Methods for increasing genetic gain in a breeding population, for increasing the probability of producing an offspring individual with a desired phenotype, and for generating an offspring individual with a desired genotype.

Abstract

Methods for increasing genetic gain in a breeding population are provided. These methods may include (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population; (b) selecting from the breeding population that could produce a subsequent progeny population; (c) inferring or determining haplotypes based on genotypes with respect to the plurality of genomically broad markers for the breeding population; (d) simulating a cross of the breeding pair to produce a subsequent generation, each member of the offspring generation possessing a simulated genotype; (e) calculating a genetic potential value of the offspring generation; (f) repeating steps (b) - (e) one or more times, where each iteration of step (b) uses a different breeding pair; (g) classifying each simulated cross;(e) and (h) selecting one or more optimal breeding pairs based on ranking, where the selected optimal breeding pair(s) are predicted to produce offspring with the highest genetic gain. Methods are also provided for choosing breeding pairs predicted to produce offspring with phenotypes, methods for increasing the probability of producing offspring individuals with desired phenotypes, methods for generating offspring individuals with desired genotypes and / or phenotypes, offspring produced from this, and cells, seeds, parts and tissue cultures thereof.

Description

Methods for increasing genetic gain in a breeding population, for increasing the probability of producing an offspring individual with a desired phenotype, and for generating an offspring individual with a desired genotype. Cross-reference to related application.

[0001] The subject matter disclosed in this document claims the benefit of U.S. Provisional Patent Application Serial No. 61 / 418,135, entitled METHODS FOR INCREASING GENETIC GAIN IN A BREEDING POPULATION, filed November 30, 2010, disclosure of which is incorporated herein by reference in its entirety. TECHNICAL DOMAIN

[0002] The subject matter disclosed relates to plant molecular genetics and reproduction, particularly to methods for increasing genetic gain from whole-genome selections in breeding populations. PREVIOUS TECHNIQUE

[0003] In plant reproduction, conventional selection is based on phenotypic evaluations of offspring in breeding populations. Offspring are typically phenotyped during the growing season, and superior individuals are selected based on their phenotypic scores. For most field crops, there is only one growing season per year. Therefore, a limitation of phenotypic selection is that it is routinely limited to one cycle per year. Another frequent disadvantage of phenotypic selection is the influence of environmental noise on the phenotypic expression of traits. This environmental noise can cause biases in selection and decrease the selection efficiency of phenotypic selection.

[0004] The development of molecular technologies facilitates Petition 870210067187, dated 07 / 23 / 2021, page 5 / 110 2 / 91 methods for using molecular markers to accelerate selective breeding processes. One such molecular technology is marker-assisted selection (MAS; also known as marker-aided breeding). With MAS, one or more quantitative trait loci (QTLs) associated with a trait of interest are first identified, and then analyses of these QTLs are employed in subsequent selections (Lande and Thompson, 1990). In general, MAS can be performed over several cycles per year, and genetic gain can be increased by intensive selection of target QTLs.

[0005] However, it is often difficult to identify all QTLs that are associated with a particular trait of interest, thus reducing the overall effectiveness of MAS (Utz et al. 1999; Jannink et al., 2010). This occurs due to a lack of QTLs with small effects during QTL identification due to various technical reasons (e.g., low heritability, small samples, etc.). Failure to identify such QTLs can make MAS difficult and / or inefficient to use for improving important traits such as crop yield. Furthermore, QTL effects may be overestimated (Beavis, 1994), which further reduces the efficiency of MAS (Jannink et al., 2010).

[0006] Genome-wide selection (GWS; Meuwissen et al., 2001) is a technique that has been proposed to address some of the disadvantages of MAS (Bernardo and Yu, 2007; Jannink et al., 2010). The GWS strategy incorporates all available genetic markers for a given genome into a predictive model simultaneously, thus reducing the risks of missing or inaccurately calculating the effects of QTLs with smaller effects. Each marker is generally considered a putative QTL, and all markers are combined to predict the genomic reproduction values ​​(GBV) of the Petition 870210067187, dated 07 / 23 / 2021, page 6 / 110 3 / 91 offspring with GWS. Simulations and empirical studies have verified advantages of GWS in relation to MAS and PS (Meuwissen et al., 2001; Bernardo and Yu, 2007; Hayes et al., 2009; Lorenzana and Bernardo, 2009; Luan et al., 2009).

[0007] Typically, GWS can be used to select superior offspring based on their own particular GBVs. (Bernardo & Yu, 2007; Jannink et al., 2010). For example, GWS is commonly used to estimate the effects of all analyzed markers based on a training population, allowing the calculation of a global GBV for each offspring based on the offspring's genome. The offspring can then be ranked in relation to GBV, and superior offspring can be promoted to one or more additional breeding cycles and Yu.

[0008] However, a given selection strategy may not be ideal for GWS. For example, crossing two main selected lines in a given cycle does not necessarily generate offspring with high breeding efficiency.

[0009] Therefore, new methods are needed that overcome the BUT based on conventional GBV to maximize the benefits of GWS over selective breeding strategies. SUMMARY

[0010] This summary lists various modalities of the subject matter presently disclosed, and in many cases, lists variations and permutations of these modalities. This summary is merely exemplary of the numerous and varied modalities. Mention of one or more representative characteristics of a particular modality is likewise exemplary. Such a modality may typically exist with or without the characteristic(s) mentioned; likewise, these characteristics may be applied to other modalities of the subject matter presently disclosed, whether listed in this summary or not. For Petition 870210067187, dated 07 / 23 / 2021, p. 7 / 110 4 / 91 To avoid excessive repetition, this summary does not list or suggest all possible combinations of such characteristics.

[0011] In some embodiments, the presently disclosed individual provides methods for increasing genetic gain in a breeding population. In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce a segregating offspring population; (c) inferring or determining the haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) stimulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, wherein the genetic potential value of the offspring generation is the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e); and (h) select one or more breeding pairs based on the ranking from step (d); Petition 870210067187, dated 07 / 23 / 2021, page 8 / 110 5 / 91 (g), where it is predicted that crossing the breeding pair selected in step (g) will generate offspring with greater genetic gain. In some modalities, the methods currently disclosed also include repeating steps (b)-(e) and (g), so that at least one average performance value calculated in step (e) exceeds a predetermined value.

[0012] The presently disclosed subject matter also provides, in some embodiments, methods for selecting breeding pairs predicted to produce offspring with desired phenotypes. In some embodiments, the methods comprise (a) estimating effects with respect to a trait of interest from a plurality of genomically broad markers in a biparental breeding population comprising a plurality of potential breeding partners; (b) selecting a first and a second breeding partner from the biparental breeding population wherein the haplotype of each of the first and second breeding partners is known or predictable with respect to the plurality of genetic markers; (c) inferring or determining haplotypes with respect to the plurality of genomically broad markers for the first and second breeding partners;(d) stimulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, wherein the genetic potential value of the offspring generation is the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different; Petition 870210067187, dated 07 / 23 / 2021, page 9 / 110 6 / 91 second breeding partner or both from the breeding population; classification (g) classify each simulated cross from step (d) based on the potential genetic values ​​calculated in step (e); and (h) select one or more breeding pairs based on the classification from step (g), where the breeding pair is predicted to produce offspring with the desired phenotype.

[0013] The subject matter now disclosed also provides, in some embodiments, methods for increasing the probability of producing offspring with the desired phenotypes. In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce a segregating offspring population; (c) inferring the haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) stimulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, where the genetic potential value of the offspring generation can be calculated as the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member, or it can be calculated based on the right tail or the left tail of the distribution of genomic breeding values; (f) repeat the steps; Petition 870210067187, dated 07 / 23 / 2021, page 10 / 110 7 / 91 (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) ranking each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e); and (h) selecting one or more breeding pairs based on the ranking from step (g), wherein each or more breeding pairs is predicted to have a higher probability of producing offspring with the desired phenotype versus other breeding pairs in the breeding population.

[0014] The presently disclosed subject matter also provides methods for generating offspring individuals with desired genotypes. In some embodiments, the methods comprise (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce a segregating offspring population; (c) inferring haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) stimulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, where the genetic potential value of the offspring generation is the average of the genomic reproduction values ​​of the simulated member genotypes; Petition 870210067187, dated 07 / 23 / 2021, page 11 / 110 8 / 91 of the offspring generation; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e); (h) select one or more breeding pairs based on the ranking from step (g); and (i) breed the one or more breeding pairs selected in step (h) to generate an offspring individual with the desired genotype.

[0015] The presently disclosed subject also provides methods for generating offspring individuals with desired genotypes. In some embodiments, the methods comprise (a) estimating the effects with respect to a trait of interest of a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce a segregating offspring population; (c) inferring the haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, where the genetic potential value of the offspring generation is the average of the genomic reproduction values ​​of the simulated member genotypes; Petition 870210067187, dated 07 / 23 / 2021, page 12 / 110 9 / 91 of the offspring generation; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e); (h) select one or more breeding pairs based on the ranking from step (g); and (i) breed the one or more breeding pairs selected in step (h) to generate an offspring individual with the desired genotype.

[0016] In some embodiments of the methods presently disclosed, each breeding partner is a plant. In some embodiments, the plant is selected from the group consisting of maize, wheat, barley, rice, beet, sunflower, winter rapeseed, canola, tomato, pepper, melon, watermelon, broccoli, cauliflower, Brussels sprouts, lettuce, spinach, sugarcane, coffee, cocoa, pine, poplar, eucalyptus, apple, and grape. In some embodiments, the plant is maize.

[0017] In some embodiments of the methods presently disclosed, each breeding partner is an isogenic individual.

[0018] In some forms of the methods currently disclosed, the breeding partners are the same individual.

[0019] In some embodiments of the methods currently disclosed, one or more genetic markers are selected from the group consisting of a single nucleotide polymorphism (SNP), an insertion / deletion (indel), a simple sequence repeat (SSR), a restriction fragment length polymorphism (RFLP), a random amplified DNA polymorphic marker (RAPD), a cleaved amplified sequence polymorphic marker (CAPS), a Diversity Matrix Technology (DArT) marker, an amplified fragment length polymorphism (AFLP), and combinations thereof. Petition 870210067187, dated 07 / 23 / 2021, page 13 / 110 10 / 91

[0020] In some embodiments of the methods currently disclosed, one or more genetic markers comprise at least one marker present in each of 5 cM, 3 cM, 2 cM, 1 cM, 0.5 cM or 0.25 cM in the genomes of the breeding partners.

[0021] In some embodiments of the methods currently disclosed, the inference step, the simulation step, the calculation step, or combinations thereof, include consideration of the expected recombination rates between adjacent broad genomic markers. In some embodiments, the recombination rate between at least one of one or more genetic markers and the genetic locus associated with the desired phenotype is zero.

[0022] In some embodiments of the methods currently disclosed, the inference step, the simulation step, or both are performed by a suitably programmed computer.

[0023] In some embodiments of the methods currently disclosed, the simulation stage comprises simulating at least 100, 500 or 1000 offspring in the generation of offspring.

[0024] In some embodiments of the methods presently disclosed, the estimation comprises estimating the effects on the desired phenotype of genomic-wide plurality markers based on best linear one-sided phenotypic predictions (BLUPs) and genotypic marker data in the biparental breeding population using best linear one-sided genomic-wide prediction (GBLUP). In some embodiments, the estimation comprises estimating genetic variation by retained maximum likelihood (REML) estimation based on phenotypic data from multiple locations using Equation (1), as defined in this document.

[0025] In some embodiments of the methods currently disclosed, the inference involves using a minimum recombination principle (MRP). Petition 870210067187, dated 07 / 23 / 2021, page 14 / 110 11 / 91

[0026] In some embodiments of the methods currently disclosed, the breeding population consists of n members and the replication comprises simulating all n(n-1) / 2 unique matings of the members of the breeding population.

[0027] In some embodiments of the methods now disclosed, the feature of interest comprises at least two independent features of interest. In some embodiments, the methods now disclosed further comprise assigning to each independent feature of interest an importance value relative to the other independent features.

[0028] In some embodiments of the methods currently disclosed, the selection of one or more breeding pairs based on the classification of stage (g) comprises selecting the breeding pairs for which the genetic potential values ​​of the offspring generations are classified in the 20%, 10%, 5% or 1% highest.

[0029] The subject matter now disclosed also provides, in some embodiments, offspring generated by the methods now disclosed. In some embodiments, the offspring is a plant.

[0030] The subject matter now disclosed also provides, in some embodiments, plant cells generated by the methods now disclosed, including, but not limited to, their seeds and / or their offspring.

[0031] Thus, it is an object of the subject presently disclosed to provide methods for increasing genetic gain in breeding populations.

[0032] An object of the subject matter presently disclosed, set forth above in this document and which is wholly or partially encompassed by the subject matter presently disclosed, other objects will become apparent as the description goes on, when considered Petition 870210067187, dated 07 / 23 / 2021, page 15 / 110 12 / 91 together with the attached figures described in more detail below. BRIEF DESCRIPTION OF THE FIGURES

[0033] Figure 1 is a comparison of conventional breeding methods with an exemplary method of the subject currently disclosed named the Genome-Wide Selection Simulated Marker-Assisted Recurrent Progeny-Wide Selection (GWS-SMART). A first parent (P1) and a second parent (P2) are generated to create a first generation (F1), which is used to create a breeding population. In the conventional breeding method represented on the left side of Figure 1, marker-assisted selection (MAS) and / or genome-wide selection (GWS) are employed in an attempt to identify members of the breeding population for further breeding. In subsequent breeding generations, however, the selected lines (superior lines) are chosen based only on phenotypic values ​​and / or estimated breeding values ​​calculated from the MAS and / or GWS approaches.

[0034] In the GWS-SMART approach depicted on the right side of Figure 1, virtual populations are simulated to evaluate the genetic merit of possible crosses of members of the breeding population (in some modalities, each possible cross is simulated), and the simulated genotypes based on all markers used in the breeding population are used to inversely evaluate the relative merits of individual crosses of the breeding population. Based on this inverse evaluation, particularly desirable crosses (superior crosses) can be selected for further breeding and / or superior lines can be selected for further intercrossing.

[0035] Figure 2 is the simulation scheme used for comparison MAS, GWS, and GWS-SMART are described in EXAMPLE 1. Petition 870210067187, dated 07 / 23 / 2021, page 16 / 110 13 / 91

[0036] Figure 3 is an exemplary scheme for use GWS-SMART in plant reproduction as described in EXAMPLE 1.

[0037] Figure 4 is a comparison of exemplary genetic gains due to GWS-SMART, basic genome-wide selection (GWS), and marker-assisted selection (MAS), described in EXAMPLE 1. DETAILED DESCRIPTION Definitions

[0038] Although the following terms are considered to be well understood by a person skilled in the art, the following definitions are established to facilitate the explanation of the subject matter now disclosed.

[0039] All technical and scientific terms used in this document, unless otherwise defined below, are intended to have the same meaning as generally understood by a person skilled in the art. References to techniques employed in this document are intended to refer to techniques as commonly understood in the art, including variations on those techniques and / or substitutions for equivalent techniques that would be evident to a person skilled in the art.

[0040] In accordance with established patent law convention, the terms "a," "an," and "the" refer to one or more when used in this application, including in the claims. For example, the phrase "a marker" refers to one or more markers. Similarly, the phrase "at least one," when used in this document, refers to an entity, and refers to, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100 or more of that entity, including, but not limited to, integer values ​​between 1 and 100, and greater than 100. Similarly, the term "plurality" refers to at least two and thus refers, for example, to 2, Petition 870210067187, dated 07 / 23 / 2021, p. 17 / 110 14 / 91 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, or more than that entity, including, but not limited to, integer values ​​between 1 and 100, or greater than 100.

[0041] For use in the present invention, the term and / or, when used in the context of a list of entities, refers to entities that are present individually or in combination. Thus, for example, the sentence A, B, C and / or D includes A, B, C and D individually, but also includes any and all combinations and subcombinations of A, B, C and D.

[0042] Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, etc. used in the descriptive report and claims shall be understood as being modified in all instances by the term approximately. The term approximately, as used in this document, when referring to a measurable value, such as a quantity of mass, weight, time, volume, concentration or percentage, shall encompass variations of ±20% in some embodiments, ±10% in some embodiments, ±5% in some embodiments, ±1% in some embodiments, ±0.5% in some embodiments and ±0.1% in some embodiments of the specified value, as such variations are appropriate for performing the disclosed methods.In this sense, unless otherwise indicated, the numerical parameters set forth in this descriptive report and in the attached claims are approximations that may vary depending on the desired properties to be obtained from the subject matter disclosed herein.

[0043] As used in this document, the term allele refers to a variant or alternative sequence form at a genetic locus. In diploids, unique isolates are inherited by an individual offspring separately from each parent at each locus. The two alleles of a given locus present at a Petition 870210067187, dated 07 / 23 / 2021, page 18 / 110 15 / 91 diploid organisms occupy corresponding places on a pair of homologous chromosomes, although a person normally versed in the technique understands that the alleles in any particular individual do not necessarily represent all the alleles that are present in the species.

[0044] As used in this document, the phrase associated with refers to a recognizable and / or analyzable relationship between the two entities. For example, the phrase associated with a trait refers to a locus, gene, allele, marker, phenotype, etc., or the expression thereof, the presence or absence of which may influence a measure, degree, and / or rate at which the trait is expressed in an individual or a plurality of individuals.

[0045] As used in this document, the term backcrossing, and grammatical variations thereof, refers to a process in which a farmer crosses an offspring individual with one of its parents; for example, a first F1 generation with one of its parent genotypes of the F1 individual. In some embodiments, a backcross is performed iteratively, with an offspring individual from each successive reverse cross generation being itself backcrossed with the same genotype as the parent.

[0046] As used in this document, the term breeding population refers to a group of individuals from which breeding individuals and potential mates are selected. In some embodiments, a breeding population is a segregating population.

[0047] As used in this document, the term chromosome is used in its technically recognized sense, meaning a self-replicating genetic structure containing genomic DNA and having, in its nucleotide sequence, a linear array of genes. Petition 870210067187, dated 07 / 23 / 2021, page 19 / 110 16 / 91

[0048] As used in this document, the terms cultivar and variety refer to a group of similar plants that, due to structural and / or genetic characteristics and / or performance, can be distinguished from other members of the same species.

[0049] As used in this document, the term elite lineage refers to any lineage that is substantially homozygous and that has resulted from breeding and selection for superior agronomic performance.

[0050] As used in this document, the term gene refers to a hereditary unit, including a sequence of DNA that occupies a specific location on a chromosome and that contains the genetic instructions for a particular characteristic or aspect in an organism.

[0051] As used in this document, the phrase genetic gain refers to an amount of performance increase that is achieved through artificial genetic improvement programs. In some embodiments, genetic gain refers to an increase in performance that is achieved after one generation (see Allard, 1960).

[0052] As used in this document, the phrase genetic map refers to an ordered list of loci generally related to the relative positions of the loci on a particular chromosome.

