Computer-implemented methods for identifying and predicting non-human animal traits

Abstract

The present disclosure provides computer-implemented methods for assisting a user in identifying the likelihood that a non-human animal has or will have, does not have or will not have, or is at risk for developing, one or more traits. The methods disclosed herein also include a method for identifying and / or recommending one or more products or services for a non-human animal, where the one or more products or services are specific for one or more traits identified in or projected to appear in a non-human animal.

Description

[0001] COMPUTER-IMPLEMENTED METHODS FOR IDENTIFYING AND PREDICTING NON-HUMAN ANIMAL TRAITS

[0002] Background

[0003] Animal owners, caretakers, and breeders have long relied on overt phenotypic proxies of animal health and performance in order to select for and identify animals that have desired traits. Such proxies include personal testimony from the animal seller, morphological features, age, abilities, show scores, show records and pedigree of the animal. Selection strategies based on these parameters may, and often does, result in inadvertent co-selection of undesired traits (e.g., disease conditions, undesirable temperament) that may be genetically linked with a desired trait and that are unrecognized by the animal enthusiast until phenotypes associated with the undesired trait present in the animal.

[0004] The technological development of clinical diagnostic testing for veterinary use has enabled more accurate identification of genetic markers associated with specific traits. Nonetheless, existing diagnostic platforms generally focus on single gene-trait correspondences that emphasize selection of breed or health traits, thus limiting the ability to provide a holistic assessment of an animal in order to predict its future health and performance or to select for a set of desired traits (e.g., traits that create a specific suitability when combined). Furthermore, diagnosis or identification of any trait in an animal does not inform an animal owner, caretaker or breeder for ways to care for or manage the specific trait. That information must instead be researched and sought out by one caring for the animal. Therefore, there exists a need for an integrative diagnostic and / or selection platform that evaluates a broad spectrum of animal traits to facilitate identification of animals having desirable characteristics suited for the intended use of the animal or preferred by the owner, as well as information about products or services that would be suitable to highlight and optimize an animal’s desirable traits and manage or treat any undesirable traits.

[0005] Summary of the Disclosure

[0006] In one aspect, the disclosure features a method for assisting a user in identifying one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services for a nonhuman animal including: (a) generating a probability index for the non-human animal using information about the non-human animal stored in a computer-implemented database system, wherein the probability index includes an array of probability values pertaining to the likelihood that the non-human animal includes one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) traits; (b) converting the probability index of step (a) into a report, wherein the report identifies: (i) the non-human animal as having, not having, or at risk of developing the one or more traits; and (ii) the one or more products or services for the non-human animal, wherein the one or more products or services are specific for the one or more traits; and (c) providing, based on the report of step (b), information about the one or more products or services or information about a service provider for the one or more products or services.

[0007] In another aspect, the disclosure features a method including: (a) generating a probability index for a non-human animal using a computer-implemented database system including information about the non-human animal stored therein, wherein the probability index includes an array of probability values pertaining to a likelihood that the non-human animal includes one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) traits; (b) converting the probability index of step (a) into a report, wherein the report identifies one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services specific for the one or more traits of the non-human animal; and (c) transmitting the report to a user.

[0008] In some embodiments, the method further includes providing remote access to the computer- implemented database system to a user over a network connection, wherein the user can access, through a graphical user interface: (a) the report; (b) the information about the one or more products or services (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more); and / or (c) the information about the non-human animal.

[0009] In some embodiments, the user can update the information about the non-human animal.

[0010] In some embodiments, the user can provide feedback regarding the perceived accuracy of the report.

[0011] In some embodiments, the method further includes an option for the user to purchase, lease, or access the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services.

[0012] In some embodiments, the method further includes providing an option for the user to communicate with a service provider that provides the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services, and / or one or more additional users (e.g., one, two, three, four, five, six, or more).

[0013] In some embodiments, the option for the user to communicate with the service provider and / or with the one or more additional users includes a written, verbal, digital, analog, or electronic means of communication.

[0014] In some embodiments, the user is provided with an option to send the service provider and / or the one or more (e.g., one, two, three, four, five, or six) additional users a written message.

[0015] In some embodiments, the user is provided with a web address or uniform resource locator (URL) for the service provider.

[0016] In some embodiments, the option for the user to communicate with the one or more (e.g., one, two, three, four, five, six, or more) additional users includes a share feature.

[0017] In some embodiments, the share feature includes a written, verbal, digital, analog, or electronic means of sharing and / or rating the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services.

[0018] In some embodiments, the one or more products or services include at least 2 or more (e.g., 2 to 10) products or services.

[0019] In some embodiments, the one or more products or services include at least 3 or more (e.g., 3 to 10) products or services.

[0020] In some embodiments, the one or more products or services include at least 4 or more (e.g., 4 to 10) products or services.

[0021] In some embodiments, the one or more products or services include at least 5 or more (e.g., 5 to 10, e.g., at least 6, at least 7, at least 8, at least 9, or at least 10 or more) products or services.

[0022] In some embodiments, the one or more products or services include at least 10 or more (e.g., 10 to 20, or more) products or services. In some embodiments, at least one of the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services is marked as recommended.

[0023] In some embodiments, the information about the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products or services are provided based on geographical location.

[0024] In some embodiments, the services include veterinary services, training services, education services, ancestry analysis services, pedigree services, or grooming services, parentage services, husbandry services, care services, enrichment services, facility services, boarding services, recreational services, monitoring services (e.g., services that include microchip insertion and / or monitoring accompanying of the microchip), and pet-sitting services.

[0025] In some embodiments, the products include a medication, a wearable product, a hygiene-related product, a breed-specific product, a trait specific product, an edible product, an environmental enrichment product, an enhancing product, a training product, a sporting product, an exercise product, an enrichment product, safety product, a furniture product, and a recreational product.

[0026] In some embodiments, the method further includes providing an incentive for the user to purchase or access the one or more products or services.

[0027] In some embodiments, the incentive includes a coupon, discount, bonus item, sale, upgrade, entry into a sweepstakes, rewards points, or early access.

[0028] In another aspect, the disclosure features a computer-implemented method for predicting a likelihood that a non-human animal includes one or more traits, the method including: (a) generating a probability index for the non-human animal using information about the non-human animal stored in a computer-implemented database system, wherein the probability index includes an array of probability values pertaining to the likelihood that the non-human animal includes the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) traits; and (b) converting the probability index of step (a) into a report, wherein the report identifies: (i) the non-human animal as having, not having, or at risk of developing the one or more traits; and (ii) a predictive image or a rendering of the non-human animal.

[0029] In some embodiments, the method further includes selecting an age or life-stage of the non- human animal.

[0030] In some embodiments, the predictive image or rendering of the non-human animal depends on the age or life-stage of the non-human animal.

[0031] In some embodiments, the age of the non-human animal is selected from 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, or 35 years.

[0032] In some embodiments, the life-stage of the non-human animal is selected from newborn, neonate, infant, adolescent, juvenile adult, senior or geriatric.

[0033] In some embodiments, the method further includes, prior to step (a): (a) receiving information about the non-human animal; and / or (b) storing the information about the non-human animal in the computer-implemented database system.

[0034] In some embodiments, the method further includes storing the report in the computer- implemented database system. In some embodiments, the information about the non-human animal was uploaded from a secondary source into the computer-implemented database system.

[0035] In another aspect, the disclosure features a computer-implemented method for predicting a likelihood that a non-human animal includes one or more traits (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more), the method including: (a) training a predictive computer model using a physical computing device and a machine learning algorithm, wherein the physical computing device includes a plurality of data about a plurality of non-human animals; (b) receiving information about the non-human animal; (c) generating a probability index by applying the predictive computer model, wherein the probability index includes an array of probability values pertaining to the likelihood that the non-human animal includes the one or more traits (d) converting the probability index of step (c) into a report; and / or (e) presenting the report of step (d) to a user on a graphical user interface.

[0036] In some embodiments, the plurality of data of step (a) includes a training data set.

[0037] In some embodiments, the information about the non-human animal of step (a) is received from a secondary source.

[0038] In some embodiments, the training the predictive computer model includes inputting the training data set into (i) a supervised machine learning model, (ii) an unsupervised machine learning model, and / or (iii) a semi-supervised machine learning model.

[0039] In some embodiments, the method further includes: (a) the training data set utilized by the supervised machine learning model is a labeled training data set; (b) the training data set utilized by the unsupervised machine learning model is an unlabeled training data set; and / or (c) the training data set utilized by the semi-supervised machine learning model is a combination of both the labeled and unlabeled training data sets.

[0040] In some embodiments, the unsupervised machine learning model includes a process of classifying similar data within the unlabeled training data, thereby generating one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) clusters of similar data.

[0041] In some embodiments, the unsupervised machine learning further includes a process of identifying commonalities within the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) clusters of similar data.

[0042] In some embodiments, the unsupervised machine learning further includes a process of identifying outlying data within the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) clusters of similar data.

[0043] In some embodiments, the training the predictive computer model further includes a validation step and / or a testing step.

[0044] In some embodiments, the method further includes transmitting the report to the user.

[0045] In some embodiments, the report is automatically transmitted to the user.

[0046] In some embodiments, the method further includes providing remote access to the computer- implemented database system to a user over a network connection, wherein the user can access, through a graphical user interface: (a) the report; and / or (b) the information about the non-human animal.

[0047] In some embodiments, the user can update the information about the non-human animal.

[0048] In some embodiments, the user can provide feedback regarding perceived accuracy of the report. In some embodiments, the report is updated based on the feedback provided by the user.

[0049] In some embodiments, the information about the non-human animal includes genetic information. In some embodiments, the information about the non-human animal further includes genetic information and non-genetic information about the non-human animal and / or a related non-human animal.

[0050] In some embodiments, the genetic information includes a genetic profile.

[0051] In some embodiments, the genetic profile includes a DNA profile, an RNA profile, a gene expression profile, and / or a methylation profile.

[0052] In some embodiments, the genetic information includes results of previously performed genetic testing.

[0053] In some embodiments, the genetic testing includes sequencing of the whole genome of the non- human animal.

[0054] In some embodiments, the genetic testing includes performing an analysis of an entire genome of the non-human animal.

[0055] In some embodiments, the analysis of the entire genome includes a genome wide association study (GWAS).

[0056] In some embodiments, the genetic testing includes evaluating one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) single nucleotide polymorphisms (SNP).

[0057] In some embodiments, the genetic testing incldues genotyping the non-human animal for any one of the following genetic markers: agouti signaling protein (ASIP), RALY heterogeneous nuclear ribonucleoprotein (RALY), Semaphorin 3 (SEMA3), growth hormone receptor (GHR), SMAD family member 2 (SMAD2), beta-defensin 103 (CBD103), dickkopf WNT signaling pathway inhibitor 4 (DKK4), HPS3 biogenesis of lysosomal organelles complex 2 subunit 1 (HPS3), HPS4 biogenesis of lysosomal organelles complex 3 subunit 2 (HPS4), HPS5 biogenesis of lysosomal organelles complex 2 subunit 2 (HPS5), HPS6 biogenesis of lysosomal organelles complex 2 subunit 3 (HPS6), KIT proto-oncogene, receptor tyrosine kinase (KIT), major facilitator superfamily domain containing 12 (MFSD12), melanocyte inducing transcription factor (MITF), proteasome 20S subunit beta 7 (PSMB7), solute carrier family 26 member 4 (SLC26A4), tyrosinase (TYR), tyrosinase related protein 1 (TYRP1 ), melanocortin 1 receptor (MC1 R), keratin 71 (KRT71 ), fibroblast growth factor 5 (FGF5), G protein-coupled receptor 22 (GPR22), mitochondrial intermediate peptidase (MIPEP), melanocortin 5 receptor (MC5R), chr1 :78870578: T>C, ACE_chr9:12461897: A>C, melanophilin (MLPH), methionine sulfoxide reductase B3 (MSRB3), AHHS65D, R-spondin 2 (RSPO2), forkhead box I3 (FOXI3), serum / glucocorticoid regulated kinase family, member 3 (SGK3), stanniocalcin 2 (STC2), AHX1 JD9, premelanosome protein (PMEL), T brachyury, T- box transcription factor T, OCA2 melanosomal transmembrane protein (OCA2), usherin (USH2A), dishevelled segment polarity protein 2 (DVL2), SPARC related modular calcium binding 2 (SMOC2), thrombospondin 2 (THBS2), insulin like growth factor 1 (IGF1 ), insulin like growth factor 1 receptor (IGF1 R), insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2), Dopamine receptor D4 (DRD4), general transcription factor Hi (GTF2I); GTF2I repeat domain contain 1 (GTF2IRD1 ), methionine sulfoxide reductase B3 (MSRB3), proopiomelanocortin (POMC), very low-density lipoprotein receptor (VLDLR), adenine phosphoribosyltransferase (APRT), glial cell derived neurotrophic factor (GDNF) ,anillin (ANLN), ubiquitin specific peptidase 31 (USP31 ), fibrinogen alpha chain (FGA), glial fibrillary acidic protein (GFAP), enamelin (ENAM), solute carrier family 24 member 4 (SLC24A4), sacsin molecular chaperone (SACS), protein kinase, DNA-activated, catalytic subunit (PRKDC), insulin like growth factor binding protein 5 (IGFBP5), glutamate metabotropic receptor 1 (GRM1 ), Bardet-Biedl syndrome 2 (BBS2), leucine rich repeat LGI family member 2 (LGI2), glycoprotein IX platelet (GP9), purinergic receptor P2Y12 (P2RY12), bone morphogenetic protein 3 (BMP3), FERM domain containing kindlin 3 (FERMT3), bestrophin 1 (BEST1 ), serine active site containing 1 (SERAC1 ), anoctamin 6 (ANO6), tyrosyl-tRNA synthetase 2 (YARS2), bridging integrator 1 (BIN1 ), protein tyrosine phosphatase-like A (PTPLA), inositol 1 ,4,5-trisphosphate receptor type 1 (ITPR1 ), RAB24, member RAS oncogene family (RAB24), Rai GTPase activating protein catalytic subunit alpha 1 (RALGAPA1 ), selenoprotein P (SELENOP), sorting nexin 14 (SNX14), solute carrier family 25 member 12 (SLC25A12), solute carrier family 6 member 3 (SLC6A3), SET binding factor 2 (SBF2), integrin subunit alpha 10 (ITGA10), fibroblast growth factor 4 (FGF4), fibroblast growth factor 5 (FGF5), ADAM metallopeptidase with thrombospondin type 1 motif 20 (ADAMTS20), distal-less homeobox 6 (DLX6), non-homologous end joining factor 1 (NHEJ1 ), complement C3 (C3), cyclic nucleotide gated channel subunit beta 3 (CNGB3), nephrocystin 4 (NPHP4), IQ motif containing B1 (IQCB1 ), RPGR interacting protein 1 (RPGRIP1 ), NAD(P) dependent steroid dehydrogenase-like (NSDHL), solute carrier family 5 member 5 (SLC5A5), SIX homeobox 6 (SIX6), thyroid peroxidase (TPO), melanin-concentrating hormone receptor 2 (MCHR2), cytochrome b5 reductase 3 (CYB5R3), choline O-acetyltransferase (CHAT), cholinergic receptor nicotinic epsilon subunit (CHRNE), Collagen-like Tail Subunit of Asymmetric Acetylcholinesterase (COLQ), retinoid isomerohydrolase RPE65 (RPE65), copper metabolism domain containing 1 (COMMD1 ), ATPase copper transporting alpha (ATP7A), ATPase copper transporting beta (ATP7B), solute carrier family 35 member D1 (SLC35D1 ), solute carrier family 37 member 2 (SLC37A2), adaptor related protein complex 3 subunit beta 1 (AP3B1 ), inositol polyphosphate-5-phosphatase E (INPP5E), solute carrier family 3 member 1 (SLC3A1 ), solute carrier family 7 member 9 (SLC7A9), very low density lipoprotein receptor (VLDLR), ATPase sarcoplasmic / endoplasmic reticulum Ca2+ transporting 2 (ATP2A2), myosin VI IA (MYO7A), protein tyrosine phosphatase receptor type Q (PTPRQ), SP110 nuclear body protein (SP110), superoxide dismutase 1 (SOD1 ), FAM20C golgi associated secretory pathway kinase (FAM20C), pyruvate dehydrogenase kinase 4 (PDK4), RNA binding motif protein 20 (RBM20), titin (TTN), rhodopsin (RHO), family with sequence similarity 83 member H (FAM83H), collagen type VII alpha 1 chain (COL7A1 ), EPS8 signaling adaptor L2 (EPS8L2), serine / threonine kinase 38 like (STK38L), N-myc downstream regulated 1 (NDRG1 ), coiled-coil domain containing 66 (CCDC66), plakophilin 1 (PKP1 ), ADAM metallopeptidase with thrombospondin type 1 motif 2 (ADAMTS2), collagen type V alpha 1 chain (COL5A1 ), tenascin XB (TNXB), keratin 10 (KRT10), brevican (BCAN), dynamin 1 (DNM1 ), unc-93 homolog B1 , TLR signaling regulator (UNC93B1 ), coagulation factor VII (F7), coagulation factor XI (F11 ), collagen type IV alpha 4 chain (COL4A4), FANCD2 and FANCI associated nuclease 1 (FAN1 ), mitofusin 2 (MFN2), keratin 16 (KRT16), alpha-L-fucosidase 1 (FUCA1 ), ATP binding cassette subfamily B member 4 (ABCB4), integrin subunit alpha 2b (ITGA2B), olfactomedin like 3 (OLFML3), galactosylceramidase (GALC), phosphofructokinase, muscle (PFKM), glucose-6-phosphatase (G6PC), amylo-alpha-1 , 6-glucosidase, 4- alpha-glucanotransferase (AGL), galactosidase beta 1 (GLB1 ), hexosaminidase subunit alpha (HEXA), hexosaminidase subunit beta (HEXB), coagulation factor VIII (F8), coagulation factor IX (F9), patatin like phospholipase domain containing 8 (PNPLA8), potassium voltage-gated channel interacting protein 4 (KCNIP4), solute carrier family 12 member 6 (SLC12A6), FYVE and coiled-coil domain autophagy adaptor 1 (FYCO1 ), heat shock transcription factor 4 (HSF4), spectrin beta, erythrocytic (SPTB), desmoglein 1 (DSG1 ), family with sequence similarity 83 member G (FAM83G), SUV39H2 histone lysine methyltransferase (SUV39H2), solute carrier family 2 member 9 (SLC2A9), catalase (CAT), folliculin interacting protein 2 (FNIP2), alkaline phosphatase, biomineralization associated (ALPL), abhydrolase domain containing 5, lysophosphatidic acid acyltransferase (ABHD5), aspartic peptidase retroviral like 1 (ASPRV1 ), NIPA like domain containing 4 (NIPAL4), patatin like phospholipase domain containing 1 (PNPLA1 ), solute carrier family 27 member 4 (SLC27A4), high mobility group AT-hook 2 (HMGA2), amnion associated transmembrane protein (AMN), cubilin (CUBN), acyl-CoA synthetase long chain family member 5 (ACSL5), MAP3K7 C-terminal like (MAP3K7CL), pitrilysin metallopeptidase 1 (PITRM1 ), DIRAS family GTPase 1 (DIRAS1 ), L-2-hydroxyglutarate dehydrogenase (L2HGDH), NHL repeat containing E3 ubiquitin protein ligase 1 (NLHRC1 ), autophagy related 4D cysteine peptidase (ATG4D), transglutaminase 1 (TGM1 ), contactin associated protein 1 (CNTNAP1 ), calpain 1 (CAPN1 ), NADH:ubiquinone oxidoreductase core subunit S7 (NDUFS7), gap junction protein alpha 9 (GJA9), muskelin 1 (MKLN1 ), integrin subunit beta 2 (ITGB2), N-acyl phosphatidylethanolamine phospholipase D (NAPEPLD), plasminogen (PLG), sarcoglycan alpha (SGCA), sarcoglycan delta (SGCD), potassium voltage-gated channel subfamily Q member 1 (KCNQ1 ), leprecan-like 1 (LEPREL1 ), laminin subunit beta 3 (LAMB3), tubulin beta 1 class VI (TUBB1 ), Carbohydrate Sulfotransferase 6 (CHST6), CDK5 regulatory subunit associated protein 2 (CDK5RAP2), myosin heavy chain 9 (MYH9), retinol binding protein 4 (RBP4), alpha-L-iduronidase (IDUA), N-acetyl-alpha-glucosaminidase (NAGLU), arylsulfatase B (ARSB), N-sulfoglucosamine sulfohydrolase (SGSH), glucuronidase beta (GUSB), dystrophin (DMD), collagen type VI alpha 1 chain (COL6A1 ), myostatin (MSTN), ADAMTS like 2 (ADAMTSL2), iron-sulfur cluster assembly factor IBA57 (IBA57), myeloperoxidase (MPO), chloride voltage-gated channel 1 (CLCN1 ), myotubularin 1 (MTM1 ), hypocretin receptor 2 (HCRTR2), spectrin beta, non-erythrocytic 2 (SPTBN2), activating transcription factor 2 (ATF2), phospholipase A2 group VI (PLA2G6), tectonin beta-propeller repeat containing 2 (TECPR2), VPS11 core subunit of CORVET and HOPS complexes (VPS11 ), 2', 3'- cyclic nucleotide 3' phosphodiesterase (CNP), ATPase cation transporting 13A2 (ATP13A2), CLN5 intracellular trafficking protein (CLN5), palmitoyl-protein thioesterase 1 (PPT1 ), cathepsin D (CTSD), tripeptidyl peptidase 1 (TPP1 ), arylsulfatase G (ARSG), CLN6 transmembrane ER protein (CLN6), major facilitator superfamily domain containing 8 (MFSD8), CLN8 transmembrane ER and ERGIC protein (CLN8), sodium voltage-gated channel alpha subunit 8 (SCN8A), lipoxygenase homology PLAT domains 1 (LOXHD1 ), solute carrier family 45 member 2 (SLC45A2), collagen type IX alpha 2 chain (COL9A2), solute carrier family 13 member 1 (SLC13A1 ), exostosin glycosyltransferase 2 (EXT2), collagen type I alpha 1 chain (COL1 A1 ), collagen type I alpha 2 chain (COL1A2), serpin family H member 1 (SERPINH1 ), phosphatidylinositol glycan anchor biosynthesis class N (PIGN), anti-Mullerian hormone receptor type 2 (AMHR2), LIM homeobox 3 (LHX3), POU class 1 homeobox 1 (POU1 F1 ), corticotropin releasing hormone receptor 1 (CRHR1 ), polycystin 1 , transient receptor potential channel interacting (PKD1 ), ALX homeobox 4 (ALX4), limb development membrane protein 1 (LMBR1 ), slingshot protein phosphatase 2 (SSH2), Rho guanine nucleotide exchange factor 10 (ARHGEF10), RAB3 GTPase activating protein catalytic subunit 1 (RAB3GAP1 ), alpha glucosidase (GAA), kallikrein B1 (KLKB1 ), coiled-coil domain 39 molecular ruler complex subunit (CCDC39), NME / NM23 family member 5 (NME5), alanine-glyoxylate and serine-pyruvate aminotransferase (AGXT), ADAM metallopeptidase with thrombospondin type 1 motif 10 (ADAMTS10), ADAM metallopeptidase with thrombospondin type 1 motif 17 (ADAMTS17), SEL1 L adaptor subunit of ERAD E3 ubiquitin ligase (SEL1 L), cyclic nucleotide gated channel subunit alpha 1 (CNGA1 ), cyclic nucleotide gated channel subunit beta 1 (CNGB1 ), interphotoreceptor matrix proteoglycan 2 (IMPG2), intraflagellar transport 122 (IFT122), MER protooncogene, tyrosine kinase (MERTK), NECAP endocytosis associated 1 (NECAP1 ), S-antigen visual arrestin (SAG), solute carrier family 4 member 3 (SLC4A3), tetratricopeptide repeat domain 8 (TTC8), phosphodiesterase 6B (PDE6B), ADAM metallopeptidase domain 9 (ADAM9), RD3 regulator of GUCY2D (RD3), chromosome 2 open reading frame 71 (C2orf71 ), FAM161 centrosomal protein A (FAM161 A), photoreceptor disc component (PRCD), kirre like nephrin family adhesion molecule 2 (KIRREL2), NPHS1 adhesion molecule, nephrin (NPHS1 ), pyruvate dehydrogenase phosphatase catalytic subunit 1 (PDP1 ), pyruvate kinase L / R (PKLR), AT-hook transcription factor (AKNA), fol liculin (FLCN), collagen type IX alpha 3 chain (COL9A3), phosphodiesterase 6A (PDE6A), family with sequence similarity 134 member B (FAM134B), recombination activating 1 (RAG1 ), MDM2 binding protein (MTBP), collagen type XI alpha 2 chain (COL11 A2), NK2 homeobox 8 (NKX2-8), potassium inwardly rectifying channel subfamily J member 10 (KCNJ10), HES family bHLH transcription factor 7 (HES7), ATPase Na+ / K+ transporting subunit beta 2 (ATP1 B2), ATP binding cassette subfamily A member 4 (ABCA4), solute carrier family 6 member 5 (SLC6A5), solute carrier family 19 member 3 (SLC19A3), RAS guanyl releasing protein 1 (RASGRP1 ), vacuolar protein sorting 13 homolog B (VPS13B), collagen type VI alpha 3 chain (COL6A3), scavenger receptor class F member 2 (SCARF2), von Willebrand factor (VWF), xanthine dehydrogenase (XDH), molybdenum cofactor sulfurase (MOCOS), ectodysplasin A (EDA), collagen type IV alpha 5 chain (COL4A5), retinitis pigmentosa GTPase regulator (RPGR), interleukin 2 receptor subunit gamma (IL2RG), proteolipid protein 1 (PLP1 ), melanocyte inducing transcription factor (MITF), endothelial PAS domain-containing protein 1 (EPAS1 ), Immunoglobulin Superfamily member 1 gene (IGSF1 ), and glutamate receptor interacting protein 1 (GRIP1 ).

[0058] In some embodiments, the method further includes genetically modifying one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) of the genetic markers.

[0059] In some embodiments, the non-genetic information is gathered from one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) of: (i) someone knowledgeable about the nonhuman animal pertaining to the one or more traits (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more); (ii) a medical history of the non-human animal, including results from one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, ore more) clinical assays performed using a biological sample from the non-human animal and / or results from one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) diagnostic tests on the non-human animal; (iii) a score assigned to the non-human animal during competition; and (iv) media content regarding the non-human animal.

[0060] In some embodiments, the media content includes an image, a video, and / or a rendering of the non-human animal.

[0061] In some embodiments, the information about the non-human animal includes epigenetic information.

[0062] In some embodiments, the epigenetic information includes a DNA methylation profile, a histone modification profile, and / or a chromatin remodeling profile.

[0063] In some embodiments, the epigenetic information was obtained by sequencing methods. In some embodiments, the information about the non-human animal includes blood type information.

[0064] In some embodiments, the blood type information includes an ABO blood group classification selected from the group consisting of: Type A, Type B, Type AB, or Type O.

[0065] In some embodiments, the information about the non-human animal includes telomere information. In some embodiments, the telomere information includes telomere length and / or a rate of telomere shortening or elongation.

[0066] In some embodiments, the one or more traits are selected from: coat color, coat color modifier, coat color intensity, coat texture, coat thickness, coat type, facial marking, leg marking, leg length, webbed paws, water-repellent coat, shedding, eye color, skin color, body size, tail shape, tail length, head shape, ear erectness, ear length, ear position, speed, gait, temperament, muscle type, jumping ability, catching ability, hunting ability, herding ability, behavior, fear, aggression, performance, ability, drive, adaptability, health, species, breed, and breeding group.

