Chromosomal biomarkers

Abstract

Methods for analyzing chromosomal regions and interactions related to physical performance.

Description

[Technical field]

[0001] The present invention relates to detecting chromosomal interactions. [Background technology]

[0002] Physical performance is complex and cannot be predicted using available methods. It is clear that coordination, flexibility, precision, power, speed, endurance, balance, cognitive efficiency, and timing are related to performance. This has applications not only in sports medicine but also in fitness and physical training regimes. Summary of the Invention

[0003] The inventors have used an approach that analyzes subgroups of the population to identify chromosomal interactions associated with physical performance. Our work allows classification and adjustment of physical performance in a completely new way that is more sensitive and tailored to the individual than genomic or protein classifications, and reflects the individual's personal history.

[0004] Thus, the present invention is a method for detecting a chromosomal condition representing a subgroup in a population, comprising determining whether a chromosomal interaction relating to said chromosomal condition exists within a defined region of the genome, said subgroup being related to physical performance in an individual; and the chromosomal interactions are optionally identified by a method for determining which chromosomal interactions are associated with chromosomal states corresponding to physical performance subgroups of a population, the method comprising contacting a first set of nucleic acids from subgroups having various chromosomal states with a second set of index nucleic acids and allowing complementary sequences to hybridize, the nucleic acids of the first and second sets of nucleic acids representing ligated products comprising sequences from both chromosomal regions that came together in the chromosomal interaction, and the pattern of hybridization between the first and second sets of nucleic acids allowing the determination of which chromosomal interactions are specific to a physical performance subgroup; and Chromosomal interactions (i) corresponds to any one of the chromosomal interactions shown in any of Tables 3, 7, 8, 9, 25, and 30; and / or (ii) corresponds to any one of the chromosomal interactions shown in any of Tables 13, 14, 18, 22, 23, and 24; and / or (iii) corresponds to any one of the chromosomal interactions shown in Tables 31 or 32; and / or (iv) is present in a region of 4000 bases that includes or is adjacent to (i), (ii), or (iii); and / or (v) is present in any one of the regions or genes listed in Table 21, 24, 25, 30, 31, or 32. The present invention provides a method for

[0005] In a preferred embodiment, the present invention is a method for detecting a chromosomal condition representing a subgroup in a population, comprising determining whether a chromosomal interaction relating to said chromosomal condition exists within a defined region of the genome, said subgroup being related to physical performance in an individual; and hand, (a) corresponds to any of the chromosomal interactions shown in any of Tables 33, 34, 35, 36, 37, 38, 39, 40 or 41, or any of Figures 16, 17 or 18; and / or (b) is present in a region of 4,000 bases that includes or is adjacent to (a); Either A specific combination of chromosomal interactions Provide a way to be classified. [Brief description of the drawings]

[0006] [Figure 1] 1 shows a graphical representation and summary of the analysis of Example 1. [Diagram 2] Venn diagram of the 80 potential markers identified in analysis 2 and the 65 markers identified in part 2 and screened in part 3 based on exercise response is shown. [Diagram 3]Markers of high response to strength training are shown. A shows that the high responders are corrected and the low responders are reprogrammed such that the training program makes the markers detectable. B shows that the high responders are corrected and the low responders are reprogrammed such that the training program makes the markers undetectable. C shows that the high responders are fixed and the low responders are also fixed, indicating no change in the chromatin landscape for this particular interaction. [Figure 4] The overlap of analyses is shown, with the left circle representing the 38 additional predictive markers in analysis 2, the middle circle showing the 42 overlapping predictive markers examined in parts 2 and 3, and the right circle showing the 23 markers examined in parts 2 and 3 that are response to training markers. [Diagram 5] FIG. 11 is a Venn diagram showing the composition of the 80 markers identified in Analysis 2. [Figure 6] Confusion matrix results are shown. Classification of the training set based on sprinters and stayers. [Figure 7] Venn diagram comparing markers identified by Glmnet with markers identified based on Boshloo p-values. [Figure 8] 1 shows the distribution curve for aerobic capacity in the Epifit study. [Figure 9] 1 shows the distribution curve for relative strength ratio (Kg:Kg) in the Epifit study. [Figure 10] The genetic characteristics of the horses and the VENN ratios of reported ORFs and ORFs with significant CCS are shown. [Figure 11] VENN plots of the top 250 loops in sedentary subjects compared to two athletic groups. [Figure 12] Shows horse genetic characteristics and VENN comparison of reported ORFs with ORFs with significant common CCS in sedentary subjects. [Figure 13] A comparison between training and desk work is shown. [Figure 14] A preferred method for detecting chromosomal interactions is presented. [Figure 15] Venn diagrams of CV and intensity baseline and 8-week key markers are shown. [Figure 16] Baseline CV markers based on high and low CV groups are shown. [Figure 17] Baseline strength markers based on exact P values ​​for high and low intensity groups of associations are shown. [Figure 18] Ten Episwitch equine markers identified from the UK cohort are shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] EMBODIMENTS OF THE PRESENT DISCLOSURE The present invention relates to a panel of epigenetic markers for the control and stratification of physical performance, in particular strength and endurance. The markers are preferably stable and also allow the detection of predisposition in the physiology to certain stimuli such as physical training.

[0008] The present invention also includes monitoring physical performance or its responsiveness to a particular stimulus, such as a particular training regime. Thus, in one embodiment, the present invention, in one aspect, provides a "live" ongoing readout of physical performance status, allowing an individual to be provided with personalized stimuli that reflect their individual needs.

[0009] The present invention provides a method for selecting an individual for a given physical challenge, such as a race or a particular training session. The present invention also provides a method for selecting or designing a training or fitness regime, for example, for a particular individual.

[0010] The present invention also provides methods for predicting physical performance, including whether an individual has high muscle strength or endurance, or both, as measured, for example, by certain methods described herein.

[0011] The invention allows for classification of individuals as "fit", "strong" or "sedentary". The invention allows for stratification of "baseline" individuals entering a training program for better predisposition to either strength or endurance - a de facto predictive biomarker of response. The invention also allows for stratification of early biomarker evidence of response to training in individuals - a de facto early response biomarker to monitor training progression.

[0012] Any of the markers disclosed herein may be used in the methods of the invention, including any marker disclosed in any table or figure. Preferred markers are set forth below. - Tables 3, 7, 8, 9, 25, and 30; and - Tables 13, 14, 18, 22, 23, and 24; and - Table 31 or 32; and - Tables 33 to 41; and -Figures 16 to 18.

[0013] physical ability The present invention relates to the determination of physical performance. The method of the present invention can detect responsiveness to a stimulus related to physical performance, for example, training, such as strength or endurance training. Training typically involves subjecting an individual to physical exercise, for example, exerting force, physically moving, running, or performing a specific physical activity (for example, as disclosed herein) for a specific period of time. The method of the present invention can be used to detect high or low response to a stimulus, such as high or low response to any specific training or physical activity disclosed herein. Preferably, the present invention detects response to strength or endurance training.

[0014] The present invention can be used to select individuals suitable for physical activities such as sports. Preferred sports include strength or endurance sports. Preferred endurance sports are racing or running. Thus, the present invention can be used to select individuals suitable for any particular activity mentioned herein, such as training.

[0015] The process of the present invention The process of the present invention includes a classification system for detecting chromosomal interactions related to physical performance.This classification can be performed by using the Episwitch™ system described herein, which is based on the bridge regions of chromosomes that are gathered at chromosomal interactions, by cutting chromosomal DNA, and then ligating the nucleic acid present at the bridge entity, and inducing ligated nucleic acid that has sequences from both regions that form chromosomal interactions.The detection of this ligated nucleic acid allows the detection of the presence or absence of a specific chromosomal interaction.

[0016] Chromosomal interactions can be identified using the methods described above, in which a first and a second population of nucleic acids are used. These nucleic acids can also be generated using Episwitch™ technology.

[0017] Epigenetic interactions relevant to the present invention As used herein, the terms "epigenetic" and "chromosomal" interactions refer to interactions typically between distal regions of loci on chromosomes, which interactions are dynamic and change, form, or break depending on the state of the chromosomal regions.

[0018] In certain processes of the invention, chromosomal interactions are detected by first generating ligated nucleic acids that contain sequences from both regions of the chromosomes that are usually part of the interaction. In such processes, both regions can be crosslinked by any suitable means. In a preferred embodiment, the interaction is crosslinked using formaldehyde, but may be crosslinked by any aldehyde, or D-biotinyl-e-aminocaproic acid-N-hydroxysuccinimide ester or digoxigenin-3-O-methylcarbonyl-e-aminocaproic acid-N-hydroxysuccinimide ester. Paraformaldehyde can crosslink DNA strands that are 4 angstroms apart. Preferably, the chromosomal interactions are on the same chromosome, optionally 2-10 angstroms apart.

[0019] Chromosomal interactions can reflect the state of a chromosomal region, for example, whether it is transcribed or repressed.The chromosomal interactions that are specific to the subgroups defined herein are found to be stable, and therefore provide a reliable means of measuring the difference between two subgroups.

[0020] In addition, it is believed that chromosomal interactions specific to a certain feature (such as physical performance) usually occur early in a biological process, compared to other epigenetic markers, such as changes to methylation or histone protein binding, and can provide a "live" readout of an individual's state. Thus, the process of the present invention can detect early stages of a biological process as well as continuous monitoring. Chromosomal interactions can also reflect the current state of an individual, and therefore can be used to evaluate changes in physical performance. Furthermore, there is little variation in relevant chromosomal interactions between individuals in the same subgroup. Detecting chromosomal interactions is highly informative, with up to 50 possible different interactions per gene, so the process of the present invention can interrogate 500,000 different interactions.

[0021] Preferred marker set As used herein, the term "marker" or "biomarker" refers to a specific chromosomal interaction that can be detected (classified) in the present invention. Specific markers are disclosed herein, any of which can be used in the present invention, or any of which can be used in any combination with other specific markers or combinations disclosed herein. Preferably, a set of markers may be used, for example, in the combinations or numbers disclosed herein. Specific markers disclosed in the tables herein are preferred, and markers present in the genes or regions described in the tables herein are also preferred. These can be classified by any suitable method, such as, for example, PCR or probe-based methods disclosed herein, such as qPCR methods. Markers are defined herein by location or by probe and / or primer sequence.

[0022] Location and causes of epigenetic interactions Epigenetic chromosomal interactions may overlap and include regions of chromosomes that are shown to code for related or undescribed genes, but may also be in intergenic regions. The chromosomal interactions detected in the present invention may be caused by changes to the underlying DNA sequence, environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modifications, chromatin remodeling, and specific local DNA interactions. The changes that lead to chromosomal interactions may be caused by changes to the underlying nucleic acid sequence that do not directly affect gene products or gene expression patterns themselves. Such changes may be, for example, SNPs inside and / or outside genes, gene fusions, and / or deletions of intergenic DNA, microRNA, and non-coding RNA. For example, it is known that approximately 20% of SNPs are in non-coding regions, and therefore the process described is also useful in non-coding situations. In one embodiment, the regions of chromosomes that form interactions together are less than 5kb, 3kb, 1kb, 500 base pairs, or 200 base pairs apart on the same chromosome.

[0023] The chromosomal interaction to be detected is preferably within any of the genes mentioned in Table 21. However, it may be, for example, up to 50,000, up to 30,000, up to 20,000, up to 10,000, or up to 5000 bases upstream from the gene. It may be, for example, up to 50,000, up to 30,000, up to 20,000, up to 10,000, or up to 5000 bases downstream from the gene. It may be, for example, upstream or downstream from the coding sequence by any of these specific base numbers.

[0024] Subgroups, Time Points and Personalization One objective of the present invention is to determine a characteristic associated with physical performance. This may be at one or more defined time points for the same individual, for example at least 1, 2, 5, 8 or 10 different time points. The period between the time points may be at least 20, 50, 80 or 100 days. Typically, testing of the individual (by the method of the present invention) occurs before, during or after a physical stimulus is applied. The testing may determine a predisposition to a particular type of response, or a response to the actual stimulus.

[0025] As used herein, "subgroup" preferably refers to a population subgroup (subgroup within a population), more preferably a subgroup within a human or equine population. The present invention includes applying and detecting physical stimuli to specific subgroups in a population. The inventors have discovered that chromosome interactions differ between subsets (e.g., at least two subsets) in a given population. Identifying these differences allows categorization of individuals, which allows for personalized stimuli, such as training, or for selecting individuals for special physical activities.

[0026] Generate ligated nucleic acids Certain aspects of the invention utilize ligated nucleic acids, particularly ligated DNA, which contain sequences from both regions that converge in a chromosomal interaction and therefore provide information about the interaction. The EpiSwitch™ method described herein uses the generation of such ligated nucleic acids to detect chromosomal interactions.

[0027] Thus, the process of the present invention comprises the steps (including methods comprising these steps): (i) cross-linking epigenetic chromosomal interactions present at chromosomal loci, preferably in vitro; (ii) optionally, isolating the crosslinked DNA from the chromosomal locus; (iii) cleaving the crosslinked DNA, e.g., by restriction digestion with an enzyme that cuts it at least once (particularly an enzyme that cuts at least once within the chromosomal locus); (iv) ligating the cross-linked cleaved DNA ends (particularly to form a DNA loop); and (v) optionally identifying the presence of ligated DNA and / or DNA loops, in particular using techniques such as PCR (polymerase chain reaction), to identify the presence of specific chromosomal interactions; The method includes producing a ligated nucleic acid (e.g., DNA) by

[0028] These steps may be performed to detect chromosomal interactions for any of the embodiments described herein. The steps may also be performed to generate the first and / or second nucleic acid sets described herein.

[0029] PCR (polymerase chain reaction) can be used to detect or identify the ligated nucleic acid, for example the size of the PCR product produced can indicate that there is a specific chromosomal interaction and therefore can be used to identify the state of the locus. In a preferred embodiment, at least one, two or three primers or primer pairs shown in Table 24, 25 or 30 are used in the PCR reaction. In another preferred embodiment, at least one, two or three primers or primer pairs shown in Table 31 or 32 are used in the PCR reaction. Those skilled in the art will be aware of the myriad of restriction enzymes that can be used to cut DNA within a chromosomal locus of interest. It will be clear that the particular enzyme used will depend on the locus being investigated and the sequence of the DNA located therein. A non-limiting example of a restriction enzyme that can be used to cut DNA as described in the present invention is TaqI.

[0030] EpiSwitch™ Technology and Other Embodiments The EpiSwitch™ technology relates to the use of microarray EpiSwitch™ marker data in the detection of phenotype-specific epigenetic chromosomal conformation signatures. Embodiments such as EpiSwitch™ that utilize ligated nucleic acids in the manner described herein have several advantages. For example, nucleic acid sequences from the first nucleic acid set of the present invention either hybridize or do not hybridize with the second nucleic acid set, so that their stochastic noise is at a low level. This provides a binary result that allows a relatively simple way of measuring complex mechanisms at the epigenetic level. The EpiSwitch™ technology also has a fast processing time and low cost. In one embodiment, the processing time is 3-6 hours.

[0031] Samples and sample treatment The process of the present invention will usually be carried out on a sample. The sample will usually contain DNA from an individual. It will usually contain cells. In one embodiment, the sample is obtained by minimally invasive means, and can be, for example, a blood sample. DNA can be extracted and cut into pieces with standard restriction enzymes. This can predetermine which chromosome conformations are retained and detected by the EpiSwitch™ platform. Due to the synchronization of chromosome interactions between tissues and blood, including horizontal transfer, blood samples can be used to detect chromosome interactions in tissues.