[0053] As used in this document, the term genetic marker refers to a nucleic acid sequence (e.g., a polymorphic nucleic acid sequence) that has been identified as being associated with a trait locus and / or allele of interest and that is indicative of and / or can be used to verify the presence or absence of the trait, locus, and / or allele of interest in a cell or organism. Examples of genetic markers Petition 870210067187, dated 07 / 23 / 2021, p. 20 / 110 17 / 91 includes, but is not limited to, genes, DNA- or RNA-derived sequences (e.g., chromosomal subsequences that are specific to specific sites on a given chromosome), promoters, any untranslated regions of a gene, microRNAs, short inhibitory RNAs (siRNAs; also called small inhibitory RNAs), quantitative feature sites (QTLs), transgenes, mRNAs, double-stranded RNAs, transcription profiles, and methylation patterns.

[0054] As used in this document, the phrase genome-wide selection (GWS) refers to methods for increasing the genetic gain of a species that utilize markers located throughout the species' genome to predict genomic breeding values ​​(GBVs) of individuals. Unlike methods such as marker-assisted selection (MAS), GWS does not rely on the use of markers that have been previously identified as being linked to loci (e.g., QTLs) associated with any given trait of interest. Instead, each marker is generally considered a putative QTL, and all markers are combined to predict genomic breeding values ​​(GBVs) of offspring using the GWS method.

[0055] As used in this document, the term genotype refers to the genetic makeup of an organism. The expression of a genotype can give rise to a phenotype (i.e., observable characteristics) of an organism. An individual's genotype, compared to a reference genotype or the genotype of one or more other individuals, can provide valuable information related to current or predictive phenotypes. The term genotype therefore refers to the genetic component of a phenotype of interest, a plurality of phenotypes of interest, and / or an entire cell or organism. Genotypes can be characterized indirectly Petition 870210067187, dated 07 / 23 / 2021, p. 21 / 110 18 / 91 using markers and / or characterized directly by nucleic acid sequencing.

[0056] As used in this document, the phrase determining an individual's genotype refers to the determination of at least part of an individual's genetic makeup and, in particular, may refer to the determination of genetic variability in an individual that can be used as an indicator or predictor of a corresponding phenotype. The determined genotype may, in some embodiments, be the entire genomic sequence of an individual, but generally, much less sequence information is usually considered. The determined genotype may be as minimal as the determination of a single base pair, as in the identification of one or more polymorphisms in the individual.

[0057] Furthermore, determining a genotype may involve determining one or more haplotypes. Additionally, determining an individual's genotype may involve determining one or more polymorphisms exhibiting linkage disequilibrium with at least one polymorphism or haplotype having genotypic value. As used in this document, the terms genotypic value and genomic reproduction value (GBV) refer to a measurable degree to which one or more haplotypes and / or genotypes affect the expression of a phenotype associated with a trait, and this can be considered as a contribution of the haplotype(s) and / or genotype(s) to a trait. In some embodiments, GBV can be calculated by regression of a phenotype onto haplotypes.

[0058] In some embodiments, determining an individual's genotype may involve identifying at least one polymorphism of at least one gene and / or at a locus. In some embodiments, determining an individual's genotype may involve identifying at least one haplotype of at least one gene and / or at least one Petition 870210067187, dated 07 / 23 / 2021, p. 22 / 110 19 / 91 locus. In some modalities, determining an individual's genotype may involve identifying at least one unique polymorphism for at least one haplotype of at least one gene and / or at least one locus.

[0059] As used in this document, haplotype refers to the collective characteristic or characteristics of a number of closely linked loci within a given gene or group of genes, which can be inherited as a unit. For example, in some embodiments, a haplotype may comprise a group of closely related polymorphisms (e.g., single nucleotide polymorphisms; SNPs). In some embodiments, a haplotype is a characterization of a plurality of loci on a single chromosome (or a region thereof) of a pair of homologous chromosomes, wherein the characterization is indicative of which loci and / or alleles are present on the single chromosome (or region thereof).

[0060] As used in this document, linkage disequilibrium (LD) refers to a statistical measure derived from the strength of the association or co-occurrence of two distinct genetic markers.Several statistical methods can be used to summarize the LD between two markers, but in practice, only two, termed D' and r2, are widely used (see, for example, Devlin and Risch 1995; Jorde, 2000).

[0061] As such, the phrase linkage disequilibrium refers to a change in the expected relative frequency of gamete types in a population of many individuals in a single generation, such that two or more loci act as genetically linked loci. If the frequency in a population of allele S is x, that of allele s is x', that of allele B is y' and that of allele b is y', then the expected frequency of genotype SB is xy, that of SB is x'y and that of sb is x'y', and any deviation from these frequencies in the population is an example of linkage disequilibrium. Petition 870210067187, dated 07 / 23 / 2021, p. 23 / 110 20 / 91

[0062] As used in this document, the term heterozygous refers to a genetic condition that exists in a cell or organism when different alleles reside at corresponding locations on homologous chromosomes. As used in this document, the term homozygous refers to a genetic condition that exists when identical alleles reside at corresponding locations on homologous chromosomes. It is noted that both of these terms can refer to single nucleotide positions, multiple nucleotide positions (contiguous or not), and / or entire locations on homologous chromosomes.

[0063] As used in this document, the term hybrid, when used in the context of a plant, refers to a seed and the plant from which the seed develops that results from the crossing of at least two genetically different plant parents.

[0064] As used in this document, the term hybrid, when used in the context of nucleic acids, refers to a double-helix (or higher-order) nucleic acid molecule (a duplex) formed by hydrogen bonding between complementary nucleotide bases. The terms hybridization and annealing refer to the process by which single strands of nucleic acid sequences form double-helix (and higher-order) segments through hydrogen bonding between complementary bases.

[0065] As used in this document, in the context of a plant, the terms improved and superior, and grammatical variants, refer to a plant (or a part, offspring or tissue culture thereof) that, as a consequence of having (or not having) a particular allele of interest, expresses a phenotype of interest or expresses a phenotype of interest to a greater or lesser degree (as desired) in relation to another plant (or a part, offspring or tissue culture thereof) that does not have (or has) the particular allele of interest. Petition 870210067187, dated 07 / 23 / 2021, p. 24 / 110 21 / 91

[0066] As used in this document, the term isogeny refers to an individual or lineage that is substantially or completely homozygous. It should be noted that the term may refer to individuals or lineages that are substantially or completely homozygous across their entire genomes, or that are substantially or completely homozygous with respect to subsequences of their genomes that are of particular interest.

[0067] As used in this document, the term introgress and grammatical variants thereof (including, but not limited to, introgression, introgressed, and introgressing) refers to natural and artificial processes by which one or more genomic regions of an individual are moved into the genome of another individual to create germplasm that has a new combination of genetic loci, haplotypes, and / or alleles. Exemplary methods for introgressing a trait of interest include, but are not limited to, breeding an individual that has the trait of interest with an individual that does not have it, and backcrossing an individual that has the trait of interest with a recurrent parent.

[0068] As used in this document, the term isolate refers to a nucleotide sequence (e.g., a genetic marker) that is free of sequences that normally flank one or both sides of the nucleotide sequence in a genome. As such, the phrase isolated and purified genetic marker may be, for example, a recombinant DNA molecule, provided that one of the nucleic acid sequences normally found flanking the genetic marker in a naturally occurring genome is removed or absent. Thus, isolated nucleic acids include, without limitation, recombinant DNA that exists as a separate molecule (including, but not limited to, genomic DNA fragments produced by polymerase chain reaction (PCR) or Petition 870210067187, dated 07 / 23 / 2021, page 25 / 110 22 / 91 restriction endonuclease treatment) with less than the complete entirety of its naturally occurring flanking sequences present, as well as recombinant DNA that is incorporated into a vector, a self-replicating plasmid, and / or into an individual's genomic DNA as part of a hybrid or fusion nucleic acid molecule.

[0069] As used in this document, the term linkage refers to a phenomenon in which alleles on the same chromosome tend to be transmitted together more frequently than would be expected by chance if their transmission were independent.Thus, two alleles on the same chromosome are considered linked when they diverge from each other in the next generation in some modes, less than 50% of the time, in some modes less than 25% of the time, in some modes less than 20% of the time, in some modes less than 15% of the time, in some modes less than 10% of the time, in some modes less than 9% of the time, in some modes less than 8% of the time, in some modes less than 7% of the time, in some modes less than 6% of the time, in some modes less than 5% of the time, in some modes less than 4% of the time, in some modes less than 3% of the time, in some modes less than 2% of the time, in some modes less than 1% of the time; in some modes less than 0.5% of the time, and in some modes less than 0.1% of the time.

[0070] As such, linkage normally implies and can also refer to physical proximity on a chromosome. Thus, two sites are linked if they are comprised, in some embodiments, within 20 centimorgans (cM), in some embodiments 15 cM, in some embodiments 12 cM, in some embodiments 10 cM, Petition 870210067187, dated 07 / 23 / 2021, p. 26 / 110 23 / 91 in some modalities 9 cM, in some modalities 8 cM, in some modalities 7 cM, in some modalities 6 cM, in some modalities 5 cM, in some modalities 4 cM, in some modalities 3 cM, in some modalities 2 cM, in some modalities 1 cM from each other, in some modalities 0.5 cM from each other, and in some modalities 0.1 cM from each other. Similarly, a locus of the currently disclosed subject matter is linked to a marker (e.g., a genetic marker) if it is, in some modalities, within 20, 15, 12, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.5, or 0.1 cM of the marker.

[0071] As used in this document, the phrase linkage group refers to all genes or genetic features that are located on the same chromosome. Within a linkage group, those loci that are sufficiently close physically may exhibit linkage in genetic crossovers. Since the probability of a crossover between two loci increases with the physical distance between the two loci on a chromosome, loci that are removed far from each other in a linkage group may not exhibit any detectable linkage in direct genetic testing. The term linkage group is primarily used to refer to genetic loci that exhibit linkage behavior in genetic systems where chromosome assignments have not yet been made.Thus, in the present context, the term linkage group is synonymous with the physical entity of a chromosome, although a person versed in the technique will understand that a linkage group can also be defined as corresponding to a region (i.e., less than the entirety) of a given chromosome.

[0072] As used in this document, the term locus refers to a position on a chromosome of a species, and which may encompass, in some forms, a single nucleotide, in Petition 870210067187, dated 07 / 23 / 2021, p. 27 / 110 24 / 91 in some forms, multiple nucleotides, and in some forms, more than multiple nucleotides in a given genomic region. In some forms, the terms locus and gene are used interchangeably.

[0073] As used in this document, the terms marker and molecular marker are used interchangeably to refer to an identifiable position on a chromosome, the inheritance of which can be monitored, and / or a reagent that is used in methods to visualize differences in nucleic acid sequences present at such identifiable positions on chromosomes. Thus, in some embodiments, a marker comprises a known or detectable nucleic acid sequence.Examples of markers include, but are not limited to, genetic markers, protein composition, peptide levels, protein levels, oil composition, oil levels, carbohydrate composition, carbohydrate levels, fatty acid composition, fatty acid levels, amino acid composition, amino acid levels, biopolymers, starch composition, starch levels, fermentable starch, fermentation yield, fermentation efficiency, energy yield, secondary compounds, metabolites, morphological characteristics, and agronomic characteristics.Molecular markers include, but are not limited to, restriction fragment length polymorphisms (RFLPs), amplified random polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLPs), single-strand conformation polymorphisms (SSCPs), single nucleotide polymorphisms (SNPs), insertion / deletion mutations (indels), single sequence repeats (SSRs), microsatellite repeats, amplified sequence characterization regions (SCARs), amplified cleaved sequence polymorphic markers (CAPS), and isozyme markers, technologies based on... Petition 870210067187, dated 07 / 23 / 2021, page 28 / 110 25 / 91 microarray, TAQMAN® markers, ILLUMINA® GOLDENGATE® assay markers, nucleic acid sequences or combinations of the markers described in this document, which can be used to define a specific genetic and / or chromosomal location.

[0074] In some embodiments, a marker corresponds to an amplification product generated by the amplification of a nucleic acid with one or more oligonucleotides, for example, by the polymerase chain reaction (PCR). As used in this document, the phrase "corresponds to an amplification product," in the context of a marker, refers to a marker that has a nucleotide sequence that is equal to or the inverse complement of (allowing for mutations introduced by the amplification reaction itself and / or naturally occurring and / or artificial allelic differences) an amplification product that is generated by the amplification of a nucleic acid with a given set of oligonucleotides.In some embodiments, amplification is by PCR, and the oligonucleotides are PCR primers that are designed to hybridize with opposite strands of a genomic DNA molecule to amplify a genomic DNA sequence present between the sequences to which the PCR primers hybridize in the genomic DNA. The amplified fragment resulting from one or more amplification cycles using such an arrangement of primers is a double-stranded nucleic acid, one strand of which has a nucleotide sequence comprising, in the order 5' to 3', the sequence of one of the primers, the sequence of the genomic DNA located between the primers, and the reverse complement of the second primer. Typically, the normal primer is assigned to the primer that has the same sequence as a subsequence of the (arbitrarily assigned) top strand of a double-stranded nucleic acid to be amplified, so that the top strand of the amplified fragment... Petition 870210067187, dated 07 / 23 / 2021, page 29 / 110 26 / 91 includes a nucleotide sequence that, in the 5' to 3' direction, is identical to the normal primer sequence—the sequence located between the normal and reverse primers of the top strand of the genomic fragment—the reverse complement of the reverse primer. Therefore, a marker that matches an amplified fragment is a marker that has the same sequence as one of the strands of the amplified fragment.

[0075] As used in this document, the term marker assay refers to a method for detecting a polymorphism at a specific location using a particular method, such as, but not limited to, measuring at least one phenotype (e.g., seed color, oil content, or a visually detectable characteristic such as corn and soybean grain yield, plant height, flowering time, lodging rate, disease resistance, aluminum tolerance, iron deficiency chlorosis tolerance, and grain moisture); nucleic acid-based assays including, but not limited to, restriction fragment length polymorphism (RFLP), single base extension, electrophoresis, sequence alignment, allele-specific oligonucleotide hybridization (ASO), randomly amplified polymorphic DNA (RAPD), microarray-based technologies, TAQMAN® assays, ILLUMINA® GOLDENGATE® assay analysis, nucleic acid sequencing technologies;Peptide and / or polypeptide analyses; or any other technique that can be used to detect a polymorphism in an organism at a location of interest.

[0076] As used in this document, the term native characteristic refers to any monogenic or polygenic characteristic existing in a given germplasm of an individual. When identified through the use of molecular marker(s), the information obtained can be used for germplasm improvement through selective breeding of the predicted populations. Petition 870210067187, dated 07 / 23 / 2021, page 30 / 110 27 / 91 as disclosed in this document.

[0077] As used in this document, the phrase nucleotide sequence identity refers to the presence of identical nucleotides at corresponding positions in the two polynucleotides. Polynucleotides have identical sequences if the nucleotide sequence in the two polynucleotides is the same when aligned for maximum correspondence. Sequence comparison between two or more polynucleotides is generally performed by comparing portions of the two sequences within a comparison window to identify and compare local regions of sequence similarity, the comparison window generally being about 20 to 200 contiguous nucleotides.The percentage of sequence identity for polynucleotides, such as 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 98, 99, or 100% sequence identity, can be determined by comparing two optimally aligned sequences in a comparison window, where the portion of the polynucleotide sequence in the comparison window may include additions or deletions (i.e., gaps) compared to the reference sequence for optimal alignment of the two sequences.

[0078] The percentage can be calculated by any method generally applicable in the field of molecular biology. In some embodiments, the percentage is calculated by: (a) determining the number of positions where the identical nucleic acid base occurs in both sequences to the number of matched positions; (b) dividing the number of matched positions by the total number of positions in the comparison window; and (c) multiplying the result by 100 to determine the percentage of sequence identity. Optimal sequence alignment for comparison can also be performed by computerized implementations of known algorithms, or by visual inspection. The comparison algorithms of Petition 870210067187, dated 07 / 23 / 2021, page 31 / 110 28 / 91 sequence and multiple sequence alignment software available are, respectively, the Basic Local Alignment Search Tool (BLAST; Altschul et al., 1990; Altschul et al., 1997) and ClustalW (Larkin et al., 2007), both available online. Other suitable programs include, but are not limited to, GAP, BestFit, Plot Similarity, and FASTA, which are part of the Wisconsin Accelrys GCG® package, available from Accelrys, Inc. of San Diego, California, United States of America. In some embodiments, a percentage of sequence identity refers to sequence identity relative to the total length of one of the sequences being compared. In some embodiments, a calculation to determine a percentage of sequence identity does not include, in the calculation, any nucleotide positions where any of the compared nucleic acids includes an n (i.e., where any nucleotide could be present at that position).

[0079] The term phenotype refers to any observable property of an organism, produced by the interaction of the organism's genotype and the environment. A phenotype can encompass variable expressivity and penetrance of the phenotype. Exemplary phenotypes include, but are not limited to, a visible phenotype, a physiological phenotype, a susceptibility phenotype, a cellular phenotype, a molecular phenotype, and combinations thereof.

[0080] As used in this document, the phrase phenotypic marker refers to a marker that can be used to discriminate between different phenotypes.

[0081] As used in this document, the term plant refers to an entire plant, its organs (i.e., leaves, stems, roots, flowers, etc.), seeds, plant cells, and their offspring. The term plant cell includes, without limitation, cells within seeds, suspension cultures, embryos, meristematic regions, callus tissue, Petition 870210067187, dated 07 / 23 / 2021, page 32 / 110 29 / 91 leaves, buds, gametophytes, sporophytes, pollen, and microspores. The phrase plant part refers to a part of a plant, including single cells and cell tissues, such as plant cells, that are intact in plants, cell clusters, and tissue cultures, from which plants can be regenerated. Examples of plant parts include, but are not limited to, individual cells and tissues of pollen, ovules, leaves, embryos, roots, root tips, anthers, flowers, fruits, stems, buds, and seeds; as well as implants, rhizomes, protoplasts, stalks, and the like.

[0082] As used in this document, the term polymorphism refers to the presence of one or more variations of a nucleic acid sequence at a location in a population of one or more individuals. The sequence variation may be a base or bases that are different, inserted, or deleted. Polymorphisms can be, for example, single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs), and indels, which are insertions and deletions. Furthermore, the variation may be in a transcriptional profile or in a methylation pattern. The polymorphic sites of a nucleic acid sequence can be determined by comparing the nucleic acid sequences at one or more locations in two or more germplasm entries. As such, in some embodiments, the term polymorphism refers to the occurrence of two or more genetically determined alternative variant sequences (i.e., alleles) in a population. A polymorphic marker is the location where the divergence occurs.Exemplary markers have at least two (or, in some forms, more) alleles, each occurring at a frequency greater than 1%. A polymorphic site can be as small as a base pair (e.g., a single nucleotide polymorphism; SNP).

[0083] As used in this document, the term population Petition 870210067187, dated 07 / 23 / 2021, page 33 / 110 30 / 91 refers to a genetically heterogeneous collection of plants that, in some forms, share a common genetic origin.

[0084] As used in this document, the term primer refers to an oligonucleotide that is capable of annealing to a target nucleic acid (in some embodiments, annealing specifically to a target nucleic acid), allowing a DNA polymerase to bind, thus serving as a starting point for DNA synthesis when placed under conditions in which synthesis of the primer extension product is induced (e.g., in the presence of nucleotides and a polymerization agent, such as DNA polymerase, and at a suitable temperature and pH). In some embodiments, a plurality of primers is used to amplify nucleic acids (e.g., using polymerase chain reaction; PCR).