[0067] In some embodiments, the coat color, coat color modifier, or coat color intensity is selected from a group consisting of amber champagne, amber champagne dun, amber cream, amber dun, amber dun pearl, apricot dun, apricot pearl, agouti, bay, bay cream pearl, bay double cream, bay pearl, black, black bay, black cream pearl, black double cream, black dun, black pearl, black / red, blanket appaloosa, blood bay, blue roan, brindle, brown, buckskin, buttermilk buckskin, champagne, champagne amber pearl, champagne classic pearl, champagne dun, champagne dun pearl, champagne gold pearl, champagne pearl, chesetnut, chestnut cream pearl, chocolate, classic champagne, classic champagne dun, classic cream, classic dun, cream, cream champagne, cream grullo, cream pearl, cremello, cocoa, dapple grey, dark bay, dark brown, dark chestnut, dominant white, dun, dun bay cream, dun bay double cream, dun black cream, dun black double cream, dun chestnut cream, dun chestnut double cream, dun pearl, dun pearl bay, dun pearl black, dunalino, dunolino, dunskin, dunskin splash, flaxen chestnut, fleabitten grey, frame overo, frosted appaloosa, grey, gold champagne, gold cream, gold dun, gold dun pearl, grey, grullo, half blanket, leopard appaloosa, light grey, liver chestnut, marbled appaloosa, merle, overo, palomino, pearl, perlino, perlino dun, pseudocream classic, red, red chestnut, red dun, red roan, rose grey, sabino, saddle tan, seal brown, silver, silver amber champagne, silver amber dun, silver bay cream, silver bay cream pearl, silver bay double cream, silver black, silver black cream pearl, silver black double cream, silver champagne, silver champagne dun, silver champagne pearl, silver classic champagne, silver classic champagne pearl, silver cream pearl, silver dapple, silver dapple bay, silver dapple buckskin, silver dapple cream grullo, silver dapple dun, silver dapple dunskin, silver dapple grullo, silver dapple pearl, silver dapple perlino, silver dapple perlino dun, silver dun, silver dun bay cream, silver dun bay double cream, silver dun black, silver dun black double cream, silver dun pearl, silver dun pearl bay, silver dun pearl black, silver pearl bay, silver pearl black, silver red, silver smoky cream, skewbald, smoky black, smoky cream, smoky grullo, snowflake appaloosa, sorrel, splashed white, steel grey, sun, tobiano, tovero, tri-color, white, white spotting, apron, barring on body, barring on shoulder, belly spots (large or small), belly stripe, belted, bend or spots on body, bend or spots on head, birdcatcher spots, black spots, blagdon, blanket with roaning, blanket with spots, blaze, blaze with freckling, body spots, body white, brindle, brow spots, calico, coon / skunk tail, dapples, dilute, dorsal stripe, double dilute, ermine markings, few white hairs on body, fewspot, flaxen mane / tail, flea-bitten, fleshmark, frosted, grullo, heart marking, highlights in mane / tail, lacing pattern, leopard spotted, lightning marks, line back, frosting mane / tail, maximum tobiano, maximum overo, maximum white, maximum white sabino, medicine hat, minimal overo, minimal sabino, minimal tobiano, mixed mane / tail, mottled, no marking, overo, pangare, paw prints, pearl, piebald, pinto markings, rabicano, roan, roaning on body, rose, rump spots, sabino, skewbald, smudge marks, smutty, snip, snowcap, sooty, splash markings, star, stripe, tobiano, umbilical spots, white head, white mane / tail, white throat latch, white tipped ears, white with fading edges, white with ragged edges, white with smooth edges, wither spot , no markings, solid, white blaze, black and tan, saddle tan, creeping tan, merle, harlequin, salty licorice, tan point, sable, blue-based sable, red-fawn, platinum, Isabella, blue brindle, red pied, black pied, brown-based sable, lilac-based sable, black-based sable, sable merle agouti, watermarking, ticking, urajiro, countershading, blue tick, blue roan, panda spotting, whitehead, piebald, cocoa, shaded sable, clear sable, recessive black, recessive red, cocker sable, sighthound domino grizzle, ancient domino, northern domino, seal tan, ghost, dominant black, yellow, melanistic mask, dominant yellow, black saddle, brindle merle, bi-color, tri-color, fawn, landseer, wheaten, wild boar, wolf color, sandy, peppered, orange and white, grizzle, blenheim, apricot, mustard, parti-color, ruby, salt and pepper, mahogany, charcoal, lemon, domino, camo, camouflage, cafe-au-lait, silver-beige, and white star,

[0068] In some embodiments, the facial marking is selected from a group consisting of apron face, badger face, bald face, blaze, interrupted stripe, face, snip, star, stripe markings, both eyes amber, both eyes blue, both eyes brown, both eyes green, both eyes tiger, eyebrows, face mask, few white hairs on forehead, left eye amber, left eye blue, left eye brown, left eye green, left eye partial blue, left eye tiger, partial bald face, pigment around eye, right eye amber, right eye blue, right eye brown, right eye green, right eye partial blue, right eye tiger, white around eye, white chin, white jaw, white lip, white nose, melanistic mask, white head, freckled, and white sclera

[0069] In some embodiments, the coat texture, coat thickness, coat type, or coat shedding is selected from a group consisting of smooth, rough, curly, straight, downy, spiky, brindle, high shedding, low shedding, medium shedding, double coat, single coat, bald, lions-mane ruff, and furnishings.

[0070] In some embodiments, the body size is selected from a group consisting of small, medium, large, extra-large, giant, small to medium, medium to large, large to extra-large, and extra-large to giant.

[0071] In some embodiments, the tail shape is curly, straight, screwtail, long, short, bob-tail, curved, high or medium or low set.

[0072] In some embodiments, the behavior is selected from a group consisting of attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement, herding, non-herding, sight based hunting, scent based hunting, scent based tracking, water affinity, water aversion.

[0073] In some embodiments, the aggression is selected from a group consisting of dog aggression, unfamiliar dog aggression, familiar dog aggression, rivalry aggression, stranger aggression, owner aggression, farm aggression, animal aggression, non-canine animal aggression, spatial aggression, territoriality, and touch sensitivity.

[0074] In some embodiments, the fear is selected from a group consisting of dog fear, stranger fear, escaping, non-social fear, separation anxiety, grooming fear, veterinary stress, veterinary fear, novel environment fear, fear of water, fear of flying, noise fear, and separation urination.

[0075] In some embodiments, the temperament is selected from a group consisting of vigilant, curious / vigilant, curious, spooky, non-spooky, hot, cold, and medium, aggressive, relaxed, average, suspicious, friendly, out-going, reserved, shy, aloof.

[0076] In some embodiments, the health includes variants of one or more (e.g., one, two, three, four, five, six, seven, eight, nine, or ten) genes associated with one or more disease or non-disease conditions. In some embodiments, the disease or non-disease conditions are selected from a group consisting of degenerative myelopathy, copper toxicosis, 2,8-dihydroxyadenine urolithiasis, acral mutilation syndrome, acute respiratory distress syndrome, alexander disease, amelogenesis imperfecta, autosomal recessive severe combined immunodeficiency, bald thigh syndrome, Bandera’s neonatal ataxia, Bardet-Biedl syndrome 2, progressive retinal atrophy, benign familial juvenile epilepsy, Bernard- Soulier syndrome, bleeding disorder, brachycephaly, p-Mannosidosis, canine leukocyte adhesion deficiency type III, canine multifocal retinopathy 1 , canine multifocal retinopathy 2, canine scott syndrome, cardiac arrhythmia, cardiomyopathy and juvenile mortality, centronuclear myopathy, cerebellar ataxia, cerebellar ataxia 2, cerebellar cortical degeneration, cerebellar degeneration-myositis complex, cerebral dysfunction, Charcot-Marie tooth disease, chondrodysplasia, disproportionate short-limbed, chondrodystrophy with or without chondrodysplasia, chondrodystrophy and intervertebral disc disease, cleft lip with or without palate and syndactyly, cleft palate, collie eye anomaly, complement 3 deficiency, cone degeneration, cone-rod dystrophy, cone-rod dystrophy 1 , cone-rod dystrophy 2, congenital dyshormonogenic hypothyroidism with goiter, congenital eye malformation, congenital hypothyroidism, congenital hypothyroidism with goiter, congenital idiopathic megaesophagus risk factor, congenital methemoglobinemia, congenital myasthenic syndrome, congenital stationary night blindness, copper toxicosis protective mutation, craniomandibular osteopathy, cystic renal dysplasia and hepatic fibrosis, cystinuria, cystinuria type l-A, cystinuria type l-B, cystinuria type I l-A, cystinuria type I l-B, dandy-walkerlike malformation, deafness and vestibular dysfunction, degenerative myelopathy, demyelinating polyneuropathy, dental hypomineralisation, dilated cardiomyopathy, dilated cardiomyopathy risk factor, dominant progressive retinal atrophy, dystrophic epidermolysis bullosa, early retinal degeneration, early- onset progressive polyneuropathy, early-onset pra, ectodermal dysplasia, Ehlers-Danlos syndrome, embryonic lethality, epidermolytic hyperkeratosis, episodic falling, exercise-induced collapse, factor VII deficiency, factor XI deficiency, familial nephropathy, Fanconi syndrome, fetal onset neuroaxonal dystrophy, focal non-epidermolytic palmoplantar keratoderma, GM1 gangliosidosis, GM2 gangliosidosis, glanzmann thrombasthenia type I, glaucoma, globoid cell leukodystrophy, glycogen storage disease type IIIA, glycogen storage disease type IA, glycogen storage disease VII, hemophilia A, hemophilia B, hereditary ataxia, hereditary cataracts HSF4-related, hereditary elliptocytosis, hereditary footpad hyperkeratosis, hereditary nasal parakeratosis, hereditary nephritis, hyperuricosuria, hypocatalasia, hypomyelination, hypophosphatasia, ichthyosis, inflammatory myopathy, inguinal cryptorchidism, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, juvenile dermatomyositis, juvenile encephalopathy, juvenile myoclonic epilepsy, I-2- hydroxyglutaric aciduria, lagotto storage disease, lamellar ichthyosis, laryngeal paralysis and polyneuropathy, leonberger polyneuropathy type 2, lethal acrodermatitis, ligneous membranitis, limbgirdle muscular dystrophy, limb-girdle muscular dystrophy, type 2F, long qt syndrome, lung developmental disease, multidrug resistance 1 medication sensitivity, macrothrombocytopenia, macular corneal dystrophy, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, mucopolysaccharidosis I, mucopolysaccharidosis, type IIIA, mucopolysaccharidosis, type VII, muscular dystrophy, muscular hypertrophy, musladin-lueke syndrome, myeloperoxidase deficiency, myotonia congenita, myotubular myopathy, myotubular myopathy 1 , narcolepsy, neonatal cerebellar cortical degeneration, neonatal encephalopathy with seizures, neuroaxonal dystrophy, neuronal ceroid lipofuscinosis 1 , neuronal ceroid lipofuscinosis 10, neuronal ceroid lipofuscinosis 12, neuronal ceroid lipofuscinosis 4a, neuronal ceroid lipofuscinosis 5, neuronal ceroid lipofuscinosis 6, neuronal ceroid lipofuscinosis 7, neuronal ceroid lipofuscinosis 8, nonsyndromic hearing loss, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, osteochondromatosis, osteogenesis imperfecta, paroxysmal dyskinesia, persistent mullerian duct syndrome, pituitary dwarfism, pituitarydependent hyperadrenocorticism, polycystic kidney disease, polydactyly, polyneuropathy with ocular abnormalities and neuronal vacuolation, pompe disease, prekallikrein deficiency, primary ciliary dyskinesia, primary ciliary dyskinesia, primary hyperoxaluria, primary lens luxation, primary open angle glaucoma, primary open angle glaucoma and lens luxation, progressive early-onset cerebellar ataxia, progressive retinal atrophy, progressive retinal atrophy i, progressive retinal atrophy type III, progressive rod-cone degeneration, pyruvate dehydrogenase phosphatase 1 deficiency, pyruvate kinase deficiency, recurrent inflammatory pulmonary disease, renal cystadenocarcinoma and nodular dermatofibrosis, rodcone dysplasia 1 , rod-cone dysplasia 3, sensory neuropathy, severe combined immunodeficiency, shar- pei autoinflammatory disease, skeletal dysplasia 2, spinocerebellar ataxia, spinocerebellar ataxia with myokymia and / or seizures, spondylocostal dysostosis, spongy degeneration with cerebellar ataxia, Stargardt disease, subacute necrotizing encephalopathy, T locus, thrombopathia, trapped neutrophil syndrome, ullrich congenital muscular dystrophy, Van den Ende-Gupta syndrome, Von Willebrand's disease type 1 , Von Willebrand's disease type 2, Von Willebrand's disease type 3, X-linked ectodermal dysplasia, X-linked hereditary nephropathy, X-linked retinal dysplasia, X-linked severe combined immunodeficiency, X-linked tremors, xanthinuria type 1 , xanthinuria type II, XX Disorder of sex development, canine degenerative myelopathy, progressive retinal atrophy, cone-rod dystrophy 3, progressive retinal atrophy, rod-cone dysplasia 4, and retinal dysplasia / oculoskeletal dysplasia 1 .

[0077] In some embodiments, the species of the non-human animal is selected from: Alopex lagopus, Atelocynus microtis, Canis adustus, Canis aureus, Canis familiaris, Canis latrans, Canis lupus, Canis mesomelas, Canis rufus, Canis simensis, Cerdocyon thous, Chrysocyon brachyurus, Cuon alpinus, Lycaon pictus, Nyctereutes procyonoides, Otocyon megalotis, Pseudalopex culpaeus, Pseudalopex fulvipes, Pseudalopex griseus, Pseudalopex gymnocercus, Pseudalopex sechurae, Pseudalopex veturus, Speothos venaticus, Urocyon cinereoargenteus, Urocyon littoralis, Vulpes bengalensis, Vulpes cana, Vulpes chama, Vulpes corsac, Vulpes ferrilata, Vulpes macrotis, Vulpes pallida, Vulpes rueppelli, Vulpes velox, Vulpes vulpes, and Vulpes zerda.

[0078] In some embodiments, the species of the non-human animal is Canis familiaris.

[0079] In some embodiments, the breeding group of the non-human animal is selected from: a herding group; a hound group; a toy group; a non-sporting group; a sporting group; a terrier group; and a working group.

[0080] In some embodiments, the breed of the non-human animal is selected from the group consisting of: Aidi, Affenpinscher, Hound, Africanis, African Hairless, Terrier, Akita, Alaskan Klee Kai, Alaskan Malamute, Bulldog, Spaniel, Eskimo Dog, American Sport Dog, Shepherd, Armant, Aussiedoodle, Australian Cattle Dog, Kelpie, Labradoodle, Azawakh, Basenji, Bassador, Basset, Bassugg, Beagador, Beagle, Beaglier, Collie, Beauceron, Whippet, Belgian Groenendael, Belgian Laekenois, Belgian Malinois, Sheepdog, Belgian Tervuren, Bergamasco, Bernedoodle, Bernese Mountain Dog, Bichon Frise, Bichon Yorkie, Bich-poo, Blue Lacy, Boerboel, Bolognese, Borador, Border Jack, Bordoodle, Borzoi, Bouvier Des Flandres, Boxador, Boxer, Bracco Italiano, Braque D'Auvergne, Briard, Brittany, Bugg, Bullmastiff, Bull Pei, Canaan Dog, Cane Corso, Cane Corso Italiano, Corgi, Catahoula Leopard Dog, Cavachon, Cavapom, Cavapoo, Cavapoochon, Cava Tzu, Cheagle, Retriever, Chihuahua, Chinook, Chinese Crested, Chipoo, Chi Staffy Bull, Chiweenie, Chorkie, Chow Chow, Chug, Chusky, Cirneco Dell'Etna, Cockachon, Cockador, Cockapoo, Cojack, Coton De Tulear, Dachshund, Dalmatian, Dameranian, Dobermann, Dogue de Bordeaux, Dorkie, Doxiepoo, Setter, Mountain Dog, Eurasier, Lapphund, Spitz, French Bull Jack, Frenchie Staff, French Pin, Frug, Gerberian Shepsky, Pointer, Pinscher, German Sheprador, Goberian, Goldendoodle, Golden Dox, Grand Bleu De Gascogne, Great Dane, Great Pyrenees, Greenland Dog, Griffon, Hamiltonstovare, Harrier, Havanese, Horgi, Hovawart, Hungarian Kuvasz, Hungarian Puli, Hungarian Pumi, Hungarian Vizsla, Irish Doodle, Italian Spinone, Jack-A-Bee, Jackahuahua, Jack-A-Poo, Jackshund, Jacktzu, Japanese Chin, Japanese Shiba, Jug, Keeshond, Kokoni, Komondor, Kooikerhondje, Korean Jindo, Labrador, Lachon, Lagotto Romagnolo, Lancashire Heeler, Large Munsterlander, Leonberger, Lhasa Apso, Lhasapoo, Lhatese, Lbwchen, Lurcher, Mai-Shi, Maltese, Maltichon, Maltipom, Malti-Poo, Mastiff, Mexican Hairless, Poodle, Schnauzer, Miniature Schnoxie, Morkie, Newfoundland, New Zealand Huntaway, Northern Inuit, Norwegian Buhund, Papillon, Peek-a-poo, Pekingese, Pitsky, Pomapoo, Pomchi, Pomeranian, Pomsky, Portuguese Podengo, Water Dog, Presa Canario, Pug, Pugalier, Pugapoo, Puggle, Pugzu, Rescue Dog, Rhodesian Ridgeback, Rottweiler, Russian Toy, Saluki, Samoyed, Schipperke, Schnoodle, Segugio Italiano, Shar Pei, Sheepadoodle, Shih-poo, Shih Tzu, Shollie, Shorkie, Siberian Cocker, Siberian Husky, Sloughi, Small Munsterlander, Springador, Sprocker, Sprollie, Sproodle, Stabyhoun, Staffador, Staffy Jack, St. Bernard, Swedish Vallhund, Tamaskan, Terri-Poo, Turkish Kangal Dog, Weimaraner, Westiepoo, Yorkie Russell, and Yorkipoo, or a mixed breed thereof.

[0081] In some embodiments, the non-human animal is selected from the group consisting of selected from the group consisting of a dog, horse, cattle, sheep, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non-human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig.

[0082] In another aspect, the disclosure features a computer-implemented method for producing, identifying, and / or visualizing a virtual non-human animal including one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) predetermined traits, the method including: (a) selecting the one or more predetermined traits of interest for the non-human animal; (b) generating a probability index for the non-human animal using information stored in a computer-implemented database system, wherein: (i) the information stored in the computer-implemented database system includes data related to a plurality of non-human animals including the one or more predetermined traits selected in step (a); and (ii) the probability index includes an array of probability values pertaining to the likelihood that the non- human animal includes the one or more predetermined traits also includes a plurality of nonpredetermined traits; (c) converting the probability index of step (b) into a report, wherein the report identifies: (i) the non-human animal as having or at risk of developing the one or more predetermined traits (ii) the non-human animal as having, not having, or at risk of developing the plurality of nonpredetermined traits; and (iii) a predictive image or a rendering of the non-human animal; and (d) presenting the report of step (c) to a user on a graphical user interface.

[0083] In some embodiments, the information stored in the computer-implemented database system was obtained from a secondary source.

[0084] In some embodiments, the method further includes selecting an age or life-stage of the non- human animal. In some embodiments, the predictive image or rendering of the non-human animal depends on the age or life-stage of the non-human animal.

[0085] In some embodiments, the age of the non-human animal is selected from 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, or 35 years.

[0086] In some embodiments, the life-stage of the non-human animal is selected from newborn, neonate, infant, adolescent, juvenile adult, senior or geriatric.

[0087] In some embodiments, the method further includes identifying one or more (e.g., one, two, three, four, five or more) breeding pairs likely to produce the non-human animal including the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) predetermined traits.

[0088] In some embodiments, the method further includes selecting the one or more (e.g., one, two, three, four, five or more) breeding pairs.

[0089] In some embodiments, the method further includes breeding one or more (e.g., one, two, three, four, five, or more) of the selected breeding pairs.

[0090] In some embodiments, the method further includes birthing the non-human animal including the one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) predetermined traits.

[0091] Brief Description of the Drawings

[0092] FIG. 1 is a graphical output of a user’s input data. The graphical output includes an image of an animal (e.g., a canine). The graphic of the animal, in this case, a poodle, may be: (1 ) a representative image of the user’s animal (e.g., a representative image of the dog’s breed); (2) a predicted rendering of the user’s animal by artificial intelligence (Al); or (3) an actual image of the user’s animal. The graphical output further includes the results of the user’s input data. Input data may include, but is not limited to, genetic profiling data (e.g., DNA sequencing data) on hundreds (e.g., 100, 150, 200, 250, 300, 350, 400, 450, 500, or more) of genes associated (e.g., causative or correlative) with genetic traits. The results of the user’s input data may identify genetic traits related to, for example, an animal’s behavior (e.g., separation anxiety, attention seeking (e.g., barking), etc.), personality (e.g., aggression, intelligence, worker, etc.), color (e.g., black, white, brown, a combination thereof, etc.), coat (e.g., propensity for shedding) and / or health (allergies, obesity, arthritis, etc.).

[0093] FIG. 2 is a graphic of user instructions for obtaining a saliva sample from the user’s animal (e.g., a canine).

[0094] FIG. 3 is a graphical output of a user’s input data. The graphical output includes an image of the animal (e.g., a representative image of the breed; an Al-generated rendering based on the user’s input data; or an actual image of the animal), the results of the user’s input data (e.g., genetic profiling data), and a call-to-action (CTA) button (i.e. , a clickable button) capable of providing the user with additional information. This figure specifically shows an image and genetic profiling results of a dog named Fido, along with a clickable button capable of providing the user with helpful hints and resources specific to Fido’s results.

[0095] FIG. 4 is a graphical output of the CTA button described in FIG. 3, which provides recommended products (e.g., dog collars, shampoos, toys, medications, and the like) or services (e.g., report, analyses, and / or consultations) for the animal (e.g., canine) based on the results of the user’s input data. The recommended products, services, or tailored recommendations may be provided or approved by the same entity that is providing the graphical output and / or by a third-party.

[0096] FIG. 5 is a flow diagram for generating a user interface (e.g., a mobile application and / or a website). The user interface can receive a variety of user input data from multiple sources and may output graphics of the user’s input data, animal features (e.g., image and / or traits), and / or results (e.g., see FIGS. 1-4).

[0097] FIG. 6 is a graphic of an initial user interface, such as the front page of a mobile application and / or website. A CTA button (as shown labeled as “Activate Al Predictor”) can initiate the process of retrieving (e.g., uploading and / or downloading), analyzing, and outputting the results of a user’s input data (e.g., see FIGs. 1-4).

[0098] FIG. 7 is a graphical output of a user’s input data. The graphical output includes an Al-generated image of a virtual animal, based on the input DNA (e.g., based on a genetic profile), along with a summary of the results of the user’s input data.

[0099] FIG. 8 is a representative report showing the genetic profile of a canine (“Canine Genetics Report”). The report includes the results of a user’s input data (e.g., based on the canine’s genetic profile). FIG. 9 is a schematic of an exemplary workflow utilizing the computer-implemented database system of the disclosure to assisting a user in identifying one or more products or services for a nonhuman animal based on one or more traits that the non-human animal has or is at risk of developing.

[0100] Definitions

[0101] As used herein, the terms “database” and “database system” refer to an organized collection of related data stored digitally on a computer system (e.g., a non-transitory storage medium). In the context of the present disclosure, a database system may store information (e.g., genetic and non-genetic information) about a plurality of non-human animals pertaining to one or more traits. The database system may be part of a larger computer software system that allows a user to engage with one or more databases (e.g., by way of a computer-implemented graphical user interface, as is disclosed herein). The databases are linked but may be integrated into a single platform or distributed over multiple different platforms. Information in the database system may be interrogated by a user by providing input information pertaining to a specified set of selection criteria (e.g., one or more traits desired in a non- human animal) set by the user utilizing a graphical user interface. The database system may be configured for interrogation by a user to perform functions such as, e.g., adding, removing, modifying, and retrieving data (e.g., data about a non-human animal pertaining to one or more traits) within the system. Database systems may be physically instantiated on database servers that store the database information and accessed by a user by way of a graphical user interface. Information within the database or the database system that may be interrogated by a user may include genetic information (e.g., including telomere length information and other age-related and genetic-damage related information), epigenetic information, and non-genetic information (e.g., blood-type information).

[0102] As used herein, the term “trait” means a characteristic of an organism which manifests itself in a phenotype, and refers to a biological, behavioral, or any other measurable characteristic(s), which can be any variable that can be quantified in or from a biological sample or organism, which can then be used either alone or in combination with one or more other quantified variables to characterize an animal. Many traits are the result of the expression of a single gene, but some are polygenic, i.e. , they result from coordinated expression of more than one gene. A “phenotype” is an outward appearance or other measurable characteristic of an organism. Many different traits can be inferred by the methods disclosed herein. As used herein, a trait may be “predetermined” if the expression of the trait was selected for by a user (e.g., animal owner, breeder, caretaker, seller, or buyer) using the methods and systems of the present disclosure. Non-limiting examples of predetermined traits include coat color, coat color modifier, coat color intensity, coat texture, coat thickness, facial marking, leg marking, eye color, skin color, mane color, tail color, speed, gait, temperament, and health.

[0103] As used herein, the term “non-human animal” or “animal” refers to any non-human mammal, including cattle, sheep, dog, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non- human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig, among others. A non-human animal may refer to a companion animal, a farm animal, a service animal, or an animal used for leisure or sporting activities.

[0104] As used herein, the term “probability index” refers to an array of probability values pertaining to the likelihood of a non-human animal having one or more traits. The array of probability values may include individual probability values corresponding to the likelihood of expression of a specific trait in the non-human animal.

[0105] As used herein, the terms “user interface” and “graphical user interface” refer to a component of a computer software program instantiated on a general-purpose computer or an electronic hand-held device that allows a user to engage with a computer program (e.g., a software program) to achieve a specific goal (e.g., identifying the likelihood of a non-human animal having one or more traits). The user interface may be part of a larger system containing a database system and subsystems (such as, e.g., a database system disclosed herein), as well as engines for searching, comparing, saving, exporting, and transforming information using a variety of mathematical operations (e.g., statistical analysis). A user interface generally requires the user to provide one or more inputs (e.g., one or more traits of interest), which the interface routes to the appropriate software system (e.g., database and / or engine) or subsystem. The user interface may then receive and display output information (e.g., a probability index containing an array of probability values pertaining to the likelihood of the animal having one or more traits) to the user based on the processing of the provided inputs by the appropriate systems. The user interface may include systems that are functionally connected to physical input hardware such as a peripheral device, such as a keyboard and a mouse, and output hardware, such as a display monitor, speakers, and printer(s).

[0106] As used herein, the term “report” refers to a representation of a probability index displayed on a graphical user interface. The report may include information regarding one or more traits of a non-human animal or indicia regarding the likelihood of a non-human animal having, not having, or at risk of developing at least one or more traits (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700, 800, 900, 1000, or more). The report can include any combination of numerical values, images, and / or words. The report may also include information regarding services, products, or service providers specific to or beneficial to the non-human animal (e.g., based on the presence or absence of the one one or more traits).

[0107] As used herein, the phrase “having one or more traits” or “has one or more traits” refers to a non- human animal that includes, displays, or contains / possesses (e.g., has a genetic or proteomic basis for) of the one or more traits. For example, a non-human animal that has a trait dependent on a specific genotype necessarily contains the specific gene or allele that produces the trait. Additionally, an animal that will display a trait in the future (e.g., as determined by a probability index) is considered to have that trait. Similarly, the phrase “not having one or more traits” or “does not have one or more traits” refers to a non-human animal that does not include, display, or contain / possess (e.g., based on a genotypic or proteomic information) the one or more traits. For example, a non-human animal that does not have a trait dependent on a specific genotype necessarily does not contain or possess the specific gene or allele that produces the trait. Furthermore, the phrase “at risk of developing” the one or more traits refers to a non-human animal that does not currently have the one or more traits, but may develop or display the one or more traits in the future. An animal may be at risk of developing one or more traits considered to be desirable and / or undesirable.

[0108] As used herein, the phrase “share feature” refers to a functionality within a computer- implemented database system described herein that enables a user to transmit content (e.g., files, links, data, media, messages, or products) to a service provider described herein or to one or more additional users of the computer-implemented database system. The content may be transmitted by written, verbal, digital, analog, or other electronic means. In some embodiments, the share feature enables the sharing of user-generated content, such as a rating (e.g., a five star-based rating or binary rating system), review of a product or service, or ability to connect with secondary users (e.g., communicate with other users that upload genetic similarities).

[0109] Detailed Description

[0110] This disclosure generally relates to methods and computer-implemented systems (e.g., a database system and a graphical user interface) for predicting the likelihood of a non-human animal as having one or more traits (e.g., traits associated with physical appearance, morphology, health, and performance, among others). In particular, the present disclosure provides computer-implemented databases containing information associated with one or more traits (e.g., genetic and non-genetic information) for use in calculating a probability that the non-human animal has one or more traits. The methods and computer-implemented systems disclosed herein are also useful for identifying products or services that relate to one or more traits that a non-human animal may have. The methods and computer- implemented systems disclosed herein that predict the likelihood of a non-human animal as having one or more traits are also useful in predicting visual aspects of the non-human animal (e.g., physical appearance), and for use in rendering a predictive image of an animal (e.g., a virtual animal). The disclosed methods and computer-implemented systems are also applicable to machine learning algorithms, where a predictive computer model that is trained on a data set, can be applied to predict the likelihood of a non-human animal as having one or more traits. Such information may be particularly useful in making decisions pertaining to products or services for use by, with, or for the non-human animal. Such information may also be useful in making decisions pertaining to purchasing, medical treatment, breeding, and destiny determination (e.g., discipline) of the animal.

[0111] The sections that follow describe the methods and computer-implemented systems useful for predicting the likelihood of a non-human animal having one or more traits in detail. While the descriptions below are directed towards dogs, the present disclosure can also be used with other non-human animals including, but not limited to horses, cattle, sheep, cats, camels, pigs, goats, alpacas, donkeys, llamas, red foxes, mice, rats, ferrets, non-human primates, rabbits, gerbils, hamsters, chinchillas, or guinea pigs, among others.

[0112] Computer-implemented database systems

[0113] The present disclosure provides a database system for storing, analyzing, retrieving, and updating information about multiple non-human animals (e.g., horse, cattle, sheep, dog, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non-human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig, among others) having one or more traits. The database system disclosed herein also includes information about a plurality of products and services that are associated with one or more traits (e.g., health, disease, behavior, color, among others) that a non-human animal may have or be at risk of developing. The database system can include a variety of system components (e.g., subsystems). Information about the non-human animal can be uploaded into various datasets of the system (e.g., uploaded from a secondary source) or stored in the various datasets of the system, categorized according to the source and / or type of information, and partitioned into unique database subsystems. An exemplary database subsystem can include a dataset of non-human animals having a specific genetic profile associated with one or more traits (e.g., a genetic profile subsystem) and characterized by, for example, expression of a specific gene(s) or lack thereof, presence or absence of a specific variant of a gene(s) (e.g., a gene having a single nucleotide polymorphism (SNP)), presence or absence of genetic mutations (e.g., indels, substitutions (transitions, transversions, silent, missense, or nonsense), duplication, repeat expansion, or frameshift). A separate database subsystem can contain information about the animal gathered from one or more secondary sources (e.g., a secondary source information subsystem). An additional separate database subsystem can contain information about products and / or services that may be of use to the non-human animal.