[0032] Properties of the Nucleic Acids of the Invention The invention relates to certain nucleic acids, such as ligated nucleic acids, described herein as being used or produced in the process of the invention. These may be the same as the first and second nucleic acids described herein, or may have any of the properties of the first and second nucleic acids. The nucleic acids of the invention typically comprise two portions, each of which comprises a sequence from one of the two regions of a chromosome that converge in a chromosomal interaction. Typically, each portion is at least 8, 10, 15, 20, 30, or 40 nucleotides in length, for example 10-40 nucleotides in length. Preferred nucleic acids include sequences from any of the genes, particularly those in which the nucleic acid is listed in any table, such as Table 21. Typically, preferred nucleic acids include the specific probe sequences listed in Tables 24, 25 or 30, or fragments and / or homologues of such sequences. Preferred nucleic acids also include the specific probe sequences listed in Tables 31 or 32, or fragments and / or homologues of such sequences. Preferably, the nucleic acid is DNA. Where specific sequences are provided, it is understood that the invention may use complementary sequences as required in certain embodiments.

[0033] Primers shown in Tables 24, 25 or 30 may also be used in the present invention as described herein. In one embodiment, primers are used that include any of the sequences shown in Tables 24, 25 or 30; or fragments and / or homologues of any of the sequences shown in Tables 24, 25 or 30.

[0034] Primers shown in Tables 31 or 32 may also be used in the present invention as described herein. In one embodiment, primers are used that include any of the sequences shown in Tables 31 or 32; or fragments and / or homologues of any of the sequences shown in Tables 31 or 32.

[0035] A second set of nucleic acid "index" sequences The second set of nucleic acids has the function of being a set of index sequences and is essentially a set of nucleic acid sequences suitable for identifying subgroup-specific sequences. They can represent "background" chromosomal interactions and may or may not be selected in some way. They are generally a subset of all possible chromosomal interactions.

[0036] The second nucleic acid set may be derived by any suitable method. They may be computationally derived or they may be based on chromosomal interactions in an individual. They typically represent a larger population group than the first nucleic acid set. In one particular embodiment, the second nucleic acid set represents all possible epigenetic chromosomal interactions in a particular gene set. In another particular embodiment, the second nucleic acid set represents a majority of all possible epigenetic chromosomal interactions present in the population described herein. In one particular embodiment, the second nucleic acid set represents at least 50% or at least 80% of epigenetic chromosomal interactions in at least 20, 50, 100 or 500 genes, such as 20-100, or 50-500 genes.

[0037] The second nucleic acid set typically represents at least 100 possible epigenetic chromosomal interactions that modify, regulate, or in some way mediate disease state / phenotype in a population.The second nucleic acid set may represent chromosomal interactions that affect physical performance in a species.The second nucleic acid set typically includes sequences that represent epigenetic interactions that are both associated and unassociated with physical performance subgroups.

[0038] In one particular embodiment, the second set of nucleic acids is at least partially derived from naturally occurring sequences in a population, and typically obtained by in silico methods. The nucleic acids may further comprise single or multiple mutations compared to the corresponding portion of the nucleic acid present in the naturally occurring nucleic acid. Mutations include deletions, substitutions, and / or additions of one or more nucleotide base pairs. In one particular embodiment, the second set of nucleic acids may comprise sequences representing homologs and / or orthologs with at least 70% sequence identity with the corresponding portion of the nucleic acid present in the naturally occurring species. In another particular embodiment, at least 80% sequence identity or at least 90% sequence identity with the corresponding portion of the nucleic acid present in the naturally occurring species is provided.

[0039] Properties of the second set of nucleic acids In one particular embodiment, there are at least 100 different nucleic acid sequences in the second nucleic acid set, preferably at least 1000, 2000, or 5000 different nucleic acid sequences, and up to 100,000, 1,000,000, or 10,000,000 different nucleic acid sequences. A typical number would be 100-1,000,000, such as 1,000-100,000 different nucleic acid sequences. All of these or at least 90% or at least 50% would represent different chromosomal interactions.

[0040] In one particular embodiment, the second nucleic acid set represents chromosomal interactions at at least 20 different loci or genes, preferably at least 40 different loci or genes, and more preferably at least 100, at least 500, at least 1000, or at least 5000 different loci or genes, such as 100 to 10,000 different loci or genes. The length of the second nucleic acid set is suitable for them to specifically hybridize to the first nucleic acid set according to Watson-Crick base pairing and allow identification of subgroup-specific chromosomal interactions. Typically, the second nucleic acid set will include two portions whose sequences correspond to two chromosomal regions that converge in a chromosomal interaction. The second nucleic acid set typically includes nucleic acid sequences that are at least 10, preferably 20, and more preferably 30 bases (nucleotides) in length. In another embodiment, the nucleic acid sequences can be at most 500, preferably at most 100, and more preferably at most 50 base pairs in length. In a preferred embodiment, the second set of nucleic acids comprises nucleic acid sequences of between 17 and 25 base pairs. In one embodiment, at least 100, 80%, or 50% of the second set of nucleic acid sequences have a length as described above. Preferably, the different nucleic acids do not have any overlapping sequences, e.g., at least 100%, 90%, 80%, or 50% of the nucleic acids do not have the same sequence over at least 5 consecutive nucleotides.

[0041] The same set of second nucleic acids may be used with different first nucleic acid sets representing different characteristics of a subgroup, with the second nucleic acid set acting as an "index," i.e., the second nucleic acid set may represent a "universal" collection of nucleic acids that can be used to identify chromosomal interactions associated with the different characteristics.

[0042] First set of nucleic acids The first nucleic acid set is usually from a subgroup related to physical performance. The first nucleic acid may have any of the characteristics and properties of the second nucleic acid set mentioned herein. The first nucleic acid set is usually from a sample from an individual that has undergone the treatment and processing described herein, particularly the cross-linking and cleavage steps of EpiSwitch™. Typically, the first nucleic acid set represents all or at least 80% or 50% of the chromosomal interactions present in the sample taken from the individual.

[0043] Typically, the first set of nucleic acids represents a smaller population of chromosomal interactions across the loci or genes represented by the second set of nucleic acids compared to the chromosomal interactions represented by the second set of nucleic acids, i.e., the second set of nucleic acids represents a background or index set of interactions at a defined set of loci or genes.

[0044] Nucleic acid library Any of the types of nucleic acid populations referred to herein may be in the form of a library that includes at least 200, at least 500, at least 1000, at least 5000, or at least 10,000 different nucleic acids of that type, such as a "first" or "second" nucleic acid. Such libraries may be in the form of arrays. The libraries may, for example, include all of the nucleic acids disclosed in any of the tables disclosed herein, or all of the probe sequences disclosed in any of the tables disclosed herein.

[0045] Hybridization The present invention requires a means for allowing fully or partially complementary nucleic acid sequences from a first set of nucleic acids and a second set of nucleic acids to hybridize. In one embodiment, all of the first set of nucleic acids and all of the second set of nucleic acids are contacted in a single assay, i.e., in a single hybridization step. However, any suitable assay can be used.

[0046] Labeled Nucleic Acids and Hybridization Patterns The nucleic acid referred to herein can be preferably labeled with an independent label, such as a fluorophore (fluorescent molecule) or a radioactive label, which can assist in the detection of hybridization that can be achieved.Certain labels can be detected under UV light.For example, the hybridization pattern on the array described herein represents the difference in epigenetic chromosome interactions between two subgroups, thus providing a process of epigenetic chromosome comparison and determining which epigenetic chromosome interactions are specific to the subgroup of the population of the present invention.

[0047] The term "pattern of hybridization" broadly covers the presence or absence of hybridization between a first set of nucleic acids and a second set of nucleic acids, i.e., which particular nucleic acids from the first set hybridize to which particular nucleic acids from the second set, and is not limited to any particular assay or technique, but requires a surface or array capable of detecting the "pattern."

[0048] Select subgroups with special characteristics The present invention provides a process comprising detecting the presence or absence of chromosomal interactions, typically 5-20 or 5-500 such interactions, preferably 20-300 or 50-100 interactions, to determine the presence or absence of a trait associated with physical performance in an individual. Preferably, the chromosomal interactions are in any of the genes referred to herein or in any of the tables herein. In one embodiment, the chromosomal interactions classified are those represented in any of Tables 3, 7, 8, 9, 25 and 30. In another embodiment, the chromosomal interactions are those represented in any of Tables 13, 14, 18, 22, 23 and 24. In a preferred embodiment, the chromosomal interactions classified are those represented in Tables 31 or 32. In one embodiment, the chromosomal interactions classified are those from any of Tables 33, 34, 35, 36, 37, 38, 39 or 40, or any of Figures 16, 17 or 18. The relevant chromosomal interactions may be present or absent for a given trait, whereby the presence or absence of an interaction indicates the presence of that trait.

[0049] Individuals being tested The individual to be tested is preferably a human or a horse. The human is an athlete or a sportsman. The human is usually 30 years old or younger. The horse can be any type of horse mentioned herein, such as a thoroughbred. The horse can be a racehorse. The horse can be a horse that is not a racehorse but is optionally being considered for selection as a racehorse. The horse can be less than 500 days old, such as less than 200 days old, or less than 100 days old.

[0050] Favorable gene regions, loci, genes, and chromosomal interactions For all embodiments of the invention, preferred genetic regions, loci, genes, and chromosomal interactions are referred to in a table, for example any of Tables 3, 7, 8, 9, 25 and 30 (preferably for the human classification), or any of Tables 13, 14, 18, 22, 23 and 24 (preferably for the equine classification), or Tables 31 or 32 (preferably for the human classification). Typically, in the process of the invention, chromosomal interactions are detected from at least 1, 2, 10, 30, or 50 genes listed in Table 21. Chromosomal interactions may be upstream or downstream of any of the genes referred to herein, for example 50 kb upstream or 20 kb downstream from the coding sequence.

[0051] In one embodiment, at least 5, 10 or all chromosomal interactions in Table 3 are classified. In one embodiment, interactions with at least the top 5 or 10 high odds ratios in Table 3 are classified.

[0052] In one embodiment, at least 5, 10, 15 or all chromosomal interactions in Table 7 are classified. In one embodiment, interactions with at least the smallest 5, 10 or 15 average p-values ​​in Table 7 are classified.

[0053] In one embodiment, at least 5, 10, 15, 20 or all chromosomal interactions in Table 8 are classified. In one embodiment, interactions with at least the smallest 5, 10, 15, or 20 average p-values ​​in Table 8 are classified.

[0054] In one embodiment, at least 5, 10, 15, 20 or all chromosomal interactions in Table 9 are classified. In one embodiment, interactions with at least the smallest 5, 10, 15, or 20 average p-values ​​in Table 9 are classified.

[0055] In one embodiment, at least 5, 10, 15 or all chromosomal interactions in Table 13 are classified. In one embodiment, interactions with at least the smallest 5 or 10 exact Bauschlow p-values ​​in Table 13 are classified.

[0056] In one embodiment, at least 5, 10, 15 or all of the chromosomal interactions in Table 14 are classified.

[0057] In one embodiment, at least 5, 10, 20, 30 or all chromosomal interactions of Table 18 are classified. In one embodiment, interactions with at least the smallest 5, 10, 20 or 30 exact Bauschlow p-values ​​of Table 18 are classified.

[0058] In one embodiment, at least 5, 10 or all of the chromosomal interactions in Table 22 are classified. In one embodiment, interactions with at least the top 5 or 10 high odds ratios in Table 22 are classified.

[0059] In one embodiment, at least 5, 10, 15 or all of the chromosomal interactions in Table 23 are classified.

[0060] In one embodiment, at least 5, 10, 20, 30 or all chromosomal interactions of Table 24 are classified. In one embodiment, interactions having at least the top 5, 10, 20 or 30 GLMET values ​​of Table 24 are classified.

[0061] In one embodiment, at least 5, 10, 20, 30, 40, 50 or all chromosomal interactions of Table 25 are classified. In one embodiment, interactions with at least the smallest 5, 10, 20, 20, 30, 40, 50 adjusted p-values ​​of Table 25 are classified. In one embodiment, at least markers 1-30 of Table 25 are classified. In another, at least markers 31-77 of Table 25 are classified.

[0062] In one embodiment, at least 5, 10, 20, 30, 40, 50, 150, 180 or all chromosomal interactions of table 30 are classified. In one embodiment, interactions with at least the smallest 5, 10, 20, 20, 30, 40, 50, 150 or 180 adjusted p-values ​​of table 30 are classified. In one embodiment, at least markers 1-50 of table 30 are classified. In another embodiment, at least markers 51-100 of table 30 are classified. In another embodiment, at least markers 101-150 of table 30 are classified. In one embodiment, at least markers 151-202 of table 30 are classified.

[0063] In one embodiment, at least 5, 10 or all of the chromosomal interactions in Table 31 are classified.

[0064] In one embodiment, at least five or all of the chromosomal interactions in Table 32 are classified.

[0065] In one embodiment, at least 5, 10, 20, 30, 40, 50, 150, 180, 200, 250 or all chromosomal interactions of Table 33 are classified. In one embodiment, interactions with at least the smallest 5, 10, 20, 20, 30, 40, 50, 150, 180 or 250 adjusted p-values ​​of Table 33 are classified. In one embodiment, at least markers 1-50 of Table 33 are classified. In another embodiment, at least markers 51-100 of Table 33 are classified. In another embodiment, at least markers 101-150 of Table 33 are classified. In one embodiment, at least markers 151-202 of Table 33 are classified. In one embodiment, at least markers 202-320 of Table 33 are classified.

[0066] In one embodiment, at least 5, 10, 20, 30, 40, 50 or all chromosomal interactions in Table 34 are classified.

[0067] In one embodiment, at least 5, 10, 20, 30 or all of the chromosomal interactions in Table 35 are classified.

[0068] In one embodiment, at least 5, 10, 15 or all of the chromosomal interactions in Table 36 are classified.

[0069] In one embodiment, at least 5, 10, 20, 30, 40, 50, 150, 180 or all chromosomal interactions of Table 37 are classified. In one embodiment, interactions having at least the smallest 5, 10, 20, 20, 30, 40, 50, 150 or 180 adjusted p-values ​​of Table 37 are classified. In one embodiment, at least markers 1-50 of Table 37 are classified. In another embodiment, at least markers 51-100 of Table 37 are classified. In another embodiment, at least markers 101-150 of Table 37 are classified. In one embodiment, at least markers 151-202 of Table 37 are classified.

[0070] In one embodiment, at least 5, 10 or all of the chromosomal interactions in Table 38 are classified.

[0071] In one embodiment, at least five or all of the chromosomal interactions in Table 39 are classified.

[0072] In one embodiment, at least 5, 10 or all of the chromosomal interactions in Table 40 are classified. In one embodiment, at least 3 "shared" chromosomal interactions in Table 40 are classified. In one embodiment, at least 7 "strength" chromosomal interactions in Table 40 are classified.