[0085] As used in this document, the term probe refers to a nucleic acid (e.g., a single-stranded nucleic acid or a strand of a double-stranded or higher-order nucleic acid) that can form a hydrogen-bonded duplex with a complementary sequence in a target nucleic acid sequence. Typically, a probe has sufficient length to form a stable, sequence-specific duplex molecule, and as such, it can be employed in some embodiments to detect a sequence of interest present in a plurality of nucleic acids.

[0086] As used in this document, the term offspring refers to any plant that results from natural or assisted reproduction of one or more plants. For example, offspring plants may be generated by crossing two plants (including, but not limited to, crossing two plants). Petition 870210067187, dated 07 / 23 / 2021, p. 34 / 110 31 / 91 dissociated, backcrossing of a plant to a parent plant, intercrossing of two plants, etc.), but they can also be generated by self-pollination of a plant, creating a double haploid, or other techniques that would be known to a person normally versed in the art. As such, a descendant plant can be any plant resulting as offspring from vegetative or sexual reproduction of one or more parent plants or their descendants. For example, a descendant plant can be obtained by cloning or self-pollination of a parent plant or by crossing two parent plants, and includes self-pollinating generations as well as F1 or F2 generations, or even later generations.An F1 generation is a first generation of offspring produced from the parents, at least one of which is being used for the first time as a donor of a trait, while the offspring of the second generation (F2) or subsequent generations (F3, F4 and so on) are, in some embodiments, specimens produced from self-pollination (including, but not limited to, double haploidization), intercrossing, backcrossing or other crosses of F1 individuals, F2 individuals and so on. An F1 generation can, in this way, be (and, in some embodiments, is) a hybrid resulting from a cross between two true breeding parents (i.e., parents that are true breeding are, individually, homozygous for a trait of interest or an allele thereof and, in some embodiments, are isogenic), while an F2 generation can be (and, in some embodiments, is) offspring resulting from the self-pollination of the F1 hybrids.

[0087] As used in this document, the phrase quantitative trait locus (QTLs) refers to a genetic locus or loci that control, to some extent, a particular trait. Petition 870210067187, dated 07 / 23 / 2021, p. 35 / 110 32 / 91 degree, a numerically representable characteristic that, in some modalities, is continuously distributed. When a QTL can be indicated by multiple markers, the genetic distance between the endpoint markers is indicative of the QTL size.

[0088] As used in this document, the term recombination refers to an exchange of DNA fragments between two DNA molecules or chromatids of paired chromosomes (a crossover) over a region of similar or identical nucleotide sequences. A recombination event is understood here to refer to a meiotic crossover.

[0089] As used in this document, the phrases selected allele, desired allele, and allele of interest are used interchangeably to refer to a nucleic acid sequence that includes a polymorphic allele associated with a desired trait. Note that a selected allele, desired allele, and / or allele of interest may be associated with an increase in a desired trait or a decrease in a desired trait, depending on the nature of the phenotype that one wishes to generate in an introgressed plant.

[0090] As used in this document, the phrase significant QTL markers refers to QTL markers that are characterized by a statistical test LOD that is greater than the estimated empirical threshold LOD of 5000 permutations (see Churchill and Doerge, 1994).

[0091] As used in this document, the phrase single nucleotide polymorphism or SNP refers to a polymorphism that constitutes a single base pair difference between two nucleotide sequences. As used in this document, the term SNP also refers to differences between two nucleotide sequences that result from simple one-series changes in Petition 870210067187, dated 07 / 23 / 2021, p. 36 / 110 33 / 91 view of the other that occur at a single site in the sequence. For example, the term SNP is intended to refer not only to sequences that differ by a single nucleotide as a result of a nucleic acid substitution in one relative to the other, but also to sequences that differ by 1, 2, 3 or more nucleotides as a result of a deletion of 1, 2, 3 or more nucleotides at a single site in one of the sequences compared to the other. It could be understood that, in the case of two sequences that differ from each other only by virtue of a deletion of 1, 2, 3 or more nucleotides at a single site in one of the sequences compared to the other, this same scenario could be considered an addition of 1, 2, 3 or more nucleotides at a single site in one of the sequences compared to the other, depending on which of the two sequences is considered the reference sequence.Single site insertions and / or deletions are therefore also considered to be encompassed by the term SNP.

[0092] As used in this document, the phrase strict hybridization conditions refers to the conditions under which a polynucleotide hybridizes to its target subsequence, typically to a complex mixture of nucleic acids, but essentially to no other sequence. Strict conditions are sequence-dependent and may differ under different circumstances.

[0093] Longer sequences typically hybridize specifically at higher temperatures. An extensive guide to nucleic acid hybridization is found in Tijssen, 1993. Generally, stringent conditions are selected, being about 5-10 °C below the thermal melting point (Tm) for the specific sequence at a defined ionic strength pH. Tm is the temperature (under defined ionic strength, pH, and nucleic acid concentration) at Petition 870210067187, dated 07 / 23 / 2021, p. 37 / 110 34 / 91 that 50% of the target complementary probes hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Exemplary stringent conditions are those in which the salt concentration is less than about 1.0 M sodium ions, typically about 0.01 to 1.0 M sodium ions (or other salts) at pH 7.0 to 8.3, and the temperature is at least about 30 °C for short probes (e.g., 10 to 50 nucleotides) and at least about 60 °C for long probes (e.g., more than 50 nucleotides).

[0094] Strict conditions can also be achieved with the addition of destabilizing agents, such as formamide. Additional exemplary strict hybridization conditions include 50% formamide, 5x SSC, and 1% SDS incubating at 42 °C; or SSC 1% SDS, incubating at 65 °C; with one or more washes in 0.2x SSC and 0.1% SDS at 65 °C. For PCR, a temperature of about 36 °C is typical for low-rigor amplification, although annealing temperatures can range between about 32 °C and 48 °C (or higher) depending on primer length. Further guidance for determining hybridization parameters is provided in various references (see, for example, Ausubel et al., 1999).

[0095] As used in this document, the phrase Essay TAQMAN® refers to the detection of sequences in real time using PCR based on the TAQMAN® assay sold by Applied Biosystems, Inc. of Foster City, California, United States of America. For an identified marker, a TAQMAN® assay can be developed for application in the breeding program.

[0096] As used in this document, the term tester refers to a lineage used in a test cross with one or more other lineages, wherein the tester and the lineage(s) Petition 870210067187, dated 07 / 23 / 2021, p. 38 / 110 35 / 91 tested is / are genetically different. A tester may be an isogenic lineage to the cross lineage.

[0097] As used in this document, the terms trait and trait of interest refer to a phenotype of interest, a gene that contributes to a phenotype of interest, and a nucleic acid sequence associated with a gene that contributes to a phenotype of interest. Any trait that would be desirable to test for or against in subsequent generations can be a trait of interest. Exemplary non-limiting traits of interest include yield, disease resistance, agronomic traits, abiotic traits, grain composition (including, but not limited to, protein, oil, and / or starch composition), insect resistance, fertility, silage, and morphological traits. In some embodiments, two or more traits of interest are tested for and / or against (individually or collectively) offspring individuals.

[0098] As used in this document, the term transgene refers to a nucleic acid molecule introduced into an organism or its ancestors by some form of artificial transfer technique. The artificial transfer technique thus creates a transgenic organism or a transgenic cell. It is understood that the artificial transfer technique can occur in an ancestral organism (or a cell thereof and / or that may develop in the ancestral organism) and also in any descendant individual that has the artificially transferred nucleic acid molecule or a fragment thereof, and is still considered transgenic even if one or more natural and / or assisted creations result in the artificially transferred nucleic acid molecule being present in the descendant individual. II Methods to Increase Genetic Gain Petition 870210067187, dated 07 / 23 / 2021, page 39 / 110 36 / 91

[0099] In some embodiments, the presently disclosed individual provides methods for increasing genetic gain. An exemplary embodiment of the presently disclosed methods is called the Genome-Wide Selection Simulated Marker-Assisted Recurrent Descent Test (GWS-SMART). It combines computer simulations and GWS to improve genetic gains in production programs. In some embodiments, GWS-SMART simulates the offspring generated from some or all possible crosses of the lines present in a breeding population, identifying those crosses with acceptably high probabilities of producing superior offspring, and selecting suitable individuals for follow-up (see Figure 1 and Figure 3).

[0100] As such, methods for increasing genetic gain through the use of computer simulations in plant breeding are provided here in some embodiments. With GWS-Smart, it is possible to simulate the offspring of some or all possible crosses of a breeding population. After that, breeding pairs are selected for which the simulated crosses are predicted, resulting in acceptably high probabilities of producing superior offspring.

[0101] Thus, in some embodiments, the methods presently disclosed comprise (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner would produce Petition 870210067187, dated 07 / 23 / 2021, page 40 / 110 37 / 91 a subsequent population; (c) infer or determine haplotypes with respect to the plurality of broad genomic markers for the first breeding partner and the second breeding partner based on the marker genotypes of these partners and a given genetic map; (d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, where the genetic potential value of the offspring generation is the average of the genomic breeding value of each simulated genotype of each member of the offspring generation; or calculate a distribution or frequency of occurrence for one or more of the genotypes of one or more members of the offspring generation;(f) repeat steps (b)-(e) one or more times, where in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential value calculated in step (e); and (h) select one or more ideal breeding pairs based on the ranking from step (g), wherein the cross of the breeding pair selected in step (g) is predicted to generate offspring with the highest genetic gain. An exemplary implementation of GWS-SMART is shown in Figure 3. II. A. To Provide or Estimate Effects

[0102] The subject matter disclosed herein includes, in some embodiments, the step of providing or estimating the effects (also known as phenotypic effects or marker effects) with respect to a trait of interest of a plurality of genomically broad markers in a breeding population, Petition 870210067187, dated 07 / 23 / 2021, page 41 / 110 38 / 91 comprising a plurality of potential breeding partners. Effects with respect to a trait of interest of a plurality of genomically broad markers may be known (e.g., previously estimated, and therefore provided for the methods of the subject presently disclosed) or may be estimated again.

[0103] To estimate the effects, the breeding population acts as a reference population. As used in this document, the term reference population refers to a population of individuals (e.g., plants) for which genotype and phenotype information is available (e.g., known, discernible, or inferable) with respect to genomically broad markers and a trait of interest. In some embodiments, members of reference populations may be genotyped with respect to a plurality of genomically broad markers. Observing genotyped members of the reference population with respect to the phenotype of the trait of interest (herein referred to as phenotyping) facilitates the determination of the effects of the presence or absence of each of a plurality of genetic markers that are associated with the trait of interest (herein referred to as effects, phenotypic effects, or marker effects).

[0104] In some embodiments, the reference population provided includes members whose genomes collectively and / or individually include all the genomically broad markers for which effects are to be estimated. In some embodiments, the reference is a biparentally segregating population, for example, a double haploid (DH) population, a recombinant isogenic lineage (RIL) population, an Fn population (i.e., a population that has undergone n = 2, 3, 4, 5, 6 or more generations of inbreeding or self-pollination) or a combination thereof. Petition 870210067187, dated 07 / 23 / 2021, page 42 / 110 39 / 91 (collectively referred to in this document as a DH-Fn-RIL population). In some embodiments, a Dh / Fn / RIL population is itself generated from an F1 population resulting from a cross between two parents (in some embodiments, where one or both parents are pure). In some embodiments, the members of the DH / Fn / RIL population are pure or substantially pure, such that each member of the population is homozygous at substantially all or all loci. An advantage of using such a population is that it can facilitate the ability to infer haplotype structures or linkage phases by genotyping members with respect to each genomically broad marker. For example, a DH or RIL individual will have one of two possible homozygous genotypes at each locus: QQ or qq.

[0105] For an F2, F3, F4 or later population, two exemplary methods for inferring haplotypes of Fn population members are provided below.

[0106] As an example and not as a limitation, suppose that genotypic and phenotypic data for a trait of interest are collected from a reference population that has been collectively grown at multiple locations with one or more replicates at each location. Taking into account the genotypic and phenotypic data, the effect of each marker can be estimated using any of several possible strategies including, but not limited to, least squares estimation, best linear unbiased prediction (BLUP) and derivatives thereof (e.g., genomic BLUP or GBLUP) or one of the Bayesian estimation methods (e.g., BayesA and BayesB; see Meuwissen et al., 2001, for a discussion of these approaches).

[0107] In some modalities, the GBLUP method is employed to estimate the markers. One advantage of using GBLUP Petition 870210067187, dated 07 / 23 / 2021, p. 43 / 110 40 / 91 is the ability to include considerations of genetic variance, which is the sum of the genetic variations of all locations associated with a given trait of interest, and also environmental variance, which can be related to many environmental factors including, but not limited to, differences in soil, temperature, water, fertilizers, etc. In some embodiments, these variance components can be calculated using restricted maximum likelihood estimation (REML; Henderson, 1975; see, for example, Harville, 1977) based on phenotypic data from multiple locations using the model of equation (1): Yij = m + Gigi + Ljbj + eij(1)

[0108] where y is the phenotype of line i at location j (which is an observable characteristic of a trait of interest); μ is the overall mean of the phenotype of a trait; G is the indicator variable representing the genotype of line i; g is the genotypic effect of line i, which can be considered as a sum of the QTL effects; Lj is the indicator variable, with 1 indicating that the line was phenotyped at location j and 0 indicating that the line was not phenotyped at location j; bj is the effect of location j caused by differences in water, soil, temperature, and / or other factors; and eij is the residual of the phenotype of line i at location j after eij ~ N (0, σe2). Here, g is assumed to be a random effect after g ~ N (0, σg2), and bj is a fixed effect. The genetic variance σg2 and the environmental variance σe2 can be estimated by REML (Henderson, 1975).In the model, the g parameter can be calculated by a BLUP procedure (see, for example, Henderson, 1975), and the BLUPs of each lineage are used as phenotypes in the following model.

[0109] The effect of each marker can be estimated based on the Phenotypic BLUPs and genotypic marker data from a training population using the best one-sided linear genomic prediction. Petition 870210067187, dated 07 / 23 / 2021, page 44 / 110 41 / 91 wide (GBLUP; Meuwissen et al., 2001). A linear exemplar model for GBLUP is: m Yi = μ+Σ(νΡ+ ei (2)

[0110] where y is the phenotypic BLUP of lineage i, μ is the overall mean, zy is the genotype of marker j for lineage i, gj is the effect of marker je, and ei is the residual after ei ~ N(0, oe2). In some embodiments, the BLUP phenotype can be considered as the mean of phenotypes of a lineage at various locations. Since in these embodiments a mixed model was employed to calculate this quantity, it can be referred to as a BLUP phenotype in the context of mixed model theory (Henderson, 1975). In the model, μ is considered to be a fixed effect and gj is considered to be a random effect, following a normal distribution gj ~ N(0, ogj2). It is also considered that each marker has equal genetic variation expressed by Equation (2a): □□□□□□□□□□□□(2a)

[0111] with m being the total number of markers used (Meuwissen et al., 2001; Bernardo and Yu, 2007; Lorenzana and Bernardo 2009; Jannink et al., 2010). Based on the model, the variance-covariance matrix V for phenotype y is expressed by equation (2b): mv=Z <z^)+W j=l (2b)

[0112] where Zj is a vector of genotypic scores for marker j by n individuals in a population and eal(nxn) is an identity matrix with diagonal elements 1 and the others 0. The global μ, a fixed effect, can be estimated as defined in Equation (2c): Petition 870210067187, dated 07 / 23 / 2021, page 45 / 110 42 / 91 μ (XTV“1X)'1XTV'1y (2c)

[0113] with X being a vector of numbers one, and the effect of marker j being able to be calculated as defined below in equation (2d): (2d)

[0114] In some forms, one or more of equations (1), (2), (2a), (2b), (2c) and (2d) are calculated by a suitably programmed computer. II. B. Haplotype Inference

[0115] Members of the breeding / reference population serve as potential breeding partners, and crosses between different potential breeding partners are simulated to determine which of the breeding partners are most likely to produce offspring with desirable genotypes and therefore desirable phenotypes. Thus, putative offspring can be simulated from crosses between members of the breeding population. In some embodiments, a breeding population has n members, and all possible crosses between members of the breeding population (i.e., a total of n(n-1) / 2 total crosses) are simulated.

[0116] To simulate any given cross, haplotypes in relation to the genomic-wide markers that are located on each chromosome must be inferred. In some embodiments, haplotype inference is based on the genotypes of the members of the breeding population, taking into account the genetic distances between each two adjacent markers.

[0117] As discussed above, inferring haplotypes from markers of DH or RIL individuals is relatively straightforward. In title Petition 870210067187, dated 07 / 23 / 2021, page 46 / 110 43 / 91 example and not limitation, if the genotypes of two individuals with respect to ten (10) SNP markers on a given chromosome are represented as AABBABBAAB and BBAAAABAAA, where A represents a homozygous genotype with two identical 0 alleles from one parent of a particular DH or RIL individual and B represents the genotype with two identical 1 alleles from the other parent, then the haplotype structures for the two individuals can be represented as: 001 101 100 001 101 100 1 110,000,100,000 e110 000 1 0 0 0' respectively.

[0118] Inferring marker haplotypes in individuals from Fn-type populations is, however, more complex. Two algorithms have been developed to solve the problem. In the first algorithm, the haplotype structure is inferred based on the minimum recombination principle (MRP). The MRP defends the proposition that genetic recombination is rare and, therefore, haplotypes with fewer recombinants should be preferred in haplotype reconstruction (o'Connell, 2000; Gusfield, 2002; Qian and Beckmann, 2002).

[0119] By way of example and not limitation, suppose that the genotype AHBBAHHHHB is observed in an Fn type individual, where A is a homozygous genotype with two identical 0 alleles from one parent (in some embodiments, the parent is removed several generations if n > 1), B is a homozygous genotype with two identical 1 alleles from the other parent, and H is the heterozygous genotype (i.e., a genotype with both 0 and 1 alleles). In the present example, there is only one H between the first and fifth markers, and thus, the haplotype of the individual with respect to these five markers can be expressed as: □ οι 10 oii io Petition 870210067187, dated 07 / 23 / 2021, p. 47 / 110 44 / 91

[0120] Turning now to the sixth marker, the individual is also heterozygous for this marker. The result is that there could be two possible haplotypes, which can be described as follows: ooi loo oo 1 io 011 101 θ 01 100'

[0121] For any haplotype structure, the number of recombinant events that could generate the haplotype structure is five, since a complete lack of recombination would have resulted in regions of the chromosome with all 0 or all 1, but no regions on the chromosome with both 0 and 1. Thus, to generate the upper haplotype of the upper structure, a recombination event between the second and third markers could create the change from 0 to 1, and between the fourth and fifth, it would create the change from 1 to 0. Regarding the lower haplotype of the upper structure, a recombinant event between the first and second markers would create the change from 0 to 1, a recombination event between the fourth and fifth markers would create a change from 1 to 0, and a recombination event between the fifth and sixth markers would create the change back to 1 (i.e., a total of five for the upper haplotype structure).A similar analysis of the lower haplotype structure reveals that it would be produced by recombination events between the second and third markers, the fourth and fifth markers, and the fifth and sixth markers of the upper haplotype, and between the first and second and the fourth and fifth in the lower haplotype.