[0114] Use of the database system to identify the likelihood of a non-human animal having one or more traits

[0115] The present disclosure provides methods for using genetic and non-genetic information about one or more non-human animals that have been integrated into a database and that can be interrogated in order to assist in the determination of whether or not a non-human animal has, does not have, or is at risk of developing one or more traits. Such methods can be used to inform an owner of a non-human animal about one or more desired physical characteristics, behavioral characteristics, and / or predispositions that the animal may develop over time. For example, dog enthusiasts have historically selected for specific traits in their dogs by making purchasing or adopting decisions based on information relating to the dog’s pedigree, fitness, appearance, health, and behavior. Accordingly, someone who is looking to acquire a dog would identify the trait(s) most important to them and choose a dog or dog breed that is most likely to have those trait(s). This strategy is encumbered by various sources of error that introduce uncertainty of achieving the target phenotype in the dog. For example, breeding decisions based on pedigree have long been made on the basis of self-reported testimony of the dog seller and / or based on an assumption that the dog inherits 50% of its genetic information from each parent. There are at least two problems with this approach, namely that (1 ) pedigree assignments based on presumed genetic inheritance often underestimate the degree of genetic relatedness (e.g., inbreeding) between the parents; and (2) small changes in genotype can produce profound changes in phenotype, thus potentially confounding the initial judgment of the traits that the dog may possess.

[0116] Presently, dog breeders rely on aggregated information about a dog’s traits (e.g., behavior, physical appearance, aggression, size, among others) that are largely rooted in observation about the traits that are typically present in a breed of dog. In purebred dogs, some of these traits are more predictable (e.g., greyhounds as fast sprinters), and such dogs are prized for their predictable traits (e.g., greyhound racing). However, inbreeding due to genetic relatedness of parent animals for the purpose of producing offspring with the desired traits often leads to an increase in the offspring animal having an increased likelihood of negative health outcomes or life expectancy (see, e.g., Yordy et al. Conserv. Genet. 21 (1 ):137-148, 2020). Some common health outcomes of inbreeding in popular dog breeds are well documented (e.g., the short face of French bulldogs may make their breathing less efficient), and tests for common inbreeding conditions may exist. However, in the case that a test exists for a specific health outcome, the health outcome is not always tested for, especially in breeds where the health outcome may not be common. For example, a mutation in the ABCB1 gene is most commonly, but not exclusively, found in collie-type breeds and can lead to neurotoxic symptoms and other adverse reactions when the animal is treated with certain medication (see, e.g., Beckers et al. PLOS One 17(8):e0273706). An owner that is aware of medication sensitivity caused by an ABCB1 mutation can inform their veterinarian and avoid complicated, expensive, and potentially life-threatening adverse events that would be incurred if the animal were treated with the wrong type of medication. The absence of predictive information about an animal’s ultimate phenotype, related to trait(s) that an owner may find desirable as well as undesirable, substantially increases the risk and cost associated with an unfavorable outcome. The present disclosure provides a holistic approach for identifying the likelihood of a non-human animal having a desired phenotype (or not having an undesired phenotype) by providing an integrated database system capable of analyzing and correlating an unprecedented number of genetic and non-genetic characteristics in order to identify the animal’s phenotype with a high probability. Such a database system combines, stores, organizes, and correlates vast quantities of disjointed genetic and non-genetic information pertaining to one or more physical and / or behavioral traits, which a user can then leverage to determine with a high certainty if their animal has, does not have, or is at risk of having desired and / or undesired phenotypes.

[0117] The methods disclosed herein address the problem faced by animal owners and breeders discussed above by determining the likelihood of an animal having one or more (and typically multiple) desirable or undesirable characteristics by drawing from multiple sources of information integrated within a single user-friendly platform that may be instantiated, for example, as an online computer-implemented user-interface system in communication with a computer-implemented database system, as is described herein. The user platform may be part of a larger computer-implemented system that includes a remote graphical user interface, one or more digital database systems having one or more database subsystems, one or more engines for searching, comparing, storing, and manipulating information (e.g., through mathematical operations such as, e.g., statistical analysis), and computer hardware (e.g., local or remote) to implement the platform, as described in detail below. The user may be any party interested in procuring, identifying, and / or producing a non-human animal (e.g., dog, cattle, sheep, horse, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non-human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig, among others) such as, for example, an animal owner, breeder, seller, caretaker, or first-time buyer.

[0118] Thus, the disclosed methods and systems may be applied to determine the likelihood of a non- human animal having, not having, or at risk of developing one or more traits according to the steps of the following methods. First, using information about a non-human animal (e.g., genetic and / or non-genetic information), a computer-implemented database system disclosed herein is used to generate a probability index for the non-human animal. The probability index includes an array of probability values (e.g., at least 6.25%, 12.5%, 25%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or 100%) pertaining to the likelihood that the animal has, does not have, or is at risk of developing the one or more traits. For example, if an animal is being evaluated for the likelihood of medication sensitivity, the information about the animal would likely include genetic sequencing information that includes at least all or part of the ABCB1 gene. That genetic information would be evaluated by the database system, where the database system determines the likelihood of the animal having the relevant ABCB1 mutation, and relays that information in the form of a probability index. The probability index is subsequently converted into a report that can be displayed on a graphical user interface (see, e.g., FIGS. 3, 4, 7 and 8). In some embodiments, the report is transmitted to the user. In some embodiments, the report is automatically transmitted to the user. The computer-implemented database systems of the present disclosure may be capable of receiving (e.g., uploading) information (e.g., genetic and / or non-genetic information) about a non-human animal (e.g., the non-human animal of interest), storing said information in the computer-implemented database system, storing the generated probability index in the computer-implemented database system, storing the report in the computer-implemented database system, and storing any other relevant information regarding the non-human animal within the computer-implemented database system.

[0119] The probability index described herein utilizes information (e.g., genetic and / or non-genetic information). When genetic information is used to determine the probability index for one or more specific traits, the information (e.g., genetic variations, mutations, deletions, insertions, frame shifts, simple changes, substitutions and duplications) is considered using established or predicted rules of function (e.g., variants for melanistic masking will produce black markings on a dog’s face). A summary (e.g., a report) of likely (e.g., having, not having, at risk of developing one or more traits) outcomes for the animal (e.g., dog) is subsequently generated. For example, the probability index can indicate whether an animal’s (e.g., a dog) genome contains variants that will produce a specific phenotype(s) (e.g., a genetic variant for masking of the snout (e.g., the “E” locus, MC1R), variants for facial furnishings (e.g., IC locus, RSP02), specific variants for tail length, (e.g., T-box), variants for copper toxicosis or Wilson’s disease (e.g., ATP7B)), and a report summarizes these outcomes. The report would inform an owner, buyer, caretaker, or veterinary practitioner that this dog would likely have a dark / black muzzle with facial hair (e.g., bearded) and a short tail (e.g., naturally bobbed tail) along with food sensitivities due to an inability to properly metabolize copper. If properly managed, an owner or caretaker could avoid liver failure in this animal by providing a diet low in copper and seek out medical management, both of which are known and could be recommended or identified. As such, medications (e.g., ursodial, furosemide) along with specialized dog food (e.g., Science Diet l / D) could be identified as relevant solutions, and information about said products could be provided to the owner.

[0120] Use of the database system to identify products or services relevant to one or more traits

[0121] The methods of the present disclosure also include providing information and / or recommendations to a user about one or more products and / or services that are related to one or more traits of interest (or to a specific non-human animal). Information about products and services is intended to be integrated into the database system (e.g., in a subsystem), and correlated with specific traits for which the products and / or services might be of use to the non-human animal, for use by the non-human animal, or for use with the non-human animal. Such methods can be used to manage any undesirable traits that an animal may have, highlight desirable traits, and keep an animal healthy. Presently, to manage a trait in an animal, an owner or breeder would first have to be aware of the existence of the trait, and second would need to research ways to manage the trait. For example, a dog which has been determined to have Ehlers-Danlos syndrome, would likely benefit from veterinary services provided by a veterinary practitioner that has experience with treating and managing this specific disease in dogs. In some embodiments, the methods of the present disclosure may provide information to a user about a veterinary practitioner that may have experience treating and managing a health condition that the user’s animal may have or may be at risk for developing. In some embodiments, the information about a particular service or product provided to a user may be based on geographical area or map (e.g., a veterinary practitioner with experience treating or managing Ehlers-Danlos syndrome near the user’s location). A report that pertains to the likelihood of a non-human animal as having, not having, or at risk of developing one or more traits may also include information about product(s) or service(s) related to the one or more traits. For example, a noise reduction collar may be particularly useful for a dog that is determined to have a high likelihood of barking. Similarly, a dog that has a high likelihood of having aggressive behavior towards other dogs may benefit from a training class more than a dog that does not have a likelihood of displaying aggressive behaviors towards other dogs. A report that finds a dog to have a high likelihood of both barking behavior and displaying aggressive behavior towards other dogs may also include information pertaining to a noise reduction collar and training services (see, e.g., FIG. 4). This concept may be applicable to any relevant product or service. An exemplary workflow to assist a user in identifying one or more products or services for a non-human animal is shown in FIG. 9.

[0122] The methods described herein address the problem of correlating information about one or more specific products or services with one or more traits, and, thereby, allow a user to simply and easily access and share (e.g., via a share feature) that information. Thus, the disclosed methods and systems may be applied to assist a user in identifying one or more products or services for a non-human animal that that are specific for one or more traits that the non-human animal has been determined to be likely to have or to be at risk of developing according to the steps of the following methods. First, using information about a non-human animal (e.g., genetic and / or non-genetic information), a computer- implemented database system disclosed herein is used to generate a probability index for the non-human animal. The probability index includes an array of probability values (e.g., at least 6.25%, 12.5%, 25%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or 100%) pertaining to the likelihood that the animal has, does not have, or is at risk of developing the one or more traits. For example, if an animal is being evaluated for the likelihood of shedding, the information about the animal may include genetic sequencing information that includes at least all or part of the SD locus (e.g., MC5R gene; see e.g., FIG. 8, and Table 1). That genetic information would be evaluated by the database system, where the database system determines the likelihood of the animal having the relevant genotype, and relays that information in the form of a probability index. The probability index is subsequently converted into a report that also includes information about one or more products or services that can be used to help reduce the shedding phenotype (e.g., grooming services, shampoos, brushes, among others), which can then be displayed on a graphical user interface (see, e.g., FIG. 8). In some embodiments, the report is transmitted to the user. In some embodiments, the report is automatically transmitted to the user.

[0123] A report may include information about any number of products or services specific for one or more traits. In some embodiments the information about the one or more products or services may include at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55,

[0124] 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83,

[0125] 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99, or 100 or more products or services. In some embodiments, the information about the one or more products or services may include at least between

[0126] 1 -2, 1 -5, 1 -10, 1 -15, 1 -20, 1 -25, 1 -30, 1 -35, 1 -40, 1 -45, 1 -50, 1 -55, 1 -60, 1 -65, 1 -70, 1 -75, 1 -80, 1 -85, 1 - 90, 1 -95, 1 -100, 1 -150, 1 -200, 1 -250, 1 -300, 1 -350, 1 -400, 1 -450, 1 -500; 500-450, 500-400, 500-350, 500-300, 500-250, 500-200, 500-150, 500-100, 500-50, 500-25, 500-5; 100-90, 100-80, 100-70, 100-60, 100-50, 100-40, 100-30, 100-20, 100-10; 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90; 5-15, 10-25, 20-35, 25-40, 30-45, 35-50, 40-55, 45-60, 50-65, 55-70, 60-75, 65-80, 70-85, 75-90, 80-95, or 85- 100 products or services.

[0127] The products may include but are not limited to products such as a medication, a wearable product, a hygiene-related product, a breed-specific product, a trait specific product , an edible product, an environmental enrichment product, an enrichment product, an enhancing product, a training product, a sporting product, an exercise product, safety product, a furniture product, and a recreational product.

[0128] The services may include but are not limited to services such as veterinary services, training services, education services, ancestry analysis services, pedigree services, or grooming services, parentage services, husbandry services, care services, enrichment services, facility services, boarding services, recreational services, and monitoring services.

[0129] In some embodiments, an incentive may be provided for a user to purchase or access the one or more products or services. Such an incentive may include but is not limited to a coupon, discount, bonus item (e.g., a free item), sale, upgrade, entry into a sweepstakes, rewards points, or early access (e.g., pre-sale, priority status, access before others).

[0130] In some embodiments, a recommended product and / or service may be shared (e.g., via a share feature) with one or more users of the database system described herein. For example, a recommended product, a recommended service, and / or other content generated by the user (e.g., files, links, data, media, messages) may be transmitted (e.g., by written, verbal, digital, analog, or other electronic means) to a service provider and / or another user of a database system described herein. This ability to share products, services, and user-generated content is sometimes referred herein as a “share feature.” In some embodiments, the share feature includes a hierarchical rating system (e.g., a five star rating system) or binary rating system (e.g., a “like” or “dislike” system) for a recommended product or service being shared. In some embodiments, the share feature includes the option for the user to leave a written review of the recommended product or service. In some embodiments, the share feature includes the option for users to communicate with other users who have uploaded genetic similarities.

[0131] Use of the database system to produce or visualize a virtual non-human animal

[0132] The present disclosure also includes methods for producing, identifying, and / or visualizing a virtual non-human animal (e.g., an animal that is not in existence) with one or more traits. To visualize a virtual animal, a user can first identify one or more predetermined traits of interest. The one or more predetermined traits may be selected from: coat color, coat color modifier, coat color intensity, coat texture, coat thickness, coat type, facial marking, leg marking, leg length, shedding, eye color, skin color, body size, tail shape, head shape, ear erectness, speed, gait, temperament, behavior, fear, aggression, performance, ability, health, species, breed, and breeding group. A user may select at least one predetermined trait of interest, but may select at least 2 (e.g., 2, 5, 10, 20, 40, 80, 100, 200, 300, 500, 1000, or more) predetermined traits for visualization of the virtual animal. Any traits required to predict an image of the virtual animal that is not selected by the user (e.g., non-predetermined traits) may be automatically populated. For example, a user may be interested in a virtual animal with the predetermined traits of small size, long hair, and both eyes blue. The probability index generated by this selection of predetermined traits includes an array of probability values pertaining to the likelihood that the virtual animal having the predetermined traits also has a plurality of non-predetermined traits. This probability index is then converted into a report that identifies the virtual non-human animal as at risk of developing the one or more predetermined traits, as having, not having, or at risk of developing a plurality of nonpredetermined traits, and a predictive image or rendering of the virtual animal. The report that includes the predictive image of the non-human animal can be presented to the user with the graphical user interface described herein.

[0133] The foregoing methods for providing a predictive image of a virtual animal (e.g., an animal that does not exist), may also be used to predict the image or likeness of a non-human animal that does exist. For example, instead of selecting the predetermined traits for assessment by the probability index, the probability would utilize the information (e.g., genetic, non-genetic information) about the non-human animal. Any traits required to produce the predictive image would be populated by the probability index as determining the likelihood of a non-human animal having, not having, or at risk of developing one or more traits dependent on the information about the non-human animal. The resulting probability index is then converted into a report along with the predictive image of the animal, for visualization by the user on a graphical user interface.

[0134] The image prediction described herein can be further refined based on the life-stage or age of an animal that a user is interested in visualizing. A user can select a life stage from the list of newborn, neonate, infant, adolescent, juvenile adult, senior or geriatric. A user can also select the age of an animal as 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, or 35 years of age.

[0135] The methods provided herein further provide for methods of producing a virtual non-human animal. As described above, a user may select one or more pre-determined traits of interest for the non- human animal, where the probability index is generated using information stored in the computer- implemented database system related to a plurality of non-human animals that have the one or more predetermined traits, and the probability index includes an array of probability values pertaining to the likelihood that the non-human animal that has the one or more predetermined traits also has a plurality of non-predetermined traits. The probability index is converted into a report that identifies the likelihood of the non-human animal as having or at risk of developing the one or more predetermined traits, and as having, not having, or at risk of developing the plurality of non-predetermined traits, in addition to a predictive image of the non-human animal. The method may further include identifying one or more breeding pairs that are likely to produce the non-human animal. One or more breeding pairs may then be selected, and subsequently bred. A non-human offspring animal that includes the one or more predetermined traits may then be birthed following a successful breeding, thus producing the non-human animal.

[0136] The methods may also be used to identify and / or recommend one or more products or services to a user of the database that relate to one or more traits or characteristics of the virtual non-human animal. For example, a virtual non-human animal produced by the database based on user selected traits and / or characteristics input into the platform may be identified by the database as likely to have or to be susceptible to develop one or more disease conditions or undesired traits. The database may identify and / or recommend one or more products or services to the user based on the disease condition or undesired trait determined by the database as being likely to arise in the animal if the user were to attempt to produce the animal in the real world (e.g., through breeding or genetic manipulation). Additionally, the user may have the option to share the identified or recommended product or service with other users of the database systems described herein (e.g., via a share feature).

[0137] Use of the database system to train a predictive computer model

[0138] The methods of the present disclosure include methods for predicting the likelihood of a nonhuman animal as having one or more traits through the use of a predictive computer model. The predictive computer model described herein uses a physical computing device that may be in networkconnection with the computer-implemented database system of the present disclosure. Training a predictive computer model with a plurality of data about a plurality of non-human animals can increase the accuracy of predicting the likelihood of an animal as having, not having, or at risk of developing one or more traits. The quality of the training data set that the predictive computer model is trained on, and the robustness of the training has downstream effects on the accuracy of the predictive computer model. A well-trained predictive computer model may be able to correlate information (e.g., genetic information or non-genetic information) about a non-human animal (e.g., the non-human animal of interest) with the likelihood of that non-human animal having, not having, or at risk of developing one or more traits that could otherwise be unidentified without the use of the predictive computer model.

[0139] The methods described herein include predicting the likelihood of a non-human animal (e.g., a dog) having, not having, or at risk of developing one or more traits, where the predictive computer model is first trained using a plurality of data about a plurality of non-human animals. Following the training of the predictive computer model, information (e.g., genetic information, non-genetic information) about a non-human of interest may be received (e.g., added to the computer-implemented database system, retrieved from storage in the computer-implemented database system, or other appropriate method of accessing the information), where a probability index is generated for the non-human animal. The probability index includes an array of probability values (e.g., at least 6.25%, 12.5%, 25%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or 100%) pertaining to the likelihood that the animal has, does not have, or is at risk of developing the one or more traits. The probability index is then converted into a report, which can then be presented to the user by way of a graphical user interface as described herein. The predictive computer model may also be utilized for identification of one or more products or services for a non-human animal that are specific for the one or more traits. The predictive computer model may also be used in any of the methods described herein.

[0140] Machine Learning

[0141] The present disclosure provides methods (e.g., computer-implemented methods) utilizing machine learning techniques for accomplishing a variety of tasks (e.g., predicting a likelihood that a non- human animal comprises one or more traits described herein; and assisting a user in identifying one or more products or services for a non-human animal based on the one or more traits). For example, the methods described herein may include the step of training a predictive computer model to analyze a complex data set (e.g., phenotype data, genotype data, and / or gene expression data) and / or media content (e.g., images, videos, and other renderings) from a non-human animal and provide a likelihood (e.g., by way of a probability index) that said non-human animal has one or more traits described herein. Machine training typically includes a learning phase (e.g., supervised learning, unsupervised learning, and / or semi-supervised learning), a validation phase, and a testing phase.

[0142] During the learning phase of machine training, a user may input a training data set (e.g., a labeled training data set, and unlabeled training data set, or a combination of both a labeled and an unlabeled training data set) into a supervised machine learning model, an unsupervised machine learning model, and / or a semi-supervised machine learning model. Conventionally, a supervised machine learning model utilizes a labeled training data set, an unsupervised machine learning model utilizes an unlabeled training data set, and a semi-supervised machine learning model utilizes a combination of both the labeled and unlabeled training data sets. Briefly, data labeling is the process of identifying raw data (e.g., phenotype data, genotype data, gene expression data images, text files, and videos) and adding one or more meaningful and informative labels to provide context so that the machine learning model can learn from it.

[0143] The process by which the unsupervised learning occurs is classically broken down into the following three phases.

[0144] (1 ) Clustering: looking for data that is similar and does or does not "cluster" together;

[0145] (2) Associated Rule Mining: looking at clusters and identifying commonalities or "rules" that apply to said cluster;

[0146] (3) Outlier Detection: locating rule breakers that do not fit to a certain degree into the identified commonality / rule.

[0147] Therefore, the unsupervised machine learning utilized in the methods described herein may include: a process of classifying similar data within the unlabeled training data (thereby generating one or more clusters of similar data); a process of identifying commonalities within the one or more clusters of similar data; and a process of identifying outlying data within the one or more clusters of similar data.

[0148] Machine training the predictive computer model may further include a validation step (e.g., testing a known set of data and seeing if a predicted outcome is true) and / or testing step (e.g., testing an unknown set of data and seeing if a predicted outcome is true). The quality of a trained predictive computer model can be dependent on its ability to pass the validation and testing phase. Typically, a predicative accuracy (e.g., a percentage of accurate test predications) greater than or equal to 60% (e.g., 70%, 75%, 80%, 85%, 90%, 95%, or more) is ideal, with at least 70% (e.g., 75%, 80%, 85%, 90%, 95%, or more) being preferable. In some embodiments, validation is done by comparison of the prediction with empirical results (e.g., evaluating the accuracy of breed prediction). In some embodiments, results of the validation step can be used to modify any rules of the machine learning algorithm, or as feedback for a new run of the model.

[0149] A variety of other machine training / learning techniques are well-known to those of skill in the art. For example, learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (e.g., a user’s input data) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a classification tree (e.g., a random forest) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem. Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (e.g., decision / classification trees such as random forests, classification and regression trees (C&RT), and boosted trees), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, and Bayesian learning in belief networks), reinforcement learning (e.g., passive learning) in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning actionvalue functions, applications of reinforcement learning, and genetic algorithms and evolutionary programming. Other learning statistical classifier systems include support vector machines (for example, Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss- Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ).

[0150] Information stored in the database

[0151] Multiple sources of information may be used in the systems and methods of the present disclosure to identify a non-human animal having, not having, or at risk of developing one or more traits. Among these sources, genetic information, epigenetic information, blood type information, and telomere information about the animal may be a critical factor in determining its ultimate phenotype. Without wishing to be bound by any theory, expression of one or more traits in the non-human animal may result from expression of one or more genes. Thus, the present disclosure provides methods for using information (e.g., genetic, epigenetic, blood type, and telomere information) to increase the likelihood of determining a non-human animal as having, not having, or at risk of developing one or more traits. The present disclosure also provides methods of using information (e.g., genetic, epigenetic, blood type, and telomere information) to produce a composition analysis for a non-human animal that assists in determining ancestral breed composition as well as parentage determination. The genetic basis for a large number of animal traits, predisposition for physical skills, and health and disease conditions has been established to date, as is well-known in the art, and this information can be incorporated into the system database and interrogated by a user to establish, e.g., a probability index, as described herein, which can be used to assess the phenotype of the animal.

[0152] Genetic Information a. Genetic markers of physical traits

[0153] The presence of genes or gene variants that affect coat color and markings in dogs can be tested and verified using methods described herein and known in the art. This information can be included in the database system and interrogated in order to produce a probability index, as is described herein. Patterns and color of facial markings in dogs may also be genetically determined. Furthermore, genetic information about the color of leg markings may be employed using the methods of the disclosure. Leg markings can be reported in the database for each reference animal and used as a selection criterion for the desired offspring animal.

[0154] Conformation is yet another physical trait that may be predicted through genetic profiling of an animal in accordance with the methods disclosed herein. Conformation deals with the physical attributes of the animal (e.g., a dog) and encompasses dimensions such as bone structure, musculature, and body proportions in relation to each other and the intended use of the animal. Conformation attributes can also be reported in the database for each reference animal. Conformation varies by dog breeding group (e.g., sporting, hound, working, terrier, toy, non-sporting, and herding), and further varies by specific breed within the breeding group (e.g., the toy breeding group includes affenpinschers, biewer terriers, brussels griffons, cavalier king Charles spaniels, chihuahuas, Chinese cresteds, English toy spaniels, Havanese, Italian greyhounds, Japanese chin, maltese, toy Manchester terriers, miniature pinschers, papillons, Pekingese, Pomeranians, toy poodles, pugs, Russian toy, shih tzu, silky terriers, toy fox terriers, and Yorkshire terriers) . Conformational traits may be measured in proportion, substance, height at the withers, withers height to body length ratio, head proportionality, facial expression, eye color, ear erectness, ear symmetrically, skull shape, muzzle shape, lip color, bite level, tail type, forequarters, hindquarters, coat harshness, coat color, coat furnishings, and pigmentation of paw pads, among others. Examples of genes associated with physical traits are found in Table 1 below.

[0155] Table 1. Exemplary Genes Associated with Phenotype / Appearance b. Genetic markers of behavioral traits

[0156] The methods and systems disclosed herein encompass genetic profiling of non-human animals for use in producing and / or rendering (e.g., virtually) an animal having an increased probability of exhibiting one or more desired physical and / or behavioral traits or predispositions. In the context of dogs, but also applicable to other non-human animals, such traits and predispositions include, but are not limited to temperament, speed, and gait. Information about these traits can be added to the database for each reference animal and can be used as a selection criterion for the desired offspring animal.

[0157] A non-limiting example of genes associated with non-human animal behavior is temperament. Animal temperament generally refers to stable individual patterns in behavior that are largely genetically determined and independent of learning. Non-limiting examples of behavioral traits pertaining to temperament in dogs may include, for example, curiosity, dominance / submissiveness, passivity, aggression, anxiety, impulsiveness, gazing, plasticity, work ethic, sloth, sociability, neuroticism, playfulness, territoriality, friendliness, intelligence, and obedience among others. Prior studies in dogs have demonstrated a genetic link between genes and behavior, such as, e.g., polymorphisms in the dopamine receptor D4 (DRD4) gene and gazing traits (Hori et al. Open J. Anim. Sci. 3:54-58, 2013). Variants of the same gene also show an association with impulsive behavior. Temperament information (genetic or phenotypic) can be reported in the database for each reference animal. For example, genetic correlations between the DRD4 gene and temperament can be employed according to the methods described herein to determine the likelihood that an animal will have a specific type of temperament.

[0158] Additional gene-behavior correlations (e.g., see Table 2 below) may be employed using the methods and systems disclosed herein to produce an animal having a desired temperament.

[0159] According to the present disclosure, temperament may be assessed in a non-human animal using genetic testing by, e.g., determining the presence of one or more polymorphisms in the DRD4 gene in the animal, as is discussed above. Additionally, or alternatively, the temperament of the non-human animal may be self-reported by a person familiar with the animal, such as an owner, breeder, seller, or caretaker. Such subjective assessments may be made using conventional methods, such as, e.g., rating the animal’s temperament on a scale from 1 to 10. A non-human animal assessed as having a selfreported temperament of 1 will generally be non-reactive, calm, and easy (i.e., “bombproof”). An animal assessed as having a self-reported temperament of 10 is generally high-energy, extremely spooky, and difficult to manage (i.e., “hot”). The present disclosure also allows for verification of self-reported assessments of animal temperament by means of genetic testing.

[0160] Table 2. Exemplary Genes Associated with Behavior c. Genetic diagnostic testing

[0161] As is described above, systems and methods of the disclosure can be used to identify a non- human animal (e.g., dog, cattle, sheep, horse, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non-human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig, among others) as having one or more traits based on the expression levels (e.g., mRNA expression level or protein expression level) of one or more biomarkers in a biological sample obtained from the animal. The biological sample can include, for example, cells, tissue (e.g., a tissue sample obtained by hair sample or biopsy, such as, e.g., fresh frozen or formalin-fixed paraffin embedded tissue), blood, serum, plasma, urine, sputum, nail clippings, cheek swab, hair follicles, cell-free fetal DNA, mitochondria, a gamete (e.g., oocyte or spermatozoon), or an embryo. Numerous methods of determining biomarker expression levels, or expression profiling, are known in the art, including, but not limited to, microarrays, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative real-time PCR (qPCR), Northern blots, Western blots, Southern blots, NanoString nCounter technologies (e.g., those described in U.S. Patent Application Nos. US 2011 / 0201515, US 2011 / 0229888, and US 2013 / 0017971 , each of which is incorporated by reference in its entirety), next generation sequencing (e.g., RNA-Seq techniques), and proteomic techniques (e.g., mass spectrometry or protein arrays).

[0162] Accordingly, the one or more biomarkers may be a nucleotide sequence (e.g., DNA or RNA) encoding a gene of interest. The nucleotide sequence may be embodied in a single- or double-stranded polynucleotide. The presence or absence of expression of a particular gene marker can be assessed using any of the methods known in the art for determining the nucleotide composition of a polynucleotide sequence. For example, the nucleotide sequence of a gene of interest can be assessed using nucleic acid sequencing technologies including but not limited to Sanger sequencing methods, Next Generation Sequencing (NGS; e.g., pyrosequencing, sequencing by reversible terminator chemistry, sequencing by ligation, and real-time sequencing) such as those offered on commercially available platforms (e.g., Illumina (e.g., AmpliSeq), Qiagen, Pacific Biosciences, Thermo Fisher, Roche, and Oxford Nanopore Technologies). Clonal amplification of target sequences for NGS may be performed using real-time polymerase chain reaction (also known as RT-qPCR) on commercially available platforms from Applied Biosystems, Roche, Stratagene, Cepheid, Eppendorf, or Bio-Rad Laboratories. Additionally, emulsion PCR methods can be used for amplification of target sequences using commercially available platforms such as Droplet Digital PCR by Bio-Rad Laboratories. Genomic sequencing may also be performed over the entire genome of a cell obtained from a non-human animal in order to detect a biomarker of interest. Exemplary analyses of an entire genome include but are not limited to a genome wide association study (GWAS) analysis. Other genes not described in the present disclosure may also be assayed according to known methods and information about those genes can also be incorporated into the database system for interrogation by a user according to the methods described herein. The results of a genetic analysis of an animal can also be incorporated into the database of the present disclosure and interrogated according to the systems and methods described herein.