[0073] In one embodiment, at least 5, 10, 20, 30, 40, 50 or all chromosomal interactions in Table 41 are classified. In one embodiment, interactions with at least the smallest 5, 10, 20, 20, 30, 40, 50 adjusted p-values ​​in Table 41 are classified. In one embodiment, at least markers 1-30 in Table 41 are classified. In another embodiment, at least markers 31-77 in Table 41 are classified. [Table 41 is shown in an abbreviated form to avoid duplicating information from Table 25 for the same marker set. It is understood that the smallest p-values ​​referred to herein can be obtained from Table 25.]

[0074] In one embodiment, at least 5, 8 or all of the chromosomal interactions in FIG. 16 are classified.

[0075] In one embodiment, at least 5, 10 or all of the chromosomal interactions in FIG. 17 are classified.

[0076] In one embodiment, at least 5, 8 or all of the chromosomal interactions in FIG. 18 are classified.

[0077] Typically, at least 5, 10, 15, 20, 30, 40 or 70 chromosomal interactions are classified from any of the genes or regions disclosed in the tables or portions of the tables disclosed herein. Typically, the classified chromosomal interactions are present in at least 20, 50 or all of the genes referred to in Table 21.

[0078] For all embodiments of the present invention, preferred gene regions, loci, genes, and chromosomal interactions are referred to in Tables 24 and 30.

[0079] In one embodiment, the locus (including the gene and / or location where the chromosomal interaction is detected) may contain a CTCF binding site. This is any sequence that can bind to the transcriptional repressor CTCF. The sequence may consist of or include a CCCTC sequence, which may be present in one, two or three copies in the locus. The CTCF binding site sequence may include the sequence CCGCGNGGNGGCAG (in IUPAC notation). The CTCF binding site may be within at least 100, 500, 1000 or 4000 bases of the chromosomal interaction, or within any of the chromosomal regions shown in Tables 24 or 30.

[0080] Thus, typically sequences from both regions of the probe (i.e., from both sites of chromosomal interaction) can be detected. In preferred embodiments, a probe that contains or consists of the same or complementary sequence as any of the probes shown in the tables is used in the process. In some embodiments, a probe that contains a sequence that is homologous to any of the probe sequences shown in the tables is used.

[0081] Tables provided herein The table shows probe (Episwitch™ marker) data and gene data representing chromosomal interactions related to physical performance. The probe sequences represent sequences that can be used to detect ligated products produced from both sites of the gene region that converges on the chromosomal interaction. That is, the probes contain sequences that are complementary to the sequences of the ligated products. The first two sets of Start-End locations represent the probe locations and the second two sets of Start-End locations represent the relevant 4kb region. The following information is provided in the table of probe data: - HyperG_States: p-value for the probability of finding that number of significant Episwitch™ markers at a locus based on the parameters of hypergeometric enrichment - Probe_Count Total: The total number of Episwitch™ conformations tested at that locus - Probe_CountSig: the number of Episwitch™ conformations found to be statistically significant at that locus -FDR HyperG: Hypergeometric p-value corrected for multiple testing (false discovery rate) -Sig Proportion: Proportion of significant Episwitch™ markers relative to the number of markers tested at that locus -logFC: logarithm base 2 of epigenetic ratio (FC) -AveExpr: average log2-expression for probes across all arrays and channels -T: Moderated t-statistics -p-value: Unadjusted p-value -adj.p.Val: adjusted p-value or q-value -B: The B-statistic (lod or B) is the log odds that the gene is differentially expressed. -FC: Non-log Fold Change -FC_1: Non-logarithmic fold change around zero - LS: The binary value relates to the FC_1 value. FC_1 values ​​less than 1.1 are set to -1, FC_1 values ​​greater than 1.1 are set to 1. Between these values ​​the value is 0.

[0082] The table also shows the genes in which the relevant chromosomal interactions were found to occur. The p-values ​​in the table of loci are the same as HyperG_Stats (p-value for the probability of finding that number of significant Episwitch™ markers at the locus based on the parameters of hypergeometric enrichment).

[0083] The probe is designed to be 30 bp away from the Taq1 site. For PCR, PCR primers are also designed to detect the ligated product, but at a different position from Taq1.

[0084] Probe Position: Start1 - 30 bases upstream of TaqI on fragment 1 End1 - TaqI restriction site on fragment 1 TaqI restriction site on Sart2-fragment 2 End2 - 30 bases downstream of TaqI on fragment 2

[0085] 4kb sequence location: Start1 - 4000 bases upstream of TaqI on fragment 1 End1 - TaqI restriction site on fragment 1 TaqI restriction site on Sart2-fragment 2 End2 - 4000 bases downstream of TaqI on fragment 2

[0086] GLMNET values ​​associated with the procedure for fitting the full lasso or elastic-net regularization (λ(elastic-net) set to 0.5).

[0087] Preferred embodiments for sample preparation and chromosomal interaction detection Methods for preparing samples and detecting chromosome conformation are described herein. Optimized (non-conventional) versions of these methods can be used, for example as described in this section.

[0088] Typically, samples are at least 2 × 10 5 Samples will contain up to 5 x 10 cells. 5 In one embodiment, the sample may contain 2×10 cells. 5 ~5.5×10 5 The cell population will contain 100 cells.

[0089] The cross-linking of epigenetic chromosomal interactions present at chromosomal loci is described herein. This can be performed before cell lysis occurs. Cell lysis can be performed for 3-7 minutes, such as 4-6 or about 5 minutes. In some embodiments, cell lysis is performed for at least 5 minutes and less than 10 minutes.

[0090] Digesting DNA with a restriction enzyme is described herein. Typically, DNA restriction is carried out at about 55° C. to about 70° C., such as about 65° C., for a period of about 10 to 30 minutes, such as about 20 minutes.

[0091] Preferably, a frequent-cutter restriction enzyme is used that results in fragments of the ligated DNA having an average fragment size of up to 4000 base pairs. Optionally, the restriction enzyme results in fragments of the ligated DNA having an average fragment size of about 200-300 base pairs, such as about 256 base pairs. In one embodiment, typical fragment sizes are 200 base pairs to 4,000 base pairs, such as 400-2,000 or 500-1,000 base pairs.

[0092] In one embodiment of the EpiSwitch Method, a DNA precipitation step is not performed between the DNA restriction digestion step and the DNA ligation step.

[0093] DNA ligation is described herein. Typically, DNA ligation is carried out for 5 to 30 minutes, such as about 10 minutes.

[0094] Proteins in the sample may be enzymatically digested, for example with a proteinase, optionally proteinase K. Proteins may be enzymatically digested for a period of about 30 minutes to 1 hour, for example about 45 minutes. In one embodiment, digestion of the proteins, for example proteinase K digestion, is not followed by a step of crosslink reversal or phenol DNA extraction.

[0095] In one embodiment, PCR detection preferably provides a binary readout for the presence / absence of the ligated nucleic acid and is capable of detecting single copies of the ligated nucleic acid.

[0096] FIG. 14 shows a preferred method for detecting chromosomal interactions.

[0097] Methods and Uses of the Invention The method of the present invention can be described in various ways: it is a method of making a ligated nucleic acid comprising the steps of (i) in vitro cross-linking chromosomal regions that converge at a chromosomal interaction; (ii) excising the cross-linked DNA or subjecting it to restriction digestion cleavage; and (iii) ligating the cross-linked and cleaved DNA ends to form a ligated nucleic acid, detection of which can be used to determine the chromosomal status at a locus, and preferably - the locus may be any of the loci, regions or genes mentioned in any of the tables, and / or - the chromosomal interaction may be any chromosomal interaction corresponding to any of the probes mentioned herein or disclosed in any of the tables, and / or - the ligated product may have or contain (i) a sequence that is the same as or homologous to any of the probe sequences disclosed in any of the tables herein; or (ii) a sequence that is complementary to (ii). It can be described as a method.

[0098] The method of the present invention is a method for detecting chromosomal states representing different subgroups within a population, comprising the steps of determining whether chromosomal interactions are present or absent within defined epigenetically active regions of the genome, preferably comprising: Subgroups are defined by the presence or absence of physical ability and / or - the chromosomal status may be at any locus, region, or gene mentioned in any table; and / or -The chromosomal interaction can be any of those mentioned in any of the tables or any of those corresponding to any of the probes disclosed in the tables. It can be described as a method.

[0099] The process of the invention is a method of making a ligated nucleic acid comprising the steps of: (i) in vitro cross-linking chromosomal regions that converge at a chromosomal interaction; (ii) excising the cross-linked DNA or subjecting it to restriction digestion cleavage; and (iii) ligating the cross-linked and cleaved DNA ends to form a ligated nucleic acid, wherein detection of the ligated nucleic acid can be used to determine the chromosomal status at a locus, and preferably - the locus may be any of the loci, regions or genes mentioned in any of the tables; and / or the chromosomal interaction may be any chromosomal interaction corresponding to any of the probes mentioned herein or disclosed in any of the tables; and / or - the ligated product may have or contain (i) a sequence that is the same as or homologous to any of the probe sequences disclosed in any of the tables; or (ii) a sequence that is complementary to (ii). It can be described as a method.

[0100] The method of the present invention is a method for detecting chromosomal states representing different subgroups within a population, comprising the steps of determining whether chromosomal interactions are present or absent within defined epigenetically active regions of the genome, preferably comprising: Subgroups are defined by the presence or absence of physical ability and / or - the chromosomal status may be at any locus, region, or gene mentioned in any table; and / or -The chromosomal interaction can be any of those mentioned in any of the tables or any of those corresponding to any of the probes disclosed in the tables. It can be described as a method.

[0101] The invention includes the detection of chromosomal interactions at any locus, gene or region mentioned in any table, such as Table 24 or 30. The invention includes the use of the nucleic acids and probes described herein to detect chromosomal interactions, for example, the use of at least 1, 5, 10, 50, 100, 200 such nucleic acids or probes to detect chromosomal interactions at at least 1, 5, 10, 50, 100 different loci or genes. The invention includes the detection of chromosomal interactions using any primer or primer pair listed in Table 24 or 30, or using variants of these primers (sequences comprising the primer sequences or comprising fragments and / or homologs of the primer sequences) described herein.

[0102] The invention includes the detection of chromosomal interactions at any locus, gene or region mentioned in Table 24 or 30. The invention includes the use of the nucleic acids and probes described herein to detect chromosomal interactions, for example, the use of at least 1, 5, 10, 50, 100, 200, 250, 300 such nucleic acids or probes to detect chromosomal interactions at at least 1, 5, 10, 50, 100, 200, 250, 300 different loci or genes. The invention includes the detection of chromosomal interactions using any primer or primer pair listed in Table 24 or 30, or using variants of these primers (sequences comprising the primer sequences or comprising fragments and / or homologs of the primer sequences) described herein.

[0103] When analyzing whether a chromosomal interaction occurs "within" a defined gene, region, or location, both parts of the chromosomes that come together in the interaction are within the defined gene, region, or location, or in some embodiments only a portion of the chromosomes is within the defined gene, region, or location.

[0104] Use of the Methods of the Invention to Identify New Training or Fitness Regimens Knowledge of chromosomal interactions can be used to identify new fitness or training regimes. The present invention provides methods and uses of the chromosomal interactions defined herein to identify or design novel drugs.

[0105] Homolog Homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein.Such homologs typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% homology, for example, over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a portion of nucleic acid that is derived from a region of chromosome involved in chromosomal interaction.Homology can be calculated based on nucleotide identity (sometimes referred to as "hard homology").

[0106] Thus, in certain embodiments, homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein with reference to percent sequence identity.Typically, such homologs have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% sequence identity, for example, over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a portion of the nucleic acid that is derived from a region of a chromosome involved in chromosomal interactions.

[0107] For example, the UWGCG package provides the BESTFIT program (e.g., used with its default settings) that can be used to calculate homology and / or sequence identity percentage (Devereux et al (1984) Nucleic Acids Research 12, p387-395). The PILEUP and BLAST algorithms can be used to calculate homology and / or sequence identity percentage and / or align sequences (e.g., to identify equivalent or corresponding sequences (typically with their default settings)), as described, for example, in Altschul SF (1993) J Mol Evol 36:290-300; Altschul, S, F et al (1990) J Mol Biol 215:403-10.

[0108] Software for performing BLAST analysis is publicly available through the National Center for Biotechnology Information. The algorithm involves first identifying high-scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence that match when aligned with words of the same length in the database sequence or meet a threshold score T with a certain positive value. T is referred to as the neighborhood word score threshold (Altschul et al, supra). These initial neighborhood word hits act as seeds to initiate searches to find HSPs that contain them. The word hits are extended in both directions along each sequence as far as the cumulative alignment score can be increased. The extension of the word hits in each direction is stopped when the cumulative alignment score falls by an amount X from the value at which it reached its maximum; the accumulation of one or more negatively scoring residue alignments causes the cumulative score to go to zero or below; or when the end of either sequence is reached. The BLAST algorithm parameters W5 T and X determine the sensitivity and speed of the alignment. The BLAST program uses as defaults a wordlength (W) of 11, a BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89:10915-10919) of 50, alignment (B) of 10, expectation (E) of 10, M=5, N=4, and a comparison of both strands.

[0109] BLAST algorithm performs statistical analysis of the similarity between two sequences; see, for example, Karlin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90:5873-5787. One measure of similarity provided by BLAST algorithm is the minimum sum probability (P(N)), which provides an indication of the probability that a match between two polynucleotide sequences would occur by chance. For example, a sequence is considered to be similar to another sequence if the minimum sum probability of comparing a first sequence with a second sequence is less than about 1, preferably less than about 0.1, more preferably less than about 0.01, and most preferably less than about 0.001.

[0110] Homologous sequences typically differ by 1, 2, 3, 4 or more bases (which may be nucleotide substitutions, deletions, or insertions), such as less than 10, 15, or 20 bases. These changes may be measured over any of the regions mentioned above for calculating % homology and / or sequence identity.

[0111] The homology of a "primer pair" can, for example, be calculated by considering the two sequences as a single sequence (as if the two sequences were concatenated) and then compared to another primer pair, again considered as a single sequence.

[0112] array The second nucleic acid set can be bound to the array, and in one embodiment, there are at least 15,000, 45,000, 100,000, or 250,000 different second nucleic acids bound to the array, preferably corresponding to at least 300, 900, 2000, or 5000 loci. In one embodiment, one, or a plurality, or all of the different populations of second nucleic acids are bound to more than one individual region of the array, effectively repeating in the array, allowing for error detection. The array is based on the Agilent SurePrint G3 Custom CGH microarray platform. Detection of the binding of the first nucleic acid to the array can be carried out by a two-color system.

[0113] Forms of matter referred to herein Any of the substances, such as nucleic acids or therapeutic agents, referred to herein may be in purified or isolated form. They may be in a form different from that found in nature, e.g., they may be present in combination with other substances with which they do not occur in nature. Nucleic acids (including portions of the sequences defined herein) may have sequences different from those found in nature, e.g., at least one, two, three, four, or more nucleotide changes in the sequences described in the section on homology. Nucleic acids may have heterologous sequences at the 5' or 3' ends. Nucleic acids may be chemically different from those found in nature, e.g., they may be modified in some way, but preferably still capable of Watson-Crick base pairing. Where appropriate, the nucleic acids are provided in double-stranded or single-stranded form. The invention provides all of the specific nucleic acid sequences referred to herein in single-stranded or double-stranded form, and thus includes the complementary strand to any sequence disclosed.