[0122] However, since the individual is homozygous at markers 1, 3, 4, and 5, recombination events occurring between markers 3 and 5 would be undetectable when only genotypes are considered. As a consequence, the structures of Petition 870210067187, dated 07 / 23 / 2021, page 48 / 110 The haplotypes of these 45 / 91 individuals can be simplified based on the homozygous genotypes of the second and sixth markers, as follows: 1O 0 | F ]i OI1

[0123] For the superior haplotype structure, the number of recombinant events is two (i.e., one recombination event between alleles 0 and 1 on the superior chromosome and one recombination event between alleles 1 and 0 on the other chromosome), and the number of recombination events is 0 for the inferior haplotype structure (the chromosome has only alleles from the second parent).

[0124] Therefore, applying the basic MRP proposition, the second haplotype structure (i.e., the one showing the least recombination) is the one with which the analysis continues. As a result, the haplotype structure of the first six markers can be described as: OOI 1 OQ 11 10 1

[0125] Applying the same strategies to the remaining markers, it is possible to reconstruct the haplotype structure of the entire chromosome region. As a result, the haplotype structure of all ten markers can be described as: 001 100 000 1 011 101 111 1

[0126] A second exemplary method for inferring haplotypes is based on certain genotypes of descendant (i.e., genotyped) lineages. This method is a chain inference in the sense Petition 870210067187, dated 07 / 23 / 2021, p. 49 / 110 46 / 91 which first infers the linkage phase of the first two markers (the first and second markers) and then calculates the linkage phase between the second and third markers. This process is repeated until the last marker on the same chromosome is considered.

[0127] By way of example, and not limitation, consider an F4 breeding population. The genotypes of cycle 0 could be known as the members of this population were genotyped. Genotypic data from cycle 1 could also be available after a selection cycle using currently disclosed methods is performed. Starting with the first and second markers, since haplotype information can be easily inferred from homozygous genotypes, consider the case where individual 1 of cycle 0 is AA, and individual 2 is HH. It is also possible to use the segregation of their offspring to infer the haplotype of individual 2. Once genotypic data are determined in each cycle, it is possible to reconstruct the haplotype structure of an individual based on its genotype in combination with the genotypes of its offspring.If genotype data from cycle 1 are not available, MRP is used to infer it from its own genotype, as established above. However, the ability to employ offspring genotypes increases the accuracy of the inference.

[0128] To do this, the genotypic segregation of offspring individuals in cycle 1 of two individuals and origin is analyzed. In cycle 1, the frequency of each of the four gamete types (i.e., O0, 11, O1, and I0 from an AA and HH cross) can be calculated. If the gamete with the highest observed frequency is OO or I1, then the haplotypes for individual 2 are: Q 1 Petition 870210067187, dated 07 / 23 / 2021, pp. 50 / 110 47 / 91 Otherwise, the haplotype is 0 1

[0129] After the haplotypes for the first and second markers have been resolved, the same approach can be used to infer the haplotypes of the remaining markers.

[0130] In some forms, one or more haplotype inferences are performed using a suitably programmed computer. II. C. Selection of Breeding Pairs and Simulation of Crossbreeding of the Same

[0131] After the haplotypes of each individual are inferred, crosses between pairs of individuals can be simulated to produce putative (i.e., simulated) offspring. In some embodiments, all possible crosses are simulated, so that in a breeding population of n individuals, n(n-1) / 2 crosses are simulated. For each cross, a plurality of offspring is simulated using basic meiosis theory. The use of larger numbers of simulated progeny per cross can help ensure that there are a sufficient number of different genotypes generated in each simulation to differentiate between crosses where the parents from which the breeding population is derived are very genetically similar. In some embodiments, at least 50, 100, 250, 500, or 1000 offspring genotypes are simulated per cross.

[0132] An exemplary approach to simulating the genotype of offspring from the haplotypes of their two parent lines is demonstrated below. First, a gamete and / or haplotype is generated from each parent by the process of meiosis. Assume that the structure of Petition 870210067187, dated 07 / 23 / 2021, page 51 / 110 48 / 91 haplotype in relation to a given chromosome (e.g., chromosome A) of a parent is: yttm! m —H--Hl---H1 011 101 1111 and the genetic linkage map of the aforementioned chromosome in the relevant region is: 0.05 0.07 0.15 0.03 0.04 0.20 0.13 0.17 0.18 where each numerical value displayed in the regions between the markers corresponds to a recombination frequency that was determined for that region.

[0133] A particular chromosome (each haplotype above corresponding to a chromosome of a chromosome pair) is then selected to start with a probability of 0.50, since in meiosis each of the homologous chromosomes has an equal chance of forming a gamete with another chromosome from a different homologous pair. For example, the top chromosome might be selected. The haplotype at the first locus of this chromosome is 0. The allele at the second locus is then simulated as follows. The genetic linkage map described above indicates that a recombination event would be expected in the marker 1-marker 2 interval in about 5% of (simulated) meioses. A random number is then generated from a uniform distribution of [0, 1], and if the generated random number is less than 0.05, the simulation incorporates a recombination event between these two loci. Otherwise, no recombination event is incorporated.If no recombination event is simulated between the first and second markers, the 0 allele on the second marker is... Petition 870210067187, dated 07 / 23 / 2021, page 52 / 110 49 / 91 simulated to transfer along with the 0 allele at the first marker to the next generation. The resulting haplotype with respect to the first and second loci is thus described as: 0 and the simulation continues with respect to the upper chromosome.

[0134] If, however, the random number taken with respect to the first and second markers is greater than 0.05, the focus is shifted to the lower chromosome (since the genetic material on the lower chromosome starting at the second marker is considered to have been exchanged into the upper chromosome as a result of the recombination event between the first and second markers). This simulation approach continued until the last locus (in the example, the tenth locus) was considered. An exemplary haplotype (which can also be considered as an exemplary gamete) thus obtained can be represented as: 001 101 110 1

[0135] This haplotype / gamete is then simulated to match a simulated gamete to be derived from the other parent. Such a simulated gamete could be described as: 000 100 111 0

[0136] The combination of these two simulated gametes results in a simulated offspring with the following haplotype structure on the two corresponding homologous chromosomes, which can be described as: 001 101 110 1 000 100 111 0 Petition 870210067187, dated 07 / 23 / 2021, p. 53 / 110 50 / 91

[0137] The genotype of this haplotype can be translated as AAHBAHBBHH, where the genotype at each locus is defined as A if two alleles are Os, B if two alleles are 1s, and H if the two alleles are O and 1. For convenience in subsequent calculations, the three genotype types A, B, and H can be coded as -1, +1, and 0. These codes (also referred to here as scores) can be used for the calculation of marker and GBV effects in the practice of the subject matter presently disclosed, as described in more detail here.

[0138] In some modes, one or more of the simulations are run by a suitably programmed computer. II. D. Calculation of Genetic Potential Value (GPV) Based on Simulated Offspring Genotypes

[0139] Once haplotypes are determined and / or inferred, the genomic reproducibility values ​​(GBVs) of individuals with such haplotypes can be calculated from the corresponding genotypes. To calculate the GBVs of the simulated offspring genotypes, Equation (5) can be used: yi = A+É(zijêj)(5)j=l where gj is the effect estimated using Equation (2b) from a reference population and zij is the genotype of marker j of individual i. It can be observed that the GBV of a simulated progeny individual can be calculated by summing the effects of each marker present in the simulated progeny individual. It can also be observed that this prediction model is an additive model that corresponds to the additive model used to estimate the marker effects in the training population. Specifically, x is defined as -1 if the genotype is A; 0 if the genotype is H and 1 if the genotype is B (assuming the symbol system A, B and H described above in this Petition 870210067187, dated 07 / 23 / 2021, p. 54 / 110 51 / 91 document is used for genotyping each marker).

[0140] Each cross (and therefore each breeding pair) can thus be evaluated by analyzing the offspring that are simulated. In some modalities, the average of all GBVs calculated for the simulated offspring of a cross can be used as a measure of the GPV of the cross.

[0141] In some embodiments, however, it is recognized that the mean may not reflect the total genetic variation in the simulated offspring of any given cross and therefore may not be ideal for measuring the genetic potential to obtain offspring with many favorable alleles due to recombination. For this reason, in some embodiments, only those offspring of any given simulated cross that have GBVs that fall outside one standard deviation of the mean of all GBVs of all simulated offspring of the simulated cross are used as the GPV of the cross. If the family (i.e., the collection of simulated progeny from any particular breeding pair) exhibits large genetic variation, a high GPV is expected. Otherwise, the mean should be small. This criterion may result in the selection of a family with a high mean and high genetic variation, which, in some embodiments, may be desirable.In some approaches, currently published methods assess the distribution or frequency of GBVs across all offspring in a population in order to identify those lineages that fall within the right tail area of ​​the distribution.

[0142] The discussion above refers to the use of currently disclosed methods for selecting breeding pairs with respect to a single phenotype of interest. In some embodiments, however, currently disclosed methods can be used to select breeding pairs with respect to multiple phenotypes of interest. In some embodiments, multiple GBV Petition 870210067187, dated 07 / 23 / 2021, p. 55 / 110 52 / 91 traits (GBV-MT) can be used as a measure of the genetic merit of multiple traits. An exemplary approach to calculating MT-GBV is as follows: MT-GBVk=£(w,. í=l GBVkí-Mm(GBVk) Max(GB Vt)-Mm(GB Vk)?(6a) or MT-GBVk(WiGBVkÍ~Min <GBVk)) (6b) tf Std(GBVk)

[0143] where MT-GBVk denotes the genomic estimated reproduction value of multiple traits of line ki, wi (i = 1, 2, 3,... t with f being the number of traits considered) is the weight (relative importance) of trait i, which can be predetermined as desired (including, but not limited to, by breeders based on their breeding goals and experience); GBVki is the genomic estimated reproduction value of line k for trait i; Min(GBVk) is the minimum GBV value for trait k; Max(GBVk) is the maximum GBV value for trait k; Mean(GBVk) is the mean GBV for trait k; and Std(GBVk) is the standard deviation of GBVs for trait k. In some embodiments, two techniques are employed for each equation to normalize the GBV of each trait to avoid the influence of scaling different traits on selection.

[0144] In some embodiments, one or more of equations (5), (6a) and (6b) are executed by a suitably programmed computer. II. E. Classification of Intersections based on GPVs

[0145] After the GPVs of each cross are calculated, they can be ranked based on GPVs. In some modalities, the highest-ranked crosses can be selected and effectively crossed to produce a subsequent generation. The following generation can then be considered a Petition 870210067187, dated 07 / 23 / 2021, page 56 / 110 53 / 91 new F1 and reanalyzed using the methods described here and represented in figure 4.

[0146] It is observed that it is possible that some lines in the breeding population could be used more frequently than others in the production of new F1 generations. A potential disadvantage of this in relation to long-term selection (i.e., cycling by the methods currently disclosed several times) could be the decrease in the homozygous trait due to a limited number of lines used for crossing.

[0147] To solve this problem, a second approach can be used. Regarding this second approach, after the superior breeding pairs have been identified from the GPV classification, the crosses with the highest classifications can be improved by observing other highly ranked lines to add an additional line that is not part of the highest classification. This process can be repeated until a minimum number of genetically different lines are employed.

[0148] As such, trait improvement can continue by repeating the methods iteratively, with the breeding pairs identified in one cycle being used to generate the breeding population(s) (in some forms, the segregation populations) of the next cycle. The haplotype structures of individuals from the first to the last selection cycle can be traced, since haplotypes are used to run the simulations in each cycle. After several cycles, the final offspring population can be tested and evaluated at various locations, and lines with acceptable performance can be promoted to a subsequent breeding stage and / or can be employed as a new variety, as desired. Petition 870210067187, dated 07 / 23 / 2021, page 57 / 110 54 / 91

[0149] III. Methods for Selecting Breeding Pairs Expected to Produce Offspring Having Desired Phenotypes and / or Genotypes and Methods for Producing Them

[0150] The presently disclosed subject matter also provides methods for selecting breeding pairs predicted to produce offspring with desired phenotypes and / or genotypes. In some embodiments, the methods comprise (a) estimating or providing effects with respect to the trait(s) of interest from a plurality of genomically broad markers in breeding populations (in some embodiments, biparental breeding populations) comprising a plurality of potential breeding partners; (b) selecting breeding partners from breeding populations wherein the haplotypes of each of the breeding partners are known or predictable with respect to the plurality of genetic markers; (c) inferring and / or determining haplotypes with respect to the plurality of genomically broad markers for the breeding partners;(d) simulate crosses between breeding partners to produce generations of offspring, each member of the offspring generations comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generations, wherein the genetic potential values ​​are the averages of the genomic breeding value of each simulated genotype of each member of the offspring generations; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e); and (h) select one or more breeding pairs based on the ranking from step (g), wherein the breeding pairs are predicted to; Petition 870210067187, dated 07 / 23 / 2021, page 58 / 110 55 / 91 produce offspring with the desired phenotypes and / or genotypes.

[0151] In some embodiments, it may be desirable to produce offspring with the desired phenotypes and / or genotypes. In such embodiments, the methods may additionally comprise (i) breeding one or more breeding pairs selected in step (h) to generate offspring individuals with the desired phenotypes and / or genotypes.

[0152] IV. Methods for Increasing the Probability of Producing Offspring with Desired Phenotypes and / or Genotypes

[0153] The subject matter now disclosed also provides methods for increasing the probability of producing offspring with desired phenotypes. In some embodiments, the methods comprise (a) providing effects with respect to traits of interest from a plurality of genomically broad markers in breeding populations comprising pluralities of potential breeding partners; (b) selecting, from the breeding populations, breeding pairs comprising the first and second breeding partners, wherein the crossing of the first and second breeding partners could produce a segregating offspring population; (c) inferring haplotypes with respect to the plurality of genomically broad markers for the first and second breeding partners;(d) simulate crosses between the first and second breeding partners to produce generations of offspring, each member of the offspring generations comprising a simulated genotype; (e) calculate genetic potential values ​​of the offspring generations, wherein the genetic potential values ​​of the offspring generations are the averages of the genomic reproduction values ​​of each simulated genotype of each member of the offspring generations; (f) repeat steps (b)-(e) one or more times, where in; Petition 870210067187, dated 07 / 23 / 2021, page 59 / 110 56 / 91 each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) ranking each simulated cross from step (d) based on the genetic potential value calculated in step (e); and (h), selecting one or more breeding pairs based on the ranking from step (g), wherein each of one or more breeding pairs is predicted to have a higher probability of producing offspring having the desired phenotype versus other breeding pairs in the breeding population.

[0154] V.descent and Cells, Seeds and Tissue Cultures Derived from the Same

[0155] The subject matter now disclosed also provides offspring generated by the methods now disclosed. In some embodiments, the offspring are plants. Exemplary plants include, but are not limited to, maize, wheat, barley, rice, beet, sunflower, winter rapeseed, canola, tomato, pepper, melon, watermelon, broccoli, cauliflower, Brussels sprouts, lettuce, spinach, sugarcane, coffee, cocoa, pine, poplar, eucalyptus, apple, and grape.

[0156] Cells, seeds, and offspring from additional generations of the offspring plants generated by the methods disclosed in this document are also provided. In some embodiments, the offspring plants and all parts and offspring derived therefrom are isogenic to generate offspring plants that are substantially or completely homozygous at all loci.

[0157] In some embodiments, the offspring plants, parts thereof and / or descendants of the subsequent generation derived therefrom comprise a transgene. As used in this document, the term transgene refers to a nucleic acid molecule that is or was Petition 870210067187, dated 07 / 23 / 2021, pp. 60 / 110 57 / 91 introduced into an organism or its ancestors by some form of artificial transfer technique. The artificial transfer technique, in this way, creates a transgenic organism or a transgenic cell. Examples of techniques by which this can be done are known in the art. In some embodiments, a transgenic individual is a transgenic plant and the technique used to create the transgenic plant is selected from the group consisting of Agrobacterium-mediated transformation, biolistic methods, electroporation in plant techniques and the like. Transgenic individuals can also arise from sexual crosses or by self-pollination of transgenic individuals into which exogenous polynucleotides have been introduced.

[0158] It is understood that the artificial transfer technique can occur in an ancestral organism (or a cell thereof and / or that can develop in the ancestral organism) and also in any descendant individual that has the artificially transferred nucleic acid molecule or a fragment thereof, and is still considered transgenic even if one or more natural and / or assisted creations result in the artificially transferred nucleic acid molecule being present in the descendant individual. In some embodiments, a transgene comprises a nucleic acid sequence that encodes a gene product that offers resistance to a herbicide selected from among glyphosate, sulfonylurea, imidazolinone, dicamba, glyphosinate, phenoxypropionic acid, cyclohexone, traizine, benzonitrile and broxinil.

[0159] VI. Computer-based implementations

[0160] The subject matter now disclosed also provides computer implementations of the methods now disclosed. In some embodiments, one or more steps of the disclosed methods are performed by a suitably equipped computer. Petition 870210067187, dated 07 / 23 / 2021, pp. 61 / 110 58 / 91 programmed. For example, in any of the disclosed methods, the inference step, the simulation step, or both can be performed by a suitably programmed computer. Particularly, as the complexity of genomes increases and the number of inference and / or simulation steps increases, it can be beneficial to use a suitably programmed computer to perform the necessary calculations. By way of example and not limitation, the simulation step might involve simulating at least 100, 200, 300, 400, 500, 1000 or more offspring in each generation of offspring. A suitably programmed computer can facilitate these simulations.

[0161] Alternatively or in addition, the estimation step may comprise estimating the effects on the desired phenotype of genomic-wide plurality markers based on best linear one-sided phenotypic predictions (BLIPs) and on genotypic marker data in the biparental breeding population using best linear one-sided genomic-wide prediction (GBLUP), as defined above in this document. Similarly, the estimation step may comprise estimating genetic variance by maximum retained likelihood (REML) estimation based on phenotypic data from multiple sites using Equation (1) above, and / or the inference step may comprise utilizing a minimum recombination principle (MRP), and / or the breeding population may consist of n members and the replication comprises simulating all n(n-1) / 2 unique crosses of the members of the breeding population.In any of these example methods, a properly programmed computer can facilitate these steps.

[0162] Furthermore, the methods of the subject matter now disclosed may be used in cases where the feature of interest comprises at least two independent features. Petition 870210067187, dated 07 / 23 / 2021, page 62 / 110 59 / 91 of interest. In some modalities, the methods also include assigning an importance value to each independent characteristic of interest in relation to the other independent characteristics. Here, as well, a properly programmed computer can facilitate the practice of these methods. VII. Additional Considerations

[0163] The methods of the subject matter disclosed herein may be used with isogenic or exogamous individuals, including, but not limited to, elite lines of agronomically important crop species. Thus, in some embodiments, each breeding partner is an isogenic individual.

[0164] Furthermore, it is understood that any type of marker may be employed in the methods currently disclosed. For example, in some embodiments, one or more genomically broad markers are selected from the group consisting of single nucleotide polymorphisms (SNPs), insertion / deletion (indels), simple sequence repeats (SSRs), restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs), cleaved amplified polymorphic sequence markers (CAPS), Diversity Matrix Technology (DArT) markers, amplified fragment length polymorphisms (AFLPs), and combinations thereof. In some embodiments, the one or more genomically broad markers comprise at least one marker present within each 5 cM, 3 cM, 2 cM, 1 cM, 0.5 cM, or 0.25 cM interval in the genomes of the breeding partners.