[0163] The genetic markers for use in conjunction with the disclosure described herein may be single nucleotide polymorphism (SNP), tag SNP, microsatellite, short tandem repeat (STR), simple sequence repeat (SSR), restriction fragment length polymorphism (RFLP), variable number tandem repeats (VNTRs), amplified fragment length polymorphism (AFLP), insertion-deletion polymorphism (INDEL), random amplified polymorphic DNA (RAPD), ligase chain reaction, or a simple sequence conformation polymorphisms (SSCP).

[0164] In instances in which the genetic marker is an SNP, the disclosure provides methods for using SNP information gathered from a biological sample of the animal for genetic analysis. SNPs are particularly useful for analyzing the genome of mammals. First, SNPs occur with greater frequency (approximately 10 to 100 times larger), and with greater uniformity than other polymorphic markers such as RFLPs and VNTRs. The greater frequency of SNPs means that they can be more readily identified than the other classes of polymorphisms. The greater uniformity of their distribution permits the identification of SNPs that are "closer" to a particular attribute of interest. For example, if a particular trait (e.g., speed) reflects a mutation at a particular location (e.g., a myostatin gene locus), then any polymorphism that is linked to the specific gene locus, can be used to predict the likelihood that an individual will show that trait. SNPs are also more stable than other classes of polymorphisms. Their spontaneous mutation rate is approximately 109, approximately 1 ,000 times less frequently than VNTRs, which exhibit significantly higher mutation rates. Furthermore, SNPs have the additional advantage that their allele frequency can be determined from a smaller sample size as compared to other polymorphisms such as RFLPs or VNTRs, thereby offering a higher degree of genetic resolution for determining individual identity, pedigree, and susceptibility of an animal to a particular genetic trait. As an additional benefit, SNPs cover the whole genome, which may facilitate identification of potential interactions of gene products expressed from anywhere on the genome, without requiring prior knowledge about a potential interaction between genes.

[0165] Accordingly, SNPs can be used in conjunction with the methods and systems of the present disclosure for genetic diagnostic testing and ancestry and parentage determination of a non-human animal (e.g., a dog). For example, the method of the present disclosure can be used to determine the probability that an animal, such as a dog, has a particular disease or non-disease condition (see, e.g., Table 3 below), is a member of a particular breed, or is an offspring of a specific animal or breeding pair. Detection of disease or non-disease conditions can be performed in the animal by detecting the presence of one or more disease- or non-disease condition-associated SNPs. Ancestry determination using SNP analysis can be carried out by analyzing genetic markers (e.g., SNPs) across the entire genome of the animal to identify the predominant overlapping markers present in the animal of interest and its ancestors. For the uses described above, lineage determination for a particular animal may be aided using the disclosure in conjunction with art-recognized methods.

[0166] Table 3. Exemplary Genes Associated with a Disease / Condition

[0167]

[0168]

[0169]

[0170] Epigenetic information

[0171] Some embodiments of the methods described herein use epigenetic information about the nonhuman animal. Epigenetic information regards alterations to the epigenome due to chemical modifications to the DNA or DNA-associated proteins that may influence gene expression, without altering the DNA sequence. These changes include DNA methylation, histone modification, non-coding RNA-associated gene silencing, and chromatin remodeling. Epigenetic information can be obtained by sequencing methods including bisulfite-sequencing, immunoprecipitation (e.g., methylated DNA immunoprecipitation sequencing and chromatin immunoprecipitation sequencing), chromatin accessibility analysis sequencing, long-read sequencing, and cleavage under targets & release using nuclease (CUT&RUN).

[0172] Blood Type information

[0173] Some embodiments of the methods described herein use blood type information about the nonhuman animal. Blood type information may include an ABO blood group classification (e.g., Type A, Type B, Type AB, or Type O). Blood type may be classified as positive or negative (e.g., A+, A-, B+, B-, AB+,

[0174] AB-, O+, or O-). Rh typing may be used to determine the presence of the Rh factor to classify the blood type as positive or negative. Telomere information

[0175] Some embodiments of the methods described herein use telomere information about the nonhuman animal. Telomere information can include telomere length. Telomeres shorten with each cell division; therefore, this process is linked to aging and age-related diseases. The rate of telomere shortening can be obtained from the telomere length information, therefore telomere length and the rate of shortening may be used to determine the non-human animal’s age (e.g., cellular age), lifespan, and the extent of genetic breakdown or DNA damage. Telomere information may be obtained by standard molecular techniques in the field, such as terminal restriction fragment (TRF) analysis, telomere shortest length assay (TeSLA), long-read sequencing (e.g., Nanopore or PacBio sequencing), southern blot, fluorescent in situ hybridization (FISH), quantitative polymerase chain reaction (qPCR), single telomere length analysis (STELA), molecular combing (TCA), and flow cytometry with FISH (Flow-FISH).

[0176] Non-genetic information gathered from secondary sources

[0177] While the genetic constitution of an animal may be determinative for a variety of different physical and behavioral traits, training and environmental factors also play a role. Relatedly, not all traits can presently be accounted for by genetic analysis alone. Therefore, the instant disclosure provides methods for gathering information about a non-human animal (e.g., a dog) from secondary (e.g., non-genetic) sources in order to complement genetic information about the animal. Combining genetic and non-genetic information about the animal according to the methods described herein provides a holistic animal profile that can be used to predict the phenotype of an animal with high confidence and / or to inform breeding cross-match evaluations for use in producing an animal having one or more traits.

[0178] The present disclosure allows for gathering of information from a variety of secondary sources including, but not limited to someone knowledgeable about the animal such as, e.g., the owner of the animal, an animal breeder / seller, or a caretaker; scores assigned to the animal during a competition pertaining to one or more traits; clinical / veterinary diagnostic testing performed in the animal pertaining to one or more traits; and a clinical assay of biological tissue obtained from the animal pertaining to one or more traits. Other secondary sources of information about an animal pertaining to one or more traits not described herein may be catalogued and added to the database in order to provide a more comprehensive non-genetic profile of the animal. The secondary source information can be added to the database for any reference animal and can be used, if desired, as a selection criterion for a desired animal (e.g., to facilitate the generation of a virtual animal, which can be used to assess whether or not to produce (e.g., by breeding or genetic manipulation) a real world animal with the one or more traits). a. Information gathered from someone knowledgeable about the animal

[0179] With respect to secondary source information gathered from someone knowledgeable about the animal, such information can be obtained from the owner / breeder / seller / caretaker of the animal. This information can be obtained in the form of a questionnaire. Such questionnaires can be designed to obtain detailed information about specific behaviors and skills of the animal of interest. Questionnaires are directed to a number of traits which include, but are not limited to spooking, jumping skill, speed, temperament, and gait, among others. Additionally, questionnaires relating to the health of the animal can also be gathered. Such questionnaires can include, for example, questions about the respiratory and digestive systems, lameness, bacterial, fungal, or viral susceptibility of the animal, among others. In some embodiments, information about the non-human animal may also be obtained in the form of media content (e.g., an image, video, and / or rendering of the non-human animal).

[0180] Information from someone knowledgeable about the animal may be obtained prior to performing the methods described herein (e.g., the information can be entered into a database that is part of a system described herein and / or that can be interrogated as part of the methods described herein). In some embodiments, information from someone knowledgeable about the non-human animal may be obtained after a report is generated following the methods disclosed herein. In some embodiments, information obtained after the report has been generated as a way of validating the report and / or providing user feedback. In this embodiment, someone knowledgeable about the animal may express their feedback or opinion regarding the accuracy of the report. b. Information gathered from sporting or competition events

[0181] With respect to secondary source information about the animal gathered during a sporting event or competition, such information can readily be made available as dogs often showcase their skills at various events and judged on a gamut of physical and behavioral characteristics. Judges will evaluate an animal (e.g., a dog) based upon standardized scoring and marking system used in officially sanctioned events. The factors that are considered during judgement include, but are not limited to jumping ability, speed, quality of walk, movement to music, and temperament. c. Information gathered from veterinary assays

[0182] The methods of the disclosure can also utilize non-genetic information gathered about a non- human animal from a clinical / veterinary assay obtained from the animal pertaining to one or more traits. As technology and veterinary science have advanced, clinical laboratory testing has become useful in determining the parentage, health, and any ongoing infections in the animal. Just as in humans, blood analysis can be performed to assess the health of the animal such as, for example, measuring blood sugar levels, blood urea nitrogen, amylase, total calcium, and many other clinical lab values. Equipment manufacturers have also developed new equipment which can be configured to allow X-rays, CAT scans, PET scans, and several other advanced imaging techniques applied to an animal (e.g., a dog). Therefore, a variety of information may be measured, especially those related to traits of interest, including those related or thought to relate to performance characteristics, physical structure, and / or disease predisposition. These measurements may include, but are not limited to, conformational and physiological parameters such as height, weight, limb length, limb angle, muscle volume, resting heart rate, time to resting heart rate after physical exertion, blood pressure, maximum oxygen uptake (VC max), maximum carbon dioxide production (VCC max), blood volume at rest and exercise, rebreathing measurements of lung volumes, maximum sprint speed, heart size, complete blood counter including white blood cell count, red blood cell count, hemoglobin levels, hematocrit, platelet count, and other blood cell morphology and biomarker evaluations and measurements, metabolic assays including blood sugar, protein, cholesterol, BUN, and interleukin-6 levels, DNA-based assessment of telomere length, fibrinogen levels, fecal ulcer PCR, transthyretin levels, and health parameters such as history of joint, skin, and diseases or conditions such as cardiovascular disease, orthopedic diseases, chronic obstructive pulmonary disease, pulmonary “bleeding” during extreme exertion, muscle diseases like exertional rhabdomyolysis, immune system disorders causing sarcoid tumors, and insect bite hypersensitivity. The condition may include normal, apparently normal, pre-clinical disease, overt disease, progress and / or stage of disease, undiagnosed or unclassified conditions, presence of drugs, response to exercise, response to vaccines, therapies, nutritional states, and response to environmental conditions. The disease may include inflammation or involvement of the immune system, and conditions affecting respiratory, musculoskeletal, urinary, gastrointestinal, adnexal, cardiovascular, reticuloendothelial, nervous, special senses, reproductive, and integument systems. Such conditions in the dog include laminitis, lameness, viral or bacterial disease, colic, gastritis, gastric ulcers, respiratory ailments, epistaxis, fractures, musculoskeletal damage or disorders, and joint disease, among others. The listed set of physical and physiological parameters that may be tested according to the methods described herein are not exhaustive and additional parameters may also be subject to clinical / veterinary assessment.

[0183] The aforementioned non-genetic information gathered from secondary sources may be used independently or in combination with one or more pieces of genetic information about a known or a desired non-human animal in order to produce a holistic assessment of the animal. Such an assessment may be useful for purchasing or breeding decisions by informing the buyer or breeder regarding any known predispositions or bases for genetically-linked traits or conditions, or by informing the buyer or breeder about known history of health conditions, past diagnostic assessments, and past performance in specific competitions or disciplines. Such information may be correlated within the database system, e.g., when an observed non-genetic source of information about an animal’s health or behavior is reflected in the genetic profile of the animal. Alternatively, the non-genetic information can be independent or complementary to the genetic information.

[0184] Information about products and services

[0185] The methods of the present disclosure include information and / or recommendations to a user about one or more products and / or services that are related to one or more traits of interest (or to a specific non-human animal). Information about products and services is intended to be integrated into the database system (e.g., in a subsystem), and correlated with specific traits for which the products and / or services might be of use to the non-human animal, for use by the non-human animal, or for use with the non-human animal.

[0186] A report may include information about any number of products or services specific for one or more traits. In some embodiments the information about the one or more products or services may include at least 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55,

[0187] 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83,

[0188] 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99, or 100 or more products or services. In some embodiments, the information about the one or more products or services may include at least between

[0189] 1 -2, 1 -5, 1 -10, 1 -15, 1 -20, 1 -25, 1 -30, 1 -35, 1 -40, 1 -45, 1 -50, 1 -55, 1 -60, 1 -65, 1 -70, 1 -75, 1 -80, 1 -85, 1 - 90, 1 -95, 1 -100, 1 -150, 1 -200, 1 -250, 1 -300, 1 -350, 1 -400, 1 -450, 1 -500; 500-450, 500-400, 500-350, 500-300, 500-250, 500-200, 500-150, 500-100, 500-50, 500-25, 500-5; 100-90, 100-80, 100-70, 100-60, 100-50, 100-40, 100-30, 100-20, 100-10; 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90; 5-15, 10-25, 20-35, 25-40, 30-45, 35-50, 40-55, 45-60, 50-65, 55-70, 60-75, 65-80, 70-85, 75-90, 80-95, or 85- 100 products or services. The products may include but are not limited to products such as a medication, a wearable product (e.g., collar, leash, booties, among others), a hygiene-related product, a breed-specific product, a trait specific product (e.g., blueing shampoo for white coats, copper enhancing shampoo for red coats, among others), an edible product (e.g., treats, food, among others), an environmental enrichment product, an enrichment product, an enhancing product, a training product, a sporting product, an exercise product, safety product (e.g., fencing, gates, among others), a furniture product (e.g., standalone stairs, among others), and a recreational product.

[0190] The services may include but are not limited to services such as veterinary services, training services, education services, ancestry analysis services, pedigree services, grooming services, parentage services, husbandry services, care services, enrichment services, facility services, boarding services, recreational services, monitoring services (e.g., microchip services), and pet-sitting services.

[0191] In some embodiments, an incentive may be provided for a user to purchase or access the one or more products or services. Such an incentive may include but is not limited to a coupon, discount, bonus item (e.g., a free item), sale, upgrade, entry into a sweepstakes, rewards points, or early access (e.g., pre-sale, priority status, access before others).

[0192] The products and / or services may be associated (e.g., related, correlated) with one or more traits. Table 4 below, provides non-limiting examples of traits and products or services that may be useful to a non-human animal, or the animal’s owner or caretaker. In some embodiments, information for one or more products may be provided. In some embodiments, information for one or more services may be provided. In some embodiments, information for one or more products and one or more services may be provided. Any and all brand products exemplified herein are for illustrative purposes only, and are not represented as approved or sanctioned by any provider of a product and / or service.

[0193] Table 4: Exemplary genes and traits associated with products and services

[0194] Non-human animals

[0195] The present disclosure provides methods (e.g., computer-implemented methods) for accomplishing a variety of tasks related to a non-human animal (e.g., predicting a likelihood that a non- human animal comprises one or more traits described herein; and assisting a user in identifying one or more products or services for a non-human animal). Exemplary non-human animals for use in these methods include canines (e.g., dogs, e.g., domesticated dogs), horses, cattle, sheep, cats, camels, pigs, goats, alpacas, donkeys, llamas, red foxes (e.g., vulpines), mice, rats, ferrets, primates (e.g., non-human primates), rabbits, gerbils, hamsters, chinchillas, or guinea pigs.

[0196] Traits

[0197] A non-human animal described herein may have one or more traits. The one or more traits of the present disclosure may include coat color, coat color modifier, coat color intensity, coat texture, coat thickness, coat type, facial marking, leg marking, leg length, webbed paws, water-repellent coat, shedding, eye color, skin color, body size, tail shape, tail length, head shape, ear erectness, ear length, ear position (e.g., high, medium, low), speed, gait, temperament, muscle type, jumping ability, catching ability, hunting ability, herding ability (e.g., herding, non-herding), behavior, fear, aggression, performance, ability, drive (e.g., high, medium, low), adaptability (e.g., to altitude, to loud noises), health, species, breed, and breeding group.

[0198] A non-human animal may be a canine. A canine, for example, may be any member of the Caninae subfamily, including, but not limited to, domesticated dogs, wolves, coyotes, foxes, and jackals. Exemplary canines include: an arctic fox (e.g., Alopex lagopus), a short-eared dog (e.g., Atelocynus microtis), a side-striped jackal (e.g., Canis adustus), a golden jackal (e.g., Canis aureus), a domesticated dog (e.g., Canis familiaris), a coyote (e.g., Canis latrans), a gray wolf (e.g., Canis lupus), a black-backed jackal (e.g., Canis mesomelas), a red wolf (e.g., Canis rufus), an ethiopian wolf (e.g., Canis simensis), a crab-eating fox (e.g., Cerdocyon thous), a maned wolf (e.g., Chrysocyon brachyurus), a dhole (e.g., Cuon alpinus), an African wild dog (e.g., Lycaon pictus), a raccoon dog (e.g., Nyctereutes procyonoides), a bat- eared fox (e.g., Otocyon megalotis), a culpeo (e.g., Pseudalopex culpaeus), a Darwin's fox (e.g., Pseudalopex fulvipes), a chilla (e.g., Pseudalopex griseus), a pampas fox (e.g., Pseudalopex gymnocercus), a sechuran desert fox (e.g., Pseudalopex sechurae), a hoary fox (e.g., Pseudalopex veturus), a bushdog (e.g., Speothos venaticus), a gray fox (e.g., Urocyon cinereoargenteus), a Channel Island fox (e.g., Urocyon littoralis), an Indian fox (e.g., Vulpes bengalensis), a blanford's fox (e.g., Vulpes cana), a cape fox (e.g., Vulpes chama), a corsac fox (e.g., Vulpes corsac), a Tibetan fox (e.g., Vulpes ferrilata), a kit fox (e.g., Vulpes macrotis), a pale fox (e.g., Vulpes pallida), a Ruppell's fox (e.g., Vulpes rueppelli), a swift fox (e.g., Vulpes velox), a red fox (e.g., Vulpes vulpes), and a fennec fox (e.g., Vulpes zerda).

[0199] The domesticated dog (e.g., Canis familiaris) may be any dog associated with one or more of the following breeding groups: a herding group; a hound group; a toy group; a non-sporting group; a sporting group; a terrier group; and a working group. Exemplary dog breeds within each of these groups in described below.

[0200] The following dogs are non-limiting examples of breeds belonging to the herding group: Armant dog, Australian Cattle Dog, Australian Kelpie, Australian Shepherd, Australian Stumpy Tail Cattle Dog, Basque Shepherd Dog, Bearded Collie, Beauceron, Belgian Shepherd, Bergamasco Shepherd, Berger Picard, Blue Lacy, Bohemian Shepherd, Border Collie, Bouvier des Ardennes, Bouvier des Flandres, Briard, Can de Chira, Canaan Dog, Cane Paratore, Cantabrian Water Dog, Cardigan Welsh Corgi, Carea Leones, Catahoula Leopard Dog, Catalan Sheepdog, Collie, Corsican Dog, Croatian Sheepdog, Cumberland Sheepdog, Cur, Dutch Shepherd, East European Shepherd, English Shepherd, Faroese Sheepdog, Finnish Lapphund, Gaucho sheepdog, German Shepherd, Gurdbasar, Halls Heeler, Huntaway, Icelandic Sheepdog, Koolie, Lancashire Heeler, Lapponian Herder, Lapponian Shepherd, McNab dog, Miniature American Shepherd, Mudi, New Zealand Heading Dog, Norwegian Buhund, Old English Sheepdog, Old Welsh Grey Sheepdog, Pastor Garafiano, Pastore della Lessinia e del Lagorai, Pembroke Welsh Corgi, Polish Lowland Sheepdog, Portuguese Sheepdog, Puli dog, Pumi dog, Pyrenean Sheepdog, Rottweiler, Rough Collie, Saint Miguel Cattle Dog, Sardinian Shepherd Dog, Schapendoes, Schipperke, Sheepdog trial, Shetland Sheepdog, Smithfield (dog), Smooth Collie, Spanish Water Dog, Swedish Lapphund, Swedish Vallhund, Tibetan Terrier, Welsh Corgi, Welsh Hillman, Welsh Sheepdog, White Shepherd, and White Swiss Shepherd Dog.

[0201] The following dogs are non-limiting examples of breeds belonging to the hound group: Afghan Hound, Africanis, Alpine Dachsbracke, American Foxhound, American Leopard Hound, Andalusian Hound, Artois Hound, Austrian Black and Tan Hound, Azawakh, Basenji, Basset Artesien Normand, Basset Bleu de Gascogne, Basset Fauve de Bretagne, Basset Hound, Bavarian Mountain Hound, Beagle, Beagle-Harrier, Billy, Black and Tan Coonhound, Blackmouth Cur, Bloodhound, Bluetick Coonhound, Borzoi, Bosnian Broken-haired Hound, Briquet Griffon Vendeen, Chippiparai, Cirneco dell'Etna, Combai, Coonhound, Cretan Hound, Dachshund, Drever, Dumfriesshire Black and Tan Foxhound, Estonian Hound, English Coonhound, English Foxhound, Feist, Finnish Hound, Galgo Espanol, Gascon Saintongeois, German Hound, Grand Basset Griffon Vendeen, Grand Bleu de Gascogne, Grand Fauve de Bretagne, Grand Griffon Vendeen, Greek Harehound, Greyhound, Griffon Bleu de Gascogne, Griffon Fauve de Bretagne, Hamiltonstovare, Hanover Hound, Harrier, Ibizan Hound, Indian pariah dog, Italian Greyhound, Irish Wolfhound, Istrian Coarse-haired Hound, Istrian Shorthaired Hound, Kai Ken, Kanni, Kishu Ken, Lakeland Trailhound, Lithuanian Hound, Longdog, Lurcher, Magyar agar, Mountain Cur, Mudhol Hound, Otterhound, Petit Basset Griffon Vendeen, Petit Bleu de Gascogne, Pharaoh Hound, Plott Hound, Podenco Canario, Polish Greyhound, Polish Hound, Portuguese Podengo, Posavac Hound, Rajapalayam, Rampur Greyhound, Rastreador Brasileiro, Redbone Coonhound, Rhodesian Ridgeback, Sabueso Espanol, Saluki, Schillerstbvare, Segugio dell'Appennino, Segugio Italiano a pelo forte, Segugio Italiano a pelo raso, Segugio Maremmano, Serbian Hound, Serbian Tricolour Hound, Schweizer Laufhund, Schweizerischer Niederlaufhund, Scottish Deerhound, Shikoku, Silken Windhound, Sloughi, Slovak Hound, Smalandstovare, Styrian Coarse-haired Hound, Taigan, Tatransky duric or Tatra Hound, Treeing Walker Coonhound, Trigg Hound, Transylvanian Hound, Tyrolean Hound, Ukrainian Chortai, Welsh Foxhound, Westphalian Dachsbracke, and Whippet.

[0202] The following dogs are non-limiting examples of breeds belonging to the toy group: Affenpinscher, Australian Silky Terrier, Bichon Frise, Bichon Havanais, Havanese, Bolognese, Boston Terrier, Bouledogue Frangais, French Bulldog, Caniche, Poodle, Cavalier King Charles Spaniel, Chihuahua, Chihuahueho, Chihuahua, Chin, Japanese Chin, Chinese Crested Dog, Continental Toy Spaniel, Coton de Tulear, Dwarf German Spitz: Pomeranian, Epagneul Nain Continental, Griffon Beige, Griffon Bruxellois, Brussels Griffon, Italian Greyhound, King Charles Spaniel, Kromfohrlander, Lhasa Apso, Maltese, Mi-Ki, Miniature Pinscher, Papi lion, Pekingese, Petit Brabangon, Small Brabant Griffon, Petit Chien Lion, Phalene, Pug, Russkiy Toy, Shih Tzu, Tibetan Spaniel, Tibetan Terrier, Toy Fox Terrier, Toy Manchester Terrier, Volpino Italiano, Xoloitzcuintle, and Yorkshire Terrier.

[0203] The following dogs are non-limiting examples of breeds belonging to the non-sporting group: American Eskimo Dog, Bichon Frise, Boston Terrier, Bulldog, Chinese Shar-Pei, Chow Chow, Coton de Tulear, Dalmatian, Finnish Spitz, French Bulldog, Keeshond, Lhasa Apso, Lochen, Norwegian Lundehund, Poodle , Schipperke, Shiba Inu, Tibetan Spaniel, Tibetan Terrier, and Xoloitzcuintli.

[0204] The following dogs are non-limiting examples of breeds belonging to the sporting group: American Cocker Spaniel, American Water Spaniel, Barbet, Boykin Spaniel, Bracco Italiano, Braque du Bourbonnais, Brittany, Burgos Pointer, Cesky Fousek, Chesapeake Bay Retriever, Clumber Spaniel, Corded Poodle, Curly Coated Retriever, Drentsche Patrijshond, English Cocker Spaniel, English Setter, English Springer Spaniel, Field Spaniel, Flat-Coated Retriever, German Longhaired Pointer, German Shorthaired Pointer, German Spaniel, German Wirehaired Pointer, Golden Retriever, Gordon Setter, Hungarian Wirehaired Vizsla, Irish Red and White Setter, Irish Setter, Irish Water Spaniel, Kooikerhondje, Labrador Retriever, Lagotto Romagnolo, Large Munsterlander, Nova Scotia Duck Tolling Retriever, Pointer, Portuguese Pointer, Portuguese Water Dog, Pudelpointer, Small Munsterlander, Spanish Water Dog, Spinone Italiano, Standard Poodle, Stichelhaar, Sussex Spaniel, Vizsla, Weimaraner, Welsh Springer Spaniel, Wirehaired Pointing Griffon, and Wirehaired Vizsla.

[0205] The following dogs are non-limiting examples of breeds belonging to the terrier group: Airedale Terrier, American Hairless Terrier, American Pit Bull Terrier, American Staffordshire Terrier, Australian Silky Terrier, Australian Terrier, Bedlington Terrier, Black and Tan Terrier, Black Russian Terrier, Border Terrier, Boston Terrier, Brazilian Terrier, Bull and terrier, Bull Terrier, Ca Rater Mallorqui, Cairn Terrier, Cesky Terrier, Chilean Terrier, Dandie Dinmont Terrier, English Toy Terrier (Black & Tan), Fell Terrier, Fox Terrier, Glen of Imaal Terrier, Hunt terrier, Irish Terrier, Jack Russell Terrier, Jagdterrier, Japanese Terrier, Kerry Blue Terrier, Lakeland Terrier, List of toy terriers, Lucas Terrier, Manchester Terrier, Miniature Bull Terrier, Miniature Fox Terrier, Norfolk Terrier, Norwich Terrier, Parson Russell Terrier, Patterdale Terrier, Rat Terrier, Ratonero Bodeguero Andaluz, Ratonero Murciano, Russell Terrier, Scottish Terrier, Sealyham Terrier, Skye Terrier, Smooth Fox Terrier, Soft-coated Wheaten Terrier, Sporting Lucas Terrier, Staffordshire Bull Terrier, Teddy Roosevelt Terrier, Tenterfield Terrier, Terrier, Tiny the Wonder, Toy Fox Terrier, Toy Manchester Terrier, Valencian Terrier, Welsh Terrier, West Highland White Terrier, Willie, Wire Fox Terrier, Working terrier, and Yorkshire Terrier.