[0114] The present invention also provides a kit for carrying out any method of the present invention, including the detection of chromosomal interactions associated with physical performance.Such kits can include specific binding agents that can detect relevant chromosomal interactions, such as agents that can detect the ligated nucleic acid produced by the method of the present invention.Preferred agents present in the kit include a probe that can hybridize to ligated nucleic acid or a primer pair that can amplify ligated nucleic acid in a PCR reaction, for example, as described herein.

[0115] The present invention also provides a device capable of detecting relevant chromosomal interactions. The device preferably comprises any specific binding agent, probe, or primer pair capable of detecting chromosomal interactions, such as any such agent, probe, or primer pair described herein.

[0116] Detection Method In one embodiment, the quantitative detection of the ligated sequence associated with chromosomal interaction is carried out by using a probe that can be detected when activated during PCR reaction, the ligated sequence comprises sequences from two chromosomal regions that are gathered in epigenetic chromosomal interaction, the method comprises contacting the ligated sequence with a probe during PCR reaction, and detecting the degree of activation of the probe, the probe binds to the ligation site.This method typically uses a dual-labeled fluorescent hydrolysis probe to make specific interaction detectable in a MIQE-compliant manner.

[0117] The probe is generally labeled with a detectable label that has an inactive state and an active state, so that it is only detected when activated. The degree of activation will be related to the degree of template (ligated product) present in the PCR reaction. Detection may be performed during the entire PCR or a part of it, for example, during at least 50% or 80% of the cycles of PCR.

[0118] The probe may include a fluorophore covalently attached to one end of the oligonucleotide and a quencher attached to the other end of the nucleotide, where the fluorescence of the fluorophore is quenched by the quencher. In one embodiment, the fluorophore is attached to the 5' end of the oligonucleotide and the quencher is covalently attached to the 3' end of the oligonucleotide. Fluorophores that can be used in the methods of the invention include FAM, TET, JOE, Yakima Yellow, HEX, Cyanine 3, ATTO 550, TAMRA, ROX, Texas Red, Cyanine 3.5, LC610, LC 640, ATTO 647N, Cyanine 5, Cyanine 5.5 and ATTP 680. Quenchers that can be used with suitable fluorophores include TAM, BHQ1, DAB, Eclipse, BHQ2 and BBQ650, and optionally, the fluorophore is selected from HEX, Texas Red and FAM. Preferred combinations of fluorophores and quenchers include FAM and BHQ1 and Texas Red and BHQ2.

[0119] Use of probes in qPCR assays The hydrolysis probes of the present invention are typically temperature gradient optimized with concentration-matched negative controls. Preferably, a single-step PCR reaction is optimized. More preferably, a standard curve is calculated. The advantage of using a specific probe that binds to the ligated sequence junction is that the specificity of the ligated sequence can be achieved without using a nested PCR approach. The methods described herein allow accurate and precise quantification of low copy number targets. The ligated sequence of the target can be purified, e.g., gel purified, prior to temperature gradient optimization. The ligated sequence of the target can be sequenced. Preferably, the PCR reaction is performed using about 10 ng, or 5-15 ng, or 10-20 ng, or 10-50 ng, or 10-200 ng of template DNA. The forward and reverse primers are designed, e.g., by being complementary to the sequences, such that one primer binds to one sequence of the chromosomal region represented by the ligated DNA sequence and the other primer binds to the other chromosomal region represented by the ligated DNA sequence.

[0120] Selection of ligated DNA targets The present invention encompasses the selection of primers and probes for use in the PCR methods defined herein, which includes selecting a primer based on its ability to bind and amplify a ligated sequence, and in particular selecting a probe based on the properties of the target sequence to which it binds at the curvature of the target sequence.

[0121] Probes are typically designed / selected to bind to ligated sequences where restriction fragments spanning the restriction sites are juxtaposed. In one embodiment of the present invention, the predicted curvature of the ligated sequences potentially associated with a particular chromosomal interaction is calculated, for example, using certain algorithms referenced herein. The curvature can be expressed in degrees per helical turn, for example 10.5° per helical turn. Ligated sequences are selected for targets with a curvature propensity peak score of at least 5° per helical turn, typically at least 10°, 15° or 20° per helical turn, for example 5°-20° per helical turn. Preferably, the curvature propensity score per helical turn is calculated for at least 20, 50, 100, 200 or 400 bases, for example 20-400 bases, upstream and / or downstream of the ligation site. Thus, in one embodiment, the target sequence of the ligated product has any of these levels of curvature. Target sequences can also be selected based on the lowest thermodynamic structural free energy.

[0122] Specific embodiments In one embodiment, only intrachromosomal interactions are classified / detected and not extrachromosomal interactions (between different chromosomes).

[0123] In particular embodiments, a particular chromosomal interaction, such as any specific interaction referred to herein (e.g., as defined by any probe or primer pair referred to herein), is not classified. In some embodiments, a chromosomal interaction is not classified as any of the genes described herein, such as any of the genes listed in Table 21.

[0124] Publications The contents of all publications mentioned herein are incorporated herein by reference and may be used to further define aspects related to the present invention.

[0125] table Table 1 shows the patient samples for the human study.

[0126] Table 2 shows the classification of responders in human studies.

[0127] Tables 3 and 4 show markers obtained from human studies that are preferably used to classify humans.

[0128] Table 5 describes the human study predisposing factors present in the subjects.

[0129] Table 6 shows markers obtained from human studies, which are preferably used for human classification.

[0130] Table 7 shows predictive markers for strength training response, which are preferably used for classification of humans.

[0131] Table 8 shows predictive markers for endurance training response, which are preferably used for classification of humans.

[0132] Table 9 shows predictive markers for either strength or endurance training response, which are preferably used for classification of humans.

[0133] Table 10 shows the samples for the equine study.

[0134] Table 11 defines the "sex" descriptions used in Table 10.

[0135] Table 12 shows the gender distribution in the equine studies.

[0136] Table 13 shows the top markers for distance horse vs. sprinter phenotype (n=32, 16 distance horses, 16 sprinters) which are preferably used to classify horses.

[0137] Table 14 shows markers discovered in humans that are applicable to horses, and their nearest genetic loci, which can be used to classify horses.

[0138] Table 15 shows the classifier calls for sprinters and distance horses.

[0139] Table 16 shows the probability scores for equine studies.

[0140] Table 17 shows the classifier decisions for naive samples from young thoroughbreds.

[0141] Table 18 shows useful markers derived from equine studies, which are preferably used to classify horses.

[0142] Tables 19 and 20 show subjects for the human studies.

[0143] Table 21 shows preferred genes for practicing the present invention.

[0144] Tables 22 and 23 show preferred markers and their associated traits derived from equine studies, which are preferably used to classify horses.

[0145] Table 24 shows markers that have been identified in equine studies and which are preferably used to classify horses.

[0146] Table 25 shows markers that have been identified in human studies and which are preferably used to classify humans.

[0147] Table 26 shows another set of preferred genes for the practice of the present invention.

[0148] Table 27 shows the pathway analysis of gene locations for the 171 chromosomal interactions shared between the strength and endurance groups.

[0149] Table 28 shows the pathway analysis of gene locations for the top 79 chromosomal interactions specific to the endurance group.

[0150] Table 29 shows the pathway analysis of gene locations for the top 79 chromosomal interactions specific to strength groups.

[0151] Tables 30-32 show markers that have been identified in human studies and which are preferably used to classify humans. To clarify the nomenclature used in tables such as Table 30: E_Trn stands for participation in endurance training. Str_Trn stands for participation in strength training. E_Ctrl means participation in the endurance control (i.e., no participation in endurance training). Str_Ctrl denotes participation in the strength control (i.e., no participation in strength training).

[0152] Table 33 shows markers derived from equine studies that are preferably used to classify horses. In the LS column, 1 means presence in sprinters while (-1) means presence in long distance horses. The "Loops Detected" column essentially decodes what +1 and -1 mean in terms of detection.

[0153] Table 34 shows markers obtained from human studies, which are preferably used to classify humans.

[0154] Table 35 shows preferred markers derived from equine studies, which are preferably used to classify horses.

[0155] Table 36 shows preferred markers derived from human studies, which are preferably used to classify humans.

[0156] Table 37 shows an updated version of Table 30. The same markers were classified in this study in humans and are preferably used to classify humans.

[0157] Table 38 shows an updated version of Table 31. The same markers have been classified in human studies and are preferably used to classify humans.

[0158] Table 39 shows an updated version of Table 32. The same markers have been classified in human studies and are preferably used to classify humans.

[0159] Table 40 shows markers corresponding to those shown in Figure 15, which are preferably used to classify humans.

[0160] Table 41 shows updated results from Table 25, where the markers are derived from human studies and are preferably used to classify humans.

[0161] Preferred Method The following numbered paragraphs define the preferred method.

[0162] 1. A method for detecting a chromosomal state representing a subgroup in a population, comprising determining whether a chromosomal interaction related to the chromosomal state exists within a defined region of the genome, the subgroup being related to physical performance in an individual; and the chromosomal interactions are optionally identified by a method for determining which chromosomal interactions are associated with chromosomal states corresponding to physical performance subgroups of a population, the method comprising contacting a first set of nucleic acids from subgroups having various chromosomal states with a second set of index nucleic acids and allowing complementary sequences to hybridize, the nucleic acids of the first and second sets of nucleic acids representing ligated products comprising sequences from both chromosomal regions that came together in the chromosomal interaction, and the pattern of hybridization between the first and second sets of nucleic acids allowing the determination of which chromosomal interactions are specific to a physical performance subgroup; and Chromosomal interactions (i) corresponds to any one of the chromosomal interactions shown in any of Tables 3, 7, 8, 9, 25, and 30; and / or (ii) corresponds to any one of the chromosomal interactions shown in any of Tables 13, 14, 18, 22, 23, and 24; and / or (iii) is present in a region of 4000 bases that includes or is adjacent to (i) or (ii); and / or (iv) is present in any one of the regions or genes listed in Table 21, 24, 25, or 30. Either way.

[0163] 2. The individual is a human and the subgroup is a human subgroup the individuals are horses and the subgroup is the horse subgroup, and Optionally: (i) the method is carried out to determine physical ability; and / or (ii) the method is carried out to detect responsiveness to a stimulus related to physical performance, preferably physical training, and optionally strength or endurance training; and / or (iii) the method is carried out to select individuals suitable for physical activity, preferably sports, and / or (iv) a method is performed to select a physical performance-related stimulus to be administered to the individual, the stimulus being a type of physical training; 2. The method described in paragraph 1.

[0164] 3 .( i) comprises all of the chromosomal interactions represented in any of Tables 3, 7, 8, 9, 25 and 30, or any of Tables 13, 14, 18, 22, 23, 24; and / or (ii) comprises at least 10%, 20%, 50%, or 80% of the chromosomal interactions in any of Tables 3, 7, 8, 9, 25, and 30, or any of Tables 13, 14, 18, 22, 23, 24; and / or (iii) are present together in at least 10, 50, or 100 of the regions or genes listed in any of Tables 21, 24, 25, or 30; and / or (iv) at least 10, 50, 100, 150, 200 or 300 chromosomal interactions are present in a 4000 base region that contains or is adjacent to a chromosomal interaction represented in any of Tables 3, 7, 8, 9, 25 and 30, or any of Tables 13, 14, 18, 22, 23, 24 A specific combination of chromosomal interactions 4. The method according to paragraph 1 or 2, which is classified.

[0165] 4. Chromosomal interactions: in a sample derived from an individual, and / or by detecting the presence or absence of DNA loops at the site of chromosomal interaction, and / or by detecting the presence or absence of distal regions of chromosomes that are assembled into a chromosomal conformation; and / or by detecting the presence of ligated nucleic acids generated during said sorting, the sequences of which comprise two regions each corresponding to a region of a chromosome recruited to the chromosomal interaction, wherein the detection of the ligated nucleic acids is preferably using (i) a probe having at least 70% identity to any of the specific probe sequences set out in Tables 24, 25, or 30, and / or (ii) a primer pair having at least 70% identity to any of the primer pairs in Tables 24, 25, or 30. The method according to any one of the above paragraphs 1 to 3.

[0166] 5. The second set of nucleic acids is from a larger group of individuals than the first set of nucleic acids; and / or the first set of nucleic acids is from at least eight individuals; and / or the first set of nucleic acids is at least four individuals from a first subgroup and at least four individuals from a second subgroup that preferably does not overlap with the first subgroup; The method according to any one of paragraphs 1 to 4 above.

[0167] 6. the second set of nucleic acids represents a non-selected group; and / or a second set of nucleic acids is bound to the array at defined locations; and / or the second set of nucleic acids represents chromosomal interactions in at least 100 different genes; and / or the second set of nucleic acids comprises at least 1,000 different nucleic acids representing at least 1,000 different chromosomal interactions; and / or The first set of nucleic acids and the second set of nucleic acids include at least 100 nucleic acids having a length of 10 to 100 nucleotide bases. The method according to any one of paragraphs 1 to 5 above.

[0168] 7. The first set of nucleic acids comprises: (i) A bridging step of chromosomal regions clustered in chromosomal interactions; (ii) cleaving the cross-linked region, optionally by enzymatic restriction digestion; and (iii) ligating the cross-linked and cleaved DNA ends to generate a first set of nucleic acids (particularly comprising ligated DNA). 7. The method according to any of the above paragraphs 1 to 6, obtained in a process comprising:

[0169] 8. at least 10-50 different chromosomal interactions are preferably grouped into 10-50 different regions or genes as arbitrarily defined in Tables 21, 24, 25, or 30; and / or (i) performed on human or equine athletes; and / or (ii) is performed as part of a training regimen, preferably after initiation of the training regimen; and / or (iii) performed on thoroughbred horses, preferably race horses; or (iv) performed on a human individual less than 20 years of age or a horse less than 18 months of age; and / or (v) The method is performed at multiple time points to assess physical performance characteristics at specific time points, optionally at least three time points, the three time points preferably being performed at least 30 days apart from each other. The method according to any one of paragraphs 1 to 7 above.

[0170] 9. The defined region of the genome comprises: (i) contains a single nucleotide polymorphism (SNP); and / or (ii) expresses microRNA (miRNA); and / or (iii) expresses a non-coding RNA (ncRNA); and / or (iv) expressing a nucleic acid sequence encoding at least 10 contiguous amino acid residues; and / or (v) expressing the control element; and / or (vii) contains a CTCF binding site The method according to any one of paragraphs 1 to 8 above.

[0171] 10. performed to identify individuals suitable for endurance training, and then preferably targeting the identified individuals for endurance training, optionally within at least 100 days out of 365 days following the identification; or Conducted to identify individuals suitable for strength and training, and then preferably targeting the identified individuals for strength training, optionally for at least 100 days out of 365 days following the identification The method according to any one of paragraphs 1 to 9 above.

[0172] 11. The method according to any of paragraphs 1 to 10 above, carried out to select individuals for racing.

[0173] 12. A method according to any of paragraphs 1 to 11 above, carried out to identify or design a drug that affects physical performance, wherein the method is used to detect whether a candidate drug can cause a change to a chromosomal state associated with a different physical performance state; the chromosomal interaction is any specific interaction or combination of interactions defined in any of the preceding paragraphs, and / or is present in any of the regions or genes listed in Tables 21, 24, 25, or 30; and / or Changes in chromosomal interactions are monitored using (i) a probe having at least 70% identity to any of the specific probe sequences set forth in Tables 24, 25, or 30, and / or (ii) a primer pair having at least 70% identity to any of the primer pairs in Tables 24, 25, or 30. method.