[0165] Furthermore, the methods of the subject matter presently disclosed provide, in some embodiments, a selection step in which one or more pairs of reproductions are selected based on the classification step of each method. The selection may take into account any aspect of the simulations, and in some Petition 870210067187, dated 07 / 23 / 2021, page 63 / 110 60 / 91 modalities, involves selecting breeding pairs with GPVs 20%, 10%, 5% or 1% higher. EXAMPLES

[0166] The following examples provide illustrative embodiments of the subject matter presently disclosed. In the light of the present disclosure and the general level of skill in the art, persons skilled in the art will realize that the following examples are intended to be merely illustrative and that various changes, modifications, and alterations may be employed without departing from the scope of the subject matter presently disclosed. EXAMPLE 1

[0167] Simulations Designed to Compare MAS, GWS, and GWS-SMART

[0168] In order to evaluate the performance of the methods currently disclosed, simulation experiments were carried out after the selection scheme described here and shown in Figure 2. In each of the 100 simulation replicates, populations of 250 individuals were generated based on ten 190 cM chromosomes with 20 uniformly spaced markers on each chromosome. The model for the simulation was: Q y.-μ+Σ (ALijaj )+ei j=!

[0169] where y was the phenotype of individual i, μ was the overall mean, qij was the genotype of QTL j of individual i, aj was the main effect of QTL j, Q was the total number of QTLs (Q = 50 in our simulation studies), and the environmental error e, for y, was sampled from a normal distribution with mean zero and variance oe2. In the jth QTL, the test crossover effect of the favorable allele was simulated as aj = Àk with λ = (Q-1 ) / (Q+1) (Lande and Thompson, 1990). The environmental noise ei was sampled from a Petition 870210067187, dated 07 / 23 / 2021, pp. 64 / 110 61 / 91 N(0, oe2) with oe2 = (Vg / H2) - Vg, where the given heritability H2 = 0.30 and the genetic variance of QTL Vg was calculated as Q V = Ϋ af > 1

[0170] Five (5) selection cycles were performed for each of GWS-SMART, GWS and MAS. In cycle 0, QTL in MAS were identified by step regression with a significance level provided of 0.05, and the QTL effects were estimated by multiple linear regression (Bernardo and Yu, 2007). In GWS and GWS-SMART, the effects of each marker were estimated by (Meuwissen et al., 2001). In cycle 0, the 5 superior lines were selected to recombine to generate cycle 1, as suggested by Bernardo and Yu, 2007. In cycle 1, MAS and GWS, the 5 superior lines were selected based on their predicted reproduction values, and these selected lines were intercrossed or recombined to produce the cycle 1 population. It is observed that the total number of crosses made based on the 5 superior lines was (5 x 4) / 2 = 10.

[0171] In GWS-SMART, as shown in Figure 3, 1000 populations were simulated from a cross between two individuals, and the sample size of each population was 50. The top 10 crosses with high GPV were actually selected to produce the next population of the cycle. The selection response was calculated as the difference between the phenotypic means of a tested population and its original population. The procedures for MAS, GWS, and GWS-SMART were repeated from cycle 0 to cycle 4.

[0172] Figure 4 shows the results of these simulations. As expected, the exemplary method of the subject currently disclosed (GWS-SMART) was superior to the traditional GWS and MAS. In cycle 1 to Petition 870210067187, dated 07 / 23 / 2021, pages 65 / 110 In cycle 4, 62 / 91, the selection response with GWS-SMART was 5% to 9% greater than with GWS, and 19% to 27% greater than with MAS. The results show the advantages of GWS-SMART over conventional GWS and MAS, although more simulation and real-world data are needed to evaluate them for different traits and different reproductive populations across different crops in the future. EXAMPLE 2

[0173] Exemplary GWS-SMART Implementations

[0174] The exemplary method of the subject presented herein was used to predict the best cycle 0 crosses based on cycle genotypic and phenotypic data. The training population was a cycle 0 double haploid (DH) maize population derived from two isogenic parents: A and B. This population was produced by crossing two isogenic parents to produce an F1, then a DH population was generated from the F1 individuals using standard double haploid techniques. The DH population was crossed with a tester T, and the phenotypes of each line were evaluated based on their test cross performance. The traits of interest in the study were grain yield and grain moisture. 344 lines in the DH population were genotyped using 240 SNP markers, and these lines were phenotyped at six locations (referred to in this document as A, B, C, D, E, and F) in five different states of the United States.

[0175] First, the BLUP for each row for grain yield and grain moisture was calculated based on the phenotypic data from the six locations, as shown in Table 1. As described in the previous section, the calculation of the BLUPs was based on model (1) using REML under a mixed model framework. These BLUPs were listed in Table 2. Table 1 Petition 870210067187, dated 07 / 23 / 2021, pages 66 / 110 63 / 91