[0206] The following dogs are non-limiting examples of breeds belonging to the working group: Akita, Alaskan Malamute, Anatolian Shepherd Dog, Bernese Mountain Dog, Black Russian Terrier, Boerboels , Boxer, Bullmastiff, Cane Corso, Chinook , Doberman Pinscher, Dogo Argentino, Dogue de Bordeaux, German Pinscher, Giant Schnauzer, Great Dane, Great Pyrenees, Greater Swiss Mountain Dog , Konondor, Kuvasz, Leoberger, Mastiff, Neapolitan Mastiff, Newfoundland, Portuguese Water Dog , Rottweiler , Saint Bernard, Samoyed , Siberian Husky, Standard Schnauzer, and Tibetan Mastiff. The non-human animal may be any domesticated dog (e.g., Canis familiaris), such as any of the following dog breeds: Affenpinscher, Afghan Hound, Africanis, Airedale Terrier, Akita, Alaskan Klee Kai, Alaskan Malamute, Alpine Dachsbracke, American Bulldog, American Cocker Spaniel, American Eskimo Dog, American Foxhound, American Hairless Terrier, American Leopard Hound, American Pit Bull Terrier, American Staffordshire Terrier, American Water Spaniel, Anatolian Shepherd Dog, Andalusian Hound, Armant dog, Artois Hound, Aussiedoodle, Australian Cattle Dog, Australian Kelpie, Australian Labradoodle, Australian Shepherd, Australian Silky Terrier, Australian Stumpy Tail Cattle Dog, Australian Terrier, Australian Working Kelpie, Austrian Black and Tan Hound, Azawakh, Barbet, Basenji, Basque Shepherd Dog, Bassador, Basset Artesien Normand, Basset Bleu De Gascogne, Basset Fauve De Bretagne, Basset Hound, Bassugg, Bavarian Mountain Hound, Beagador, Beagle, Beagle-Harrier, Beaglier, Bearded Collie, Beauceron, Bedlington Terrier, Bedlington Whippet, Belgian Groenendael, Belgian Laekenois, Belgian Malinois, Belgian Shepherd, Belgian Tervuren, Bergamasco, Bergamasco Shepherd, Berger Picard, Bernedoodle, Bernese Mountain Dog, Bichon Frise, Bichon Frise, Bichon Havanais, Havanese, Bichon Yorkie, Bich-poo, Biewer Terrier, Billy, Black and Tan Coonhound, Black and Tan Terrier, Black Russian Terrier, Blackmouth Cur, Bloodhound, Blue Lacy, Bluetick Coonhound, Boerboel, Boerboels , Bohemian Shepherd, Bolognese, Borador, Border Collie, Border Jack, Border Terrier, Bordoodle, Borzoi, Bosnian Broken-haired Hound, Boston Terrier, Bouledogue Frangais, French Bulldog, Bouvier des Ardennes, Bouvier Des Flandres, Boxador, Boxer, Boykin Spaniel, Bracco Italiano, Braque D'Auvergne, Braque du Bourbonnais, Brazilian Terrier, Briard, Briquet Griffon Vendeen, Brittany, Bugg, Bull and terrier, Bull Pei, Bull Terrier, Bulldog , Bullmastiff, Burgos Pointer, Ca Rater Mallorqui, Cairn Terrier, Can de Chira, Canaan Dog, Canadian Eskimo Dog, Cane Corso, Cane Corso Italiano, Cane Paratore, Caniche, Poodle, Cantabrian Water Dog, Cardigan Welsh Corgi, Carea Leones, Catahoula Leopard Dog, Catalan Sheepdog, Caucasian Shepherd Dog, Cava Tzu, Cavachon, Cavalier King Charles Spaniel, Cavapom, Cavapoo, Cavapoochon, Cesky Fousek, Cesky Terrier, Cheagle, Chesapeake Bay Retriever, Chi Staffy Bull, Chihuahua, Chihuahueno, Chihuahua, Chilean Terrier, Chin, Japanese Chin, Chinese Crested Dog, Chinese Shar-Pei, Chinook, Chinook , Chipoo, Chippiparai, Chiweenie, Chorkie, Chow Chow, Chow Shepherd, Chug, Chusky, Cirneco Dell'Etna, Clumber Spaniel, Cockachon, Cockador, Cockapoo, Cocker Spaniel, Cojack, Collie, Combai, Continental Toy Spaniel, Coonhound, Corded Poodle, Corgi, Corsican Dog, Coton De Tulear, Coton de Tulear, Cretan Hound, Croatian Sheepdog, Cumberland Sheepdog, Cur, Curly Coated Retriever, Dachshund, Dalmatian, Dameranian, Dandie Dinmont Terrier, Deerhound, Doberman Pinscher, Dobermann, Dogo Argentino, Dogue de Bordeaux, Dorkie, Doxiepoo, Drentsche Patrijshond, Drever, Dumfriesshire Black and Tan Foxhound, Dutch Shepherd, Dwarf German Spitz: Pomeranian, East European Shepherd, English Bulldog, English Cocker Spaniel, English Coonhound, English Foxhound, English Setter, English Shepherd, English Springer Spaniel, English Toy Terrier, Entlebucher Mountain Dog, Epagneul Nain Continental, Estonian Hound, Estrela Mountain Dog, Eurasier, Faroese Sheepdog, Feist, Fell Terrier, Field Spaniel, Finnish Hound, Finnish Lapphund, Finnish Spitz, Flat-Coated Retriever, Fox Terrier, Foxhound, French Bull Jack, French Bulldog, French Pin, Frenchie Staff, Frug, Galgo Espanol, Gascon Saintongeois, Gaucho sheepdog, Gerberian Shepsky, German Hound, German Longhaired Pointer, German Pinscher, German Shepherd, German Sheprador, German Shorthaired Pointer, German Spaniel, German Spitz, German Wirehaired Pointer, Giant Schnauzer, Glen Of Imaal Terrier, Goberian, Golden Dox, Golden Labrador, Golden Retriever, Golden Shepherd, Goldendoodle, Gordon Setter, Grand Basset Griffon Vendeen, Grand Basset Griffon Vendeen, Grand Bleu De Gascogne, Grand Fauve de Bretagne, Grand Griffon Vendeen, Great Dane, Great Pyrenees, Greater Swiss Mountain Dog, Greek Harehound, Greenland Dog, Greyhound, Griffon Beige, Griffon Bleu de Gascogne, Griffon Bruxellois, Griffon Fauve De Bretagne, Gurdbasar, Hairless Chinese Crested, Halls Heeler, Hamiltonstovare, Hanover Hound, Harrier, Havanese, Horgi, Hovawart, Hungarian Kuvasz, Hungarian Puli, Hungarian Pumi, Hungarian Vizsla, Hungarian Wirehaired Vizsla, Hunt terrier, Huntaway, Ibizan Hound, Icelandic Sheepdog, Indian pariah dog, Irish Doodle, Irish Red and White Setter, Irish Setter, Irish Terrier, Irish Water Spaniel, Irish Wolfhound, Istrian Coarse-haired Hound, Istrian Shorthaired Hound, Italian Greyhound, Italian Spinone, Jack Russell Terrier, Jack-A-Bee, Jackahuahua, Jack-A-Poo, Jackshund, Jacktzu, Jagdterrier, Japanese Akita, Japanese Chin, Japanese Shiba, Japanese Spitz, Japanese Terrier, Johnson American Bulldog, Jug, Kai Ken, Kanni, Keeshond, Kerry Blue Terrier, King Charles Spaniel, Kishu Ken, Kokoni, Komondor, Konondor, Kooikerhondje, Koolie, Korean Jindo, Korthals Griffon, Kromfohrlander, Kuvasz, Labradoodle, Labrador Retriever, Lachon, Lagotto Romagnolo, Lakeland Terrier, Lakeland Trailhound, Lancashire Heeler, Lapponian Herder, Lapponian Shepherd, Large Munsterlander, Large Munsterlander, Leoberger, Leonberger, Lhasa Apso, Lhasapoo, Lhatese, Lithuanian Hound, Lochen, Longdog, Lowchen, Lucas Terrier, Lurcher, Magyar agar, Mai-Shi, Maltese, Maltichon, Maltipom, Malti-Poo, Manchester Terrier, Maremma Sheepdog, Mastiff, McNab dog, Mexican Hairless, Mi-Ki, Miniature American Shepherd, Miniature Bull Terrier, Miniature Fox Terrier, Miniature Pinscher, Miniature Poodle, Miniature Schnauzer, Miniature Schnoxie, Morkie, Mountain Cur, Mudhol Hound, Mudi, Neapolitan Mastiff, New Zealand Heading Dog, New Zealand Huntaway, Newfoundland, Norfolk Terrier, Northern Inuit, Norwegian Buhund, Norwegian Elkhound, Norwegian Lundehund, Norwich Terrier, Nova Scotia Duck Tolling Retriever, Old English Sheepdog, Old Welsh Grey Sheepdog, Otterhound, Papillon, Parson Russell Terrier, Pastor Garafiano, Pastore della Lessinia e del Lagorai, Patterdale Terrier, Peek-a-poo, Pekingese, Pembroke Welsh Corgi, Petit Basset Griffon Vendeen, Petit Bleu de Gascogne, Petit Brabangon, Petit Chien Lion, Phalene, Pharaoh Hound, Picardy Sheepdog, Pitsky, Plott Hound, Podenco Canario, Pointer, Polish Greyhound, Polish Hound, Polish Lowland Sheepdog, Pomapoo, Pomchi, Pomeranian, Pomsky, Poodle, Poodle , Portuguese Podengo, Portuguese Pointer, Portuguese Sheepdog, Portuguese Water Dog, Posavac Hound, Powderpuff Chinese Crested, Pudelpointer, Pug, Pugalier, Pugapoo, Puggle, Pugzu, Puli dog, Pumi dog, Pyrenean Mastiff, Pyrenean Sheepdog, Pyrenean Shepherd, Rajapalayam, Rampur Greyhound, Rastreador Brasileiro, Rat Terrier, Ratonero Bodeguero Andaluz, Ratonero Murciano, Redbone Coonhound, Rescue Dog, Rhodesian Ridgeback, Rottweiler, Rough Collie, Russell Terrier, Russian Toy, Russkiy Toy, Sabueso Espanol, Saint Bernard, Saint Miguel Cattle Dog, Saluki, Samoyed, Sardinian Shepherd Dog, Schapendoes, Schillerstovare, Schipperke, Schnauzer, Schnoodle, Schweizer Laufhund, Schweizerischer Niederlaufhund, Scottish Deerhound, Scottish Terrier, Sealyham Terrier, Segugio dell'Appennino, Segugio Italiano, Segugio Italiano a pelo forte, Segugio Italiano a pelo raso, Segugio Maremmano, Serbian Hound, Serbian Tricolour Hound, Shar Pei, Sheepadoodle, Sheepdog trial, Shetland Sheepdog, Shiba Inu, Shih Tzu, Shih-poo, Shikoku, Shollie, Shorkie, Siberian Cocker, Siberian Husky, Silken Windhound, Skye Terrier, Sloughi, Slovakian Rough Haired Pointer, Slovak Hound, Smalandstovare, Small Munsterlander, Smithfield, Smooth Collie, Smooth Fox Terrier, Soft Coated Wheaten Terrier, Soft- coated Wheaten Terrier, Spanish Water Dog, Spinone Italiano, Sporting Lucas Terrier, Springador, Springer Spaniel, Sprocker, Sprollie, Sproodle, St. Bernard, Stabyhoun, Staffador, Staffordshire Bull Terrier, Staffy Jack, Standard Poodle, Standard Schnauzer, Stichelhaar, Styrian Coarse-haired Hound, Sussex Spaniel, Swedish Lapphund, Swedish Vallhund, Taigan, Tamaskan, Tatransky duric or Tatra Hound, Teddy Roosevelt Terrier, Tenterfield Terrier, Terrier, Terri-Poo, Tibetan Mastiff, Tibetan Spaniel, Tibetan Terrier, Tiny the Wonder, Toy Fox Terrier, Toy Manchester Terrier, Toy Poodle, Trailhound, Transylvanian Hound, Treeing Walker Coonhound, Trigg Hound, Turkish Kangal Dog, Tyrolean Hound, Ukrainian Chortai, Valencian Terrier, Vizsla, Volpino Italiano, Weimaraner, Welsh Corgi, Welsh Foxhound, Welsh Hillman, Welsh Sheepdog, Welsh Springer Spaniel, Welsh Terrier, West Highland White Terrier, Westiepoo, Westphalian Dachsbracke, Whippet, White Shepherd, White Swiss Shepherd Dog, Willie, Wire Fox Terrier, Wirehaired Pointing Griffon, Wirehaired Vizsla, Working Cocker Spaniel, Working terrier, Xoloitzcuintli, Yorkie Russell, Yorkipoo, and Yorkshire Terrier, or a mixed breed thereof. Any of the aforementioned dog breeds may be the subject of the methods described herein.

[0207] Bulldogs (e.g., French Bulldogs and American Bulldogs), Labrador Retrievers, Golden Retrievers, German Shepherds, Poodles, Rottweilers, Beagles, Dachshunds, German Shorthaired Pointers, Pembroke Welsh Corgi, Australian Shepherds, Yorkshire Terriers, Cavalier King Charles Spaniels, Doberman Pinschers, Boxers, Miniature Schnauzers, Cane Corso, Great Danes, and Shih Tzus are popular canine pets amongst humans and, thus, may be the subject of the methods described herein.

[0208] A non-human animal that may be the subject of a method described herein (e.g., predicting a likelihood that a non-human animal comprises one or more traits; and assisting a user in identifying one or more products or services for a non-human animal) may be any age or at any stage of development. For example, the non-human animal (e.g., canine (e.g., dog, e.g., domesticated dogs), horse, cattle, sheep, cat, camel, pig, goat, alpaca, donkey, llama, red fox (e.g., vulpine), mouse, rat, ferret, primate (e.g., non-human primate), rabbit, gerbil, hamster, chinchilla, or guinea pig) may be at an early stage of development (e.g., a newborn, a neonate, an infant, or a pup), a middle stage of development (e.g., a juvenile, an adolescent, or a young adult), or a late stage of development (e.g., adult, senior, or geriatric). In other examples, a non-human animal described herein may be 1 hour old, 2 hours old, 3 hours old, 4 hours old, 5 hours old, 6 hours old, 7 hours old, 8 hours old, 9 hours old, 10 hours old, 11 hours old, 12 hours old, 18 hours old, 24 hours old, 1 day old, 2 days old, 3 days old, 4 days old, 5 days old, 6 days old, 7 days old, 8 days old, 9 days old, 10 days old, 11 days old, 12 days old, 13 days old, 14 days old, 15 days old, 16 days old, 17 days old, 18 days old, 19 days old, 20 days old, 21 days old, 22 days old, 23 days old, 24 days old, 25 days old, 26 days old, 27 days old, 28 days old, 29 days old, 30 days old, 1 month old, 2 months old, 3 months old, 4 months old, 5 months old, 6 months old, 7 months old, 8 months old, 9 months old, 10 months old, 11 months old, 1 years old, 2 years old, 3 years old, 5 years old, 5 years old, 6 years old, 7 years old, 8 years old, 9 years old, 10 years old, 11 years old, 12 years old, 13 years old, 14 years old, 15 years old, 16 years old, 17 years old, 18 years old, 19 years old, 20 years old, or more.

[0209] For example, in the context of a domesticated dog (e.g., Canis familiaris), the dog may be a puppy (e.g., a newborn, a neonate, an infant, a pup, or a juvenile) or an adult (e.g., a senior or geriatric). Depending on the breed of the dog, a puppy may be about 0-2 weeks old (e.g., a neonate), about 0-1 years old, about 0-1 .5 years old, about 0-2 years old (e.g., about 0 years old, about 1 .5 years old, or about 2 years old), or about 3 months to about 1 years old (e.g., a juvenile, e.g., about 3 months, about 4 months, about 5 months, about 6 months, about 7 months, about 8 months, about 9 months, about 10 months, about 11 months, or about 12 months). Depending on the breed of the dog, an adult dog may be at least 1 years old, at least 1 .5 years old, at least 2 years old, at least 3 years old, at least 4 years old, at least 5 years old, at least 6 years old, at least 7 years old, at least 8 years old, at least 9 years old, at least 10 years old, at least 11 years old, at least 12 years old, at least 13 years old, at least 14 years old, at least 15 years old, at least 16 years old, at least 17 years old, at least 18 years old, at least 19 years old, at least 20 years old, or older. Depending on the breed of the dog, an adult dog is considered senior or geriatric after about 8 years of age (e.g., for larger breeds), after about 10 years of age (e.g., for medium-sized breeds), or after about 12 years of age (e.g., for smaller breeds). The distinction between a puppy and an adult in a particular breed of dog is readily apparent to one of skill in the art (e.g., a dog breeder or a veterinarian).

[0210] In some embodiments, coat color, coat color modifier, or coat color intensity is selected from a group consisting of amber champagne, amber champagne dun, amber cream, amber dun, amber dun pearl, apricot dun, apricot pearl, agouti, bay, bay cream pearl, bay double cream, bay pearl, black, black bay, black cream pearl, black double cream, black dun, black pearl, black / red, blanket appaloosa, blood bay, blue roan, brindle, brown, buckskin, buttermilk buckskin, champagne, champagne amber pearl, champagne classic pearl, champagne dun, champagne dun pearl, champagne gold pearl, champagne pearl, chesetnut, chestnut cream pearl, chocolate, classic champagne, classic champagne dun, classic cream, classic dun, cream, cream champagne, cream grullo, cream pearl, cremello, cocoa, dapple grey, dark bay, dark brown, dark chestnut, dominant white, dun, dun bay cream, dun bay double cream, dun black cream, dun black double cream, dun chestnut cream, dun chestnut double cream, dun pearl, dun pearl bay, dun pearl black, dunalino, dunolino, dunskin, dunskin splash, flaxen chestnut, fleabitten grey, frame overo, frosted appaloosa, grey, gold champagne, gold cream, gold dun, gold dun pearl, grey, grullo, half blanket, leopard appaloosa, light grey, liver chestnut, marbled appaloosa, merle, overo, palomino, pearl, perlino, perlino dun, pseudocream classic, red, red chestnut, red dun, red roan, rose grey, sabino, saddle tan, seal brown, silver, silver amber champagne, silver amber dun, silver bay cream, silver bay cream pearl, silver bay double cream, silver black, silver black cream pearl, silver black double cream, silver champagne, silver champagne dun, silver champagne pearl, silver classic champagne, silver classic champagne pearl, silver cream pearl, silver dapple, silver dapple bay, silver dapple buckskin, silver dapple cream grullo, silver dapple dun, silver dapple dunskin, silver dapple grullo, silver dapple pearl, silver dapple perlino, silver dapple perlino dun, silver dun, silver dun bay cream, silver dun bay double cream, silver dun black, silver dun black double cream, silver dun pearl, silver dun pearl bay, silver dun pearl black, silver pearl bay, silver pearl black, silver red, silver smoky cream, skewbald, smoky black, smoky cream, smoky grullo, snowflake appaloosa, sorrel, splashed white, steel grey, sun, tobiano, tovero, tri-color, white, white spotting, apron, barring on body, barring on shoulder, belly spots (large or small), belly stripe, belted, bend or spots on body, bend or spots on head, birdcatcher spots, black spots, blagdon, blanket with roaning, blanket with spots, blaze, blaze with freckling, body spots, body white, brindle, brow spots, calico, coon / skunk tail, dapples, dilute, dorsal stripe, double dilute, ermine markings, few white hairs on body, fewspot, flaxen mane / tail, flea-bitten, fleshmark, frosted, grullo, heart marking, highlights in mane / tail, lacing pattern, leopard spotted, lightning marks, line back, frosting mane / tail, maximum tobiano, maximum overo, maximum white, maximum white sabino, medicine hat, minimal overo, minimal sabino, minimal tobiano, mixed mane / tail, mottled, no marking, overo, pangare, paw prints, pearl, piebald, pinto markings, rabicano, roan, roaning on body, rose, rump spots, sabino, skewbald, smudge marks, smutty, snip, snowcap, sooty, splash markings, star, stripe, tobiano, umbilical spots, white head, white mane / tail, white throat latch, white tipped ears, white with fading edges, white with ragged edges, white with smooth edges, wither spot , no markings, solid, white blaze, black and tan, saddle tan, creeping tan, merle, harlequin, salty licorice, tan point, sable, blue-based sable, red-fawn, platinum, Isabella, blue brindle, red pied, black pied, brown-based sable, lilac-based sable, black-based sable, sable merle agouti, watermarking, ticking, urajiro, countershading, blue tick, blue roan, panda spotting, whitehead, piebald, cocoa, shaded sable, clear sable, recessive black, recessive red, cocker sable, sighthound domino grizzle, ancient domino, northern domino, seal tan, ghost, dominant black, yellow, melanistic mask, dominant yellow, black saddle, brindle merle, bi-color, tri-color, fawn, landseer, wheaten, wild boar, wolf color, sandy, peppered, orange and white, grizzle, blenheim, apricot, mustard, parti-color, ruby, salt and pepper, mahogany, charcoal, lemon, domino, camo, camouflage, cafe-au-lait, silver-beige, and white star.

[0211] In some embodiments, the facial marking is selected from a group consisting of apron face, badger face, bald face, blaze, interrupted stripe, face, snip, star, stripe markings, both eyes amber, both eyes blue, both eyes brown, both eyes green, both eyes tiger, eyebrows, face mask, few white hairs on forehead, left eye amber, left eye blue, left eye brown, left eye green, left eye partial blue, left eye tiger, partial bald face, pigment around eye, right eye amber, right eye blue, right eye brown, right eye green, right eye partial blue, right eye tiger, white around eye, white chin, white jaw, white lip, white nose, melanistic mask, white head, freckled, and white sclera.

[0212] In some embodiments, the coat texture, coat thickness, coat type, or coat shedding may be selected from a group consisting of smooth, rough, double, curly, straight, downy, spiky, brindle, high shedding, low shedding, medium shedding, double coat, single coat, bald, lions-mane ruff, and furnishings.

[0213] In some embodiments, body size may be selected from a group consisting of toy, very small, small (e.g., teacup, miniature, toy), medium, large, extra-large, giant, small to medium, medium to large, large to extra-large, and extra-large to giant.

[0214] In some embodiments, body size may be determined by animal (e.g., dog) weight. In some embodiments a small (e.g., teacup, miniature, or toy) animal (e.g., a dog) may have a weight of between about 2 lbs. to about 22 lbs. (e.g., about 2 lbs., 3 lbs., 4 lbs., 5 lbs., 6 lbs., 7 lbs., 8 lbs., 9 lbs., 10 lbs., 11 lbs., 12 lbs., 13 lbs., 14 lbs., 15 lbs., 16 lbs., 17 lbs., 18 lbs., 19 lbs., 20 lbs., 21 lbs., 22 lbs., 2-22 lbs., 3- 22 lbs., 4-22 lbs., 5-22 lbs., 6-22 lbs., 7-22 lbs., 8-22 lbs., 9-22 lbs., 10-22 lbs., 11 -22 lbs., 12-22 lbs., 13- 22 lbs., 14-22 lbs., 15-22 lbs., 16-22 lbs., 17-22 lbs., 18-22 lbs., 19-22 lbs., 20-22 lbs., 21 -22 lbs., 2-3 lbs., 2-4 lbs., 2-5 lbs., 2-6 lbs., 2-7 lbs., 2-8 lbs., 2-9 lbs., 2-10 lbs. ,2-11 lbs., 2-12 lbs., 2-13 lbs., 2-14 lbs., 2- 15 lbs., 2-16 lbs., 2-17 lbs., 2-18 lbs., 2-19 lbs., 2-20 lbs., 2-21 lbs.). An animal with a teacup body size (e.g., a small dog) may have a weight of about 4 lbs. or less (e.g., about 2 lbs., 3 lbs., 4 lbs., 2-3 lbs., 3-4 lbs., 2-4 lbs.). An animal with a miniature body size (e.g., a small dog) may have a weight of about 3 lbs. to about 12 lbs. (e.g., about 3 lbs., 4 lbs., 5 lbs., 6 lbs., 7 lbs., 8 lbs., 9 lbs., 10 lbs., 11 lbs., 12 lbs., 3-12 lbs., 4-12 lbs., 5-12 lbs., 6-12 lbs., 7-12 lbs., 8-12 lbs., 9-12 lbs., 10-12 lbs., 11 -12 lbs., 3-11 lbs., 3-10 lbs., 3-9 lbs., 3-8 lbs., 3-7 lbs., 3-6 lbs., 3-5 lbs., 3-4 lbs.). An animal with a toy body size (e.g., a small dog) may have a weight of about 5 lbs. to about 22 lbs. (e.g., about 5 lbs., 6 lbs., 7 lbs., 8 lbs., 9 lbs., 10 lbs., 11 lbs., 12 lbs., 13 lbs., 14 lbs., 15 lbs., 16 lbs., 17 lbs., 18 lbs., 19 lbs., 20 lbs., 21 lbs., 22 lbs., 5-22 lbs., 6-22 lbs., 7-22 lbs., 8-22 lbs., 9-22 lbs., 10-22 lbs., 11 -22 lbs., 12-22 lbs., 13-22 lbs., 14-22 lbs., 15- 22 lbs., 16-22 lbs., 17-22 lbs., 18-22 lbs., 19-22 lbs., 20-22 lbs., 21 -22 lbs., 5-6 lbs., 5-7 lbs., 5-8 lbs., 5-9 lbs., 5-10 lbs. ,5-1 1 lbs., 5-12 lbs., 5-13 lbs., 5-14 lbs., 5-15 lbs., 5-16 lbs., 5-17 lbs., 5-18 lbs., 5-19 lbs., 5-20 lbs., 5-21 lbs.).

[0215] In some embodiments, a medium animal (e.g., a medium dog) may have a weight of between about 22 lbs. and about 57 lbs. (e.g., 22 lbs., 23 lbs., 24 lbs., 25 lbs., 26 lbs., 27 lbs., 28 lbs., 29 lbs., 30 lbs., 31 lbs., 32 lbs., 33 lbs., 34 lbs., 35 lbs., 36 lbs., 37 lbs., 38 lbs., 39 lbs., 40 lbs., 41 lbs., 42 lbs., 43 lbs., 44 lbs., 45 lbs., 46 lbs., 47 lbs., 48 lbs., 49 lbs., 50 lbs., 51 lbs., 52 lbs., 53 lbs., 54 lbs., 55 lbs., 56 lbs., 57 lbs., 22-57 lbs., 23-57 lbs., 24-57 lbs., 25-57 lbs., 26-57 lbs., 27-57 lbs., 28-57 lbs., 29-57 lbs., SO- 57 lbs., 31 -57 lbs., 32-57 lbs., 33-57 lbs., 34-57 lbs., 35-57 lbs., 36-57 lbs., 37-57 lbs., 38-57 lbs., 39-57 lbs., 40-57 lbs., 41 -57 lbs., 42-57 lbs., 43-57 lbs., 44-57 lbs., 45-57 lbs., 46-57 lbs., 47-57 lbs., 48-57 lbs., 49-57 lbs., 50-57 lbs., 51 -57 lbs., 52-57 lbs., 53-57 lbs., 54-57 lbs., 55-57 lbs., 56-57 lbs., 22-23 lbs., 22- 24 lbs., 22-25 lbs., 22-26 lbs., 22-27 lbs., 22-28 lbs., 22-29 lbs., 22-30 lbs., 22-31 lbs., 22-32 lbs., 22-33 lbs., 22-34 lbs., 22-35 lbs., 22-36 lbs., 22-37 lbs., 22-38 lbs., 22-39 lbs., 22-40 lbs., 22-41 lbs., 22-42 lbs., 22-43 lbs., 22-44 lbs., 22-45 lbs., 22-46 lbs., 22-47 lbs., 22-48 lbs., 22-49 lbs., 22-50 lbs., 22-51 lbs., 22- 52 lbs., 22-53 lbs., 22-54 lbs., 22-55 lbs., 22-56 lbs.).

[0216] In some embodiments, a large animal (e.g., a large dog) may have a weight of between about 57 lbs. to about 99 lbs. (e.g., about 57 lbs., 58 lbs., 59 lbs., 60 lbs., 61 lbs., 62 lbs., 63 lbs., 64 lbs., 65 lbs.,

[0217] 66 lbs., 67 lbs., 68 lbs., 69 lbs., 70 lbs., 71 lbs., 72 lbs., 73 lbs., 74 lbs., 75 lbs., 76 lbs., 77 lbs., 78 lbs., 79 lbs., 80 lbs., 81 lbs., 82 lbs., 83 lbs., 84 lbs., 85 lbs., 86 lbs., 87 lbs., 88 lbs., 89 lbs., 90 lbs., 91 lbs., 92 lbs., 93 lbs., 94 lbs., 95 lbs., 96 lbs., 97 lbs., 98 lbs., 99 lbs., 57-99 lbs., 58-99 lbs., 59-99 lbs., 60-99 lbs.,

[0218] 61 -99 lbs., 62-99 lbs., 63-99 lbs., 64-99 lbs., 65-99 lbs., 66-99 lbs., 67-99 lbs., 68-99 lbs., 69-99 lbs., 70- 99 lbs., 71 -99 lbs., 72-99 lbs., 73-99 lbs., 74-99 lbs., 75-99 lbs., 76-99 lbs., 77-99 lbs., 78-99 lbs., 79-99 lbs., 80-99 lbs., 81 -99 lbs., 82-99 lbs., 83-99 lbs., 84-99 lbs., 85-99 lbs., 86-99 lbs., 87-99 lbs., 88-99 lbs.,

[0219] 89-99 lbs., 90-99 lbs., 91 -99 lbs., 92-99 lbs., 93-99 lbs., 94-99 lbs., 95-99 lbs., 96-99 lbs., 97-99 lbs., 98-

[0220] 99 lbs., 57-98 lbs., 57-97 lbs., 57-96 lbs., 57-95 lbs., 57-94 lbs., 57-93 lbs., 57-92 lbs., 57-91 lbs., 57-90 lbs., 57-89 lbs., 57-88 lbs., 57-87 lbs., 57-86 lbs., 57-85 lbs., 57-84 lbs., 57-83 lbs., 57-82 lbs., 57-81 lbs.,

[0221] 57-80 lbs., 57-79 lbs., 57-78 lbs., 57-77 lbs., 57-76 lbs., 57-75 lbs., 57-74 lbs., 57-73 lbs., 57-72 lbs., 57-

[0222] 71 lbs., 57-70 lbs., 57-69 lbs., 57-68 lbs., 57-67 lbs., 57-66 lbs., 57-65 lbs., 57-64 lbs., 57-63 lbs., 57-62 lbs., 57-61 lbs., 57-60 lbs., 57-59 lbs., 57-58 lbs.).

[0223] In some embodiments, an extra-large animal (e.g., an extra-large dog) or a giant animal (e.g., a giant dog, a horse) has a weight of about 99 lbs. or more (e.g., about 99 lbs., 100 lbs., 1 10 lbs., 120 lbs., 130 lbs., 140 lbs., 150 lbs., 160 lbs., 170 lbs., 180 lbs., 190 lbs., 200 lbs., 210 lbs., 220 lbs., 250 lbs., 300 lbs., 350 lbs., 400 lbs., 450 lbs., 500 lbs., 550 lbs., 600 lbs., 650 lbs., 700 lbs., 750 lbs., 800 lbs., 850 lbs., 900 lbs., 950 lbs., 1000 lbs., 1 100 lbs., 1200 lbs., 1300 lbs., 1400 lbs., 1500 lbs., 99-150 lbs., 99-200 lbs., 100-200 lbs., 1 10-200 lbs., 120-200 lbs., 130-200 lbs., 140-200 lbs., 150-200 lbs., 160-200 lbs., 170-200 lbs., 180-200 lbs., 190-200 lbs., 99-250 lbs., 99-300 lbs., 99-350 lbs., 99-400 lbs., 99-450 lbs., 99-500 lbs., 99-550 lbs., 99-600 lbs., 99-650 lbs., 99-700 lbs., 99-800 lbs., 99-900 lbs., 99-1000 lbs., 99-1 100 lbs., 99-1200 lbs., 99-1300 lbs., 99-1400 lbs., 99-1500 lbs., 99-2000 lbs.).