[0174] 13. A method according to paragraph 12 above, comprising selecting a target based on detection of a chromosomal interaction, and preferably screening for modulators of the target to identify agents that affect physical performance, said target optionally being a protein.

[0175] 14. The classification or detection comprises specific detection of the ligated products by quantitative PCR (qPCR) using primers capable of amplifying the ligated products and probes that bind to the ligation sites during the PCR reaction, said probes comprising sequences complementary to sequences from each chromosomal region that is clustered in the chromosomal interaction, preferably said probes comprising: an oligonucleotide that specifically binds to the ligated product, and / or a fluorophore covalently attached to the 5' end of the oligonucleotide, and / or A quencher moiety covalently attached to the 3' end of the oligonucleotide. and Optionally, the fluorophore is selected from HEX, Texas Red and FAM; and / or The probe comprises a nucleic acid sequence having a length of 10 to 40 nucleotide bases, preferably a length of 20 to 30 nucleotide bases. The method according to any one of paragraphs 1 to 13 above.

[0176] 15. Producing a report on the physical performance characteristics of an individual based on the results of the method; or Entering the results of the method into a database; or administering a specific fitness or training regimen to an individual based on the results of the method; Designing a specific fitness or training regimen for an individual based on the results of the method The method according to any one of paragraphs 1 to 14 above, further comprising:

[0177] Specific Aspects The EpiSwitch™ platform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal disease states at gene loci. The EpiSwitch™ platform identifies and monitors the fundamental epigenetic levels of gene regulation associated with the regulatory conformation of human chromosomes, also known as chromosome conformation signatures. Chromosome signatures are distinct primary steps in the cascade of gene deregulation. They are high-order biomarkers that have a unique set of advantages over biomarker platforms that utilize late epigenetic and gene expression biomarkers, such as DNA methylation and RNA profiling.

[0178] EpiSwitch™ Array Assays The custom EpiSwitch™ array screening platform is available in four densities: 15K, 45K, 100K, and 250K unique chromosome conformations, with each chimeric fragment repeated four times in the array, creating effective densities of 60K, 180K, 400K, and 100,000, respectively.

[0179] Custom-designed EpiSwitch™ arrays The 15K EpiSwitch™ array can screen the entire genome, including around 300 loci interrogated with EpiSwitch™ biomarker discovery technology. EpiSwitch™ arrays are built on the Agilent SurePrint G3 Custom CGH microarray platform; this technology provides four densities of probes: 60K, 180K, 400K, and 100,000. Since each EpiSwitch™ probe is presented as a quadruplet, the density per array is reduced to 15K, 45K, 100K, and 250K, thus allowing for statistical evaluation of reproducibility. The average number of potential EpiSwitch™ markers interrogated per locus is 50; thus, the number of loci that can be examined is 300, 900, 2000, and 5000.

[0180] EpiSwitch™ Custom Array Pipeline The EpiSwitch™ array is a two-color system with one set of samples, after EpiSwitch™ library generation, labeled with Cy5 and the other sample to be compared / analyzed (control) labeled with Cy3. The arrays are scanned using an Agilent SureScan scanner and the resulting features are extracted using Agilent Feature Extraction software. The data is then processed using the EpiSwitch™ array processing script in R. The arrays are run using the standard two-color package in Bioconductor in R: Limma * Array normalization is performed using Limma. * This was done using the intra-array normalization feature in and against the on-chip Agilent positive controls and EpiSwitch™ positive controls. Data was filtered based on Agilent Flag calls, Agilent control probes were removed, and technical replicate probes were averaged and then run in Limma. *The probes are compared and then modeled based on their differences between the two scenarios, which are corrected by using the False Discover Rate. Probes with a coefficient of variation (CV) of <30%, <=1.1 or =>1.1, and passing an FDR p-value of p=0.01 are used for further screening. To reduce the probe set, further multifactorial analysis is performed using the FactorMineR package in R.

[0181] * NOTE: LIMMA is a linear model and empirical Bayesian method for assessing differential expression in microarray experiments. Limma is an R package for the analysis of gene expression data generated by microarray or RNA-Seq.

[0182] A pool of probes is initially selected for final selection based on the parameters of adjusted p-value, FC, and <30% CV (arbitrary cutoff point). Further analysis and the final list are derived based only on the first two parameters (adjusted p-value; FC).

[0183] Statistical Pipeline To select high value EpiSwitch™ markers for transfer to the EpiSwitch™ PCR platform, the EpiSwitch™ screening arrays are processed using the EpiSwitch™ analysis package in R.

[0184] Process 1 Probes are selected based on their corrected p-value (false discovery rate, FDR), which is the product of a modified linear regression model. Probes with p-values ​​below ≦0.1 are selected and then further reduced by their epigenetic ratio (ER), probes ER must be ≦-1.1 or ≧1.1 to be selected for further analysis. The final filter is the coefficient of variation (CV), probes must be below ≦0.3.

[0185] Process 2 The top 40 markers in the statistical list are selected based on their ER for selection as markers for PCR conversion. The top 20 markers with the most negative ER load and the top 20 markers with the most positive ER load form the list.

[0186] Process 3 The resulting markers, statistically significant probes from step 1, form the basis of an enrichment analysis using hypergeometric enrichment (HE), which allows marker reduction from the significant probe list and, together with the markers from step 2, forms the list of probes that are transferred to the EpiSwitch™ PCR platform.

[0187] The statistical probes are processed by HE to determine which genetic locations exhibit statistically significant enrichment of probes, indicating which genetic locations are epicenters of epigenetic differences.

[0188] The most significant enriched loci based on corrected p-values ​​are selected for probe list generation. Genetic locations below a p-value of 0.3 or 0.2 are selected. Statistical probes mapping to these genetic locations along with the markers from step 2 form high-value markers for EpiSwitch™ PCR conversion.

[0189] Array Design and Processing Array Design 1. The loci were processed using SII software (currently v3.2) a. Retrieve the genomic sequences at these specific loci (gene sequences with 50 kb upstream and 20 kb downstream). b. Determine the probability that sequences within this region are involved in CCs. c. A specific RE is used to cut the sequence. d. Determine whether the restriction fragments are likely to interact in a particular orientation. e. Rank the likelihood of the various CCs interacting together. 2. Determine the array size, and hence the number of available probe sites (x). 3. Extract x / 4 interactions. 4. For each interaction, define 30bp of sequence from part 1 to the restriction site and 30bp to the restriction site in part 2. Check that those regions are not repeats, exclude them if so, and pick the next interaction down the list. Join both 30bp together to define the probe. 5. Create a list of x / 4 probes, plus a defined control probe, and replicate four times to be created on the array. 6. Upload the probe list to the Agilent Sure design website for custom CGH arrays. 7. Design Agilent custom CGH arrays using the probe sets.

[0190] Array Processing 1. For template production, process samples using the EpiSwitch™ Standard Operating Procedure (SOP). 2. Clean up by ethanol precipitation by the array processing laboratory. 3. Process samples as described in Agilent SureTag complete DNA labeling kit - Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis Enzymatic labelling for Blood, Cells or Tissues. 4. Scan using an Agilent C Scanner using Agilent feature extraction software.

[0191] EpiSwitch(TM) EpiSwitch™ biomarker signatures exhibit high structural stability, sensitivity, and specificity in stratifying complex disease phenotypes. The technology leverages recent breakthroughs in epigenetic chemistry, monitoring and evaluation of chromosome conformation signatures as a highly informative class of epigenetic biomarkers. Current research methodologies deployed in academic settings require 3-7 days of biochemical processing of cellular material to detect CCSs. These procedures limit sensitivity and reproducibility, and furthermore, do not have the benefit of targeted insights offered by the EpiSwitch™ analytical package at the design stage.

[0192] Computational EpiSwitch™ Array Marker Identification CCS sites in the genome are assessed directly by EpiSwitch™ arrays on clinical samples of the study cohort to identify all relevant stratified lead biomarkers. The EpiSwitch™ array platform is used for marker identification due to its high throughput capacity and ability to rapidly screen a large number of loci. The arrays used were Agilent custom CGH arrays that allow markers to be identified by interrogated in silico software.

[0193] EpiSwitch(TM) PCR Potential markers identified by the EpiSwitch™ Array are then validated by EpiSwitch™ PCR or DNA sequencer (i.e. Roche 454, Nanopore MinION, etc.). Top PCR markers that are statistically significant and show the highest reproducibility are selected for further reduction to a final EpiSwitch™ Signature set, which is validated in an independent cohort of samples. EpiSwitch™ PCR is performed by trained technicians following established standardized operating procedure protocols. Manufacturing of all protocols and reagents is under ISO 13485 and 9001 certification, ensuring the quality of work and transferability of the protocols. The EpiSwitch™ PCR and EpiSwitch™ Array biomarker platforms are compatible with both whole blood and cell line analysis. The tests are sensitive enough to detect very low copy number abnormalities using small volumes of blood. EXAMPLES

[0194] Example 1 This study concerns human epigenetic biomarkers to monitor physiological differences and predispositions associated with strength training programs. Defined biomarkers were discovered and assessed following 4 weeks of mixed training and 8 weeks of specialized training to aid in determining epigenetic predispositions to either strength or endurance training.

[0195] Participants wanted: Participants were recruited using posted flyers, electronic newsletters, local print and radio media, targeted recruitment at local athletic clubs, and word of mouth. To be eligible for enrollment in the study, participants had to meet the following "performance" criteria:

[0196] Group 1: Strength Athletes: Participants must be regular weightlifters. Examples provided: Body weight 100kg with a recent cumulative total of >550kg; bench press + squat + deadlift exercises.

[0197] Group 2: Fitness Athletes: Participants must be regular fitness athletes. Examples provided: Last 10km run time: <40 minutes; or Last 5km run time <19 minutes.

[0198] Group 3: Sedentary non-athletes: No current participation in sports or any planned physical activity for more than 3 years.

[0199] Study registration The necessary criteria for potential enrollment was subjective (participant) report of athletic ability to meet one of three distinct phenotypes: 1) strength athlete, 2) fitness athlete, 3) sedentary non-athlete. Eighty-five (n=85) male participants, ages 18-54, then provided written informed consent before enrolling in the study. Comprehensive medical and athletic histories were obtained before undergoing familiarization protocols, blood draws, and performance testing to ensure enrollment criteria were met.

[0200] Familiarization Protocol Prior to physiological assessment, participants were familiarized with study procedures, personnel, and were provided with a triaxial accelerometer (ActiGraph GT3X+, ActiGraph, Inc.), which was worn for 7 days to objectively measure participants' physical activity prior to physiological assessment.

[0201] Blood collection After an overnight fast, morning blood samples were collected from an antecubital vein by venipuncture using a 22-gauge needle into 6 ml EDTA (BDVacutainer®) blood tubes, which were gently inverted 12 times and immediately frozen at −80° C.

[0202] anthropometry Height was measured using a portable stadiometer (Seca, Birmingham, UK). Weight was measured to the nearest 0.1 kg by a commercially available scale (Body Composition Analyzer TBF-300, Tanita, Tokyo, Japan). Total body fat percentage was calculated using bioelectrical impedance analysis (BIA) using a commercially available analyzer (Body Composition Analyzer TBF-300, Tanita, Tokyo, Japan).

[0203] Muscle strength testing In the 1RM test, each subject attempted to lift a weight that they believed they could lift once with maximum effort. Subjects then added weight in increments of 2.2-4.5 kg until the heaviest load they could successfully lift once was determined. Subjects rested approximately 3-5 minutes between trials. The participant's maximal strength criterion was their combined 1RM maximum lift (kg) for the squat + bench press + deadlift exercises. Participants' relative strength ratio was calculated as [maximal strength:body weight (kg:kg)]. Squat exercise, 1 rep max (SQ 1RM) During the SQ 1RM test, each lifter assumed an upright position with the top of the bar within 3.0 cm of the apex of the front deltoids. With both hands on the bar, the lifter removed it from the rack and moved back to assume a ready position, knees extended, waiting for the referee. On command, the lifter bent their knees and lowered their body in one smooth descent until the top of their legs at the hip joints were lower than the top of their knees. The lifter then raised themselves from the bottom of the SQ to a standing position and extended their knees. On command, the lifter, with the assistance of an assistant, returned the weight to the rack. Bench Press, 1 Rep Max (BP 1RM) Participants were in a supine position with their head, shoulders, and buttocks in contact with the weightlifting bench at all times. The lifter's feet remained flat and motionless on the floor during the trial. The participant received the bar at arm's length from an assistant positioned behind the head on the bench. The participant then lowered the bar to their chest, 1–2 cm from the nipple line along the chest. Once the bar was no longer moving on the chest, the command "press" was given and the participant extended their arms and returned the weights to the starting position. Once the arms were fully extended, the referee gave the command to rack and the assistant assisted the participant in placing the weights back into the rack on the bench. Deadlift, 1 rep max (DL 1RM) For DL ​​1RM, participants' feet and hands were spaced equally apart from the center of the bar and were allowed to be closer (power style) or further away (sumo style). In one smooth motion, participants lifted the bar vertically off the floor until their knees and back were extended and their body was in an upright position. When their knees were fully extended, on command, participants lowered the bar to the floor.

[0204] Peak Aerobic Capacity Peak aerobic capacity (VO2peak) was obtained with an indirect calorimeter on an electronically braked cycle ergometer (Velotron, RacerMate, Seattle, WA, USA). Gas exchange was collected throughout the test using a metabolic cart (Moxus, AEI Technologies, Pittsburgh, PA, USA). The test consisted of 1 min of unloaded pedaling followed by a stepwise increase to 50 W for 2 min. Afterwards, participants were assessed for fatigue (inability to maintain a minimum cadence above 60 rpm, blood lactate >7 mmol.l). -1 Increase work rate to 30 W min-1 until the patient reaches a maximal heart rate of >1.15 and >90% of age-predicted maximum. -1 VO2peak was identified as the highest value at the end of the ramp protocol. Power rate (WR) was collected continuously throughout the test, and peak aerobic power was calculated using the average WR during the final 30 seconds of the test.

[0205] Phenotype confirmation Group 1: Participants had to meet at least the 90th percentile for strength. Strength Phenotype: Relative Strength Ratio * (RSR)>4.5kg:kg *RSR=[(SQ1RM kg+BP1RM kg+DL1RM kg)÷Weight (kg)]

[0206] Group 2: Participants had to meet at least the 90th percentile for aerobic capacity. Fitness Phenotype: VO2peak>51.4ml.kg -1

[0207] Group 3: Participants had to be below the 50th percentile for strength and aerobic capacity. Strength: Relative Strength Ratio * (RSR)<3kg:kg Fitness: 50th percentile: VO2peak<40.8ml.kg -1

[0208] Samples and processing The samples used in this study are shown in Table 1. EpiSwitch™ templates were prepared for each sample using the EpiSwitch™ extraction procedure. The 3C template library was quantified and the amount was normalized to 1 ng / μl. Serial dilutions were made and nested PCR was performed according to the EpiSwitch™ protocol.

[0209] Nested PCR was performed using serial dilutions made for each sample for all 65 markers identified in part 2. Appropriate controls were included for each marker, which consisted of a no-template control (NTC, all other reagents minus DNA template) to monitor for potential contamination of the PCR reagents and a genomic control (negative control) to confirm that the PCR products being detected were the specific 3C products. Nested PCR was analyzed using high-throughput capillary gel electrophoresis (LabChip GX Touch HT, PerkinElmer) to identify and size the PCR products.