[0176] Original Phenotypic Data for Grain Moisture and Yield from a Training Population Number of years Number of years Number of numbers 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 1 204.1 245.5 200.2 nd 163.7 133.8 13.08 18.52 15.42 nd 13.1 15.58 2 nd 255 212 nd 178 139.3 nd 19.26 15.62 nd 13.72 15.56 3 227.2 250.4 213.7 189.8 178.3 153.7 13.38 18.46 16.06 25.94 13.46 16.18 4 210.8 254.8 209.3 n.d. 177.1 147 14.04 20.02 16.56 n.d. 14.46 17.18 5 217.4 258.7 220.4 nd 181.3 145 14.3 19.86 16.5 nd 14.92 17.38 6 224.6 245.4 217.7 192.3 179.3 156.4 13.56 18.44 16.54 26.2 14.2 16.54 7 227.8 269.3 225.1 nd 181.7 nd 14.24 19.92 16.5 nd 14.2 nd 8 206.9 244.1 197.9 nd 172 132.2 13.26 18.38 15 nd 13.32 15.44 9 254.8 265.7 238 207.6 204.3 175 14.2 19.2 16.54 27.18 14.38 16.88 10 209.7 256.2 213.1 n.d. 173.2 137.5 13.12 18.72 15.32 n.d. 13.76 15.5 11 and 26.54 14.42 17.76 13 214.4 259 216.9 nd184 146,4 14,06 19,56 16,44 n.d. 14,44 17,18 14 204,9 245,7 200,2 n.d. 169,7 131,7 12,72 18,14 15,16 n.d. 13,22 15,02 15 215,1 252,8 208,4 n.d. 173,8 146,5 13,3 18,96 15,26 n.d. 13,24 16,14 16 205,8 248,8 201,8 n.d. 168,6 n.d. 14,26 19,94 16,58 n.d. 14,9 n.d. 17 211,8 255,1 216,3 n.d. 181,1 140,1 13,42 19,22 15,52 n.d. 13,98 16,4 18 211,5 255,6 206,5 n.d. 177 136,3 12,92 18,74 15,64 n.d. 13,66 15,4 19 n.d. 259,5 213,9 n.d. 185,8 151,9 n.d. 18,72 15,32 n.d. 13,04 15,82 20 236,2 272,1 230,7 n.d. 198,1 160,1 13,98 19,6 16,26 n.d. 14,4 17,14 21 203,9 244,1 199,3 n.d. 166,5 132,9 13,52 19,12 15,7 n.d. 13,88 15,88 22 204,6 n.d. 200,1 n.d. 167,2 130,9 12,9 n.d. 15,16 n.d. 13,48 15,98 23 200,3 244,5 192,4 n.d. 158,8 134,5 13,74 19,56 16,28 n.d. 14,6 16,94 24 n.d. 241,9 197,4 n.d. 166,4 122,8 n.d. 19,12 15,34 n.d. 13,56 15,88 25 n.d. n.d. 227,8 n.d. 194,2 158,7 n.d. 19,28 15,78 n.d. 14,18 17,12 26 220,3 261,8 218,9 n.d. 184,5 147 14,22 19,98 16,6 n.d. 14,88 17,26 27 225,2 260,9 215,6 n.d.190 152.3 13.12 19.2 16.12 na 13.88 15.82 28 213.4 254.6 205 na 174.4 142.4 13.3 18.98 15.88 25.7 13.84 15.68 29 na 267.4 221 202.8 193.7 166.5 13.18 14.86 31 233.9 266.2 219.3 203.6 192.8 161 13.6 19.5 16.32 25.94 14.42 17.26 32 209.3 249.3 202 184.1 164.8 142.6 13.38 18.78 15.5 26.1 13.44 15.86 33 240.1 275.8 230.3 210.2 201.7 166.2 13.58 19.64 16.18 26.04 14.62 16.08 34 231.9 268.4 227.5 202.2 196.7 160.5 13.4 19.36 15.92 25.66 14.08 17.08 35 213.3 255.9 207.3 187.9 174.6 143.4 12.92 18.48 15.34 25.26 13.84 15.46 36 195.7 237.3 190.8 171.1 160.7 126.4 12.64 18.14 15.1 24.86 13.14 15.48. Petition 870210067187, dated 07 / 23 / 2021, pp. 67 / 110 64 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 37 215.7 249.9 209.3 189.7 175.2 140.7 12.78 18.42 15.38 25.24 13.56 15.4 38 201.7 nd 191.6 176.7 159.3 129.1 12.02 nd 14.66 24.52 12.64 15.68 39 202.7 252.7 208.5 nd 174.6 136.6 13.08 18.86 15.5 na 13.84 15.64 40 202.2 246.7 202.3 180.5 170.4 141.6 13.28 19.14 16.14 25.88 14.22 16.68 41 210.9 249.6 201.6 185.1 166 132.9 12.34 17.98 14.52 24.82 13.12 15.58 42 207.9 253.1 202.3 185.4 177.3 140.9 12.62 18.62 15.4 25.56 13.44 15.08 43 218.4 250.6 207.5 190.3 170.1 145.2 13.14 18.38 15.32 25.2 13.34 15.5 44 221.5 259.7 215.6 190.3 181 152 14.16 19.44 16.44 26.72 14.46 16.88 45 227.3 260.4 221.5 197 182.5 152 13.9 19.16 16.3 26.64 14 16.84 46 198 241.5 190.1 na 151.4 na 13.74 19.26 16.02 na 13.48 na 47 206.8 na 203.1 na 172.6 132.1 12.28 na 14.76 na13,24 15,82 48 217,8 260,7 214,1 191,9 180 149,2 12,62 18,28 15,06 24,96 13,24 15,22 49 230,7 265,7 224,6 200,4 185,8 155,7 12,64 18,1 14,94 24,4 13,42 14,72 50 217,9 253,2 205,3 n.d. 169,3 143,3 13,44 19,26 16,22 n.d. 13,74 16,02 51 233,6 264,8 222,2 200,9 185,5 157,8 13,66 18,78 16,02 26,32 13,86 15,78 52 n.d. 249,7 209,2 188 180,4 144,3 n.d. 18,26 15,2 25,16 13,44 16,38 53 211,5 248,5 202,6 185,7 171,8 137,7 12,66 18,7 15,22 25,5 13,58 15,1 54 213,3 249,4 207,2 186,2 169,3 139,6 12,82 18,68 15,4 25,36 14 15,32 55 203,7 243,8 195 178 164 132,7 12,54 18,26 15,08 25,02 13,44 15,24 56 215,3 249,1 204 186,9 173,4 141,4 13,58 18,72 15,86 25,68 13,8 16,36 57 212,6 252,4 205,9 189,6 175,6 143,8 14,18 20 16,62 26,82 14,98 17,12 58 204,7 244,6 206,3 184,8 161,3 133,9 13 18,64 15,68 25,68 13,4 16,02 59 200,1 243,5 193,8 179,7 158,5 138,7 13,86 19,38 16,3 26,2 14,18 16,92 60 230,5 262,1 219,4 198,9 191,3 151,7 13,2 18,42 15,36 25,3 13,9 15,72 61 226,9 260,8 217,3 197,3 n.d.153,6 12,64 18,22 15,64 24,82 n.d. 15,34 62 219,1 254,6 209,3 189,8 179,1 146,1 13,7 19,38 16,28 26,34 14,24 15,98 63 212,6 252,7 203,7 188,1 163,8 137,1 13,14 18,86 15,66 25,66 13,38 16,38 64 226,9 263,9 217,5 196,9 185,3 149,5 13,1 19,14 15,7 26,14 13,74 16,74 65 235 271 223,5 205,4 192,7 163,4 13,86 19,18 16,08 26,1 14,22 16,64 66 219,6 263,5 214,4 197,6 185,9 152,3 14,14 20,06 16,84 26,54 15,08 17,26 67 218,8 257 213,2 191,8 181,8 145,4 13,86 19,32 16,32 26,66 14,64 16,76 68 213,2 247,4 202,4 184,4 166,1 141,5 14,26 20,26 17,16 26,52 14,76 16,92 69 206,3 245,2 200,5 182,7 164,4 134,1 13,44 19,16 16,04 25,64 13,7 16,62 70 204,8 244,6 196,7 181,6 169,6 138,5 13,5 19,26 16,08 26,3 14,18 17 71 224,6 265,1 227 202,2 193,2 160,3 13,78 19,6 16,32 26,62 14,76 17,5 72 201,4 248,6 202,5 n.d. 171,9 138,2 13,68 19,26 16 n.d. 14,18 16,2 73 213,2 249,9 210,1 187,5 174,8 150,1 14,52 20,28 17,18 26,94 15,04 17,74 74 213,6 246,9 200,2 182,9 170,9 136 12,52 17,6 14,5 25,1 12,9 15,36. Petition 870210067187, dated 07 / 23 / 2021, pp. 68 / 110 65 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 75 215.9 260.8 214.2 196.8 nd 149.3 14.4 20.06 17.02 26.88 nd 16.88 76 nd 249.8 204.6 184.4 176.3 140 nd 18.42 15.76 25.74 13.62 15.96 77 219.3 252.7 210.8 188.9 179 150.8 13.3 18.94 15.5 25.98 13.96 16.24 78 249.8 285.4 245.1 na 210.1 174 14.36 20.1 16.28 na 14.38 16.36 79 194.2 240.8 196.2 175.9 160 127.2 12.66 18.54 15.34 25.18 13.7 15.42 80 224.1 256.3 213.1 195.3 182.5 144.6 12.82 18.06 15.14 25.52 13.34 16.06 81 220.8 259.9 208.1 191.5 182.6 143.9 13.54 19.3 16.32 25.98 14.52 17.14 82 206.4 245.1 207 186.5 172.4 135.6 13.14 18.52 15.48 25.5 13.92 16.14 83 na 263.7 218.4 199.2 192.4 154.7 na 19.48 16.16 25.94 14.32 16.06 84 213.7 253.6 209.9 nd 173 138.2 12.84 18.24 15.12 25.14 13.32 15.78 85 203.8 251.4 206.4 186.5 165 135.5 12.32 17.98 14.9 24.78 12.78 15.52 86 nd 267.7 220.1 na 187.8 158.2 na 19 15.74 na13,88 16,48 87 209,7 244,2 200,8 183,9 169,3 135,1 13,46 18,72 15,88 26,04 13,86 15,74 88 219,1 260,4 217,5 194,3 181,2 152,1 13,4 19 15,9 26,4 14,02 16,44 89 225 258,1 212 194,7 184,6 145,7 13,24 18,86 15,54 25,2 13,88 16,5 90 230 265,1 218 203,1 190,2 155,6 12,94 18,3 15,02 25,24 13,56 15,84 91 200,6 243,6 195,2 181,4 172,1 131,8 12,68 18,52 15,46 25 13,4 15,96 92 220,8 256,8 213,7 192,1 180,2 150,6 14,52 20,2 17,12 27,02 15,38 17,56 93 219,8 257,4 214,9 n.d. 184,7 144,4 13,44 19 15,76 n.d. 14,02 16,6 94 n.d. 257,1 217 193,4 178,8 153,3 n.d. 19,38 16,3 26,02 14,7 16,66 95 231,1 264,8 220,2 201,8 188,7 157,6 12,5 18,26 14,96 25 13,18 15,16 96 217,2 254,6 206,4 189 179 140,9 13,8 19,1 16,12 26,5 14,46 16 97 216,9 251 210,4 190,3 168,2 140 13,28 18,76 15,78 26,3 13,52 16,84 98 223,9 258,5 215,6 196,5 186,9 149,2 12,84 18,82 15,32 25,34 13,5 15,42 99 212,3 252,1 206,7 184,3 170,3 139,2 13,34 18,72 15,62 25,08 13,98 15,6 100 210 257,9 213,9 n.d. 183,1 142,3 14,3 20,24 16,9 n.d.14.9 17.5 101 219.7 254.4 213.8 192.5 184.5 152.9 13.54 19.14 15.9 26.24 13.94 16.46 102 217.4 256.4 213.4 193.6 183.2 149.1 13.24 18.8 15.92 25.82 14.28 16.52 103 230.6 267.2 223.9 200.3 190.2 159.3 13.34 19.36 15.88 25.82 14.08 16.04 104 203.2 241.9 197.2 177.6 161.5 132.1 13.86 19.36 16.7 26.7 14.12 16.32 105 208 250.9 205.3 183.3 164.4 139.2 13.56 19.32 16.14 26.98 14.38 16 106 228.8 259.8 216 193 173.3 149.5 13.6 19.06 15.66 26.58 13.58 16.12 107 203.3 245.2 203.3 179.7 162.7 131.5 12.82 18.82 15.44 26.24 13.6 15.7 108 228.9 261.2 218.6 196.6 177.9 152.2 12.96 18.74 15.72 26.12 13.52 15.86 109 231.8 264.1 216.4 195.7 179.9 154.4 14.56 19.88 17.08 27.5 14.62 16.68 110 216.3 247.7 206.7 183.3 168 138.7 13.82 19.4 16.34 26.74 14.26 16.84 111 209.6 245.1 206.7 182.7 154.5 139.3 13 18.52 15.64 26.34 13 16.62 112 196.7 237.5 198.8 173.3 156.9 131.4 12.7 18.58 15.42 25.96 13.4 16.32. Petition 870210067187, dated 07 / 23 / 2021, pp. 69 / 110 66 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 113 226 263.2 226.7 196.5 183.9 156.8 15.14 20.68 17.86 28.04 15.46 18.26 114 223.1 253.3 212.7 189.8 174 153.6 13.14 18.22 15.8 26.5 13.46 16.06 115 224.2 254.2 219.4 189 175.8 148.3 13.5 18.94 16.12 27.38 14.18 15.94 116 213.4 251.3 213.6 na 177.3 138.5 13.82 19.12 15.86 na 14.24 16.04 117 216.4 255 211.6 186.7 169.1 153.3 13.84 19.98 17 27.86 14.92 17.26 118 225 263.9 222.7 196.1 179.8 159.3 13.88 19.84 16.4 27.12 14.74 16.5 119 213.1 250.2 210.1 185.6 na 13.44 18.96 16.08 26.42 na 120 223.7 264.9 222.8 na 188.6 152.1 13.5 18.94 15.74 na 13.6 16.4 121 212.8 252 213.5 187.7 169.9 144.4 13.28 19.18 15.8 26.92 13.6 15.64 122 na 259.9 216.7 192.8 174.2 151.3 n.d. 19.94 16.98 27.66 14.9 16.4 268.2 227.9 na 197.8 153.3 na 19.86 16.12 na14,26 17,36 125 232,1 263,7 219,6 196,4 178,7 158 12,7 18,48 15,08 26 13,6 15,36 126 n.d. 257 218,6 n.d. 181,2 143,2 n.d. 19,68 15,98 n.d. 14,26 16,78 127 232,2 264,6 224,5 197,6 183,2 153,8 14,16 20,18 16,8 27,44 14,66 17,06 128 211,4 243,3 200 177,6 164,9 131,2 12,82 18,52 15,12 25,84 13 15,76 129 233,8 261 222 197,8 188,1 163,3 14,3 19,52 16,7 27,36 14,46 17,22 130 214,8 246,7 205,1 182,1 159,5 141,5 13,44 19,16 15,82 26,36 13,52 15,88 131 225,6 261,6 213,9 n.d. 171,3 153,3 13,1 18,6 15,56 n.d. 13 15,94 132 237,1 270,9 225 201 179,4 161,7 13,74 19,22 16,22 26,66 13,58 16,12 133 221 260,1 218,4 n.d. 188,6 150,1 13,88 20,02 16,22 n.d. 14,4 17,54 134 249,7 280,9 235,5 211 202,7 175,8 14,28 20,26 17,1 27,6 15,04 17,96 135 217,3 251,4 214,3 188,2 n.d. 140,2 12,32 17,68 14,98 26,16 n.d. 15,84 136 219,7 n.d.213,5 186,9 173,5 146,6 13,38 18,96 16,24 27,14 14,06 17,14 137 227,6 262,3 216,6 196,8 184,6 155,8 14,72 20,66 17,52 28,1 15,52 17,3 138 230,6 264,5 223 194,6 186,6 153,5 13,74 19,52 16,4 27,12 14,3 16,34 139 223,7 n.d. 213,8 191,3 178,3 149 15,22 n.d. 17,6 28,4 15,58 17,42 140 227,1 258,6 222,3 191,7 182,9 151,8 13,22 18,8 15,82 26,5 13,7 15,82 141 204,4 245,8 n.d. 176,9 152 132,7 13,28 19,06 n.d. 27,28 13,5 16,6 142 216,1 252,9 209,8 190,1 171,9 143,3 13,04 19,14 15,9 26,6 13,72 16,46 143 239,6 269,4 227,4 200,8 191,8 163,5 13,16 18,88 15,46 26,16 13,66 15,54 144 220,7 258,1 215 189,7 176,6 145,9 14,06 19,86 16,48 26,84 14,58 17,46 145 230,4 269,1 228,1 200,3 179,2 154,4 13,02 18,54 15,62 26,08 13,16 16,4 146 234,6 263,5 226,8 n.d. 185,8 154,7 14,06 19,22 16,3 n.d. 14,42 17,3 147 219,1 256,6 210,3 n.d. 183,5 142 13,6 18,74 15,76 n.d.13.76 16.04 148 212.9 243.7 201.2 181.1 157.1 140.7 13.22 18.38 15.58 26.58 13.2 15.96 149 219.8 248.4 208.5 186 163.5 145.5 13.98 19.46 16.56 27.2 14.28 16.62 150 210.2 248.7 203.9 183.1 166.7 138.7 13.7 19.32 16.56 27.04 14.52 16.6. Petition 870210067187, dated 07 / 23 / 2021, pp. 70 / 110 67 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 151 222.9 251 214.1 186.3 172.9 144.1 14.34 19.5 16.58 27.3 14.7 17.18 152 228.8 258.8 222.4 196.4 174.6 150.4 13.1 18.56 15.84 26.54 13.28 16.38 153 212.3 249 206.8 184 nd 138.2 12.66 18.42 15.48 25.62 nd 15.72 154 224.3 262.8 221.6 nd 180.8 153.6 13.12 18.62 15.02 225.1 255.6 213.8 192.1 179.5 149.8 12.78 18.34 15.48 26.16 13.3 16.44 156 213.1 250.9 206.2 186.1 170 141.5 13.38 19.56 15.94 27.44 14.24 16.08 157 219.5 259.4 217.5 191.1 174 143.9 12.98 18.42 15.58 26.04 13.32 16.36 158 217.1 258.5 211.1 na 181.3 144.3 13.18 19.14 15.66 na13,56 15,6 159 225,1 259,8 218,8 188,5 178,9 151,6 13,14 18,92 16 27 14,04 16,9 160 208,8 244,5 202,7 175,9 159,7 133,4 13,16 18,52 15,54 26,12 13,48 15,44 161 213,2 251,2 210,5 186,3 171 140 12,24 17,68 14,84 25,58 12,92 15,84 162 255,3 283,5 240,3 214,4 204,4 180,3 13,2 18,48 15,3 26,2 13,58 15,96 163 210,3 249,6 209,4 181,2 172,5 140,9 14,68 20,56 17,62 28,14 15,4 17,98 164 219,4 249,3 207,1 188 169,3 142,9 14,1 19,58 16,48 27,48 14,46 16,62 165 233,4 265,5 226,1 197,1 189,3 154,5 12,96 18,74 15,3 26,58 13,28 15,96 166 226,9 256,8 215,1 189,6 176,6 156,4 14,08 19,78 16,52 27,06 14,46 17,02 167 217 248,1 210,6 n.d. 166,3 n.d. 14,02 19,46 16,44 n.d. 14,04 n.d. 168 230,5 n.d.218,3 193,7 180,2 152,7 14,04 19,52 16,72 26,86 14,38 16,14 169 235,2 268,5 227,6 201,3 188,5 168,6 13,22 19,1 16,16 26,46 13,62 16,4 170 206,7 246,7 198 177,8 158,2 133 12,38 18,1 15,06 25,5 13,16 15,5 171 208,1 246,3 206,3 184,3 169,4 144 13,2 18,7 15,7 26,54 13,74 16,22 172 226,1 258,2 219 192,6 179,8 154,2 13,42 19,42 15,98 27,32 14 16,48 173 237,3 269,9 232,4 204,3 193,1 168,2 13,98 19,72 16,5 27,82 14,68 16,98 174 217,2 251,1 215,1 185,4 174,3 151,1 13,52 19,44 16,1 26,46 14,3 16,74 175 221,8 254,4 211,7 189,7 166,4 144,3 12,04 17,48 14,44 25,08 11,84 14,82 176 230,7 264,4 224,2 191,9 181,3 n.d. 13,04 18,32 15,3 26,48 13,46 n.d. 177 244 n.d.234.7 202.9 192.9 165.7 13.84 19 16.08 27.28 14.02 16.62 178 219.6 253.8 206.5 185.9 172.5 144.8 13.22 18.82 15.9 27.1 14 16.88 179 216.7 245.2 205.4 183.1 161.1 137.4 13.96 19.18 16.18 27.52 13.76 16.86 180 222.9 253.3 214.3 187 177.4 152.4 13.02 18.58 15.58 25.96 13.56 16.26 181 219.4 251.6 206.2 187.4 168.6 145.9 12.84 18.7 15.74 26.16 13.7 16.6 182 221.9 258.7 220 191.4 179.4 153.3 12.14 17.74 14.66 25.8 12.78 15.02 183 231.2 260.9 219.7 194.8 185.4 154.4 13.32 19.1 16.04 26.44 13.8 15.92 184 218.5 256.1 211.3 186.4 171.9 143.4 13.84 19.46 16.4 26.78 14.34 17.08 185 229.5 277.2 233 na 200.2 160.2 13 18.96 15.62 na 13.88 16.22 186 221.8 257.3 219.1 188.5 179.9 150.3 12.6 18.2 15.66 25.62 13.58 15.9 187 221.6 253 215 na 174.3 148.4 13.24 18.92 16.44 na 14.3 17.28 188 220.3 na 224 194.4 187.7 155.8 14.06 na 17.04 27.3 14.92 17.66. Petition 870210067187, dated 07 / 23 / 2021, pp. 71 / 110 68 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 189 213.6 250.9 208.1 185.2 173.7 144.1 14.08 19.6 16.98 26.56 14.88 18.06 190 212 250.5 217.7 188.9 170.4 147.7 13.06 18.78 15.9 25.94 13.38 17.04 191 221.4 252.7 217.6 192.8 180.6 157.1 13.1 18.72 15.68 25.46 13.62 16.94 192 226.4 262.2 225 200.2 186.7 167.2 13.3 18.94 16.42 25.88 14.4 17.52 193 203.9 na 206 na 167.5 130 13.22 18.78 15.52 na 13.42 16.62 194 242.3 270 237.1 207.8 191.3 167.2 13.98 19.34 16.64 26.06 14.1 17.62 195 230.4 259.1 223.8 199.1 192 159.3 13.7 19.26 16.56 26.36 14.42 17.02 196 234.3 263.3 227.2 202.8 193.6 162.6 13 18.5 16.26 25.6 14.02 16.28 197 215.1 257.7 216.1 192.7 179.1 155.8 13.46 18.94 16.62 25.84 14.08 17.3 198 221.6 255.9 218.5 190 174.2 153 14.14 19.34 16.74 26.12 14.86 17.28 199 na 241.3 203.1 179.7 167.6 135.7 na 18.82 16.18 25.46 13.88 16.64 200 215.2 257.2 212.2 na 170.6 136.9 13.6 19.06 15.72 na13,32 16,04 201 236,7 265,6 227,3 202,4 195,9 171,9 13,22 18,66 15,86 25,72 14 17,32 202 220,1 256,2 217,5 189,7 n.d. 155,4 11,96 17,58 15,04 24,36 n.d. 16,1 203 229 261,4 221,8 198,4 190,2 162,8 13,24 18,5 16,22 25,64 14,08 17,68 204 245,1 276,1 237 213,7 208,9 173,1 13,12 18,42 16,14 26,18 13,74 17,16 205 n.d. 254 212,4 187,4 166,4 148,4 n.d. 18,32 15,92 25,74 12,92 16,14 206 n.d. 263,6 220,1 197,1 184,6 156,9 n.d. 18,84 16,12 26,08 13,82 17,16 207 223,7 262,4 224,8 200 192,9 162,6 13,74 19,6 16,48 26,26 14,32 17,18 208 218 247,9 215 184,8 176,1 142,2 13,32 18,72 15,84 25,48 13,98 16,7 209 235,5 268,6 228,4 202,8 193 167 14,3 19,68 17,3 26,94 14,88 17,56 210 222,4 257 218,5 192,2 170,8 150,1 13,86 19,46 16,74 26,36 14,14 17,92 211 196,9 243,6 202,8 n.d. 160,5 128,2 13,08 18,5 15,28 n.d. 13,24 15,8 212 205,9 241,4 208 178,5 164,3 136,6 13,78 19,64 16,54 26,18 14,24 17,78 213 222,4 n.d. 219,9 193,9 183,8 159,4 13,4 18,86 16,44 25,68 14,02 17,1 214 n.d. 259 213,3 n.d. 182,6 152,3 n.d.19.42 16.38 na 14.58 16.86 215 207 246.7 206 na 172.9 132.6 13.76 19.4 16.22 na 14.48 16.2 216 228.1 259.3 221.6 195.2 188 155.3 13.16 18.42 15.9 25.96 13.88 16.28 217 233.8 266.4 229.5 na 192.5 161.8 13.26 19.08 16.2 na 13.9 16.92 218 220.1 251.9 217.5 190.2 170.4 151.3 12.72 18.48 15.72 25.66 13.26 16.28 219 222.2 252.1 210.9 189.9 172.1 148 12.74 18.56 15.76 25.04 13.02 16.06 220 227.9 265.4 227.6 202.5 187.1 163.8 13.02 18.54 16.28 25.92 14.12 16.48 221 216.2 248.4 211.8 183.6 173.6 140 13.38 18.96 16.08 25.56 13.82 17.46 222 230.8 264.5 227.2 202.2 195.3 168.8 12.66 18.28 15.54 25.66 13.28 16.66 223 229.5 266.9 228.7 199.3 182.7 160.5 12.66 18.2 15.38 24.72 13.04 16.54 224 191.4 241.2 193.9 na 160 129 13.04 19.1 15.48 na 13.44 16.06 225 233 265.1 226.7 204 190.8 161.4 13.24 18.5 15.8 25.6 14.12 16.86 226 221.9 251.6 213.4 190.5 180.8 149.1 13.46 18.44 16.04 25.44 13.86 16.46. Petition 870210067187, dated 07 / 23 / 2021, pp. 72-110 69 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 227 227.4 257 219 193 180 158.6 13.54 18.64 16.24 26.28 14.38 16.8 228 230.4 260.8 223.9 197.8 189.9 157.2 13.24 18.94 16.02 25.74 14.22 17.22 229 246.1 277.7 235.4 211.4 207.9 178.1 13.44 19.4 16.56 25.84 14.22 17.4 230 na 259.4 217.6 191.6 181.1 151.6 na 18.84 16.02 26.22 14 16.54 231 226.3 257.5 219.7 193.6 180.9 153.2 13.1 18.46 15.56 24.9 13.28 16.12 232 236.8 266.8 235.1 201.1 196.7 165.9 13.06 18.84 15.96 26.1 14.1 16.58 233 220.6 262.8 220.7 na 182.3 147.5 13.58 19.1 15.62 na 13.76 15.88 234 219.2 255.3 215.5 193 183.2 151.4 13.16 19.02 15.92 25.72 13.78 16.58 235 208.9 241.9 203.9 na 162 138.9 13.12 18.66 15.56 na13,3 17,28 236 231,8 265,7 225 200 191,7 163,5 14,8 20,22 17,4 27,14 15,32 19,08 237 209,4 245,6 205,8 186,1 173,8 141,2 13,34 18,66 16,08 25,74 14,12 17,16 238 218,6 254,7 221,6 191,1 176,3 151 13,82 19,18 16,66 26,82 14,32 18,24 239 210,2 249,6 215,4 187,3 n.d. 144,4 12,6 18,26 15,56 25,06 n.d. 16,62 240 216,6 251,8 213,7 188,1 171,6 146,5 14,02 19,32 16,6 26,26 14,62 17,1 241 234,9 272 238,1 207,7 194,3 174,7 13,48 19,14 16,24 25,58 13,72 17,32 242 238,8 269,1 235,4 205,9 190,4 163,3 13,14 18,9 16,22 26,08 13,5 17,6 243 226 259,3 221,5 194,2 185,7 n.d. 13,44 18,62 16,02 25,66 13,74 n.d. 244 205,5 n.d. 197,9 178 153,1 138,8 13 n.d. 15,68 25,18 12,96 16,64 245 234,1 274,1 235,6 n.d. 205,5 160,8 14,2 19,62 16,26 n.d.14,4 17,5 246 198,7 231,7 196,8 172,3 157 128,1 12,5 18,2 15,5 24,92 13,4 16,44 247 240,2 271,4 231,7 207,1 195,2 166,2 14,12 19,7 16,92 26,68 14,7 17,44 248 210,6 250 212,3 184,7 173 142,9 12,9 18,94 15,86 25,52 13,9 17,12 249 223,4 253,5 219,6 191,9 178,4 149,9 12,92 18,32 15,72 25,42 13,44 16,98 250 215,7 253,4 212,6 189 173,8 147,6 13,28 18,82 16 26,28 14,16 16,44 251 212,5 255,2 209,2 n.d. 182,2 144,6 14,62 20,28 16,5 n.d. 15,18 16,96 252 224,4 266,9 219,4 n.d. 186,2 153 13,36 19,06 15,62 n.d. 13,74 16,98 253 224,3 258,6 219,5 197,1 186,9 155,8 13,72 19,22 16,94 26,2 14,72 17,56 254 227,4 265,1 221,6 n.d. 191,9 n.d. 13,84 19,36 16,18 n.d. 14 n.d. 255 218,1 248,8 211,6 n.d. 176,2 147,8 13,88 19,38 16,32 n.d.14.44 16.96 256 225.5 260.8 221.5 196.8 179.9 156.8 13.68 19.54 16.44 26.74 13.96 17.62 257 221.3 251.2 213.2 189.6 171.9 147.5 13.34 18.74 16.26 25.82 13.8 17.02 258 212.1 252.8 213.9 187.3 171.1 144.9 12.68 18.14 15.44 25.58 13.02 15.74 259 223.6 256.6 224.8 194.6 184.4 160.9 14.14 19.42 16.88 26.42 14.56 17.84 260 211.2 na 205.9 179 164.8 139 14 19.54 16.48 25.8 14.18 16.9 261 237.2 267.6 232.9 205.1 196 163.2 13.24 18.6 15.7 25.46 13.64 16.88 262 217.3 255.1 217.6 193.4 180.8 157.5 14.32 20.26 17.3 26.76 15 18.24 263 na 259.7 226.1 194.5 185.4 164.7 na 19.12 16.28 26.24 13.94 17.34 264 214.8 258.3 213.8 nd 181.9 137.7 12.44 18.18 14.82 nd 12.88 15.2. Petition 870210067187, dated 07 / 23 / 2021, pp. 73 / 110 70 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 265 229.2 258 224.3 195.9 183.7 156.2 13.96 19.14 16.58 26.52 14.26 16.88 266 211.5 226.2 200.4 176.1 162.7 140.4 12.68 17.7 15.32 25.52 13.42 16.3 267 241.9 256.6 229 198.7 179.4 167.9 13.16 18.36 15.68 26 12.82 16.48 268 231.5 250.4 222 194 190.4 161.1 13.92 19.54 17.12 26.48 14.46 17.12 269 231.6 246.5 217 193.1 182.5 149.2 13.16 18.02 15.96 25.76 13.42 16.86 270 227.1 251 221.9 191.5 182.3 155.8 14.16 19.42 16.82 26.94 14.6 16.98 271 196.5 244 199.6 na 160.4 131 13.72 19.5 16.08 na 13.88 16.72 272 217.7 238 210.5 182.1 173.2 150.8 13.48 18.84 15.92 26.06 13.78 16.92 273 239.9 252.3 225 196.4 183.2 163.3 13.84 18.52 16.14 26.78 13.54 17.56 274 227.2 245.2 218.2 188.1 175.4 147.8 13.52 18.82 16.24 26.42 14 16.5 275 217.4 260.2 215.8 na 170.3 140.3 13.52 18.86 15.56 na13,14 15,96 276 241,7 254,7 225,5 196,9 182,8 164,1 13,94 19,46 16,58 26,34 14,14 17,98 277 203,2 248,3 209 n.d. 166 140,5 13,08 18,72 15,56 n.d. 13,34 16,1 278 224,8 238,6 214 184 170,7 141,9 12,56 17,16 15,04 25,5 12,92 16,16 279 223,9 239,6 206,3 183,9 165,5 142,2 13,6 18,76 16,02 26,1 13,32 16,82 280 225,2 263,8 220,2 n.d. 187,8 155,3 13,46 19,36 15,76 n.d. 14,12 16,52 281 223,4 264,7 223,5 n.d. 183 150,1 13,26 19,08 15,5 n.d. 13,28 16,32 282 229,2 243,8 218,6 188,4 176 154,6 13 18,36 15,86 25,8 13,3 16,48 283 234,1 247,7 214,4 192,4 180 149,9 13,34 18,58 15,9 25,72 13,4 16,98 284 209,3 252,7 208,2 n.d. 167,6 n.d. 13,54 19,2 15,98 n.d. 14,34 n.d. 285 229,3 248,5 217,6 191,7 182,1 160,5 13,62 19,08 16,2 26,94 14,24 17,2 286 228,3 n.d. 218,9 194,4 183,9 159 13,24 18,42 16 26,4 13,86 16,3 287 n.d. 251,6 207,1 n.d. 177,8 138,5 n.d. 19,38 16,2 n.d. 14,24 15,86 288 224,5 238,2 210,4 183,8 168,8 148,4 12,86 17,94 15,5 26,14 13,68 15,9 289 234,5 255,4 222,7 195,3 184,6 n.d.13,7 18,78 16,66 26,4 13,9 n.d. 290 243,4 261,5 232,3 203,3 194,1 167,9 12,48 17,72 15,38 25,4 13,06 16 291 n.d. n.d. 222,9 n.d. 192,9 154,8 n.d. n.d. 16,54 n.d. 14,68 16,86 292 235,8 255 221,7 196,3 187,7 159 13,7 19,12 16,08 26,4 14,22 16,54 293 229,1 251,3 226,1 197,7 178,7 164,7 12,62 18,02 15,32 25,42 12,78 16,2 294 215,1 255 208,3 n.d. 168,5 n.d. 13,72 19,56 15,86 n.d. 13,46 n.d. 295 234,9 246,4 222,6 194,4 186,6 158,8 13,42 18,42 16,16 26,26 13,8 16,84 296 253,4 266 236 208 207,7 171,4 14,7 20,08 17,22 27,18 15,22 18,72 297 235,6 248,6 219,7 191,7 182,1 155,4 13,38 18,66 16,12 26,64 13,7 16,86 298 220,9 240,3 206,9 n.d. 169,2 145,5 13,64 18,88 16,28 n.d. 14,04 16,66 299 228,5 248,4 221,9 190,4 174,9 150,5 12,44 17,9 15,06 25 13,22 15,32 300 234,4 251,7 221,1 189,2 174,7 153,7 13,28 18,2 15,56 26,18 13,16 16,44 301 231,7 247 215,5 190,2 181,4 150,3 13,66 18,82 16,36 26,68 14,06 16,24 302 231,7 249,2 220,8 188,6 181 150,9 13,12 18,04 15,42 25,54 13,52 16,28. Petition 870210067187, dated 07 / 23 / 2021, pp. 74 / 110 71 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 303 201.7 223 191.9 165.3 144.3 nd 12.28 17.3 14.86 24.64 12.08 nd 304 204.5 251.5 ndnd 167.5 131.2 12.96 18.72 ndnd 13.22 16.1 305 228.9 242.3 212.2 189.3 177.1 154.6 13.4 18.46 15.88 26.78 13.84 16.88 306 214.1 232.3 205.8 179.3 167.4 na 13.5 18.9 16.06 26.78 14.1 na 307 220.9 247 213.6 188.7 167.2 145.6 12.98 18.26 15.78 25.84 12.96 16.96 308 222.6 236.6 206.7 184.4 171 150.9 12.82 17.6 15.32 25.42 13.18 16.2 309 217.6 238.3 206.9 184.8 172.1 145.5 12.48 17.6 15.38 25.1 12.88 16.02 310 226.8 245.9 209.1 189.1 170.8 145 12.74 17.64 15.36 25.34 13.32 15.74 311 207.7 251.8 210.8 na 173.6 138 13.24 18.9 15.52 na 13.48 15.4 312 na 247 221.5 189.3 182.3 155.8 nd 19.2 16.5 27.12 14.44 16.88 313 213.7 233.6 202.5 178.7 173.1 142 14.76 20.24 17.36 27.68 15.32 18.44 314 215.5 261.5 213.3 na 186.6 147.3 13.76 19.94 15.98 na14,62 16,96 315 201,6 248 203,7 n.d. 169,1 n.d. 13,24 19,12 15,56 n.d. 13,56 n.d. 316 236,1 253,3 231,8 198,3 188,7 163,9 14,02 19,28 16,82 26,96 14,46 17,18 317 227,2 244,3 212,8 184,4 177,4 147,7 13,42 18,96 16,5 26,68 14,12 16,36 318 247,3 263,6 235,3 205 202,1 169,3 14,92 20,38 17,62 27,58 15,34 18,08 319 210,2 255,7 212,3 n.d. 176,4 137,6 13,92 19,76 15,98 n.d. 14,1 17,22 320 228,2 241,5 210,5 186,8 176,2 149,6 13,08 18,24 16,02 26,44 13,56 15,94 321 234,1 252,3 216,2 194 177 157,3 12,64 17,64 15,28 25,76 12,46 16,46 322 211,4 238,3 205,2 179,6 168,9 138,3 12,46 17,64 15,04 25,16 13,06 16,02 323 207,1 223,5 197,3 170,7 156 131,9 12,84 17,98 15,62 26 13,42 16,92 324 214,6 253,3 214,5 n.d. 167,7 138,5 13,8 19,46 15,74 n.d. 13,42 16,62 325 233,9 250,2 221,4 192,3 178,5 151,3 13,02 17,94 15,48 25,4 13,42 16,26 326 214,8 257 210,5 n.d. 175,3 141,3 13,12 18,6 15,08 n.d.13,38 15,4 327 235 251,2 220,5 193,9 188,2 160,3 13,98 19,14 16,24 26,76 14,02 17,08 328 234,3 251,7 218,1 194,1 181,6 154,9 13,9 19,12 16,62 26,72 14,16 16,8 329 212,6 250,7 208,6 n.d. 168,9 134,3 13,48 18,78 15,42 n.d. 13,96 15,92 330 235,1 248,9 223,1 192,3 186,7 154 13,74 18,86 16,02 26,14 14,1 17,02 331 219,6 n.d. 214,4 n.d. 183,5 144,1 13,22 18,94 15,98 n.d. 13,96 16,38 332 228,7 n.d. 227 n.d. 196,2 161,9 13,76 n.d. 16,26 n.d. 14,26 16,96 333 219,3 238,1 206 181,5 166,1 147,4 12,86 18,38 15,58 26,14 13,66 16,28 334 221,6 241,5 214,4 187,3 174,2 144 12,46 17,6 14,92 24,98 12,88 15,52 335 231,9 251,1 221,7 195,9 188,9 160 13,58 18,68 16,04 26,36 14,26 17,1 336 259,5 n.d. 244,3 214,1 206,7 n.d. 14,36 n.d. 17,14 26,96 15,06 n.d. 337 232,4 245,2 214,6 192,5 186,2 158,1 13,56 18,74 16,52 26,48 14,1 17,08 338 222,8 261,9 217,8 n.d. 183,5 149,1 13,76 19,9 16,08 n.d. 14,54 16,88 339 222,8 237,5 207 184,2 169,4 142 12,76 17,96 15,2 25,28 13,02 15,22 340 227 242,2 212,8 184,4 175,7 n.d.13.08 18.16 15.5 26.08 13.4 na Petition 870210067187, dated 07 / 23 / 2021, pp. 75 / 110 72 / 91 Grain Yield Grain Moisture Line ID 7201 7212 7220 7330 8504 8644 7201 7212 7220 7330 8504 8644 341 203.6 243.2 197.3 nd 158.6 128.8 13.08 18.8 14.98 nd 12.8 15.28 342 247.1 259 234.2 203.6 184.6 166.7 13.1 18.12 15.56 25.5 12.76 15.78 343 ndnd 204.6 nd 160.3 130.9 nd 19.64 16.06 na 14.02 16.12 344 212.8 256.3 211.8 na 139 13.1 18.84 15.56 na 15.5 Note: Grain yield is expressed in alqueires / acre and grain moisture is expressed as a percentage. ND: Not determined. Table 2