[0224] In some embodiments, an animal (e.g., a dog) may have a body size that is considered to be in between two size groups (e.g., small to medium, medium to large, large to extra-large, large to giant, extra-large to giant). A body size that is considered to be in between two size groups may have a weight of about + / - 10 lbs. For example, a small to medium dog may have a weight of between about 12 lbs. to about 32 lbs., a medium to large dog may have a weight of between about 47 lbs. to about 67 lbs., and a large to extra-large or large to giant animal may have a weight of about 89 lbs. to about 109 lbs.

[0225] In some embodiments, body size may be determined by height, length, and / or width.

[0226] In some embodiments, tail shape may be curly, straight, screwtail, long, short, bob-tail, curved, high or medium or low set.

[0227] In some embodiments, behavior may be selected from a group consisting of attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement (e.g., high, medium, low), herding, nonherding, sight based hunting, scent based hunting, scent based tracking, water affinity, water aversion.

[0228] In some embodiments, aggression may be selected from a group consisting of dog aggression, unfamiliar dog aggression, familiar dog aggression, rivalry aggression, stranger aggression, owner aggression, farm aggression, animal aggression, non-canine animal aggression, spatial aggression, territoriality, and touch sensitivity.

[0229] In some embodiments, fear may be selected from a group consisting of dog fear, stranger fear, escaping, non-social fear, separation anxiety, grooming fear, veterinary stress, veterinary fear, novel environment fear, fear of water, fear of flying, noise fear, and separation urination.

[0230] In some embodiments, temperament may be selected from a group consisting of vigilant, curious / vigilant, curious, spooky, non-spooky, hot, cold, and medium, aggressive, relaxed, average, suspicious, friendly, out-going, reserved, shy, aloof.

[0231] In some embodiments, health can include variants of one or more genes associated with one or more disease or non-disease conditions. In some embodiments, the disease or non-disease conditions are selected from a group consisting of degenerative myelopathy, copper toxicosis, 2,8-dihydroxyadenine urolithiasis, acral mutilation syndrome, acute respiratory distress syndrome, alexander disease, amelogenesis imperfecta, autosomal recessive severe combined immunodeficiency, bald thigh syndrome, Bandera’s neonatal ataxia, Bardet-Biedl syndrome 2, progressive retinal atrophy, benign familial juvenile epilepsy, Bernard-Soulier syndrome, bleeding disorder, brachycephaly, p-Mannosidosis, canine leukocyte adhesion deficiency type III, canine multifocal retinopathy 1 , canine multifocal retinopathy 2, canine scott syndrome, cardiac arrhythmia, cardiomyopathy and juvenile mortality, centronuclear myopathy, cerebellar ataxia, cerebellar ataxia 2, cerebellar cortical degeneration, cerebellar degeneration-myositis complex, cerebral dysfunction, Charcot-Marie tooth disease, chondrodysplasia, disproportionate short-limbed, chondrodystrophy with or without chondrodysplasia, chondrodystrophy and intervertebral disc disease, cleft lip with or without palate and syndactyly, cleft palate, collie eye anomaly, complement 3 deficiency, cone degeneration, cone-rod dystrophy, cone-rod dystrophy 1 , cone-rod dystrophy 2, congenital dyshormonogenic hypothyroidism with goiter, congenital eye malformation, congenital hypothyroidism, congenital hypothyroidism with goiter, congenital idiopathic megaesophagus risk factor, congenital methemoglobinemia, congenital myasthenic syndrome, congenital stationary night blindness, copper toxicosis protective mutation, craniomandibular osteopathy, cystic renal dysplasia and hepatic fibrosis, cystinuria, cystinuria type l-A, cystinuria type l-B, cystinuria type I l-A, cystinuria type I l-B, dandy-walker-like malformation, deafness and vestibular dysfunction, degenerative myelopathy, demyelinating polyneuropathy, dental hypomineralisation, dilated cardiomyopathy, dilated cardiomyopathy risk factor, dominant progressive retinal atrophy, dystrophic epidermolysis bullosa, early retinal degeneration, early-onset progressive polyneuropathy, early-onset pra, ectodermal dysplasia, Ehlers-Danlos syndrome, embryonic lethality, epidermolytic hyperkeratosis, episodic falling, exercise- induced collapse, factor VII deficiency, factor XI deficiency, familial nephropathy, Fanconi syndrome, fetal onset neuroaxonal dystrophy, focal non-epidermolytic palmoplantar keratoderma, GM1 gangliosidosis, GM2 gangliosidosis, glanzmann thrombasthenia type I, glaucoma, globoid cell leukodystrophy, glycogen storage disease type 11 IA, glycogen storage disease type I A, glycogen storage disease VII, hemophilia A, hemophilia B, hereditary ataxia, hereditary cataracts HSF4-related, hereditary elliptocytosis, hereditary footpad hyperkeratosis, hereditary nasal parakeratosis, hereditary nephritis, hyperuricosuria, hypocatalasia, hypomyelination, hypophosphatasia, ichthyosis, inflammatory myopathy, inguinal cryptorchidism, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, juvenile dermatomyositis, juvenile encephalopathy, juvenile myoclonic epilepsy, l-2-hydroxyglutaric aciduria, lagotto storage disease, lamellar ichthyosis, laryngeal paralysis and polyneuropathy, leonberger polyneuropathy type 2, lethal acrodermatitis, ligneous membranitis, limbgirdle muscular dystrophy, limb-girdle muscular dystrophy, type 2F, long qt syndrome, lung developmental disease, multidrug resistance 1 medication sensitivity, macrothrombocytopenia, macular corneal dystrophy, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, mucopolysaccharidosis I, mucopolysaccharidosis, type IIIA, mucopolysaccharidosis, type VII, muscular dystrophy, muscular hypertrophy, musladin-lueke syndrome, myeloperoxidase deficiency, myotonia congenita, myotubular myopathy, myotubular myopathy 1 , narcolepsy, neonatal cerebellar cortical degeneration, neonatal encephalopathy with seizures, neuroaxonal dystrophy, neuronal ceroid lipofuscinosis 1 , neuronal ceroid lipofuscinosis 10, neuronal ceroid lipofuscinosis 12, neuronal ceroid lipofuscinosis 4a, neuronal ceroid lipofuscinosis 5, neuronal ceroid lipofuscinosis 6, neuronal ceroid lipofuscinosis 7, neuronal ceroid lipofuscinosis 8, nonsyndromic hearing loss, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, osteochondromatosis, osteogenesis imperfecta, paroxysmal dyskinesia, persistent mullerian duct syndrome, pituitary dwarfism, pituitarydependent hyperadrenocorticism, polycystic kidney disease, polydactyly, polyneuropathy with ocular abnormalities and neuronal vacuolation, pompe disease, prekallikrein deficiency, primary ciliary dyskinesia, primary ciliary dyskinesia, primary hyperoxaluria, primary lens luxation, primary open angle glaucoma, primary open angle glaucoma and lens luxation, progressive early-onset cerebellar ataxia, progressive retinal atrophy, progressive retinal atrophy i, progressive retinal atrophy type III, progressive rod-cone degeneration, pyruvate dehydrogenase phosphatase 1 deficiency, pyruvate kinase deficiency, recurrent inflammatory pulmonary disease, renal cystadenocarcinoma and nodular dermatofibrosis, rodcone dysplasia 1 , rod-cone dysplasia 3, sensory neuropathy, severe combined immunodeficiency, shar- pei autoinflammatory disease, skeletal dysplasia 2, spinocerebellar ataxia, spinocerebellar ataxia with myokymia and / or seizures, spondylocostal dysostosis, spongy degeneration with cerebellar ataxia, Stargardt disease, subacute necrotizing encephalopathy, T locus, thrombopathia, trapped neutrophil syndrome, ullrich congenital muscular dystrophy, Van den Ende-Gupta syndrome, Von Willebrand's disease type 1 , Von Willebrand's disease type 2, Von Willebrand's disease type 3, X-linked ectodermal dysplasia, X-linked hereditary nephropathy, X-linked retinal dysplasia, X-linked severe combined immunodeficiency, X-linked tremors, xanthinuria type 1 , xanthinuria type II, XX Disorder of sex development, canine degenerative myelopathy, progressive retinal atrophy, cone-rod dystrophy 3, progressive retinal atrophy, rod-cone dysplasia 4, and retinal dysplasia / oculoskeletal dysplasia 1 . Suitability

[0232] In the context of identifying a desirable trait (e.g., one or more desirable traits) in a non-human animal, a large part of what makes a specific trait desirable, or undesirable, is dependent on the use of the non-human animal as determined by a user, owner, or caretaker of the non-human animal. An animal (e.g., a dog) may be better suited for a particular discipline or use as compared to a different discipline or use. For example, a breeder may provide German Shepherds and Labradors to various canine service programs. These programs may include training or service programs for seeing eye guide dogs, search and rescue dogs, military dogs, substance-detection dogs (e.g., explosive detection, drug detection), police canine units, service dogs, emotional support dogs. The breeder may have multiple pups from multiple parent dogs, but all consistent for breed (e.g., German Shepherd, Labrador). Each pup can be evaluated (e.g., genetic testing) and profiled for appropriate future size (e.g., IGF1 , IGF1 R, IGF2BP2, SMAD2, STC2, ACE), temperament (e.g., DRD4, GNAT3, CD36, IGF1 ) and health (e.g., multiple loci) in accordance with the discipline or task skill set required by each training or service program. German Shepherds and Labradors suitable to the seeing eye guide dogs program may require a temperament profile for a less aggressive (e.g., IGSF1 ), less fearful (e.g., CD36, GNAT3), likely less sociable, and more focused dog with a lower energy and low barking tendency. Conversely, a dog that has increased aggression, fearlessness, higher energy, high drive, and likely to bark often and loudly, may not be suitable for training as a seeing eye dog, but instead be more suitable for the police canine unit. For a search and rescue animal, high energy, focus, and fearlessness may be desirable traits, along with barking tendency, low aggression, and possibly adaptability to high-altitude (e.g., EPAS1 ). In this scenario, pups that would be best suited for seeing eye dog programs, police canine units, or search and rescue training may all come from the same litter (e.g., from the same parent dogs) or from different litters (e.g., different parent dogs). Further, pups that would be best suited for each type of training or service program may not be distinguishable by a breeder, caretaker, or owner prior to the age that the pup would be required to enter such a training or service program. Therefore, without proper evaluation of the pup’s traits (e.g., temperament, aggression, fear), a pup may be entered into an inappropriate program (e.g., a training or service program that the pup is not best suited for), and may fail out of such a program.

[0233] An animal (e.g., a dog), may be suitable for more than one discipline or use. In some embodiments, an animal may be suitable for 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more disciplines or uses. Non-limiting examples of a discipline (e.g., use) that a non-human animal (e.g., a dog) may be suitable for include allergy detection, autism, diabetic alert, guide, service, hearing, mobility-assistance, seeing eye, psychiatric service (e.g., for post-traumatic stress disorder, anxiety, depression, agoraphobia, among others), seizure alert, emotional support, therapy, working, detection (e.g., explosive detection, cancer detection, disease detection (e.g., bacterial, viral, chronic, among others), drug detection (e.g., narcotics), truffle tracking, truffle hunting), search and rescue, military, police, crisis response, comfort, delivery, pulling, herding, guarding, hunting, agility, flyball, movement to music, obedience, jumping, dock jumping, rally obedience, lure coursing, catching (e.g., disc catching), tracking, speed, navigation, weaving, tunneling, heeling, staying, retrieving, teamwork, communication, precision, digging, scenting, companion, farm, leisure, sporting, distance running, sprinting, and racing, among others. Database architecture

[0234] The database system(s) of the present disclosure may be interrogated to identify an animal having, not having, or at risk of developing one or more traits. As described herein, a computer- implemented user interface can be used to receive user input pertaining to one or more traits of interest of the animal. The input dataset can be converted and expanded by a conversion / formatting engine of the system into a more versatile format and stored in a separate database subsystem (e.g., a user input subsystem). A comparison engine of the database system can be used to perform a comparison between information in the user input subsystem and the genetic profile and / or secondary source information subsystems to identify candidate non-human animals having one or more traits. The comparison engine can tabulate a list of all possible combinations of traits and then perform a comparison of those combinations with traits contained within the genetic profile subsystem and / or the secondary source information subsystem. The comparison engine can store those combinations that are found to occur and meet certain selection criteria in a separate database subsystem (e.g., a match subsystem) along with a numerical frequency of occurrence obtained as a count during the comparison. A statistical computation engine of the system can perform statistical computations based on the genetic and / or non-genetic information of the animal to obtain results (e.g., numerical probability values) for the likelihood of an animal as having, not having, or at risk of developing the one or more traits as well as, for example, the likelihood of the animal having a lethal genotype or of the animal having or developing a genetic disorder.

[0235] According to the present disclosure a database system can be used which contains a subsystem for accessing user input information pertaining to one or more traits, a second subsystem for accessing a set of database subsystems containing genetic and non-genetic information associated with a plurality of trait-positive and trait-negative animals, a data processing subsystem for identifying combinations of genetic and non-genetic traits associated with trait-positive animals, but not with trait-negative animals, and a calculating subsystem for determining a set of statistical results that indicates a strength of association between the combinations of genetic and non-genetic traits in trait-positive animals with the user input information. The system can also include a communications subsystem for retrieving at least some of genetic and non-genetic traits from at least one external database; a ranking subsystem for ranking the co-occurring traits according to the strength of the association of each co-occurring trait with the user input information; and a storage subsystem for storing the set of statistical results indicating the strength of association between the combinations of genetic and non-genetic traits and the user input information.

[0236] The database system of the present disclosure can further include a subsystem for accessing information about one or more products or services that are associated with one or more traits. The system can also include a communications subsystem for retrieving at least some products and / or services from at least one external database; a ranking subsystem for ranking the products and / or services that are associated with the traits according to the strength of the association of each product or service with each trait; and a storage subsystem for storing the set of statistical results indicating the strength of the association between the combination of products and one or more traits.

[0237] In a particular example, the computer-implemented database system may include a database subsystem(s) containing genetic information pertaining to one or more traits that can be used to identify an existing non-human animal having the one or more traits. Such database subsystems may be instantiated as, e.g., a referential table containing information about genetic determinants of a particular phenotypic trait. The referential table may include, e.g., information pertaining to expression of a specific gene or a combination of genes or the allelic variants thereof that would identify a non-human animal as having the desired trait(s) or that would indicate that a non-human animal expressing said gene or combination of genes would be capable of producing an offspring animal having the desired trait. Said referential table may also include information pertaining to a mutation(s) or polymorphism(s) (e.g., SNP) in a non-coding region (e.g., intron, 5’ untranslated region (UTR), or 3’ UTR) or regulatory region (e.g., promoter, enhancer, or silencer) of a particular gene that may affect its expression.

[0238] For example, in the case of coat color, the database system may contain a referential table containing information pertaining to the genetic determinants of coat color in the non-human animal (e.g., a dog). A user wishing to identify a dog having a particular desired coat color could use the user interface of the present disclosure to use coat color as a selection criterion. The user interface, being operably connected to the database system of the disclosure, may query the database system to identify the genes that are expressed in a dog to produce the desired coat color. Subsequently, the database system can search across the animals already catalogued in the database system to identify one or more non-human animals expressing the gene or combination of genes that produce the coat color in dogs. Table 1 provides a referential table for coat color selection that may be used in conjunction with the database system of the disclosure.

[0239] In another example, the database system of the disclosure can similarly be used to assess an animal having a particular coat color with specific coat color modifications (i.e., markings, such as, e.g., any one of the coat color markings described herein.). Similarly, the database system may contain a referential table containing information pertaining to the genetic determinants of coat color modifications in the dog. A user wishing to identify a dog having a particular desired coat color with a desired modification(s) could use the user interface of the present disclosure to use coat color modification as a selection criterion. The user interface can query the database system to identify the genes that are expressed in a dog to produce the desired coat color and modifications. Subsequently, the database system can search across the animals already catalogued in the database system to identify one or more non-human animals expressing the gene or combination of genes that produce the coat color and modifications in dogs.

[0240] In another example, the database system of the disclosure can be used to produce an animal that does not express one or more disease or non-disease conditions (e.g., any one of the disease or non-disease conditions described in Table 3). To this end, the database system may contain a referential table containing information pertaining to the genetic determinants of a plurality of disease or non-disease conditions. A user wishing to identify an animal that does not exhibit a particular disease or non-disease condition can use the user interface of the present disclosure to select one or more disease or non- disease conditions that are desirably avoided in the animal. The user interface can query the database system to identify the genes that are expressed in an animal (e.g., a dog) to produce the disease or non- disease condition. Subsequently, the database system can search across the animals already catalogued in the database system to identify one or more non-human animals that do not express the gene(s) that produce the disease or non-disease condition in an animal (e.g., a dog).

[0241] In yet another example, the database system of the disclosure can be used to produce an animal that exhibits a particular behavioral (e.g., curiosity / vigilance) or ability (e.g., gaited / non-gaited / carrier) trait. To this end, the database system may contain a referential table containing information pertaining to the genetic determinants of a plurality behavioral / ability traits. A user wishing to identify an animal (e.g., a dog) exhibiting one or more behavioral / ability traits can use the user interface of the present disclosure to select one or more behavioral / ability traits desired in the animal (e.g., a dog). The user interface can query the database system to identify the genes that are expressed in an animal to produce the one or more behavioral / ability traits. Subsequently, the database system can search across the animals already catalogued in the database system to identify one or more non-human animals that express the gene(s) that produce the behavioral / ability trait in an animal (e.g., a dog).

[0242] As is described herein, the database system(s) (and / or subsystem(s)) can also be interrogated to identify one or more traits associated with an animal (e.g., a dog) and subsequently can identify one or more products or services that may benefit the animal based on the one or more traits identified. The database system(s) (and / or subsystem(s)) can also provide information regarding the company (or companies) that produce the product(s) and / or service(s) to the user.

[0243] Computer-implemented graphical user interface

[0244] The present disclosure features a computer-implemented, remote (e.g., web-based), graphical user interface that allows a user to execute a variety of functions including, but not limited to creating, viewing, and modifying a user profile, a non-human animal (e.g., a dog) profile, and / or a provider profile, configuring user settings, ordering and viewing diagnostic test results (e.g., genetic test results pertaining to one or more traits), searching for animals (e.g., dogs) having one or more traits, searching for providers of a specific product or service, searching for providers that provide a product and / or service associated with a trait, providing payment information and making payments pertaining to products or services rendered by the platform of the disclosure, signing up for membership to the user platform, uploading and downloading data files (e.g., test results, images of user, animal, product, or service, e.g., from a secondary source), sharing a recommended service or product (e.g., via a share feature), among others. A graphical user interface may also have a call-to-action (CTA) button (i.e. , a clickable button) capable of providing the user with additional information. The sections that follow describe the various uses and components of the computer-implemented user interface in detail.

[0245] Use of a computer-implemented user interface to identify the likelihood of a non-human animal having one or more traits

[0246] The computer-implemented user interface may be used to assist a user in the determination of whether a non-human animal has, does not have, or is at risk of developing one or more traits. The interface can be configured to allow a user to interrogate the computer-implemented database system described herein. Interrogation of the database may include selecting one or more traits of interest for the one or more non-human animals. The interrogation may further include viewing a report based on a probability index generated using information about the non-human animal. The probability index may include an array of probability values pertaining to the likelihood that the non-human animal has the one or more traits or the likelihood that the non-human animal, does not have, or is at risk of developing (e.g., may have some day in the future) the one or more traits. The report can also include one or more products or services that are specific for the one or more traits of interest. The system can display an overall assessment of the non-human animal (e.g., the report) having the one or more traits and the one or more products or services that are specific for the one or more traits. The user interface can include an option for the user to purchase, lease, access, or electronically share (e.g., via a share feature) the one or more products or services, if desired. The user interface may also provide an option for the user to communicate with a provider (e.g., the provider of one or more products and / or services) and / or additional users, including through written, verbal, digital, analog, or electronic communication mechanisms. In some instances, the user may be provided with an option to send the service provider or any additional users the recommended product or service through a share feature. In some instances, the share feature includes a written, verbal, digital, analog, or electronic means of sharing. In some instances, the share feature allows users to rate one or more products or services. In some instances, the user may be provided with an option to send the provider or an additional user(s) a written message. The user interface may also provide information a web address or a uniform resource locator (URL) that is associated with the provider. A user may additionally utilize the user-interface to providers located within a specified geographical area.

[0247] User input

[0248] The computer-implemented user interface of the disclosure allows a user to select traits from a vast catalog of physical, behavioral, and genetic traits in order to assess a non-human animal of interest as having a particular phenotype, such as a phenotype suitable for a specific discipline of the animal (e.g., any one of the disciplines disclosed herein). For example, the disclosed methods and systems are suitable for identifying a non-human animal as having, not having, or at risk of developing one or more traits. A non-human animal having one or more traits may be identified by generating the probability index of the present disclosure to identify the likelihood of a non-human animal as having, not having, or at risk of developing one or more traits. A user may also employ the methods and systems disclosed herein to identify one or more products or services that are specific for the one or more traits of interest, which, for example, can be used to treat the non-human animal, prevent or reduce the risk that the one or more traits might manifest or result in a disease state or condition, or improve the one or more traits of the animal or a condition of the animal (e.g., by training).

[0249] The computer-implemented user interface described herein allows a user to select among one or more (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) traits desired in a non-human animal from a panel of traits that may include, for example, coat color, coat color modifier, coat color intensity, coat texture, coat thickness, facial marking, leg marking, eye color, skin color, mane color, tail color, temperament, speed, gait, and health.

[0250] Diagnostic testing

[0251] The computer-implemented user interface disclosed herein allows for a user to order clinical / veterinary tests (e.g., genetic testing pertaining to pedigree determination, health status, and predispositions towards genetic conditions) to be performed on a biological sample obtained from one or more animals owned by the user (e.g., a dog). The diagnostic testing may also be used to ascertain the ancestries of an animal of interest, e.g., by producing a report containing information on one or more (or all) of the shared cM metric, kinship coefficient, heterozygosity score, inbreeding coefficient, identity-by- descent, or breed composition metrics related to the animal, as is described herein. The user may order a test for a given animal by providing information such as the name of the animal (e.g., a dog), the type of test desired to be performed, contact information of the user (e.g., telephone number), payment information (e.g., credit card information), as well as any relevant gift card or coupon code information. The user may input personal information to the platform such as, for example, salutation, full name, e- mail (and visibility thereof to other users), telephone number (and visibility thereof to other users), date of birth, user photograph (and visibility thereof to other users), and address (and visibility thereof to other users). The user interface of the disclosure also allows the user to update user settings, such as changing the user password.

[0252] User, animal, and provider profiles

[0253] User profile

[0254] The computer-implemented user interface of the present disclosure allows the user to create a personal profile page which may be viewed and modified by the user. The user may grant permission for their profile to be viewed by other users of the platform. The user profile may include information about the user including name, address or general location (e.g., city, state, country), profile picture(s), list of animals (e.g., dogs) owned by the user as well as visual displays of physical or behavioral trait metrics of the animal, a “Favorites” list including animals, products, or services designated as Favorites on the user platform, means for adding animals, products, or services of the user into a “Favorites” list, means for contacting the user (e.g., by e-mail), means for obtaining a submission form related to genetic testing of an animal, and a means for downloading test results (e.g., genetic tests) or a report pertaining to one or more traits of the animal.

[0255] Non-human animal profile

[0256] The computer-implemented user interface disclosed herein allows the user to create and to modify a profile page of a non-human animal. The animal profile may include information about the animal including name, breed, age, profile pictures, videos, physical traits including height, colors, and markings, behavioral traits including temperament, discipline, genetic information about the animal (e.g., presence of particular gene variants in the animal), information pertaining to membership in an animal registry, sporting club information, owner information (e.g., name, address, profile picture), information pertaining to other animals (e.g., dogs) owned by the same owner (e.g., name, breed, age, and picture), order status information pertaining to tests performed on tissue obtained from the animal (e.g., order identification number, date of order, status such as ready or pending), means for downloading test results (e.g., genetic test results) or a report (e.g., the report pertaining to the likelihood of the animal having, not having, or at risk of developing one or more traits) onto the user’s device, and means for editing the animal profile. The aforementioned list is not exhaustive, and other information and features may be included in the animal profile page.

[0257] Provider profile

[0258] The computer-implemented user interface disclosed herein allows a provider of one or more products or services to create and modify a provider profile page. The provider profile may include information about the product or service including name, provider type (e.g., groomer), provider location, provider profile picture or logo, information pertaining to provider members (e.g., member name, picture, area(s) of expertise), information pertaining to animals the provider has worked with (e.g., name, breed, age, picture, and visual displays of physical or behavioral trait metrics of the animal), means for contacting the provider (e.g., by e-mail), and review from users who have used the provider’s products or services. These features and information are not exhaustive and other options for the provider profile page may be included.

[0259] Hardware implementation

[0260] The user interface described herein can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. Apparatus of the disclosure can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by a programmable processor; and method steps of the disclosure can be performed by a programmable processor executing a program of instructions to perform functions of the disclosure by operating on input data and generating output. The disclosure can be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system (e.g., a database), at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0261] Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (applicationspecific integrated circuits).

[0262] To provide for interaction with a user, the disclosure can be implemented on a computer having a display device such as a CRT (cathode ray tube), LCD (liquid crystal display), or LED (light-emitting diode) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer.

[0263] The disclosure can be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that preferably includes a front-end component, such as a client computer having a user interface (e.g., such as the user interface of the present disclosure) and / or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN, a WAN, and the computers and networks forming the Internet.

[0264] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The computer system may further include portable communications devices such as a mobile telephone that also contains functions such as an Internet browser. Other portable electronic devices, such as laptops or tablet computers may also be used with the present disclosure. It should also be understood that the computer system is not a portable communications device, but may be, e.g., a desktop computer.

[0265] Examples

[0266] Example 1 : Obtaining a sample from a non-human animal

[0267] A person (e.g., an owner, a breeder, caretaker, or a veterinarian) in possession of a non-human animal, for example, a dog (e.g., a neonate, a puppy, an adult, or a senior), seeks to identify traits that the dog may have or is likely to develop in its lifetime. Using a kit containing a collection tube and a swab or a collection tube for biopsy of blood, skin, hair, or other organ tissues / biopsies, the person obtains a saliva sample from the dog by swabbing the cheeks and / or gums of the dog for about 1 second to about 60 seconds (e.g., about 5 seconds to about 30 seconds, about 10 seconds to about 30 seconds, about 25 seconds to about 50 seconds, about 30 seconds to about 60 seconds, e.g., about 1 second, about 5 seconds, about 10 seconds, about 15 seconds, about 20 seconds, about 25 seconds, about 30 seconds, about 35 seconds, about 40 seconds, about 45 seconds, about 50 seconds, about 55 seconds, or about 60 seconds) (FIG. 2). The sample is placed into a bag and / or package for shipping to a receiving facility (e.g., a laboratory) for sample processing and analysis.

[0268] Example 2: Processing and analyzing a non-human sample

[0269] A receiving facility (e.g., a laboratory) receives a sample (e.g., a salvia sample, e.g., see Example 1 ) obtained from a non-human animal and, optionally, media content (e.g., an image, a video, and / or a rendering of the non-human animal) of the non-human animal. The laboratory proceeds to process the sample (e.g., saliva sample) and analyze DNA (e.g., sequence and / or chromatin structure), RNA (e.g., mRNA), and / or proteomic profiles within the sample using standard molecular biology techniques (e.g., DNA-sequencing, methylation sequencing, RNA-sequencing, Northern blot, mass-spectrometry, chromatography, western blot, enzyme-linked immunosorbent assay (ELISA), and the like) capable of identifying and / or measuring the presence of one or more genes, mutations, expression patterns, methylation patterns, proteins, genomic biomarkers, and / or proteomic biomarkers of interest. The data obtained from this analysis, along with any optionally received media content, is stored in a computer system.

[0270] After obtaining data (e.g., DNA, RNA, and / or proteomic profiling data) from the non-human animal (e.g., a dog) and, optionally, media content (e.g., an image, a video, and / or a rendering of the non- human animal) of the non-human animal (e.g., dog), a computer implemented method is utilized for:

[0271] (a) predicting the likelihood that the non-human animal (e.g., dog) has, or is likely to develop, one or more traits (e.g., a trait associated with the animal’s coat color (e.g., coat color modifier or coat color intensity), coat texture, coat thickness, coat type, particular facial markings, particular leg markings, leg length, shedding, eye color, skin color, body size, tail shape, head shape, ear erectness, speed, gait, temperament, behavior, fear, aggression, performance, ability, health, particular species, particular breed, and / or particular breeding group); and / or

[0272] (b) identifying one or more products or services for the non-human animal (e.g., based on the likelihood that the non-human animal has, or is likely to develop, one or more traits described herein).

[0273] Example 3: Predicting the likelihood that the non-human animal has, or is likely to develop, one or more traits

[0274] A user looking to predict the likelihood that a non-human animal (e.g., a dog) has, or is likely to develop, one or more traits (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more traits) described herein (e.g., a trait associated with the animal’s coat color (e.g., coat color modifier or coat color intensity), coat texture, coat thickness, coat type, particular facial markings, particular leg markings, leg length, shedding, eye color, skin color, body size, tail shape, head shape, ear erectness, speed, gait, temperament, behavior, fear, aggression, performance, ability, health, particular species, particular breed, and / or particular breeding group) can utilize a computer implemented method described herein.

[0275] For example, the user can identify data (e.g., DNA profiling data, RNA profiling data, proteomic profiling data) associated with the one or more traits described herein, followed by training and / or using a predictive computer model to receive such information by way of a user interface (e.g., a mobile application or website, which may look like FIG. 6; see also FIG. 5 for a flow chart of user inputted data) .