[0210] Training Response Annotation Nested PCR data were analyzed using retrospective annotation of end-point outcomes of high and low responders to exercise. To generate annotations for each training group, individual increases in physiological measures due to implementing a given training regime were ranked. The top five in the ranking system were classified as high responders to that training regime, and the bottom five were classified as low responders (see Table 2).

[0211] Summary of results The analysis of part three of the project was performed in two separate ways. The first was a direct analysis of the predictive ability of the 65 markers identified in the response analysis of part two and screened in the third part of the project. The second was a parallel analysis of all the top 131 markers initially translated from the EpiSwitch™ arrays in high-performing endurance and strength athletes. This analysis rigorously evaluated the predictive potential of the markers as high and low responders to specialized training using retrospective annotation of baseline readings and endpoint results by the end of 8 weeks of training. Figure 1 shows a graphical representation and overview of the analysis.

[0212] In comparison, the two analysis streams overlap by 40.8%. In the second analysis, we identify 38 additional markers from the 131 original markers at the start of Part 2. These are prime candidates to be predictive markers of training response. The overlap is shown in Figure 2, with circles showing the raw markers identified in the analysis stream in Section 2. [Figure 2 shows a Venn diagram of the 80 potential markers identified in Analysis 2 and the 65 markers identified in Part 2 and screened in Part 3 based on exercise response.]

[0213] Analysis 1 Analysis 1 was based on three comparisons. These three questions, originally filtered for responsiveness to mixed exercise, were designed to identify 65 markers that could predict an individual's outcome to a specific training regime or to training in general. To that end, nested PCR data were analyzed in three stratified groups: 1. High vs. low response to strength training (H / L) - To identify markers that could predict the endpoint outcome of high or low response to strength training at baseline. 2. High vs. low response to cardiovascular endurance training (H / L) - To identify markers that could predict the endpoint outcome of high or low response to strength training at baseline. 3. High vs. low (H / L) response, strength and endurance groupings independent of training - to identify markers that could predict end point outcomes at baseline in response to any / all training programs at baseline.

[0214] Annotation of outcome responses to training regimes at 8 weeks (following 4 weeks of specific aerobic or strength training) was used to identify statistically significant markers of exercise response to individual training regimes. A third group examined markers that were significant across both training programs.

[0215] To ensure that the markers were not only response markers but could predict the training response H / L outcome, the binary function of the markers at baseline and 8 weeks was investigated. Only markers that showed concordance between the two time points were selected. Filtering for chromosome conformations that were stably detected before and after training and also showed statistical significance between low and high response to the training program resulted in the selection of high-quality markers that represent an individual-specific stable regulatory framework that predisposes an individual to progress physiologically well or poorly to the end result of a certain type of physical training.

[0216] Of the 65 markers filtered for exercise response to 4 weeks of mixed training, 17 were found to be statistically significant. These details are presented in Tables 3 and 4. These 17 markers represent a pool of high-quality markers for an individual's training response predisposition at baseline (naïve) before training begins. The odds ratios presented in Table 3 are a measure of how strongly the absence and detection of individual markers are associated with a high / low predicted outcome response to training in the sample population. An odds ratio of 1 indicates no difference between the two subpopulations (high and low response). The data strongly suggest that the 17 discovered predictive markers are strongly associated with a successful outcome of training in the sample population with odds ratios ranging from 2 to 12.

[0217] Each marker was also evaluated individually using the Welch t-test, which is a statistical test used to compare two sample populations and see if the population means are equal. Equal means indicate that the two populations are not significantly different. The p-values ​​shown in Table 5 are a measure of confidence that the inequality in the population means is due to real differences and not chance sampling.

[0218] A p-value cutoff of 0.3 is used to determine whether differences in chromosome conformation detection are statistically significant. As seen in other literature, this differs from the typically used value of 0.05. The 0.3 limit was experimentally derived to ensure that we captured markers that provide useful information gain in, for example, machine learning classifiers and enhance classifier performance.

[0219] The significant p-value for each marker indicates the statistical significance of the odds ratios (OR) between training types in Table 3. The data in Tables 3 and 4 show the quality and robustness of the 17 predictive markers identified.

[0220] As shown in Table 5, the binary data from marker OBD142_081.083_1.4x leads to a useful example of both a high detection rate between high and low responders to exercise, in this case aerobic endurance training, and a predisposing condition already present at baseline before training began. The data also represent a confirmed phenomenon of interference with mixed training causing regulatory disruption before chromatin regulation is reprogrammed to the original predisposing state.

[0221] We identified a regulatory framework for predictive advantage and predisposition for high-response outcomes in individuals that exists at the start. The regulatory frameworks of low responders are either preprogrammed to match those of high responders that they essentially already possess or are not altered at all. All markers shown in Figure 3 are markers of high response in strength training. Figure 6 shows the change in detection of specific markers from baseline and after 8 weeks of training (4 weeks mixed, 4 weeks strength training), with detection status on the Y-axis being 0=no detection, 1=detection, and the X-axis being 1=baseline, 2=8 weeks. Figure 3A shows that high responders are corrected and low responders are reprogrammed such that the marker is detectable by the training program. Figure 3B shows that high responders are corrected and low responders are reprogrammed such that the marker is no longer detectable by the training program. Figure 3C shows that high responders are fixed and low responders are also fixed. This indicates that there is no alteration of the chromatin landscape of this particular interaction.

[0222] Of the 17 markers identified, three are anchored in the 3'UTR of the DKK3 locus (Table 6). It is clear that the DKK3 3'UTR is a hub of epigenetic regulation of this particular locus, with a specific genomic architecture that alters regulation of training response to create predisposition accessible.

[0223] Analysis 2 The rationale for the second analysis was to examine the 131 markers screened at baseline in part 2 against the retrospective annotation of the end-point outcomes of high or low response to specialized training after 8 weeks. Many potential predictive markers may have been excluded in analysis 1 because they were largely unaffected by 4 weeks of mixed training. In contrast to analysis 1, which identified predictive markers with the additional characteristics of responsiveness to mixed exercise, analysis 2 was designed solely to identify markers predictive of an individual's response to a specific training program.

[0224] Analysis 2 was based on the same three comparisons. 1. High vs low response to strength training - To identify markers at baseline that could predict the endpoint outcome of high or low response to strength training at baseline. 2. High vs. low response to cardiovascular endurance training - To identify markers at baseline that could predict the endpoint outcome of high or low response to strength training at baseline. 3. High vs. low response independent of training, grouping strength and endurance - to identify markers that could predict endpoint outcomes at baseline in response to any / training program at baseline.

[0225] The three comparisons were investigated using the Welch t-test. The test was cross-validated by performing 1000 repetitions with randomized sample selection. This gives 1000 different sample populations for validation on the same 131 markers. Resampling and cross-validation are used to ensure that the markers can be generalized to independent datasets. Analysis output statistics for predictive markers are shown in Tables 7, 8, and 9. When overlap between comparisons was eliminated, the analysis identified 80 potential predictive markers. These 80 included all 17 good predictive markers identified in analysis 1.

[0226] Figure 4 shows the overlap of the analyses. The left circle represents the 38 additional predictive markers from analysis 2. The middle circle shows the 42 overlapping predictive markers investigated in parts 2 and 3. These include 17 markers detailed in analysis 1. The right circle shows the 23 markers investigated in parts 2 and 3 that are response to training markers. The composition of the 80 markers identified in analysis 2 is shown in the Venn diagram in Figure 5.

[0227] As an independent validation of the general methodology, and in particular of the individual markers, it is important to mention that the top significant markers with predictive power of strength or endurance training outcomes were successfully translated into markers in equine studies. Based on the evolutionary conservation of the genetic and epigenetic regulatory mechanisms underlying these markers, a study of distance and sprinter horses achieved an accuracy of 93.75% in the classification of 32 development cohorts (Figure 6 showing the confusion matrix results).

[0228] conclusion 1. Existing chromosome conformation signatures can predict subsequent response outcomes to training phenotypes, which is consistent with many validated studies on the development of predictive biomarkers for response to various treatments. 2. Statistically significant prevalence EpiSwitch® markers identified and assessed in strength / endurance training in human participants were successfully translated into validated statistically significant equine markers for stratification of sprinters and long distance horses. 3. The 10 discovered markers classify the phenotype of sprinters and distance horses with 93.75% accuracy in the available set of 32 validated samples.

[0229] Analysis 2 identified 80 strong predictive markers, including all 17 predictive / response markers previously reported in analysis 1, as well as 38 markers that could predict H / L outcome for the training response phenotype when measured at baseline.

[0230] summary We successfully identified and evaluated significant predictive biomarkers for H / L outcomes of strength / endurance training. These results achieve the project objectives. Seventeen high-quality, robust predictive stability markers sensitive to blended training were developed for use in baseline prediction of individual response outcomes to specialized training regimes. An additional 38 rigorous predictive markers were identified and assessed at baseline, independent of response to blended training, for use in baseline prediction of individual response outcomes to the specialized training regimen.

[0231] Example 2 Identification of validated signatures includes binary CCSs that are present or absent as conditional biomarkers of epigenetic regulation in individual horses during strength or endurance training.

[0232] We identified CCS biomarkers to successfully differentiate Thoroughbreds trained as either sprinters or distance horses. These markers can be used to determine and monitor predisposition when unraced young Thoroughbreds are selected to undergo training programs for sprinters and distance horses. The markers are associated with trainability, physiological monitoring, and epigenetic reprogramming during training.

[0233] Project Approach The top 50 EpiSwitch™ markers identified from human studies were translated into equine nested PCR assays designed in EpiSwitch™ and tested by the EpiSwitch™ PCR platform using Thoroughbreds with defined phenotypes: 1) sprinting, 2) distance (long distance horses), for evaluation of the best prevalent markers for sprinters and long distance horses.

[0234] sample A total of 48 samples were used, as shown in Table 10. 16 untrained young thoroughbreds. · 16 trained Thoroughbreds classified as sprinters. 16 trained Thoroughbreds classified as long distance horses.

[0235] The sex distribution between the sample types of sprinters and distance horses is balanced. The untrained male sample is biased towards male foals relative to their gelding counterparts (Table 4). This bias is accounted for in the biostatistical analysis.

[0236] Gene conversion The 131 markers identified on the Human array and converted to nested PCR were converted from the GRCh38 human genome assembly to the EquCab2.0 horse genome assembly using LiftOver (USSC). A total of 114 of these were successfully translated. Nested primer sets for PCR were designed using the internal primer design application in OBD, and 65 of the 114 markers met the rigorous criteria for successful primer design according to the EpiSwitch™ operating procedures and methods. The top 50 markers were selected for the EpiSwitch™ horse nested PCR study.

[0237] Nested PCR screening EpiSwitch™ templates were prepared for each sample using the EpiSwitch™ extraction standard operating procedure. The 3C template library was quantified and the amount normalized to 1 ng / μl. Serial dilutions were made and nested PCR was performed according to the EpiSwitch™ protocol.

[0238] Nested PCR was performed using serial dilutions made for each sample for all 50 selected markers. Nested PCR was analyzed using high-throughput capillary gel electrophoresis (LabChip GX Touch HT, PerkinElmer) to identify and size the PCR products. Data from 16 sprinters and 16 distance horses were analyzed using Bocherou's test with resampling. The top 17 markers are shown in Table 13.

[0239] In total, 17 of the 50 markers investigated were found to be statistically significant using a Bauchleau p-value cutoff of 0.2. Importantly, this selection is generated using the individual titers of the dilutions, so that each marker is generated by a p-value based on a specific titer. Re-sampling of the dataset was performed 100 times on the 66.7% partition, and from the 32 datasets, 22 samples were randomly selected and statistical tests were performed. This was repeated 100 times, and the median results are shown in Table 13. These 17 markers represent interactions arranged around 16 genomic loci. Markers OBD154_045 / 047 and OBD154_049 / 051 are unique interactions spanning the same genomic location (see Table 4). The data show that a large number of transformed markers originally discovered in human cohorts can also be applied to horses.

[0240] MTFR1 (mitochondrial fission regulator 1) is noteworthy among the identified loci because it was shown to be the most important region from equine genetics for selection in Thoroughbred horses. Because mitochondrial function and management of reactive oxygen species are important for favorable exercise responses, MTFR1 may impose strong selection pressure in Thoroughbreds by protecting mitochondrial-rich tissues from oxidative stress, a region that is favorable for racing performance and endurance.

[0241] classification A classifier based on the sprinter vs. distance horse markers identified in the trained sample was developed to allow classification of the untrained sample. Glmnet (Lasso and Elastic-Net Regularized Generalized Linear Models) was used to rank the markers by their ability to classify between sample types. The top 14 markers identified by Glmnet were then compared to the top 11 markers based on Bocherou's p-value (Table 13). As shown in Figure 7, 10 markers showed concordance and were shared between the two statistical analyses. The 10 concordant markers were selected to develop the classifier. XGBoost was used to model the markers and build the classifier.

[0242] The XGBoost model is an ensemble-based classifier that uses a series of weak classification models to generate one overall strong model. In this gradient boosting method, an initial model is created from the training data, then a second model is created that attempts to correct any classification errors from the first model. This process is repeated n times to create a final model that classifies the training set. Early stopping and then terminating the classification building chain earlier than the initially set n value is used to prevent the model from overfitting the training set. The classification of the training set based on sprinters and long distance horses is shown in Figure 6. Based on its 10 markers, the EpiSwitch™ test properly classifies sprinters and long distance horses with an accuracy of 93.75%.

[0243] The probability calls of the 10 marker classifier are shown in Table 15. By default, a probability score by the classifier >0.5 is considered a call for sprinters and <0.5 is considered a call for distance horses. Based on the classification results in Table 15, the original probability scores (>0.5 for sprinters and <0.5 for distance horses) were adjusted to match the quality classification calls of naive samples from young thoroughbreds. The established cutoffs for the classifier calls are shown in Table 16.

[0244] The classifier was applied to data of naive samples from young thoroughbreds with the decision results shown in Table 17. In four samples, the classifier was not able to determine the phenotype with a sufficiently high certainty and was classified as non-classification restricted. The remaining 12 samples were successfully determined in predicting the epigenetic profile potentially leading to good sprinters or distance horses. The 10 markers discovered classify the sprinter and distance horse phenotype with an accuracy of 93.75% in the available set of 32 validated samples.

[0245] Example 3 Further research into human studies Multinomial Glmnet regression analysis was performed based on the three annotation groups of high, medium and low responders for each training type. Each CCS identified as predictive at baseline for training response outcomes was then compared across time points for deskwork controls, excluding CCS that showed variation due to effects other than the training under study. A total of 18 CCS were identified as predictive of response to strength training and 7 to endurance training. Two CCS were shared (mixed) between training types. The results are shown in Tables 31 and 32.

[0246] The detected loops, or EpiSwitch markers present, are strict categories of predictive and stable markers of strength or endurance training. "High responder" markers are parts of the epigenetic profile that are conducive to a very good physiological response to a training program (using VO2 max and one-repetition maximum strength tests), i.e., responders in our terminology. "Low responder" is a data analysis term that denotes stable predictive markers for individuals who do not change their physiological capacity much after a training program ("non-responders").