[0177] BLUPs of 344 Lines for Moisture and Grain Yield of a Training Population Using a Mixed Model Approach Line ID Grain Yield Grain Moisture 1 Line ID Grain Yield Grain Moisture 1 135.3 15.96 27 154 16.42 2 146 16.26 28 143.5 16.29 3 149.7 16.3 29 161.3 17.21 4 145.3 17.19 30 149.5 15.33 5 149.9 17.32 31 160 16.87 6 150.1 16.62 32 139.8 16.24 7 157.3 17.14 33 167.6 16.72 8 136.5 15.9 34 161.6 16.62 9 171 17.08 35 144.7 15.96 10 143.5 16.09 36 128.5 15.65 11 175.7 16.69 37 144.4 15.88 12 158.1 16.97 38 130.5 15.32 13 149.5 17.08 39 140.7 16.19 14 136.3 15.69 40 138.5 16.6 Petition 870210067187, dated 07 / 23 / 2021, pp. 76 / 110 73 / 91 Line ID Grain Yield Grain Moisture 1 Line ID Grain Yield Grain Moisture 15 144.8 16.18 41 138.9 15.49 16 138.5 17.33 42 142.2 15.87 17 146.3 16.49 43 144.7 15.89 18 143 16.08 44 150.8 17.03 19 152.4 15.97 45 154.1 16.83 20 164.2 17.02 46 128 16.59 21 135.2 16.41 47 137.3 15.68 22 134.5 16 48 149.8 15.66 23 132.1 16.97 49 157.7 15.47 24 132.7 16.2 50 143.4 16.52 25 161.1 16.77 51 158 16.45 26 151.8 17.32 52 146 15.96 53 140.8 15.87 85 139.3 15.48 54 141.9 16 86 157.8 16.47 55 134.2 15.69 87 138.4 16.34 56 142.7 16.39 88 151.5 16.57 57 144.3 17.29 89 150.8 16.26 58 137.2 16.14 90 157.6 15.9 59 133.7 16.83 91 135.4 15.91 60 156.3 16.05 92 149.9 17.62 61 153.9 15.71 93 149.6 16.54 62 147.2 16.69 94 151.3 16.82 63 140.8 16.24 95 157.9 15.61 64 154 16.47 96 145.5 16.7 65 162.3 16.71 97 143.8 16.46 66 152.9 17.32 98 152.5 15.95 Petition 870210067187, dated 07 / 23 / 2021, pp. 77 / 110 74 / 91 Line ID Grain Yield Grain Moisture 1 Line ID Grain Yield Grain Moisture 67 148.9 16.95 99 141.9 16.12 68 140.3 17.31 100 146.9 17.48 69 136.8 16.48 101 150.4 16.58 70 137.2 16.75 102 149.7 16.48 71 159.3 17.11 103 159.1 16.47 72 138.3 16.64 104 133.6 16.87 73 145.2 17.6 105 139.7 16.76 74 139.6 15.43 106 150.9 16.48 75 150.2 17.32 107 135.6 16.17 76 142.8 16.15 108 153.3 16.21 77 147.8 16.37 109 154.4 17.38 78 177.1 17.04 110 141.2 16.92 79 130.5 15.89 111 137.5 16.25 80 150.1 15.9 112 130.5 16.13 81 148.7 16.83 113 156.1 18.19 82 140 16.18 114 148.6 16.26 83 156.9 16.62 115 149.3 16.71 84 143.3 15.82 116 144.4 16.59 117 146.3 17.47 149 143 17.03 118 155.1 17.09 150 139.7 16.98 119 143.6 16.62 151 146.2 17.27 120 155.5 16.42 152 152.6 16.34 121 144.4 16.45 153 141.2 15.94 122 150.4 17.35 154 153.8 15.89 123 153.5 16.48 155 150.1 16.15 124 161 17.05 156 142.4 16.8 Petition 870210067187, dated 07 / 23 / 2021, pp. 78 / 110 75 / 91 Line ID Gem Grain Yield Grain Moisture 1 Line ID Gem Grain Yield Grain Moisture 125 155.4 15.95 157 148.4 16.18 126 149.8 16.84 158 147.9 16.23 127 156.6 17.38 159 151.2 16.7 128 136 15.92 160 135.5 16.11 129 158.2 17.26 161 143.1 15.61 130 139.4 16.41 162 176.3 16.18 131 150.4 16.05 163 141.7 18.02 132 159.7 16.63 164 143.7 17.13 133 152.8 17.15 165 158.2 16.2 134 172.7 17.69 166 151 17.16 135 145.3 15.77 167 142.5 16.93 136 146.3 16.85 168 153.1 16.96 137 154.6 17.94 169 162 16.54 138 156.1 16.93 170 134.7 15.71 139 149.3 18.08 171 140.8 16.4 140 153.1 16.36 172 152.4 16.8 141 133.2 16.67 173 164.6 17.28 142 145 16.52 174 146.6 16.79 143 162.5 16.21 175 145.7 15.07 144 148.5 17.22 176 155.3 16.19 145 157.5 16.2 177 165.5 16.83 146 158.1 17.01 178 144.8 16.69 147 147.7 16.37 179 139.3 16.93 148 137.3 16.21 180 148.7 16.22 181 144.2 16.34 213 153.8 16.62 182 151.6 15.46 214 151.5 16.97 Petition 870210067187, dated 07 / 23 / 2021, pp. 79 / 110 76 / 91 Line ID Gem Grain Yield Grain Moisture 1 Line ID Gem Grain Yield Grain Moisture 183 155.1 16.48 215 138.8 16.78 184 145.6 17 216 155.2 16.32 185 164.8 16.33 217 161.7 16.64 186 150.3 16 218 147.8 16.09 187 147.9 16.8 219 146.8 15.94 188 154.4 17.47 220 159.6 16.44 189 143.6 17.36 221 143.3 16.58 190 145.5 16.4 222 161.9 16.08 191 151.2 16.31 223 158.5 15.84 192 158.5 16.77 224 129.2 16.22 193 135.6 16.31 225 160.6 16.4 194 166.2 16.98 226 148.7 16.34 195 157.9 16.91 227 153.2 16.68 196 161.1 16.33 228 157.3 16.6 197 150.2 16.74 229 172.9 16.84 198 149.7 17.09 230 151.7 16.55 199 137.4 16.43 231 152.6 15.98 200 144 16.34 232 164.1 16.49 201 163.7 16.51 233 152 16.38 202 150.6 15.41 234 150.4 16.41 203 157.8 16.6 235 136.9 16.37 204 172.4 16.51 236 160.1 17.96 205 145.4 16.07 237 141.4 16.56 206 155.7 16.63 238 149.7 17.18 207 158.3 16.95 239 144.4 15.98 208 145 16.39 240 145.7 17 Petition 870210067187, dated 07 / 23 / 2021, pages 80 / 110 77 / 91 Line ID Gem Grain Yield Grain Moisture 1 Line ID Gem Grain Yield Grain Moisture 209 163 17.44 241 167.2 16.62 210 149.3 17.09 242 164.2 16.61 211 132.4 16 243 154.2 16.36 212 137 17.04 244 133.4 16.06 245 166.7 17.14 277 139.1 16.16 246 128.9 15.91 278 143.4 15.65 247 165.6 17.26 279 141.3 16.48 248 143.3 16.42 280 155.6 16.62 249 150.3 16.2 281 154.1 16.28 250 146.3 16.54 282 149.3 16.2 251 146.2 17.43 283 150.6 16.37 252 155.1 16.53 284 141.5 16.72 253 154.4 154.4 285 152.4 16.9 254 157.8 157.8 286 154.8 16.42 255 146 16.95 287 143.8 16.61 256 154.2 17.01 288 143.4 16.07 257 146.7 16.54 289 155.3 16.73 258 144.7 15.85 290 164.1 15.76 259 154.8 17.22 291 157.9 17.04 260 138.5 16.84 292 156.5 16.71 261 164 16.31 293 155.3 15.81 262 151.1 17.63 294 143.7 16.62 263 157.3 16.8 295 154.6 16.53 264 146.7 15.55 296 170.6 17.83 265 155.2 16.91 297 152.9 16.6 266 134.2 15.9 298 142.2 16.67 Petition 870210067187, dated 07 / 23 / 2021, pp. 81 / 110 78 / 91 Line ID Grain Yield Grain Moisture 1 Line ID Grain Yield Grain Moisture 267 159.4 16.15 299 149.9 15.59 268 155.5 17.12 300 151.6 16.2 269 150.8 16.26 301 150.2 16.67 270 152.3 17.16 302 151.2 16.06 271 132.3 16.75 303 123.3 15.17 272 143.1 16.54 304 137.4 16.12 273 157.3 16.76 305 148.3 16.58 274 147.9 16.62 306 137.3 16.71 275 146.3 16.21 307 144.8 16.19 276 158.2 17.09 308 143.1 15.84 309 141.9 15.67 327 155.5 16.89 310 145.4 15.78 328 153.2 16.91 311 142 16.12 329 140.7 16.31 312 150.6 17.02 330 154 16.68 313 138.5 17.93 331 148.5 16.48 314 150.2 17 332 161 16.86 315 137.8 16.36 333 140.8 16.21 316 159.2 17.13 334 144.8 15.49 317 146.6 16.71 335 155.6 16.7 318 167.4 17.95 336 177.4 17.47 319 144 16.95 337 152.2 16.78 320 146.4 16.27 338 152.3 16.98 321 152.6 15.79 339 141.6 15.67 322 138.2 15.66 340 145.6 16.12 323 129.2 16.19 341 132.3 15.82 324 143.3 16.58 342 162.9 15.88 Petition 870210067187, dated 07 / 23 / 2021, pp. 82 / 110 79 / 91 Line ID Grain Yield Grain Moisture 1 Line ID Grain Yield Grain Moisture 325 152 15.99 343 134.5 16.65 326 145.3 15.94 344 144.2 16.1

[178] As model (1) was used to calculate the BLUPs shown above, the variance components, as shown in Table 3, can also be obtained. On the one hand, these variance components can be used to calculate the heritability of a trait. On the other hand, these variances will be used in calculating the effects of each marker below. Table 3

[0179] Variance components for Grain Yield and Grain Moisture in a Training Population using REML based on a mixed model approach Variance Grain Yield Grain Moisture Vg 94.27 0.32 Ve 18.23 0.11

[180] After the BLUP components of each line (Table 2) and variance (Table 3) were calculated, the effects of 240 markers with respect to the two traits, grain yield and grain moisture, were estimated based on the BLUPs of the lines and genotypes of these markers, following the approaches established above. For simplicity, these effects can be considered as the relative contribution of the individual marker to the trait of interest. The effects, along with the position of each marker based on the BLUP of 344 lines, are listed in Table 4. These effects were used to calculate the GBV of the lines in subsequent selection cycles. The overall means for grain yield and grain moisture were 148.97 and 16.51, respectively. Petition 870210067187, dated 07 / 23 / 2021, page 83 / 110 80 / 91 Table 4