[0276] Using the predictive computer model, e.g., by way of the user interface (e.g., a mobile application or website, which may look like FIG. 6), the user generates a probability index. As exemplified in FIG. 5, the user inputs the information, along with any other information / data available (e.g., from an owner, a veterinarian, a trainer, and / or a breeder) into the interface, which outputs an array of probability values pertaining to the likelihood that the non-human animal comprises the one or more traits. Optionally, the array of probability values may be converted into a report (e.g., see FIG. 8). The report may identify if the non-human animal (e.g., dog) has, does not have, or is at risk of developing the one or more traits. The report may further identify one or more products or services for the non-human animal (e.g., see Example 4).

[0277] The report may include a graphical output of a user’s input data. The graphical output includes an image of the non-human animal (e.g., dog), which may be: (1 ) a representative image of the user’s non- human animal (e.g., a representative image of the dog’s breed); (2) a predicted rendering of the user’s non-human animal (e.g., dog, e.g., see FIG. 7) by artificial intelligence (Al); or (3) an actual image of the user’s non-human animal (e.g., dog) obtained from inputted media content. The graphical output may further include the results of the user’s input data. Input data may include, but is not limited to, genetic profiling data (e.g., DNA sequencing data) on hundreds (e.g., 100, 150, 200, 250, 300, 350, 400, 450, 500, or more) of genes associated (e.g., causative or correlative) with genetic traits. The results of the user’s input data may identify genetic traits related to, for example, an animal’s behavior (e.g., separation anxiety, attention seeking (e.g., barking), etc.), personality (e.g., aggression, intelligence, worker, etc.), color (e.g., black, white, brown, a combination thereof, etc.), coat (e.g., propensity for shedding) and / or health (allergies, obesity, arthritis, etc.). Exemplary graphical outputs are shown in FIG. 1 , FIG. 3, FIG. 4, and FIG. 7.

[0278] Example 4: Assisting a user in identifying one or more products or services for a non-human animal

[0279] A user looking to identify one or more (e.g., one, two, three, four, five, six, seven, eight, nine, ten, or more) products (e.g., animal collars, shampoos, toys (e.g., for recreation or training purposes) medications, and the like) or services (e.g., veterinary services, training services, education services, ancestry analysis services, pedigree services, recreational, boarding or grooming services) for a non- human animal (e.g., a dog) can utilize the computer implemented method described in Example 3 or the method described below. An exemplary workflow to assist a user in identifying one or more products or services for a non-human animal is shown in FIG. 9.

[0280] Using computer-stored information (e.g., DNA profiling data, RNA profiling data, proteomic profiling data, and / or media content, e.g., see Example 2) about the non-human animal (e.g., data obtained from a dog sample), a user generates a probability index through a user interface (e.g., a mobile application or website, which may look like FIG. 6) containing a programmable algorithm. As exemplified in FIG. 5, the user interface inputs the stored information, along with any other information / data available (e.g., from an owner, a veterinarian, a trainer, and / or a breeder) into the interface, which then outputs a number of products and services (e.g., see FIG. 4). These products and services are curated, based on the user’s input and resulting probability index.

[0281] The probability index contains an array of probability values pertaining to the likelihood that the non-human animal has, or is likely to develop, one or more traits (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, or more traits) described herein (e.g., a trait associated with the animal’s coat color (e.g., coat color modifier or coat color intensity), coat texture, coat thickness, coat type, particular facial markings, particular leg markings, leg length, shedding, eye color, skin color, body size, tail shape, head shape, ear erectness, speed, gait, temperament, behavior, fear, aggression, performance, ability, health, particular species, particular breed, and / or particular breeding group). The probability index is converted into a report (e.g., see FIG. 8). The report may identify if the non-human animal (e.g., dog) has, does not have, or at risk of developing the one or more traits. The report may further identify one or more products or services for the non-human animal.

[0282] The user interface and / or report may include a graphical output of a user’s input data. The graphical output includes an image of the non-human animal (e.g., dog), which may be: (1 ) a representative image of the user’s non-human animal (e.g., a representative image of the dog’s breed); (2) an Al-generated, predicted (e.g., virtual) rendering of the user’s non-human animal (e.g., dog, see, e.g., FIG. 7); or (3) an actual image of the user’s non-human animal (e.g., dog) obtained from inputted media content. The graphical output may further include the results of the user’s input data. Input data may include, but is not limited to, genetic profiling data (e.g., DNA sequencing data) on hundreds (e.g., 100, 150, 200, 250, 300, 350, 400, 450, 500, or more) of genes associated (e.g., causative or correlative) with genetic traits. The results of the user’s input data may identify genetic traits related to, for example, an animal’s behavior (e.g., separation anxiety, attention seeking (e.g., barking), etc.), personality (e.g., aggression, intelligence, worker, etc.), color (e.g., black, white, brown, a combination thereof, etc.), coat (e.g., propensity for shedding) and / or health (allergies, obesity, arthritis, etc.). Exemplary graphical outputs are shown in FIG. 1 , FIG. 3, FIG. 4, and FIG. 7.

[0283] Example 5: Determining breed composition and predicting behavioral traits of a non-human animal

[0284] A user looking to identify the likelihood that a non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits and / or has a certain breed composition can utilize a computer- implemented method described herein. For example, the user may collect a sample from the non-human animal (e.g., a dog), as described in Example 1 , and send it to the associated facility to be processed, as described in Example 2. After processing and analyzing the sample, a report can be downloaded, which will provide information on the likelihood that the non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits (e.g., attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement, herding, non-herding, sight based hunting, scent based hunting, scent based tracking, water affinity, or water aversion) and / or has a certain breed composition (e.g., a breed composition including one or more of the following: Bulldog, Labrador Retriever, Golden Retriever, German Shepherd, Poodle, Rottweiler, Beagle, Dachshund, German Shorthaired Pointer, Pembroke Welsh Corgi, Australian Shepherd, Yorkshire Terrier, Cavalier King Charles Spaniel, Doberman Pinscher, Boxer, Miniature Schnauzer, Cane Corso, Great Dane, and Shih Tzu). The report may communicate the results through symbols next to specific traits (e.g., a check mark indicating the presence of a trait or a minus symbol indicating the absence of a trait) and / or a probability value indicating the likelihood of each trait in the non-human animal (e.g., dog).

[0285] Example 6: Determining breed composition and predicting physical traits, behavioral traits, and health conditions / risks of a non-human animal

[0286] A user looking to identify the likelihood that a non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits, physical traits, health conditions / risks, and / or has a certain breed composition can utilize a computer-implemented method described herein. For example, the user may collect a sample from the non-human animal (e.g., a dog), as described in Example 1 , and send it to the associated facility to be processed, as described in Example 2. After processing and analyzing the sample, a report can be downloaded, which will provide information on the likelihood that the non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits (e.g., attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement, herding, non-herding, sight based hunting, scent based hunting, scent based tracking, water affinity, or water aversion), has a certain breed composition (e.g., a breed composition including one or more of the following: Bulldog, Labrador Retriever, Golden Retriever, German Shepherd, Poodle, Rottweiler, Beagle, Dachshund, German Shorthaired Pointer, Pembroke Welsh Corgi, Australian Shepherd, Yorkshire Terrier, Cavalier King Charles Spaniel, Doberman Pinscher, Boxer, Miniature Schnauzer, Cane Corso, Great Dane, and Shih Tzu), has, or is likely to develop, certain physical traits (e.g., a specific coat color, coat texture, coat thickness, coat type, particular facial markings, particular leg markings, leg length, shedding, eye color, skin color, body size, tail shape, head shape, and / or ear erectness) and / or has, or is likely to develop, one or more health conditions / risks (e.g., cardiac arrhythmia, cardiomyopathy and juvenile mortality, centronuclear myopathy, cerebellar ataxia, cerebellar cortical degeneration, megaesophagus risk factor, congenital methemoglobinemia, congenital stationary night blindness, copper toxicosis protective mutation, craniomandibular osteopathy, factor XI deficiency, familial nephropathy, hereditary nasal parakeratosis, hereditary nephritis, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, pituitary dwarfism, pituitary-dependent hyperadrenocorticism, pyruvate kinase deficiency, severe combined immunodeficiency, spondylocostal dysostosis, subacute necrotizing encephalopathy, or T locus). The report may communicate the results through symbols next to specific traits or conditions (e.g., a check mark indicating the presence of a trait or a minus symbol indicating the absence of a trait or condition), indicating the likelihood of each trait or condition in the non-human animal (e.g., dog). Alternatively, a probability value may be used to communicate the results.

[0287] Example 7: Determining breed composition and predicting health conditions / risks of a non-human animal

[0288] A user looking to identify the likelihood that a non-human animal (e.g., a dog) has a certain breed composition (e.g., a certain genetic makeup of dog breed) and / or has, or is likely to develop, one or more health conditions / risks can utilize a computer-implemented method described herein. For example, the user may collect a sample from the non-human animal (e.g., a dog), as described in Example 1 , and send it to the associated facility to be processed, as described in Example 2. After processing and analyzing the sample, a report can be downloaded, which will provide information on the likelihood that the non- human animal (e.g., a dog) has a certain breed composition (e.g., a breed composition including one or more of the following: Bulldog, Labrador Retriever, Golden Retriever, German Shepherd, Poodle, Rottweiler, Beagle, Dachshund, German Shorthaired Pointer, Pembroke Welsh Corgi, Australian Shepherd, Yorkshire Terrier, Cavalier King Charles Spaniel, Doberman Pinscher, Boxer, Miniature Schnauzer, Cane Corso, Great Dane, and Shih Tzu), and / or has, or is likely to develop, one or more health conditions / risks (e.g., copper Toxicosis, Degenerative Myelopathy, Cystinuria, Glaucoma, Cardiomyopathy, cardiac arrhythmia, centronuclear myopathy, cerebellar ataxia, cerebellar cortical degeneration, megaesophagus risk factor, congenital methemoglobinemia, congenital stationary night blindness, craniomandibular osteopathy, factor XI deficiency, familial nephropathy, hereditary nasal parakeratosis, hereditary nephritis, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, pituitary dwarfism, pituitary-dependent hyperadrenocorticism, pyruvate kinase deficiency, severe combined immunodeficiency, spondylocostal dysostosis, subacute necrotizing encephalopathy, or T locus). The report may communicate the results through symbols next to specific traits (e.g., a check mark indicating the presence of a trait or a minus symbol indicating the absence of a trait), indicating the likelihood of each trait in the non-human animal (e.g., dog). Alternatively, a probability value may be used to communicate the results. Example 8: Predicting behavioral traits, physical traits, and health conditions / risks of a nonhuman animal

[0289] A user looking to identify the likelihood that a non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits, physical traits, and / or health conditions / risks can utilize a computer- implemented method described herein. For example, the user may collect a sample from the non-human animal (e.g., a dog), as described in Example 1 , and send it to the associated facility to be processed, as described in Example 2. After processing an analyzing the sample, a report can be downloaded, which will provide information on the likelihood that the non-human animal (e.g., a dog) has, or is likely to develop, one or more behavioral traits (e.g., attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement, herding, non-herding, sight based hunting, scent based hunting, scent based tracking, water affinity, or water aversion), has a certain physical traits (e.g., coat color, coat texture, coat thickness, coat type, particular facial markings, particular leg markings, leg length, shedding, eye color, skin color, body size, tail shape, head shape, or ear erectness) and / or has, or is likely to develop, one or more health conditions / risks (e.g., cardiac arrhythmia, cardiomyopathy and juvenile mortality, centronuclear myopathy, cerebellar ataxia, cerebellar cortical degeneration, megaesophagus risk factor, congenital methemoglobinemia, congenital stationary night blindness, copper toxicosis protective mutation, craniomandibular osteopathy, factor XI deficiency, familial nephropathy, hereditary nasal parakeratosis, hereditary nephritis, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, pituitary dwarfism, pituitary-dependent hyperadrenocorticism, pyruvate kinase deficiency, severe combined immunodeficiency, spondylocostal dysostosis, subacute necrotizing encephalopathy, or T locus). The report may communicate the results through symbols next to specific traits (e.g., a check mark indicating the presence of a trait or a minus symbol indicating the absence of a trait), indicating the likelihood of each trait in the non-human animal (e.g., dog). Alternatively, a probability value may be used to communicate the results.

[0290] Other Embodiments

[0291] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each independent publication or patent application was specifically and individually indicated to be incorporated by reference.

[0292] While the invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications and this application is intended to cover any variations, uses, or adaptations for use in the compositions and methods of the invention following, in general, the principles for use in the compositions and methods of the invention and including such departures from the present disclosure that come within known or customary practice within the art to which the invention pertains and may be applied to the essential features hereinbefore set forth, and follows in the scope of the claims.

Claims

Claims1 . A method for assisting a user in identifying one or more products or services for a non-human animal comprising:(a) generating a probability index for the non-human animal using information about the non- human animal stored in a computer-implemented database system, wherein the probability index comprises an array of probability values pertaining to the likelihood that the non-human animal comprises one or more traits;(b) converting the probability index of step (a) into a report, wherein the report identifies:(i) the non-human animal as having, not having, or at risk of developing the one or more traits; and(ii) the one or more products or services for the non-human animal, wherein the one or more products or services are specific for the one or more traits; and(c) providing, based on the report of step (b), information about the one or more products or services or information about a service provider for the one or more products or services.

2. A method comprising:(a) generating a probability index for a non-human animal using a computer-implemented database system comprising information about the non-human animal stored therein, wherein the probability index comprises an array of probability values pertaining to a likelihood that the non- human animal comprises one or more traits;(b) converting the probability index of step (a) into a report, wherein the report identifies one or more products or services specific for the one or more traits of the non-human animal; and(c) transmitting the report to a user.

3. The method of claim 1 or 2, further comprising providing remote access to the computer- implemented database system to a user over a network connection, wherein the user can access, through a graphical user interface:(a) the report;(b) the information about the one or more products or services; and / or(c) the information about the non-human animal.

4. The method of claim 3, wherein the user can update the information about the non-human animal.

5. The method of claim 3 or 4, wherein the user can provide feedback regarding perceived accuracy of the report.

6. The method of any one of claims 1 -5, further comprising an option for the user to purchase, lease, or access the one or more products or services.

7. The method of any one of claims 1 -6, wherein the method further comprises providing an option for the user to communicate with a service provider that provides the one or more products or services, and / or one or more additional users.

8. The method of claim 7, wherein the option for the user to communicate with the service provider and / or with the one or more additional users comprises a written, verbal, digital, analog, or electronic means of communication.

9. The method of claim 7 or 8, wherein the user is provided with an option to send the service provider and / or the one or more additional users a written message.

10. The method of any one of claims 7-9, wherein the user is provided with a web address or uniform resource locator (URL) for the service provider.11 . The method of claim 7, wherein the option for the user to communicate with the one or more additional users comprises a share feature.

12. The method of claim 11 , wherein the share feature comprises a written, verbal, digital, analog, or electronic means of sharing and / or rating the one or more products or services.

13. The method of any one of claims 1 -12, wherein the one or more products or services comprise at least 2 or more products or services.

14. The method of claim 13, wherein the one or more products or services comprise at least 3 or more products or services.

15. The method of claim 14, wherein the one or more products or services comprise at least 4 or more products or services.

16. The method of claim 15, wherein the one or more products or services comprise at least 5 or more products or services.

17. The method of claim 16, wherein the one or more products or services comprise at least 10 or more products or services.

18. The method of any one of claims 1 -17, wherein at least one of the one or more products or services is marked as recommended.

19. The method of any one of claims 1 -18, wherein the information about the one or more products or services are provided based on geographical location.

20. The method of any one of claims 1 -19, wherein the services comprise veterinary services, training services, education services, ancestry analysis services, pedigree services, or grooming services, parentage services, husbandry services, care services, enrichment services, facility services, boarding services, recreational services, and monitoring services.21 . The method of any one of claims 1 -20, wherein the products comprise a medication, a wearable product, a hygiene-related product, a breed-specific product, a trait specific product , an edible product, an environmental enrichment product, an enhancing product, a training product, a sporting product, an exercise product, an enrichment product, safety product, a furniture product, and a recreational product.

22. The method of any one of claims 1 -21 , further comprising providing an incentive for the user to purchase or access the one or more products or services.

23. The method of claim 22, wherein the incentive comprises a coupon, discount, bonus item, sale, upgrade, entry into a sweepstakes, rewards points, or early access.

24. A computer-implemented method for predicting a likelihood that a non-human animal comprises one or more traits, the method comprising:(a) generating a probability index for the non-human animal using information about the non- human animal stored in a computer-implemented database system, wherein the probability index comprises an array of probability values pertaining to the likelihood that the non-human animal comprises the one or more traits; and(b) converting the probability index of step (a) into a report, wherein the report identifies:(i) the non-human animal as having, not having, or at risk of developing the one or more traits; and(ii) a predictive image or a rendering of the non-human animal.

25. The method of claim 24, further comprising selecting an age or life-stage of the non-human animal.

26. The method of claim 25, wherein the predictive image or rendering of the non-human animal depends on the age or life-stage of the non-human animal.

27. The method of claim 25 or 26, wherein the age of the non-human animal is selected from 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years,18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, or 35 years.

28. The method of claim 25 or 26, wherein the life-stage of the non-human animal is selected from newborn, neonate, infant, adolescent, juvenile adult, senior or geriatric.

29. The method of claim any one of claims 1 -28, further comprising, prior to step (a):(a) receiving information about the non-human animal; and / or(b) storing the information about the non-human animal in the computer-implemented database system.

30. The method of claim 29, further comprising storing the report in the computer-implemented database system.31 . The method of any one of claims 1 -30, wherein the information about the non-human animal was uploaded from a secondary source into the computer-implemented database system.

32. A computer-implemented method for predicting a likelihood that a non-human animal comprises one or more traits, the method comprising:(a) training a predictive computer model using a physical computing device and a machine learning algorithm, wherein the physical computing device comprises a plurality of data about a plurality of non-human animals;(b) receiving information about the non-human animal;(c) generating a probability index by applying the predictive computer model, wherein the probability index comprises an array of probability values pertaining to the likelihood that the non- human animal comprises the one or more traits;(d) converting the probability index of step (c) into a report; and / or(e) presenting the report of step (d) to a user on a graphical user interface.

33. The computer implemented method of claim 32, wherein the plurality of data of step (a) comprises a training data set.

34. The computer implemented method of claim 32 or 33, wherein the information about the non- human animal of step (a) is received from a secondary source.

35. The computer implemented method of claim 33 or 34, wherein training the predictive computer model comprises inputting the training data set into (i) a supervised machine learning model, (ii) an unsupervised machine learning model, and / or (Hi) a semi-supervised machine learning model.

36. The computer implemented method of any one of claims 33-35, wherein:(a) the training data set utilized by the supervised machine learning model is a labeled training data set;(b) the training data set utilized by the unsupervised machine learning model is an unlabeled training data set; and / or(c) the training data set utilized by the semi-supervised machine learning model is a combination of both the labeled and unlabeled training data sets.

37. The computer implemented method of claim 36, wherein the unsupervised machine learning model comprises a process of classifying similar data within the unlabeled training data, thereby generating one or more clusters of similar data.

38. The computer implemented method of claim 37, wherein the unsupervised machine learning further comprises a process of identifying commonalities within the one or more clusters of similar data.

39. The computer implemented method of claim 37 or 38, wherein the unsupervised machine learning further comprises a process of identifying outlying data within the one or more clusters of similar data.

40. The computer implemented method of any one of claims 32-39, wherein training the predictive computer model further comprises a validation step and / or a testing step.41 . The computer implemented method of any one of claims 24-40, further comprising transmitting the report to the user.

42. The computer implemented method of claim 41 , wherein the report is automatically transmitted to the user.

43. The computer implemented method of any one of claims 24-42, further comprising providing remote access to the computer-implemented database system to a user over a network connection, wherein the user can access, through a graphical user interface:(a) the report; and / or(b) the information about the non-human animal.

44. The computer implemented method of claim 43, wherein the user can update the information about the non-human animal.

45. The computer implemented method of claim 44, wherein the user can provide feedback regarding perceived accuracy of the report.

46. The computer implemented method of claim 45, wherein the report is updated based on the feedback provided by the user.

47. The method of any one of claims 1 -46, wherein the information about the non-human animal comprises genetic information.

48. The method of claim 47, wherein the information about the non-human animal further comprises genetic information and non-genetic information about the non-human animal and / or a related non-human animal.

49. The method of claim 47 or 48, wherein the genetic information comprises a genetic profile.

50. The method of claim 49, wherein the genetic profile comprises a DNA profile, an RNA profile, a gene expression profile, and / or a methylation profile.51 . The method of any one of claims 47-50, wherein the genetic information comprises results of previously performed genetic testing.

52. The method of claim 51 , wherein the genetic testing comprises sequencing of the whole genome of the non-human animal.

53. The method of claim 51 , wherein the genetic testing comprises performing an analysis of an entire genome of the non-human animal.

54. The method of claim 53, wherein the analysis of the entire genome comprises a genome wide association study (GWAS).

55. The method of claim 51 , wherein the genetic testing comprises evaluating one or more single nucleotide polymorphisms (SNP).

56. The method of any one of claims 51 -55, wherein the genetic testing comprises genotyping the non-human animal for any one of the following genetic markers: agouti signaling protein (ASIP), RALY heterogeneous nuclear ribonucleoprotein (RALY), Semaphorin 3 (SEMA3), growth hormone receptor (GHR), SMAD family member 2 (SMAD2), beta-defensin 103 (CBD103), dickkopf WNT signaling pathway inhibitor 4 (DKK4), HPS3 biogenesis of lysosomal organelles complex 2 subunit 1 (HPS3), HPS4 biogenesis of lysosomal organelles complex 3 subunit 2 (HPS4), HPS5 biogenesis of lysosomal organelles complex 2 subunit 2 (HPS5), HPS6 biogenesis of lysosomal organelles complex 2 subunit 3 (HPS6), KIT proto-oncogene, receptor tyrosine kinase (KIT), major facilitator superfamily domain containing 12 (MFSD12), melanocyte inducing transcription factor (MITF), proteasome 20S subunit beta 7 (PSMB7), solute carrier family 26 member 4 (SLC26A4), tyrosinase (TYR), tyrosinase related protein 1 (TYRP1 ), melanocortin 1 receptor (MC1 R), keratin 71 (KRT71 ), fibroblast growth factor 5 (FGF5), G protein-coupled receptor 22 (GPR22), mitochondrial intermediate peptidase (MIPEP), melanocortin 5 receptor (MC5R), chr1 :78870578: T>C, ACE_chr9:12461897: A>C, melanophilin (MLPH), methionine sulfoxide reductase B3 (MSRB3), AHHS65D, R-spondin 2 (RSPO2), forkhead box I3 (FOXI3), serum / glucocorticoid regulated kinase family, member 3 (SGK3), stanniocalcin 2 (STC2), AHX1 JD9, premelanosome protein (PMEL), T brachyury, T-box transcription factor T, OCA2 melanosomal transmembrane protein (OCA2), usherin (USH2A), dishevelled segment polarity protein 2 (DVL2), SPARC related modular calcium binding 2 (SMOC2), thrombospondin 2 (THBS2), insulin like growth factor 1 (IGF1 ), insulin like growth factor 1 receptor (IGF1 R), insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2), Dopamine receptor D4 (DRD4), general transcription factor Hi (GTF2I); GTF2I repeat domain contain 1 (GTF2IRD1 ), methionine sulfoxide reductase B3 (MSRB3), proopiomelanocortin (POMC), very low-density lipoprotein receptor (VLDLR), adenine phosphoribosyltransferase (APRT), glialcell derived neurotrophic factor (GDNF) .anillin (ANLN), ubiquitin specific peptidase 31 (USP31 ), fibrinogen alpha chain (FGA), glial fibrillary acidic protein (GFAP), enamelin (ENAM), solute carrier family 24 member 4 (SLC24A4), sacsin molecular chaperone (SACS), protein kinase, DNA-activated, catalytic subunit (PRKDC), insulin like growth factor binding protein 5 (IGFBP5), glutamate metabotropic receptor1 (GRM1 ), Bardet-Biedl syndrome 2 (BBS2), leucine rich repeat LGI family member 2 (LGI2), glycoprotein IX platelet (GP9), purinergic receptor P2Y12 (P2RY12), bone morphogenetic protein 3 (BMP3), FERM domain containing kindlin 3 (FERMT3), bestrophin 1 (BEST1 ), serine active site containing 1 (SERAC1 ), anoctamin 6 (ANO6), tyrosyl-tRNA synthetase 2 (YARS2), bridging integrator 1 (BIN1 ), protein tyrosine phosphatase-like A (PTPLA), inositol 1 ,4,5-trisphosphate receptor type 1 (ITPR1 ), RAB24, member RAS oncogene family (RAB24), Rai GTPase activating protein catalytic subunit alpha 1 (RALGAPA1 ), selenoprotein P (SELENOP), sorting nexin 14 (SNX14), solute carrier family 25 member12 (SLC25A12), solute carrier family 6 member 3 (SLC6A3), SET binding factor 2 (SBF2), integrin subunit alpha 10 (ITGA10), fibroblast growth factor 4 (FGF4), fibroblast growth factor 5 (FGF5), ADAM metallopeptidase with thrombospondin type 1 motif 20 (ADAMTS20), distal-less homeobox 6 (DLX6), non-homologous end joining factor 1 (NHEJ1 ), complement C3 (C3), cyclic nucleotide gated channel subunit beta 3 (CNGB3), nephrocystin 4 (NPHP4), IQ motif containing B1 (IQCB1 ), RPGR interacting protein 1 (RPGRIP1 ), NAD(P) dependent steroid dehydrogenase-like (NSDHL), solute carrier family 5 member 5 (SLC5A5), SIX homeobox 6 (SIX6), thyroid peroxidase (TPO), melanin-concentrating hormone receptor 2 (MCHR2), cytochrome b5 reductase 3 (CYB5R3), choline O-acetyltransferase (CHAT), cholinergic receptor nicotinic epsilon subunit (CHRNE), Collagen-like Tail Subunit of Asymmetric Acetylcholinesterase (COLQ), retinoid isomerohydrolase RPE65 (RPE65), copper metabolism domain containing 1 (COMMD1 ), ATPase copper transporting alpha (ATP7A), ATPase copper transporting beta (ATP7B), solute carrier family 35 member D1 (SLC35D1 ), solute carrier family 37 member 2 (SLC37A2), adaptor related protein complex 3 subunit beta 1 (AP3B1 ), inositol polyphosphate-5-phosphatase E (INPP5E), solute carrier family 3 member 1 (SLC3A1 ), solute carrier family 7 member 9 (SLC7A9), very low density lipoprotein receptor (VLDLR), ATPase sarcoplasmic / endoplasmic reticulum Ca2+ transporting2 (ATP2A2), myosin VIIA (MYO7A), protein tyrosine phosphatase receptor type Q (PTPRQ), SP110 nuclear body protein (SP110), superoxide dismutase 1 (SOD1 ), FAM20C golgi associated secretory pathway kinase (FAM20C), pyruvate dehydrogenase kinase 4 (PDK4), RNA binding motif protein 20 (RBM20), titin (TTN), rhodopsin (RHO), family with sequence similarity 83 member H (FAM83H), collagen type VII alpha 1 chain (COL7A1 ), EPS8 signaling adaptor L2 (EPS8L2), serine / threonine kinase 38 like (STK38L), N-myc downstream regulated 1 (NDRG1 ), coiled-coil domain containing 66 (CCDC66), plakophilin 1 (PKP1 ), ADAM metallopeptidase with thrombospondin type 1 motif 2 (ADAMTS2), collagen type V alpha 1 chain (COL5A1 ), tenascin XB (TNXB), keratin 10 (KRT10), brevican (BCAN), dynamin 1 (DNM1 ), unc-93 homolog B1 , TLR signaling regulator (UNC93B1 ), coagulation factor VII (F7), coagulation factor XI (F11 ), collagen type IV alpha 4 chain (COL4A4), FANCD2 and FANCI associated nuclease 1 (FAN1 ), mitofusin 2 (MFN2), keratin 16 (KRT16), alpha-L-fucosidase 1 (FUCA1 ), ATP binding cassette subfamily B member 4 (ABCB4), integrin subunit alpha 2b (ITGA2B), olfactomedin like 3 (OLFML3), galactosylceramidase (GALC), phosphofructokinase, muscle (PFKM), glucose-6-phosphatase (G6PC), amylo-alpha-1 , 6-glucosidase, 4-alpha-glucanotransferase (AGL), galactosidase beta 1 (GLB1 ), hexosaminidase subunit alpha (HEXA), hexosaminidase subunit beta (HEXB), coagulation factor VIII (F8), coagulation factor IX (F9), patatin like phospholipase domain containing 8 (PNPLA8), potassiumvoltage-gated channel interacting protein 4 (KCNIP4), solute carrier family 12 member 6 (SLC12A6), FYVE and coiled-coil domain autophagy adaptor 1 (FYCO1 ), heat shock transcription factor 4 (HSF4), spectrin beta, erythrocytic (SPTB), desmoglein 1 (DSG1 ), family with sequence similarity 83 member G (FAM83G), SUV39H2 histone lysine methyltransferase (SUV39H2), solute carrier family 2 member 9 (SLC2A9), catalase (CAT), folliculin interacting protein 2 (FNIP2), alkaline phosphatase, biomineralization associated (ALPL), abhydrolase domain containing 5, lysophosphatidic acid acyltransferase (ABHD5), aspartic peptidase retroviral like 1 (ASPRV1 ), NIPA like domain containing 4 (NIPAL4), patatin like phospholipase domain containing 1 (PNPLA1 ), solute carrier family 27 member 4 (SLC27A4), high mobility group AT-hook 2 (HMGA2), amnion associated transmembrane protein (AMN), cubilin (CUBN), acyl-CoA synthetase long chain family member 5 (ACSL5), MAP3K7 C-terminal like (MAP3K7CL), pitrilysin metallopeptidase 1 (PITRM1 ), DIRAS family GTPase 1 (DIRAS1 ), L-2-hydroxyglutarate dehydrogenase (L2HGDH), NHL repeat containing E3 ubiquitin protein ligase 1 (NLHRC1 ), autophagy related 4D cysteine peptidase (ATG4D), transglutaminase 1 (TGM1 ), contactin associated protein 1 (CNTNAP1 ), calpain 1 (CAPN1 ), NADH:ubiquinone oxidoreductase core subunit S7 (NDUFS7), gap junction protein alpha 9 (GJA9), muskelin 1 (MKLN1 ), integrin subunit beta 2 (ITGB2), N-acyl phosphatidylethanolamine phospholipase D (NAPEPLD), plasminogen (PLG), sarcoglycan alpha (SGCA), sarcoglycan delta (SGCD), potassium voltage-gated channel subfamily Q member 1 (KCNQ1 ), leprecan-like 1 (LEPREL1 ), laminin subunit beta 3 (LAMB3), tubulin beta 1 class VI (TUBB1 ), Carbohydrate Sulfotransferase 6 (CHST6), CDK5 regulatory subunit associated protein 2 (CDK5RAP2), myosin heavy chain 9 (MYH9), retinol binding protein 4 (RBP4), alpha-L-iduronidase (IDUA), N-acetyl- alpha-glucosaminidase (NAGLU), arylsulfatase B (ARSB), N-sulfoglucosamine sulfohydrolase (SGSH), glucuronidase beta (GUSB), dystrophin (DMD), collagen type VI alpha 1 chain (COL6A1 ), myostatin (MSTN), ADAMTS like 2 (ADAMTSL2), iron-sulfur cluster assembly factor IBA57 (IBA57), myeloperoxidase (MPO), chloride voltage-gated channel 1 (CLCN1 ), myotubularin 1 (MTM1 ), hypocretin receptor 2 (HCRTR2), spectrin beta, non-erythrocytic 2 (SPTBN2), activating transcription factor 2 (ATF2), phospholipase A2 group VI (PLA2G6), tectonin beta-propeller repeat containing 2 (TECPR2), VPS11 core subunit of CORVET and HOPS complexes (VPS11 ), 2',3'-cyclic nucleotide 3' phosphodiesterase (CNP), ATPase cation transporting 13A2 (ATP13A2), CLN5 intracellular trafficking protein (CLN5), palmitoyl-protein thioesterase 1 (PPT1 ), cathepsin D (CTSD), tripeptidyl peptidase 1 (TPP1 ), arylsulfatase G (ARSG), CLN6 transmembrane ER protein (CLN6), major facilitator superfamily domain containing 8 (MFSD8), CLN8 transmembrane ER and ERGIC protein (CLN8), sodium voltagegated channel alpha subunit 8 (SCN8A), lipoxygenase homology PLAT domains 1 (LOXHD1 ), solute carrier family 45 member 2 (SLC45A2), collagen type IX alpha 2 chain (COL9A2), solute carrier family 13 member 1 (SLC13A1 ), exostosin glycosyltransferase 2 (EXT2), collagen type I alpha 1 chain (COL1A1 ), collagen type I alpha 2 chain (COL1A2), serpin family H member 1 (SERPINH1 ), phosphatidylinositol glycan anchor biosynthesis class N (PIGN), anti-Mullerian hormone receptor type 2 (AMHR2), LIM homeobox 3 (LHX3), POU class 1 homeobox 1 (POU1 F1 ), corticotropin releasing hormone receptor 1 (CRHR1 ), polycystin 1 , transient receptor potential channel interacting (PKD1 ), ALX homeobox 4 (ALX4), limb development membrane protein 1 (LMBR1 ), slingshot protein phosphatase 2 (SSH2), Rho guanine nucleotide exchange factor 10 (ARHGEF10), RAB3 GTPase activating protein catalytic subunit 1 (RAB3GAP1 ), alpha glucosidase (GAA), kallikrein B1 (KLKB1 ), coiled-coil domain 39 molecular ruler complex subunit (CCDC39), NME / NM23 family member 5 (NME5), alanine-glyoxylate and serine-pyruvate aminotransferase (AGXT), ADAM metallopeptidase with thrombospondin type 1 motif 10 (ADAMTS10), ADAM metallopeptidase with thrombospondin type 1 motif 17 (ADAMTS17), SEL1 L adaptor subunit of ERAD E3 ubiquitin ligase (SEL1 L), cyclic nucleotide gated channel subunit alpha 1 (CNGA1 ), cyclic nucleotide gated channel subunit beta 1 (CNGB1 ), interphotoreceptor matrix proteoglycan 2 (IMPG2), intraflagellar transport 122 (IFT122), MER proto-oncogene, tyrosine kinase (MERTK), NECAP endocytosis associated 1 (NECAP1 ), S-antigen visual arrestin (SAG), solute carrier family 4 member 3 (SLC4A3), tetratricopeptide repeat domain 8 (TTC8), phosphodiesterase 6B (PDE6B), ADAM metallopeptidase domain 9 (ADAM9), RD3 regulator of GUCY2D (RD3), chromosome 2 open reading frame 71 (C2orf71 ), FAM161 centrosomal protein A (FAM161 A), photoreceptor disc component (PRCD), kirre like nephrin family adhesion molecule 2 (KIRREL2), NPHS1 adhesion molecule, nephrin (NPHS1 ), pyruvate dehydrogenase phosphatase catalytic subunit 1 (PDP1 ), pyruvate kinase L / R (PKLR), AT-hook transcription factor (AKNA), folliculin (FLCN), collagen type IX alpha 3 chain (COL9A3), phosphodiesterase 6A (PDE6A), family with sequence similarity 134 member B (FAM134B), recombination activating 1 (RAG1 ), MDM2 binding protein (MTBP), collagen type XI alpha 2 chain (COL11A2), NK2 homeobox 8 (NKX2-8), potassium inwardly rectifying channel subfamily J member 10 (KCNJ10), HES family bHLH transcription factor 7 (HES7), ATPase Na+ / K+ transporting subunit beta 2 (ATP1 B2), ATP binding cassette subfamily A member 4 (ABCA4), solute carrier family 6 member 5 (SLC6A5), solute carrier family 19 member 3 (SLC19A3), RAS guanyl releasing protein 1 (RASGRP1 ), vacuolar protein sorting 13 homolog B (VPS13B), collagen type VI alpha 3 chain (COL6A3), scavenger receptor class F member 2 (SCARF2), von Willebrand factor (VWF), xanthine dehydrogenase (XDH), molybdenum cofactor sulfurase (MOCOS), ectodysplasin A (EDA), collagen type IV alpha 5 chain (COL4A5), retinitis pigmentosa GTPase regulator (RPGR), interleukin 2 receptor subunit gamma (IL2RG), proteolipid protein 1 (PLP1 ), melanocyte inducing transcription factor (MITF), endothelial PAS domain-containing protein 1 (EPAS1 ), Immunoglobulin Superfamily member 1 gene (IGSF1 ), and glutamate receptor interacting protein 1 (GRIP1 ).