[0247] Considering that "high response" is the physiological data analysis term for "responder" biomarkers and "low response" for "non-responders," the majority of predictive response biomarkers are specific to strength or endurance. However, some biomarkers reflect dual function and provide information on good responses in both endurance and strength.

[0248] The loops detected refer to the array data, while based on the PCR data, Table 31 shows strength and Table 32 shows endurance. Marker definitions and marker categories used by data analysis on the array data: Strength Controls: These are simply the reference controls for the untrained side, the readings of a group of sedentary individuals before training at baseline. This is the physiological baseline control group. Endurance Control: Same as above but for endurance. Strength training: These are the reference controls, retrospective and ultimate positive controls for strength high achievers read on a group of strength trained individuals after successful training. This is the physiological positive control group. Endurance Training: Same as before, but for endurance.

[0249] Markers in this group should be evaluated based on the following rules: high responder markers are predictive stable (since baseline) responder markers, low responder markers are the opposite, predictive stable markers of no response to training. In general, predictive markers are specific for either strength or endurance, as they were developed separately in either strength or endurance programs and in the control group. However, there is some overlap, possibly reflecting a physiological overlap of the regulatory networks involved, and some markers are good predictive markers for a good response in both strength or endurance training.

[0250] Overall summary of the research The original pool of gene sites on the screening array consisted of 1) inflammatory genes, 2) loci that have been linked to microsatellite differences in Thoroughbred horses and translated to humans. Thoroughbreds are highly inbred and the input of genetic components to phenotypic differences is very limited in these subjects - this was considered the right model to search for associations with epigenetic control sites from a chromosome conformation perspective. The fact that statistically significant sites are broadly associated with several well-known genes functionally related to strength and endurance is the result of independent selection. Importantly, the screening of potential candidates for chromosome conformation markers was driven by proprietary EpiSwitch annotations within non-coding parts of the genome, encompassing entire genes within a 100kb window of the reference locus: upstream, downstream, or within chromatin domains. At no part of the selection of CCS marker reads was a comparative evaluation of any gene expression of any nearby genes performed to aid in the selection, thus excluding any link or correlation to gene expression changes of any genes and their known functional links to physical training outcomes.

[0251] Tables 34 to 36 show the preferred strong markers. The odds ratios shown in Table 36 are a measure of how strongly the absence and detection of individual markers are associated with high / low predicted outcome response to training in the sample population. An odds ratio of 1 indicates no difference between the two subpopulations (high and low response). The data strongly suggest that the 17 discovered predictive markers are strongly associated with successful outcome of training in the sample population with odds ratios ranging from 2 to 12.

[0252] Each marker was also evaluated individually using a Welch t-test. This is a statistical test used to compare two sample populations and see if the population means are equal. Equal means indicate that the two populations are not significantly different. The p-value shown is a measure of confidence that the inequality in the population means is due to real differences and not chance sampling.

[0253] A p-value cutoff of 0.3 is used to determine whether differences in detection of chromosome conformations are statistically significant. As seen in other literature, this differs from the commonly used value of 0.05. The limit of 0.3 was experimentally derived to ensure that markers that provide useful information gain in machine learning classifiers and enhance classifier performance were captured. The significant p-value for each marker indicates the statistical significance of the odds ratios (ORs) between the training types in this table. The data in this table demonstrate the quality and robustness of the 17 predictive markers identified.

[0254] Figure 15 is a Venn diagram of significant markers at baseline and 8 weeks for CV and muscle. These markers are shown in Table 40.

[0255] Figure 16 shows baseline CV markers based on high and low CV group accurate P-values ​​for association. These are the top 10 significant markers of subjects at baseline based on CV range. The range for muscle is also significant at baseline. This shows the close association between CV and muscle range for these markers.

[0256] Figure 17 shows the baseline muscle markers based on high and low muscle group accurate P-values ​​for association.These are the top 20 significant markers of subjects at baseline based on muscle range.Their ranges for CV are also significant at baseline.This shows the close association between CV and muscle range in these markers.

[0257] Figure 18 shows the 10 Episwitch equine markers identified from the UK cohort. The figure shows the equine genomic location and homologous region in the human genome, and the type of marker (sprinter or distance horse). The same 10 markers were used to classify horses in Singapore.

[0258] [Table 1]

[0259]

Table 2

[0260]

Table 3

[0261]

Table 4

[0262]

Table 5

[0263]

Table 6

[0264]

Table 7

[0265]

Table 8

[0266]

Table 9

[0267]

Table 10

[0268]

Table 11

[0269]

Table 12

[0270]

Table 13

[0271]

Table 14

[0272]

Table 15

[0273]

Table 16

[0274]

Table 17

[0275]

Table 18

[0276]

Table 19

[0277]

Table 20

[0278]

Table 21

[0279] TIFF0007599417000033.tif220157 TIFF0007599417000034.tif231157 TIFF0007599417000035.tif232157

[0280] TIFF0007599417000036.tif213157 TIFF0007599417000037.tif220157 TIFF0007599417000038.tif226157 TIFF0007599417000039.tif73157

[0281] TIFF0007599417000040.tif133157 TIFF0007599417000041.tif226157 TIFF0007599417000042.tif231157 TIFF0007599417000043.tif193157

[0282]

Table 22

[0283]

Table 23

[0284]

Table 24

[0285] TIFF0007599417000048.tif37157 TIFF0007599417000049.tif237157

[0286] TIFF0007599417000050.tif230157 TIFF0007599417000051.tif37157

[0287] TIFF0007599417000052.tif178157 TIFF0007599417000053.tif82157

[0288] TIFF0007599417000054.tif139134 TIFF0007599417000055.tif127134

[0289] TIFF0007599417000056.tif96108 TIFF0007599417000057.tif168108

[0290]

Table 25

[0291] TIFF0007599417000060.tif231157 TIFF0007599417000061.tif237157

[0292] TIFF0007599417000062.tif225157 TIFF0007599417000063.tif231157

[0293] TIFF0007599417000064.tif215157 TIFF0007599417000065.tif209157

[0294] TIFF0007599417000066.tif234157 TIFF0007599417000067.tif235157

[0295] TIFF0007599417000068.tif245156 TIFF0007599417000069.tif245156

[0296] TIFF0007599417000070.tif229157 TIFF0007599417000071.tif229157

[0297] TIFF0007599417000072.tif236119 TIFF0007599417000073.tif245121

[0298]

Table 26

[0299]

Table 27

[0300]

Table 28

[0301]

Table 29

[0302]

Table 30

[0303] TIFF0007599417000135.tif217157

[0304] TIFF0007599417000136.tif221157

[0305] TIFF0007599417000137.tif167103

[0306] TIFF0007599417000138.tif233157

[0307] TIFF0007599417000139.tif241157

[0308] TIFF0007599417000140.tif232157

[0309] TIFF0007599417000141.tif160104

[0310] TIFF0007599417000142.tif239152

[0311] TIFF0007599417000143.tif214157

[0312] TIFF0007599417000144.tif237157

[0313] TIFF0007599417000145.tif159103

[0314] TIFF0007599417000146.tif239152

[0315] TIFF0007599417000147.tif233157

[0316] TIFF0007599417000148.tif233157

[0317] TIFF0007599417000149.tif14999

[0318] TIFF0007599417000150.tif238152

[0319] TIFF0007599417000151.tif232152

[0320] TIFF0007599417000152.tif227152

[0321] TIFF0007599417000153.tif153101

[0322] TIFF0007599417000154.tif234152

[0323] TIFF0007599417000155.tif219152

[0324] TIFF0007599417000156.tif225152

[0325] TIFF0007599417000157.tif15199

[0326] TIFF0007599417000158.tif235118

[0327] TIFF0007599417000159.tif194116

[0328] TIFF0007599417000160.tif222117

[0329] TIFF0007599417000161.tif11799

[0330]

Table 31

[0331] TIFF0007599417000163.tif93147

[0332] TIFF0007599417000164.tif92152

[0333] TIFF0007599417000165.tif94116

[0334] TIFF0007599417000166.tif92157

[0335] TIFF0007599417000167.tif94157

[0336] TIFF0007599417000168.tif98139

[0337] TIFF0007599417000169.tif91157

[0338] TIFF0007599417000170.tif9299

[0339]

Table 32

[0340] TIFF0007599417000172.tif42152

[0341] TIFF0007599417000173.tif41157

[0342] TIFF0007599417000174.tif52157

[0343] TIFF0007599417000175.tif46113

[0344] TIFF0007599417000176.tif46113

[0345] TIFF0007599417000177.tif47139

[0346] TIFF0007599417000178.tif47157

[0347] TIFF0007599417000179.tif4392

[0348]

Table 33

[0349] TIFF0007599417000182.tif95157 TIFF0007599417000183.tif166157

[0350] TIFF0007599417000184.tif52157 TIFF0007599417000185.tif210157

[0351] TIFF0007599417000186.tif222157 TIFF0007599417000187.tif225157 TIFF0007599417000188.tif109157

[0352] TIFF0007599417000189.tif178157 TIFF0007599417000190.tif88157

[0353] TIFF0007599417000191.tif127157 TIFF0007599417000192.tif142157

[0354] TIFF0007599417000193.tif77156 TIFF0007599417000194.tif201157

[0355] TIFF0007599417000195.tif2784 TIFF0007599417000196.tif23584 TIFF0007599417000197.tif1484

[0356] TIFF0007599417000198.tif201157 TIFF0007599417000199.tif64157

[0357] TIFF0007599417000200.tif151157 TIFF0007599417000201.tif111157

[0358] TIFF0007599417000202.tif100157 TIFF0007599417000203.tif153157

[0359] TIFF0007599417000204.tif220157 TIFF0007599417000205.tif223157 TIFF0007599417000206.tif108157

[0360] TIFF0007599417000207.tif229157 TIFF0007599417000208.tif37157

[0361] TIFF0007599417000209.tif178157 TIFF0007599417000210.tif92157

[0362] TIFF0007599417000211.tif119157 TIFF0007599417000212.tif134157

[0363] TIFF0007599417000213.tif7988 TIFF0007599417000214.tif19689

[0364] TIFF0007599417000215.tif29157 TIFF0007599417000216.tif228157 TIFF0007599417000217.tif13157

[0365] TIFF0007599417000218.tif208157 TIFF0007599417000219.tif62157

[0366] TIFF0007599417000220.tif155157 TIFF0007599417000221.tif107157

[0367] TIFF0007599417000222.tif232157 TIFF0007599417000223.tif158157

[0368] TIFF0007599417000224.tif60157 TIFF0007599417000225.tif206157

[0369] TIFF0007599417000226.tif228157 TIFF0007599417000227.tif37157

[0370] TIFF0007599417000228.tif170157 TIFF0007599417000229.tif92157

[0371] TIFF0007599417000230.tif123157 TIFF0007599417000231.tif147157

[0372] TIFF0007599417000232.tif72157 TIFF0007599417000233.tif193157

[0373] TIFF0007599417000234.tif21157 TIFF0007599417000235.tif216157 TIFF0007599417000236.tif16157

[0374] TIFF0007599417000237.tif191157 TIFF0007599417000238.tif61157

[0375] TIFF0007599417000239.tif173157 TIFF0007599417000240.tif233157 TIFF0007599417000241.tif150157

[0376] TIFF0007599417000242.tif88157 TIFF0007599417000243.tif178157

[0377] TIFF0007599417000244.tif36157 TIFF0007599417000245.tif224157 TIFF0007599417000246.tif9157

[0378] TIFF0007599417000247.tif198157 TIFF0007599417000248.tif54157

[0379] TIFF0007599417000249.tif16881 TIFF0007599417000250.tif10980

[0380] TIFF0007599417000251.tif111157 TIFF0007599417000252.tif154157

[0381] TIFF0007599417000253.tif61157 TIFF0007599417000254.tif201157

[0382] TIFF0007599417000255.tif221157 TIFF0007599417000256.tif31157

[0383] TIFF0007599417000257.tif197157 TIFF0007599417000258.tif233157 TIFF0007599417000259.tif126157

[0384] TIFF0007599417000260.tif111157 TIFF0007599417000261.tif154157

[0385] TIFF0007599417000262.tif60157 TIFF0007599417000263.tif209157

[0386] TIFF0007599417000264.tif233157 TIFF0007599417000265.tif33157

[0387] TIFF0007599417000266.tif19294 TIFF0007599417000267.tif8493

[0388]

Table 34

[0389] TIFF0007599417000271.tif176151 TIFF0007599417000272.tif242151 TIFF0007599417000273.tif116151

[0390]

Table 35

[0391]

Table 36

[0392]

Table 37

[0393] TIFF0007599417000277.tif154157

[0394] TIFF0007599417000278.tif83152 TIFF0007599417000279.tif69152

[0395] TIFF0007599417000280.tif134157 TIFF0007599417000281.tif220152

[0396] TIFF0007599417000282.tif153157

[0397] TIFF0007599417000283.tif74157 TIFF0007599417000284.tif45157

[0398]

Table 38

[0399] TIFF0007599417000286.tif79138 TIFF0007599417000287.tif20139

[0400] TIFF0007599417000288.tif223157

[0401]

Table 39

[0402]

Table 40

[0403]

Table 41

Claims

1. 1. A method for determining endurance or muscle strength in a human or horse individual by detecting the presence or absence of at least 10 chromosomal interactions in a sample from said individual, comprising: The detection comprises: (a) cross-linking DNA of two chromosomal regions that form a chromosomal interaction in a chromosome of the individual; (b) subjecting the cross-linked DNA to shear to separate the cross-linked DNA from the chromosome; (c) ligating the ends of the cross-linked and cleaved DNA to generate a ligated nucleic acid having DNA from both chromosomal regions that formed the chromosomal interaction; and (d) detecting the presence or absence of the ligated nucleic acid using at least 10 probes to identify the presence or absence of said chromosomal interaction. This is done by; (i) at least 10 probe sequences are selected from the probe sequences shown below from Table 25, and the individual is a human individual; - ACCGCCTCACCTCAGCTCTCCAGTGAGATCGATCCTCCCACCTAAGCTTCCCAAGTTGCT, - GACACCTCCTCTCCCTTCCCTCCCCTTCTCGAGTTATCAAAATATTTTGAGAGACAGTAT, - GACACCTCCTCTCCCTTCCCTCCCCTTCTCGATGGCCACATGTGGCCAGTGGCTCTCATG, - TGCAGAAAGTACTACAAAAAAAGAAGCTTCGAAAATGTTGGAGATGAGAGTTTCTTCACC, - GGCAGGTGGATCATTTGAGGTCAAGAGCTCGACAGAGCAAGACACCATCTCCAAAAAGAA, - TGTCCTTTCAAGGAAGGATTAGCATCCTTCGAAAGACCTATCAGGATTTCATTTGTAATG, - CCTCGTCGTCCCCTCTCTTCCTCGTTCCTCGAACATCTCCAAGTCAGATAATCATAACAA, - ATCTATTATAATGATGCAATATTGTTAATCGATTCAAAGATCAAATTAATTATTAAAGCT, - ATTTTAAATGTGGCATTTTAGGTTTATTTCGATTTTGCATAAATTGAAAAAGCTGGAGAT, - TGCTGAAAGAAAACACAATTTATTTAAGTCGAAATTTTGGAAAAGCCCTGATTTAAGTCA, - GACACCTCCTCTCCCTTCCCTCCCCTTCTCGATCACTTTGCAAAGCTTTGTTGGCTAGGC, - TAAATAAAATTGTCTTTTTTTGTCTTTCTCGATTACAGAGAACTAAGTACATTTTAAATC, - CTTAATTTTTTTTCTTTGAATGCCTCTATCGACAGTCTTCTCTCTACTTTCTACAGTGAA, - ACACCTCACCCACCCAGCTGGGCTGGCCTCGATGCCATTAAATCATCCCGTGACCTTCCT, - GGCAGGCGGATCATTTCAGGTCAGGAGTTCGACCTGAAATTTCCATACTAAATTTAAATA, - CCTCGTCGTCCCCTCTCTTCCTCGTTCCTCGACAGGAAAGCATACGGAAAAAGTTAAAGA, - AATTTCACATTGATACATTGATAGACATTCGAAATCATACACAGCATACTCTCAAACCAT, - AAATGTATAAGAACAGAAGAGAATTATCTCGACATGTCTGAAAAGTATTATCAGCCCTCT, - AAAACACCCTGAATTGGAAGAAAGAAACTCGAGGGATGAGTGTGTATCATCAAAGTCAAA, - CGCGGGGCCTTCTGGGCCAGGCGGGCCCTCGAAAAGCCCCACGCCCCCCCAGAGCTGCTG, - AATACGTGGTCAATCTAAGGATTATAGTTCGAAAAGATTAATGATGTATTGATGACACTT, - CTTGCCTAGTCTTTAATTTATTTATTTATCGACATTTTTTTCTTATCAACGAGACGATGT, - AATTAGACAACGACTATATGACTCTGTCTCGAGATTGGTCTTAACACACTATTGATTATT, - AAAAAGAGAAAAGCAGGTTAGCACATTGTCGACCCCGCCCCCGGGATGGGGGAACTGGCC, - AAATTTTCAAGTGTACGATATGGTATCATCGACCCCGCCCCCGGGATGGGGGAACTGGCC, - AAACTTACGATTACATTTAGAGATCACTTCGACCCCGCCCCCGGGATGGGGGAACTGGCC, - AAGGGCTCGGGAGCTCCCTCGGCACACCTCGAGGAGTGCCAGGCATCTACTGCTCTGTCC, - CCCCTGGCTCACCTACACAAAATTGTGCTCGACTCTACTCTTAGCCCTGCTAAATAAGTA, - ATTATATTAGTGCTGTAATAAAATTAAGTCGACACATTTGATACTGCTTATTGGGTTATT, - TACCTTGAAAAGCTCTTCAGTATGATTATCGAGCTTTAGCCATTCTAGTAATTATTAAAA, - ATGAATTAATGTTTCCTAGAAAGTTGTCTCGAAAGAAGAAAGTGTCAGGGTTCAACTGCC, - CCTCGTCGTCCCCTCTCTTCCTCGTTCCTCGATGCGGGACTGATTGTTACAGAACTGTTT, - GGGGATGGGGCCGAAATATGATTGCACTTCGAGTTTGTTTAGTTTTTATCTTCCCCATTT, - GGGGATGGGGCCGAAATATGATTGCACTTCGAACTTCAGCACCTGACCTTTGTCATCAAC, - AGAGGCTGAGGTGAAAAGATTGTTTGAGTCGAAGATCATTGTCTCATTTTTTTACTTGTT, - AGAGGCTGAGGTGAAAAGATTGTTTGAGTCGATACACTGAACAAGTGCCAGAGCAGAATA, - GGTGGGAGGATCACTTGAGGTCAGAAGTTCGATCTCCTGACCTCAAGTGATCCTCTAGCT, - TATCAAGTTAAACATTCAGACGTCTAGGTCGACTTGAAGTTCACCTAAAGTTTTCCAGTC, - ATAGTATAGAATGACAGCATGCTGGTTATCGACAAGAGTTCTTAAAAAGCCTAAATGTCA, - ATTCCACAAATATTTGTGAGCACCATCTTCGAGCTCATTAGTTCAAGACCAGCCTGGGCA, - CTCTCAACTTTGGATGTAAGAATCATCTTCGAGATTTTGACTCTCCACCCTGCCCCACAGG, - CGGTCCCTCTGCCCCGGTTACCTACCCGTCGACATGACACTTGGGTGGGGATACAGGGCC, - TCCCCCCTCTTCCTCTGCCTCCCTTCCCCTCGAAAACTGATTAAAAAGAATATTGCTGGCT, - TCACTTTTATTTATCTTACTCACTTTTCTCGAGGAATTCTCAGAATTCTCCTCAACCCAC, - TTTCTTCCAACTGAGAATCTTAAAAATCGAAATTGGATAAGGAAAAAGTGAATGTG, - TTTTAGTTTTATTTATTTAGTTATCATCTCGAAAAACAAACAACAACAATAACAGCAACCCTC, - GCAAGACTTTGTCTCAAAACAAAAGTGTTCGAAAAAGTCATCGTTTAAAAGGTAAAATGT, - TCCATTAGCTCTGCTTTCAAATACTATATCGATGTAGCTTATGTAAAATAAATGTATTAA, - TCAAAGTTACAGTTTATATAATTAGAAATCGATCTAACCTCAATTCCAGTCCCACAAATG, - TCAAGAAAAAAATAATAATAATTTTTTTTTCGAACTTATGGCTCAAGCGATCTCTTGCTT, - AAGCAAGAGAATCGCTTGAGCCATAAGTTCGATGCTGCTTTGGGAACTGAAGGTTTTTCT, - CGTAGAACTAAGATGTATTCAAAGTCAGTCGAAAAAGATTAGAAAAGTTCAACTCTAAGA, - AGGCAGGCAGATCAATGAGGTTGGGAGATCGACAAGTTCAGTAATTCTGAGGTGAGTTTT, - GTTGTTATAACTATATCATGAGACTAAGTCGAAAAAAAAAAAACAATAATTTCAGCTGTAT, - TATATTAGAAACATGTCTGAAAAAAGTATCGATAAATGTATACGTTGATGTACATTGATA, - GGTATTACCTTGATGGCCTTAAAGAAGATCGAGCCAGAGGGCCTCTGTTCATGTTTGGGC, - TCAAGAAAAAATAATAATAATTTTTTTTTCGATTCCTGCTACACATTTTGGCAGAATACT, - CATGGTTTATAATCCTTTTTTATACATTGTCGACACTGTATTTTCACAGATCACTCTGGAG, - AGAACTTCCTGATATATTTTTTTCTTTTTCGAAGGTCTTAAAATGTTTTTTAAACATGACC, - GACTCTTAAAAACAATAATATCAAACACTCGACTTGTCATTTAGTTCTTTGGGAAGCAGT, - AATTAGACACGACTATATGACTCTGTCTCGACTTTAAAGCAAGTACTTCTTGTATGCTC, - TCTTTGTTTCCTGTTTCCACTTCTTATTTCGATATTTATTGAGTGCTACTATATATATGC, - TATGAATCCCTAAATGTCACACATCAAGTCGAATTATATAAGATACCCTGAAATTTAAGG, - ATGACTGTGGAAGGTTCTGATGTCTCTGTCGATGAAATGGAGAGAGGAAAGAAAAGAA, - ATGACTGTGGAAGGTTCTGATGTCTCTGTCGACATCATTTTTACAAATAAGACCAGATGT, - AATATTGCTACTGGAAAATCTGAATCTTTTCGAAGAAAGCCCTTTGTAAGTTGTTTTCAAA, - AACTATGTGACTTTTAGCTATACGAGTTTCGACTGGGTTCTAAATAGTTAATGTCATAGT, - TAAAACTATTTTAAATGTTTTTAAAGTATCGATGTGTACTTTGACATCTGTGATGATGAT, - TTATGGCTTAGAAGTAGAAAGTCATAAATCGATTCCTAAAAATTAATGAGGTGAATAGTA, - TTTCTATGAAGAATGTTCAAGATGCAATTCGAGCTACATACTATTATATATTTTCACAGT, - CTCTATAAATTTACCAGAATATAAATTCTCGACTAAAAGTTCAGTTCTTCATTCCCACTA, - ATCTTACAAAGAATAATTCTAAGAAAAGTCGAGCATTGGAGAAAATCTCCCTTTTCTTTT, - TGTTTTTATTTTCATGTTTTAATTTTGTTCGAACTCTTGACCTCAGGTAAACCACCCACC, - AGAACTGGTTATTGATCTATTCAGGGATTCGAATAATAGATTATGAATAAATTATTCTG, - CTTTTTGAAGATTATAATCTATTAGTGATCGAGGCTTTTTTGCTTTTTTTTTTTTGAGAT, - CTATATGTAAGTTACAATATGTAAGGTATCGATAGTCACTGAGACTAATTTAATGTTATA, - TGTAGGCAGATTACCTGAAGTTAGGAGTTCGATTAGGAATAACCTATCATTAGAGTTGTT; or (ii) at least 10 probes are selected from the probe sequences shown below from Table 24, and the individual is a horse individual; - TTTAAAACTAAGTCTAACCGCCTGAAAGTCGAGAGGGAAGTGCTACGCATCCCGCGCACA, - AGAGGCTCAAAACTTCTGTCTGGTAAATTCGATTTTCTCCTATTGCTCTACATCACTCGG, - CCCCATAGGAGGCTTCTGGGAAGCCCAGTCGAGATGCATCCATGTTGTCATGGGTATAAG, - TTCCTACCACCCAAATTCTATTATTCTATCGAGAGGCACCAGGATGCTGAGAGGATGAGA, - TCGTGTTAGTTAAATCGGCCGATTTGTCTCGAGAAAAGAGAAGGACTGGGTCATGGTGGG, - CCGCCTCGCCACAGCTCTCCAGTGAGATTCGATCCTTTTAAACTTTGTTAGGCAAGACCA, - TTGATCTTTTTTAAAATAGGGATGGGGTTCGAGAGAAGGAGAATGAAGCAGAAAGCTTGT, - CTGTAACCAGAGGAGCAGAGTCTGCACATCGATTCCTCTTACTGCACTGCAGGTCCAGGG, - ACCCAAAATTTACTTGCAATCACTTTAATCGAGCTCTTGGATGTCTGGGGATTGTTCTGC, - GATAGAATTAAACATTGAAGGCAAACTTTCGATTAGGGTTTTAGTTTTCTTTAAGTTTTT, - GAGCACAGGTGCTGGATTCAGGCTTCCTTCGATTCAAACTTCCCTCACACTGACATTTTT, - GGAGAAAAAGGGGCTTTACGCAGTCCATTCGATGCAGTCAGGAGTCAGGACAGCATCTCC, - CACAATGCCTTCCCCTCTTCCACCCCTTTCGATCACTGTCCTTCGTACACTCAAGATATA, - GTAAAAACCAGCTTCCAGGGACCAGATGTCGAGATCAGTATTGAAATGCACCATTTCATT, - CATAAGAGATGACTGGCCTTTTGCTGTTTCGATGGCCCGGCTTCCTCTTTGGGACTTCAG, - TAATGAAATTGTCTTTTTGTGTCTTTCTTCGAGGATTTCTAGACTTTCTGTGCATCACTA, - AAGCATAAATTCGTTTTCCCCTTGACTTTCGAGACTCCATAAAGCACACGCACAAAGGCC, - GTAACATGCTGAATAACAACATTTCAGTTCGAGCGTTGTGGCAGAGCTTCCTTGTTACTG, - ACAGTGCTGCCTCTTCTGTGCAGATGCTTCGAAACCCATGTAATTTCATAGCTATGATGG, - TTGCCTCCATTTTAAAATTGAAGAAACTTCGAATAAAATTGAAACATCCTACCACCAACA, - CAGCTGGAGCTCAGCCGGATGCGCAACTTCGAGAAGAGATATTTGCACATCCGTGTTCGT, - AGTGGGCCACCACTGCCTAAGAACTACTTCGAGGCCAATACTACTGTCGGCCGCATCCGT, - GCCGTCGGTGGAGAAATTTAAAACTATTTCGAGTCAGTAACTGGACTCAGTTAAGGAAGG, - CACTGAGCTGCACGAAGATGCTGCCATCGATTCTCAATTGGGAAAAGCACATGGGATATG, - TTAAAAGTAAAGTAAGTAGAGGTCAGTCTCGAAGCTTTGAAGATTAGTTTTTGAACAGTG, - CAAGAATCAGTTCTGAGAAATCAATGCCTCGAGAACCAACGAAACACTGGGCCGCCTGCA, - TGAACATTATCCAGGAGCTCATACTGCTTCGATGACCACAGAGGCGCCAGAAGGCCACAG, - CAATGTACAATCTACCAGGAACTGAAGTTCGAGCAGCACTTGGCTTTCTTCCTTTTTAGA, - GAAAGCTGGGTATGGGAGTGTGCACACTTCGAGATCTCAGAAAGGGCATAACTGTATTTT, - ATTGCCTCCATTTTAAAATTGAAGAAACTCGATGGAGTTTGGGTTTAAGCACTGCTTTTT, - AGGCCTCGCCTATGAATAGCATACCAGCTCGATTATTCTTCTCTCACACAATTTCTGAAG, - CTCCGCCCGCCAGCTCTGCAGCTTCCCTTCGACGCTTCCTACTTTTCTCCAGGAAATTAT, - TCACTCCAGGACCCCCCCAGCCCTGTGTTCGAGGTTGTTTTGCCAGTCGTCTGTTTGGCA, - GGAAACACAAAGTTCTTGGCAAAGCTTCGAGTCTTGGAGGTGTTGTTATTTGCCCAAAGC, - GTGGCCTTATTTTGCAAAGGGGCAAACTTCGAAGGTGATGAAACGGAGGCCCAGAGAGGG, - TACCTCCAGTCTTGGTTATTCTGAGAGTTCGAGTCAGGATTCAGACCCAAGTCTAACTCT, - GGGTCCAAACCTGCAAATCCCCAGCTGTTCGAAAATGTGTCTGCTTTTTCATGGACACCA, - TTCCTACCACCCAAATTCTATTATTCTATCGAGAGGCTGAGATGAACAAGGCAGACGCAG, - TACCTCCAGTCTTGGTTATTCTGAGAGTTCGACAAGTGCGCTAGTGCCGCTTGTCAAGAG, - ATAAATTCCTTGTGAATTTTGATTCCTTCGAAGACTTGTTCTGTTGGAGGCATGTCCCT, - CTTCCCCAAGAATTTTGGATTATATCTCTCGAGAAAGGGTAAGGAAGAGTCAAAGCTGTA, - CAATTTCTGGGATACAGTAGTGAAGAGTCGATGGCATGGGATCTCTGCTATTTATCTCT Either way.

2. (i) is performed on samples from human or equine athletes; or (ii) performed on samples from thoroughbred horses; or (iii) performed on samples from human individuals younger than 20 years of age or on samples from horses younger than 18 months of age The method of claim 1.

3. 2. The method of claim 1, carried out to select individuals for racing.

4. The method of claim 1, wherein the detection of the ligated nucleic acid is performed by quantitative PCR (qPCR) using primers capable of amplifying the ligated nucleic acid, and the probe binds to the ligation site during the PCR reaction.

5. The probe is a fluorophore covalently attached to the 5' end of the probe, and / or A quencher covalently attached to the 3' end of the probe. The method of claim 4, comprising:

6. The method of claim 5, wherein said fluorophore is selected from HEX, Texas Red and FAM.

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