57. The method of claim 56, further comprising genetically modifying one or more of the genetic markers.

58. The method of claim 48, wherein the non-genetic information is gathered from one or more of:(i) someone knowledgeable about the non-human animal pertaining to the one or more traits;(ii) a medical history of the non-human animal, including results from one or more clinical assays performed using a biological sample from the non-human animal and / or results from one or more diagnostic tests on the non-human animal;(Hi) a score assigned to the non-human animal during competition; and (iv) media content regarding the non-human animal.

59. The method of claim 58, wherein the media content comprises an image, a video, and / or a rendering of the non-human animal.

60. The method of any one of claims 1 -59, wherein the information about the non-human animal comprises epigenetic information.61 . The method of claim 60, wherein the epigenetic information comprises a DNA methylation profile, a histone modification profile, and / or a chromatin remodeling profile.

62. The method of claim 60 or 61 , wherein the epigenetic information was obtained by sequencing methods.

63. The method of any one of claims 1 -62 wherein the information about the non-human animal comprises blood type information.

64. The method of claim 63, wherein the blood type information comprises an ABO blood group classification selected from the group consisting of: Type A, Type B, Type AB, or Type O.

65. The method of any one of claims 1 -64, wherein the information about the non-human animal comprises telomere information.

66. The method of claim 65, wherein the telomere information comprises telomere length and / or a rate of telomere shortening or elongation.

67. The method of any one of claims 1 -66, wherein the one or more traits are selected from: coat color, coat color modifier, coat color intensity, coat texture, coat thickness, coat type, facial marking, leg marking, leg length, webbed paws, water-repellent coat, shedding, eye color, skin color, body size, tail shape, tail length, head shape, ear erectness, ear length, ear position, speed, gait, temperament, muscle type, jumping ability, catching ability, hunting ability, herding ability, behavior, fear, aggression, performance, ability, drive, adaptability, health, species, breed, and breeding group.

68. The method of claim 67, wherein the coat color, coat color modifier, or coat color intensity is selected from a group consisting of amber champagne, amber champagne dun, amber cream, amber dun, amber dun pearl, apricot dun, apricot pearl, agouti, bay, bay cream pearl, bay double cream, bay pearl, black, black bay, black cream pearl, black double cream, black dun, black pearl, black / red, blanket appaloosa, blood bay, blue roan, brindle, brown, buckskin, buttermilk buckskin, champagne, champagne amber pearl, champagne classic pearl, champagne dun, champagne dun pearl, champagne gold pearl, champagne pearl, chesetnut, chestnut cream pearl, chocolate, classic champagne, classic champagne dun, classic cream, classic dun, cream, cream champagne, cream grullo, cream pearl, cremello, cocoa, dapple grey, dark bay, dark brown, dark chestnut, dominant white, dun, dun bay cream, dun bay double cream, dun black cream, dun black double cream, dun chestnut cream, dun chestnut double cream, dun pearl, dun pearl bay, dun pearl black, dunalino, dunolino, dunskin, dunskin splash, flaxen chestnut, fleabitten grey, frame overo, frosted appaloosa, grey, gold champagne, gold cream, gold dun, gold dun pearl, grey, grullo, half blanket, leopard appaloosa, light grey, liver chestnut, marbled appaloosa, merle, overo, palomino, pearl, perlino, perlino dun, pseudocream classic, red, red chestnut, red dun, red roan, rose grey, sabino, saddle tan, seal brown, silver, silver amber champagne, silver amber dun, silver bay cream, silver bay cream pearl, silver bay double cream, silver black, silver black cream pearl, silver blackdouble cream, silver champagne, silver champagne dun, silver champagne pearl, silver classic champagne, silver classic champagne pearl, silver cream pearl, silver dapple, silver dapple bay, silver dapple buckskin, silver dapple cream grullo, silver dapple dun, silver dapple dunskin, silver dapple grullo, silver dapple pearl, silver dapple perlino, silver dapple perlino dun, silver dun, silver dun bay cream, silver dun bay double cream, silver dun black, silver dun black double cream, silver dun pearl, silver dun pearl bay, silver dun pearl black, silver pearl bay, silver pearl black, silver red, silver smoky cream, skewbald, smoky black, smoky cream, smoky grullo, snowflake appaloosa, sorrel, splashed white, steel grey, sun, tobiano, tovero, tri-color, white, white spotting, apron, barring on body, barring on shoulder, belly spots (large or small), belly stripe, belted, bend or spots on body, bend or spots on head, birdcatcher spots, black spots, blagdon, blanket with roaning, blanket with spots, blaze, blaze with freckling, body spots, body white, brindle, brow spots, calico, coon / skunk tail, dapples, dilute, dorsal stripe, double dilute, ermine markings, few white hairs on body, fewspot, flaxen mane / tail, flea-bitten, fleshmark, frosted, grullo, heart marking, highlights in mane / tail, lacing pattern, leopard spotted, lightning marks, line back, frosting mane / tail, maximum tobiano, maximum overo, maximum white, maximum white sabino, medicine hat, minimal overo, minimal sabino, minimal tobiano, mixed mane / tail, mottled, no marking, overo, pangare, paw prints, pearl, piebald, pinto markings, rabicano, roan, roaning on body, rose, rump spots, sabino, skewbald, smudge marks, smutty, snip, snowcap, sooty, splash markings, star, stripe, tobiano, umbilical spots, white head, white mane / tail, white throat latch, white tipped ears, white with fading edges, white with ragged edges, white with smooth edges, wither spot , no markings, solid, white blaze, black and tan, saddle tan, creeping tan, merle, harlequin, salty licorice, tan point, sable, blue-based sable, red-fawn, platinum, Isabella, blue brindle, red pied, black pied, brown-based sable, lilac-based sable, black-based sable, sable merle agouti, watermarking, ticking, urajiro, countershading, blue tick, blue roan, panda spotting, whitehead, piebald, cocoa, shaded sable, clear sable, recessive black, recessive red, cocker sable, sighthound domino grizzle, ancient domino, northern domino, seal tan, ghost, dominant black, yellow, melanistic mask, dominant yellow, black saddle, brindle merle, bi-color, tri-color, fawn, landseer, wheaten, wild boar, wolf color, sandy, peppered, orange and white, grizzle, blenheim, apricot, mustard, parti-color, ruby, salt and pepper, mahogany, charcoal, lemon, domino, camo, camouflage, cafe-au-lait, silver-beige, and white star,69. The method of claim 67, wherein the facial marking is selected from a group consisting of apron face, badger face, bald face, blaze, interrupted stripe, face, snip, star, stripe markings, both eyes amber, both eyes blue, both eyes brown, both eyes green, both eyes tiger, eyebrows, face mask, few white hairs on forehead, left eye amber, left eye blue, left eye brown, left eye green, left eye partial blue, left eye tiger, partial bald face, pigment around eye, right eye amber, right eye blue, right eye brown, right eye green, right eye partial blue, right eye tiger, white around eye, white chin, white jaw, white lip, white nose, melanistic mask, white head, freckled, and white sclera70. The method of claim 67, wherein the coat texture, coat thickness, coat type, or coat shedding is selected from a group consisting of smooth, rough, curly, straight, downy, spiky, brindle, high shedding, low shedding, medium shedding, double coat, single coat, bald, lions-mane ruff, and furnishings.71 . The method of claim 67, wherein body size is selected from a group consisting of small, medium, large, extra-large, giant, small to medium, medium to large, large to extra-large, and extra-large to giant.

72. The method of claim 67, wherein the tail shape is curly, straight, screwtail, long, short, bob-tail, curved, high or medium or low set.

73. The method of claim 67, wherein the behavior is selected from a group consisting of attachment, barking, chasing, excitability, energy, trainability, low drive, medium drive, high drive, focused, less focused, more focused, high recall, medium recall, low recall, sharing, guarding, non-guarding, sport interest, assistance dog type / temperament, social anxiety, spook, social engagement, herding, nonherding, sight based hunting, scent based hunting, scent based tracking, water affinity, water aversion.

74. The method of claim 67, wherein the aggression is selected from a group consisting of dog aggression, unfamiliar dog aggression, familiar dog aggression, rivalry aggression, stranger aggression, owner aggression, farm aggression, animal aggression, non-canine animal aggression, spatial aggression, territoriality, and touch sensitivity.

75. The method of claim 67, wherein the fear is selected from a group consisting of dog fear, stranger fear, escaping, non-social fear, separation anxiety, grooming fear, veterinary stress, veterinary fear, novel environment fear, fear of water, fear of flying, noise fear, and separation urination.

76. The method of claim 67, wherein the temperament is selected from a group consisting of vigilant, curious / vigilant, curious, spooky, non-spooky, hot, cold, and medium, aggressive, relaxed, average, suspicious, friendly, out-going, reserved, shy, aloof.

77. The method of claim 67, wherein the health comprises variants of one or more genes associated with one or more disease or non-disease conditions.

78. The method of claim 77, wherein the disease or non-disease conditions are selected from a group consisting of degenerative myelopathy, copper toxicosis, 2,8-dihydroxyadenine urolithiasis, acral mutilation syndrome, acute respiratory distress syndrome, alexander disease, amelogenesis imperfecta, autosomal recessive severe combined immunodeficiency, bald thigh syndrome, Bandera’s neonatal ataxia, Bardet-Biedl syndrome 2, progressive retinal atrophy, benign familial juvenile epilepsy, Bernard- Soulier syndrome, bleeding disorder, brachycephaly, p-Mannosidosis, canine leukocyte adhesion deficiency type III, canine multifocal retinopathy 1 , canine multifocal retinopathy 2, canine scott syndrome, cardiac arrhythmia, cardiomyopathy and juvenile mortality, centronuclear myopathy, cerebellar ataxia, cerebellar ataxia 2, cerebellar cortical degeneration, cerebellar degeneration-myositis complex, cerebral dysfunction, Charcot-Marie tooth disease, chondrodysplasia, disproportionate short-limbed, chondrodystrophy with or without chondrodysplasia, chondrodystrophy and intervertebral disc disease, cleft lip with or without palate and syndactyly, cleft palate, collie eye anomaly, complement 3 deficiency, cone degeneration, cone-rod dystrophy, cone-rod dystrophy 1 , cone-rod dystrophy 2, congenital dyshormonogenic hypothyroidism with goiter, congenital eye malformation, congenital hypothyroidism,congenital hypothyroidism with goiter, congenital idiopathic megaesophagus risk factor, congenital methemoglobinemia, congenital myasthenic syndrome, congenital stationary night blindness, copper toxicosis protective mutation, craniomandibular osteopathy, cystic renal dysplasia and hepatic fibrosis, cystinuria, cystinuria type l-A, cystinuria type l-B, cystinuria type I l-A, cystinuria type I l-B, dandy-walkerlike malformation, deafness and vestibular dysfunction, degenerative myelopathy, demyelinating polyneuropathy, dental hypomineralisation, dilated cardiomyopathy, dilated cardiomyopathy risk factor, dominant progressive retinal atrophy, dystrophic epidermolysis bullosa, early retinal degeneration, early- onset progressive polyneuropathy, early-onset pra, ectodermal dysplasia, Ehlers-Danlos syndrome, embryonic lethality, epidermolytic hyperkeratosis, episodic falling, exercise-induced collapse, factor VII deficiency, factor XI deficiency, familial nephropathy, Fanconi syndrome, fetal onset neuroaxonal dystrophy, focal non-epidermolytic palmoplantar keratoderma, GM1 gangliosidosis, GM2 gangliosidosis, glanzmann thrombasthenia type I, glaucoma, globoid cell leukodystrophy, glycogen storage disease type IIIA, glycogen storage disease type IA, glycogen storage disease VII, hemophilia A, hemophilia B, hereditary ataxia, hereditary cataracts HSF4-related, hereditary elliptocytosis, hereditary footpad hyperkeratosis, hereditary nasal parakeratosis, hereditary nephritis, hyperuricosuria, hypocatalasia, hypomyelination, hypophosphatasia, ichthyosis, inflammatory myopathy, inguinal cryptorchidism, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, intestinal cobalamin malabsorption, juvenile dermatomyositis, juvenile encephalopathy, juvenile myoclonic epilepsy, I-2- hydroxyglutaric aciduria, lagotto storage disease, lamellar ichthyosis, laryngeal paralysis and polyneuropathy, leonberger polyneuropathy type 2, lethal acrodermatitis, ligneous membranitis, limbgirdle muscular dystrophy, limb-girdle muscular dystrophy, type 2F, long qt syndrome, lung developmental disease, multidrug resistance 1 medication sensitivity, macrothrombocytopenia, macular corneal dystrophy, may-hegglin anomaly, microphthalmia, mitochondrial dysfunction syndrome 3, mucopolysaccharidosis I, mucopolysaccharidosis, type IIIA, mucopolysaccharidosis, type VII, muscular dystrophy, muscular hypertrophy, musladin-lueke syndrome, myeloperoxidase deficiency, myotonia congenita, myotubular myopathy, myotubular myopathy 1 , narcolepsy, neonatal cerebellar cortical degeneration, neonatal encephalopathy with seizures, neuroaxonal dystrophy, neuronal ceroid lipofuscinosis 1 , neuronal ceroid lipofuscinosis 10, neuronal ceroid lipofuscinosis 12, neuronal ceroid lipofuscinosis 4a, neuronal ceroid lipofuscinosis 5, neuronal ceroid lipofuscinosis 6, neuronal ceroid lipofuscinosis 7, neuronal ceroid lipofuscinosis 8, nonsyndromic hearing loss, obesity, oculocutaneous albinism, oculoskeletal dysplasia, osteochondrodysplasia, osteochondromatosis, osteogenesis imperfecta, paroxysmal dyskinesia, persistent mullerian duct syndrome, pituitary dwarfism, pituitarydependent hyperadrenocorticism, polycystic kidney disease, polydactyly, polyneuropathy with ocular abnormalities and neuronal vacuolation, pompe disease, prekallikrein deficiency, primary ciliary dyskinesia, primary ciliary dyskinesia, primary hyperoxaluria, primary lens luxation, primary open angle glaucoma, primary open angle glaucoma and lens luxation, progressive early-onset cerebellar ataxia, progressive retinal atrophy, progressive retinal atrophy i, progressive retinal atrophy type III, progressive rod-cone degeneration, pyruvate dehydrogenase phosphatase 1 deficiency, pyruvate kinase deficiency, recurrent inflammatory pulmonary disease, renal cystadenocarcinoma and nodular dermatofibrosis, rodcone dysplasia 1 , rod-cone dysplasia 3, sensory neuropathy, severe combined immunodeficiency, shar- pei autoinflammatory disease, skeletal dysplasia 2, spinocerebellar ataxia, spinocerebellar ataxia with myokymia and / or seizures, spondylocostal dysostosis, spongy degeneration with cerebellar ataxia,Stargardt disease, subacute necrotizing encephalopathy, T locus, thrombopathia, trapped neutrophil syndrome, ullrich congenital muscular dystrophy, Van den Ende-Gupta syndrome, Von Willebrand's disease type 1 , Von Willebrand's disease type 2, Von Willebrand's disease type 3, X-linked ectodermal dysplasia, X-linked hereditary nephropathy, X-linked retinal dysplasia, X-linked severe combined immunodeficiency, X-linked tremors, xanthinuria type 1 , xanthinuria type II, XX Disorder of sex development, canine degenerative myelopathy, progressive retinal atrophy, cone-rod dystrophy 3, progressive retinal atrophy, rod-cone dysplasia 4, and retinal dysplasia / oculoskeletal dysplasia 1 .

79. The method of claim 67, wherein the species of the non-human animal is selected from: Alopex lagopus, Atelocynus microtis, Canis adustus, Canis aureus, Canis familiaris, Canis latrans, Canis lupus, Canis mesomelas, Canis rufus, Canis simensis, Cerdocyon thous, Chrysocyon brachyurus, Cuon alpinus, Lycaon pictus, Nyctereutes procyonoides, Otocyon megalotis, Pseudalopex culpaeus, Pseudalopex fulvipes, Pseudalopex griseus, Pseudalopex gymnocercus, Pseudalopex sechurae, Pseudalopex veturus, Speothos venaticus, Urocyon cinereoargenteus, Urocyon littoralis, Vulpes bengalensis, Vulpes cana, Vulpes chama, Vulpes corsac, Vulpes ferrilata, Vulpes macrotis, Vulpes pallida, Vulpes rueppelli, Vulpes velox, Vulpes vulpes, and Vulpes zerda.

80. The method of claim 79, wherein the species of the non-human animal is Canis familiaris.81 . The method of claim 67, wherein the breeding group of the non-human animal is selected from: a herding group; a hound group; a toy group; a non-sporting group; a sporting group; a terrier group; and a working group.

82. The method of claim 67, wherein the breed of the non-human animal is selected from the group consisting of: Aidi, Affenpinscher, Hound, Africanis, African Hairless, Terrier, Akita, Alaskan Klee Kai, Alaskan Malamute, Bulldog, Spaniel, Eskimo Dog, American Sport Dog, Shepherd, Armant, Aussiedoodle, Australian Cattle Dog, Kelpie, Labradoodle, Azawakh, Basenji, Bassador, Basset, Bassugg, Beagador, Beagle, Beaglier, Collie, Beauceron, Whippet, Belgian Groenendael, Belgian Laekenois, Belgian Malinois, Sheepdog, Belgian Tervuren, Bergamasco, Bernedoodle, Bernese Mountain Dog, Bichon Frise, Bichon Yorkie, Bich-poo, Blue Lacy, Boerboel, Bolognese, Borador, Border Jack, Bordoodle, Borzoi, Bouvier Des Flandres, Boxador, Boxer, Bracco Italiano, Braque D'Auvergne, Briard, Brittany, Bugg, Bullmastiff, Bull Pei, Canaan Dog, Cane Corso, Cane Corso Italiano, Corgi, Catahoula Leopard Dog, Cavachon, Cavapom, Cavapoo, Cavapoochon, Cava Tzu, Cheagle, Retriever, Chihuahua, Chinook, Chinese Crested, Chipoo, Chi Staffy Bull, Chiweenie, Chorkie, Chow Chow, Chug, Chusky, Cirneco Dell'Etna, Cockachon, Cockador, Cockapoo, Cojack, Coton De Tulear, Dachshund, Dalmatian, Dameranian, Dobermann, Dogue de Bordeaux, Dorkie, Doxiepoo, Setter, Mountain Dog, Eurasier, Lapphund, Spitz, French Bull Jack, Frenchie Staff, French Pin, Frug, Gerberian Shepsky, Pointer, Pinscher, German Sheprador, Goberian, Goldendoodle, Golden Dox, Grand Bleu De Gascogne, Great Dane, Great Pyrenees, Greenland Dog, Griffon, Hamiltonstovare, Harrier, Havanese, Horgi, Hovawart, Hungarian Kuvasz, Hungarian Puli, Hungarian Pumi, Hungarian Vizsla, Irish Doodle, Italian Spinone, Jack-A-Bee, Jackahuahua, Jack-A-Poo, Jackshund, Jacktzu, Japanese Chin, Japanese Shiba, Jug, Keeshond, Kokoni, Komondor, Kooikerhondje, Korean Jindo, Labrador, Lachon, Lagotto Romagnolo,Lancashire Heeler, Large Munsterlander, Leonberger, Lhasa Apso, Lhasapoo, Lhatese, Lbwchen, Lurcher, Mai-Shi, Maltese, Maltichon, Maltipom, Malti-Poo, Mastiff, Mexican Hairless, Poodle, Schnauzer, Miniature Schnoxie, Morkie, Newfoundland, New Zealand Huntaway, Northern Inuit, Norwegian Buhund, Papillon, Peek-a-poo, Pekingese, Pitsky, Pomapoo, Pomchi, Pomeranian, Pomsky, Portuguese Podengo, Water Dog, Presa Canario, Pug, Pugalier, Pugapoo, Puggle, Pugzu, Rescue Dog, Rhodesian Ridgeback, Rottweiler, Russian Toy, Saluki, Samoyed, Schipperke, Schnoodle, Segugio Italiano, Shar Pei, Sheepadoodle, Shih-poo, Shih Tzu, Shollie, Shorkie, Siberian Cocker, Siberian Husky, Sloughi, Small Munsterlander, Springador, Sprocker, Sprollie, Sproodle, Stabyhoun, Staffador, Staffy Jack, St. Bernard, Swedish Vallhund, Tamaskan, Terri-Poo, Turkish Kangal Dog, Weimaraner, Westiepoo, Yorkie Russell, and Yorkipoo, or a mixed breed thereof.

83. The method of any one of claims 1 -82, wherein the non-human animal is selected from the group consisting of selected from the group consisting of a dog, horse, cattle, sheep, cat, camel, pig, goat, alpaca, donkey, llama, red fox, mouse, rat, ferret, non-human primate, rabbit, gerbil, hamster, chinchilla, or guinea pig.

84. A computer-implemented method for producing, identifying, and / or visualizing a virtual non- human animal comprising one or more predetermined traits, the method comprising:(a) selecting the one or more predetermined traits of interest for the non-human animal;(b) generating a probability index for the non-human animal using information stored in a computer-implemented database system, wherein:(i) the information stored in the computer-implemented database system comprises data related to a plurality of non-human animals comprising the one or more predetermined traits selected in step (a); and(ii) the probability index comprises an array of probability values pertaining to the likelihood that the non-human animal comprising the one or more predetermined traits also comprises a plurality of non-predetermined traits;(c) converting the probability index of step (b) into a report, wherein the report identifies:(i) the non-human animal as having or at risk of developing the one or more predetermined traits(ii) the non-human animal as having, not having, or at risk of developing the plurality of non-predetermined traits; and(iii) a predictive image or a rendering of the non-human animal; and(d) presenting the report of step (c) to a user on a graphical user interface.

85. The method of claim 84, the information stored in the computer-implemented database system was obtained from a secondary source.

86. The method of claim 84, further comprising selecting an age or life-stage of the non-human animal.

87. The method of claim 84, wherein the predictive image or rendering of the non-human animal depends on the age or life-stage of the non-human animal.

88. The method of claim 86 or 87, wherein the age of the non-human animal is selected from 1 day, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years,18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, or 35 years.

89. The method of claim 86 or 87, wherein the life-stage of the non-human animal is selected from newborn, neonate, infant, adolescent, juvenile adult, senior or geriatric.

90. The method of any one of claims 84-89 further comprising identifying one or more breeding pairs likely to produce the non-human animal comprising the one or more predetermined traits.91 . The method of claim 90, further comprising selecting the one or more breeding pairs.

92. The method of claim 91 , further comprising breeding one or more of the selected breeding pairs.

93. The method of claim 92, further comprising birthing the non-human animal comprising the one or more predetermined traits.

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