[0181] Effects of Markers Estimated from Training Populations Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 1.1 0.0 -0.01 0.01 1.2 5.9 0.49 -0.01 1.3 8.3 -0.81 -0.03 1.4 23.1 1.59 0.02 1.5 27.4 -1.27 -0.08 1.6 28.5 0.27 0.12 1.7 41.3 -1.18 -0.03 1.8 42.5 1.04 0.05 1.9 45.8 1.06 -0.01 1.10 62.3 1.16 -0.03 1.11 67.3 -0.68 0.04 1.12 76.4 0.23 0.07 1.13 93.1 0.86 0.07 1.14 95.0 -0.28 -0.05 1.15 105.7 -0.26 -0.03 1.16 118.8 -1.06 0.05 1.17 127.0 0.84 -0.03 1.18 134.9 -0.04 0.09 1.19 144.3 -0.78 0.03 1.20 157.3 -0.77 -0.03 1.21 163.6 2.44 0.00 1.22 169.8 0.88 0.04 1.23 177.2 0.89 -0.05 1.24 183.3 -1.01 0.05 1.25 184.3 -0.51 -0.02 1.26 186.0 -1.06 -0.01 1.27 188.9 0.59 -0.01 1.28 206.1 -0.12 0.10 1.29 214.9 0.94 0.01 1.30 220.6 0.11 -0.01 1.31 224.6 -0.17 -0.01 1.32 233.2 -0.85 0.00 1.33 238.4 0.34 0.00 Petition 870210067187, dated 07 / 23 / 2021, pages 84 / 110 81 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 1.34 243.2 0.99 -0.04 1.35 248.9 3.54 -0.03 1.36 254.5 -1.17 -0.04 1.37 256.4 0.45 0.05 1.38 258.7 -0.34 -0.01 1.39 275.6 -2.02 -0.03 1.40 294.4 1.00 0.03 1.41 306.7 1.42 0.01 1.42 315.6 -0.88 0.02 1.43 316.2 0.10 -0.05 CHROMOSOME 2 2.1 0.0 -0.37 0.07 2.2 3.7 -0.90 -0.06 2.3 5.0 1.58 -0.01 2.4 11.4 -0.90 0.03 2.5 14.3 0.45 -0.05 2.6 33.5 1.53 0.11 2.7 38.1 0.23 0.02 2.8 50.1 -3.65 -0.09 2.9 58.5 0.64 -0.05 2.10 64.4 1.00 0.01 2.11 78.4 0.78 0.01 2.12 94.1 -0.48 0.02 2.13 94.6 1.31 0.02 2.14 104.7 -0.42 0.01 2.15 110.4 1.54 0.03 2.16 111.5 -1.56 0.06 2.17 118.2 0.07 0.08 2.18 130.1 0.83 0.00 2.19 144.4 0.15 0.01 2.20 146.7 -0.23 0.01 2.21 165.0 -1.51 -0.02 2.22 172.2 0.55 0.12 2.23 177.3 -0.67 -0.01 2.24 181.9 0.89 -0.02 2.25 184.4 0.66 -0.01 2.26 189.4 -0.15 0.06 Petition 870210067187, dated 07 / 23 / 2021, pages 85 / 110 82 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 2.27 196.6 1.10 0.01 CHROMOSOME 3 3.1 0.0 -0.69 -0.01 3.2 12.3 1.28 0.03 3.3 17.1 -0.81 0.04 3.4 28.1 1.20 -0.02 3.5 50.6 -0.96 0.03 3.6 61.5 -0.14 -0.03 3.7 72.4 2.05 0.01 3.8 76.8 -0.50 0.03 3.9 80.7 1.11 -0.01 3.10 83.6 0.06 0.00 3.11 93.6 0.02 -0.01 3.12 105.8 0.41 0.02 3.13 107.5 -0.92 0.07 3.14 125.6 1.43 0.08 3.15 126.0 1.08 0.05 3.16 128.9 0.00 -0.06 3.17 134.5 -1.43 -0.06 3.18 137.6 -0.39 0.05 3.19 140.8 0.10 -0.13 3.20 144.8 -0.20 0.02 3.21 157.3 0.00 0.06 3.22 175.2 -0.59 0.03 3.23 175.6 0.57 -0.04 3.24 175.9 0.18 0.01 3.25 179.2 -0.85 -0.07 3.26 205.2 1.62 0.04 3.27 210.9 -2.08 -0.07 3.28 222.1 0.80 -0.01 3.29 225.4 0.21 -0.02 CHROMOSOME 4 4.1 0.0 0.89 0.05 4.2 40.4 0.14 0.06 4.3 43.0 -0.80 -0.02 4.4 46.1 0.61 -0.01 4.5 56.7 -1.48 0.02 Petition 870210067187, dated 07 / 23 / 2021, pages 86 / 110 83 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 4.6 69.0 0.06 0.06 4.7 79.4 0.38 -0.06 4.8 83.8 1.89 0.07 4.9 92.3 -0.60 -0.03 4.10 99.7 -0.88 0.02 4.11 113.3 0.60 -0.07 4.12 117.3 0.46 0.05 4.13 120.0 0.42 -0.05 4.14 125.9 0.10 0.01 4.45 127.7 0.14 0.04 4.16 132.6 -0.37 -0.02 4.17 135.8 0.15 -0.01 4.18 144.2 -0.36 0.01 4.19 152.3 0.66 0.06 4.20 159.0 0.27 -0.10 4.21 165.7 1.93 0.01 4.22 171.3 -0.86 0.05 4.23 177.0 0.36 0.06 4.24 179.7 -0.62 -0.03 CHROMOSOME 5 5.1 0.0 0.27 0.02 5.2 1.5 -0.73 -0.02 5.3 10.0 -0.57 -0.12 5.4 13.1 1.47 0.06 5.5 14.0 0.63 0.06 5.6 17.5 0.08 -0.03 5.7 19.8 -1.08 0.00 5.8 24.2 -1.41 0.02 5.9 25.3 1.25 -0.01 5.10 29.0 0.94 -0.01 5.11 34.2 -0.45 0.00 5.12 42.6 0.15 -0.01 5.13 42.8 -0.84 -0.01 5.14 52.6 0.93 0.05 5.15 78.3 -0.48 -0.05 5.16 80.6 -0.32 -0.03 5.17 86.8 1.50 0.00 Petition 870210067187, dated 07 / 23 / 2021, page 87 / 110 84 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 5.18 93.0 0.90 0.04 5.19 95.1 0.12 0.01 5.20 106.2 -0.36 -0.07 5.21 110.9 0.22 -0.04 5.22 115.1 -0.47 0.05 5.23 129.1 -1.97 0.00 5.24 130.7 1.35 0.04 5.25 135.5 0.62 -0.05 5.26 138.7 -0.01 -0.02 5.27 142.6 -0.48 0.05 5.28 143.4 -0.24 -0.02 CHROMOSOME 6 6.1 0.0 -1.09 -0.02 6.2 4.7 1.22 0.07 6.3 18.5 0.83 0.05 6.4 20.5 -1.05 -0.06 6.5 28.9 0.53 0.09 6.6 97.6 -1.63 0.10 6.7 102.5 0.27 -0.05 6.8 104.1 0.01 0.02 6.9 106.4 1.01 0.01 6.10 188.3 -0.39 -0.03 6.11 197.8 0.57 -0.01 6.12 204.9 -1.69 -0.04 6.13 207.5 1.66 0.04 6.14 215.5 0.29 0.04 6.15 220.3 0.91 -0.01 6.16 224.0 0.07 0.02 6.17 227.3 0.24 0.05 6.18 232.4 0.30 0.06 6.19 236.5 -1.78 -0.07 6.20 260.4 0.00 -0.10 CHROMOSOME 7 7.1 0.0 1.09 0.01 7.2 1.8 0.83 0.03 7.3 2.1 -1.15 0.05 7.4 5.1 -1.10 -0.06 Petition 870210067187, dated 07 / 23 / 2021, pages 88 / 110 85 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 7.5 31.0 1.10 0.05 7.6 49.5 1.22 -0.02 7.7 51.2 -2.58 0.04 7.8 58.1 0.42 -0.03 7.9 59.6 0.73 0.02 7.10 61.5 0.15 -0.01 7.11 62.2 1.10 0.01 7.12 65.0 -0.66 -0.09 7.13 69.8 0.85 0.01 7.14 75.9 -1.50 0.00 7.15 90.6 0.28 0.06 7.16 98.0 -0.54 -0.03 7.17 115.4 -0.08 -0.04 7.18 118.5 -0.22 -0.04 7.19 124.5 -0.68 0.07 7.20 129.2 0.45 -0.04 7.21 130.5 -0.88 0.00 7.22 133.7 1.13 0.02 7.23 152.1 -0.38 -0.02 7.24 166.9 -1.81 0.00 7.25 175.2 1.94 -0.04 CHROMOSOME 8 8.1 0.0 -0.35 0.00 8.2 7.4 1.58 0.05 8.3 17.3 0.70 -0.02 8.4 20.2 -0.80 -0.02 8.5 29.1 0.73 0.08 8.6 35.7 -1.16 -0.05 8.7 52.6 -0.44 0.03 8.8 56.3 -0.01 0.02 8.9 72.9 -1.69 -0.01 8.10 75.4 1.40 -0.09 8.11 84.9 0.41 0.03 8.12 143.5 -0.23 -0.01 8.13 155.6 0.45 -0.01 8.14 233.6 0.58 0.01 8.15 234.8 -0.55 -0.04 Petition 870210067187, dated 07 / 23 / 2021, pages 89 / 110 86 / 91 Locus Position (cM) Effect of Yield Effect of Moisture CHROMOSOME 1 8.16 264.3 -1.47 -0.03 8.17 296.8 1.18 0.04 8.18 301.0 -0.36 -0.04 CHROMOSOME 9 9.1 0.0 -0.28 0.00 9.2 2.6 1.16 -0.03 9.3 11.1 1.17 -0.02 9.4 16.7 -2.06 -0.02 9.5 34.1 1.18 0.05 9.6 34.6 0.27 -0.04 9.7 39.6 -0.72 0.01 9.8 44.7 -0.89 0.03 9.9 53.8 0.05 -0.06 9.10 59.7 -2.35 -0.05 9.11 71.6 -0.32 -0.02 9.12 74.4 1.04 -0.05 CHROMOSOME 10 10.1 0.0 -0.38 -0.05 10.2 11.4 1.12 0.08 10.3 22.6 0.94 0.02 10.4 31.7 0.49 -0.03 10.5 34.8 -1.52 0.01 10.6 53.4 0.31 0.00 10.7 56.3 -0.20 -0.02 10.8 63.5 -0.29 0.00 10.9 70.5 1.08 0.00 10.10 75.0 -1.46 -0.01 10.11 82.0 0.95 0.01 10.12 101.2 1.02 0.01 10.13 113.1 0.14 -0.01 10.14 132.7 -0.83 0.07

[0182] Once the marker effects were calculated, GWS-SMART was used to select the top 10 crosses from the DH population. The GPV of each cross was calculated from the simulated offspring of the cross. The genotype of each offspring was simulated using GWS-SMART, and the GBV of the offspring was then... Petition 870210067187, dated 07 / 23 / 2021, pages 90 / 110 87 / 91 calculated using Equation (5). It should be noted that the offspring simulated by GWS-SMART were, in fact, the F2 offspring, since the F1 offspring have the same type of genotype from crossing two parent lines from a DH population. This GPV can also be calculated based on the right tail or left tail of the distribution of the GBVs of the offspring of a given cross. In total, from the 344 lines in the DH population, 344 * (344 - 1) / 2 = 58996 crosses are simulated to generate 58996 subsequent populations using GWS-SMART. The GPVs of each cross were calculated in GWS-SMART. Based on the GPVs, the top 10 crosses were eventually selected based on grain yield (see Table 5) and grain moisture (see Table 6), respectively. Table 5

[0183] 10 Superior Crosses Selected Based on GPV in relation to Grain Yield Crossbreed ID Lineage ID 1 Lineage ID 2 GPV 1 41 311 207.60 2 41 138 207.62 3 95 2 207.65 4 192 2 207.65 5 311 138 207.81 6 333 2 207.98 7 41 2 208.43 8 311 2 208.62 9 138 2 208.65 10 333 138 207.17 Petition 870210067187, dated 07 / 23 / 2021, pp. 91 / 110 88 / 91 Table 6

[0184] 10 Superior Crosses Selected Based on GPV in relation to Grain Moisture Crossbreed ID Lineage ID 1 Lineage ID 2 GPV 1 342 317 18.368 2 242 163 18.368 3 178 319 18.368 4 216 319 18.369 5 177 186 18.369 6 30 163 18.369 7 63 279 18.370 8 95 312 18.370 9 285 82 18.370 10 60 82 18.371

[0185] GWS-SMART can be performed based on multiple trait implementations. The only difference between multi-trait GWS-SMART and single-trait GWS-SMART, as described above, is the way of calculating GBV for a simulated offspring of a given cross. In this example, the multi-trait GBV algorithm will be used if grain yield and grain moisture are considered in the selection simultaneously as follows (Equations (6a) and (6b)). Based on multi-trait GBVs, the top 10 crosses were selected and listed in Table 7. Table 7

[0186] 10 Superior Crossovers Based on Multiple GPV Features Cross ID Line ID 1 Line ID 2 GPV of Multiple Traits 1 99 2 0.9909 2 95 2 0.9912 3 41 138 0.9914 4 192 2 0.9914 5 41 311 0.9915 6 138 311 0.9922 Petition 870210067187, dated 07 / 23 / 2021, pp. 92 / 110 89 / 91 Crossbreed ID Lineage ID 1 Lineage ID 2 GPV of Multiple Traits 7 333 2 0.9935 8 41 2 0.9952 9 138 2 0.996 10 311 2 0.9961 REFERENCES

[0187] All references listed below, as well as all references cited in this disclosure, including, but not limited to, all patents, patent applications and publications thereof, scientific journal articles and database entries (e.g., entries in the GENBANK® database and all annotations available therein) are incorporated herein by reference in their entirety, to the extent that they supplement, explain, provide a setting for, or teach the methodology, techniques and / or compositions used in this document.

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[0212] It will be understood that various details of the subject matter now disclosed may be altered without departing from the scope of the subject matter now disclosed. Furthermore, the above description is for illustrative purposes only, and not for limitation purposes.

Claims

1. A method for increasing genetic gain in a breeding population, characterized in that it comprises: (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting the breeding population from a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner could produce a segregating offspring population; (c) inferring or determining haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, where the genetic potential value of the offspring generation is the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e);and (h) select one or more breeding pairs based on Petition 870210067187, dated 07 / 23 / 2021, page 96 / 110 2 / 8 classification of step (g), wherein the crossing of the breeding pair selected in step (g) is predicted to generate offspring with greater genetic gain, and wherein the inference step, the simulation step, or both, are performed by a suitably programmed computer, wherein the offspring derived from it comprise a transgene.; 2. Method according to claim 1, characterized in that it further comprises repeating steps (b)-(e) and (g) such that at least one average performance value calculated in step (e) exceeds a predetermined value.

3. Method according to claim 1, characterized in that each breeding partner is a plant.

4. Method according to claim 3, characterized in that the plant is selected from the group consisting of corn, wheat, barley, rice, beet, sunflower, winter rapeseed, canola, tomato, pepper, melon, watermelon, broccoli, cauliflower, Brussels sprouts, lettuce, spinach, sugarcane, coffee, cocoa, pine, poplar, eucalyptus, apple and grape.

5. Method according to claim 4, characterized in that the plant is corn.

6. Method according to claim 1, characterized in that each breeding partner is a consanguineous individual.

7. Method according to claim 1, characterized in that the breeding partners are the same individual.

8. Method, according to claim 1, characterized in that one or more genetic markers are selected from the group consisting of a single nucleotide polymorphism (SNP), an insertion / deletion (indel), a simple sequence repeat (SSR), Petition 870210067187, dated 23 / 07 / 2021, page 97 / 110 3 / 8 a restriction fragment length polymorphism (RFLP), a random amplified DNA polymorphism (RAPD), a cleaved amplified polymorphic sequence marker (CAPS), a Diversity Array Technology (DArT) marker, an amplified fragment length polymorphism (AFLP) and combinations thereof.

9. Method, according to claim 1, characterized in that one or more genetic markers comprise at least one marker present in each interval of 5 cM, 3 cM, 2 cM, 1 cM, 0.5 cM, or 0.25 cM in the genomes of the breeding partners.

10. A method according to claim 1, characterized in that the inference step, the simulation step, the calculation step, or combinations thereof, includes consideration of the expected recombination rates between adjacent broad genomic markers.

11. Method, according to claim 10, characterized in that the recombination rate between at least one of one or more genetic markers and the genetic locus associated with the desired phenotype is zero.

12. Method according to claim 1, characterized in that the simulation step comprises simulating at least 100, 500 or 1000 descendants in the generation of offspring.

13. Method, according to claim 1, characterized in that the supply step comprises estimating the effects with respect to the desired phenotype of genomic-wide plurality markers based on best linear one-sided phenotypic predictions (BLUPs) and genotypic marker data in the biparental breeding population using best linear one-sided genomic-wide prediction (GBLUP).

14. Method, according to claim 1, characterized in that the inference comprises using a minimum recombination principle (MRP).

15. Method, according to claim 1, characterized in that the breeding population consists of n members and the replication comprises simulating all n(n-1) / 2 unique matings of the members of the breeding population.

16. Method according to claim 1, characterized in that the feature of interest comprises at least two independent features of interest.

17. Method according to claim 16, characterized in that it further comprises assigning to each independent feature of interest an importance value in relation to the other independent features.

18. Method according to claim 1, characterized in that the selection of one or more breeding pairs based on the ranking of step (g) comprises selecting the breeding pairs for which the genetic potential values ​​of the offspring generations are ranked in the 20%, 10%, 5% or 1% highest.

19. Method according to claim 1, characterized in that it further comprises (i) crossing one or more breeding pairs selected in step (h) to generate offspring with greater genetic gain.

20. Method for increasing the probability of producing an individual offspring with a desired phenotype, characterized in that it comprises: (a) providing effects with respect to a trait of interest from a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner could produce a segregating offspring population; (c) inferring haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, wherein the genetic potential value of the offspring generation may be calculated as the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member, or it may be calculated based on the right tail or the left tail of the distribution of genomic breeding values; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), the selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e);(e) and (h) select one or more breeding pairs based on the ranking from step (g), wherein the inference step, the simulation step, or both are performed by a suitably programmed computer, and further, wherein it is predicted that each of one or more breeding pairs has a higher probability of producing offspring with the desired phenotype versus other breeding pairs in the breeding population, wherein the offspring derived from it comprises a transgene.

21. A method for generating offspring with a desired genotype, characterized in that it comprises: (a) providing the effects with respect to a trait of interest of a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners; (b) selecting the breeding population from a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner could produce a segregating offspring population; (c) inferring haplotypes with respect to the plurality of genomically broad markers for the first breeding partner and the second breeding partner;(d) simulate a cross between the first breeding partner and the second breeding partner to produce a generation of offspring, with each member of the offspring generation comprising a simulated genotype; (e) calculate a genetic potential value of the offspring generation, wherein the genetic potential value of the offspring generation is the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both, from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e);(h) select one or more breeding pairs based on the classification from step (g), and (i) reproduce the one or more breeding pairs selected in step (h) to generate an offspring individual with a desired genotype, wherein the inference step, the simulation step, or both are performed by a suitably programmed computer, wherein the offspring derived therefrom comprise a transgene.

22. Method for increasing genetic gain in a breeding population, characterized in that it comprises: (a) providing effects with respect to a trait of interest of a plurality of genomically broad markers in a breeding population comprising a plurality of potential breeding partners, wherein the provisioning step comprises estimating the effects with respect to the desired phenotype of the plurality of genomically broad markers based on the best linear one-sided phenotypic predictions (BLUPs) and genotypic marker data in the biparental breeding population using the best linear one-sided genomically broad prediction (GBLUP), and further, wherein the estimation comprises estimating genetic variation by retained maximum likelihood (REML) estimation based on phenotypic data from various loci using Equation (1): y ij =μ+ϋ igi +L jbj +e ij(1), where: yij is a phenotype of line i at locus j;μ is a general mean of the desired phenotype of a trait; Gi is a variable indicator representing the genotype of line i; gi is a genotypic effect of line i, relative to gi~N(0,ag2); Petition 870210067187, dated 23 / 07 / 2021, page 102 / 110 8 / 8 (b) select from the breeding population a first breeding pair comprising a first breeding partner and a second breeding partner, wherein the crossing of the first breeding partner and the second breeding partner could produce a segregating offspring population; (c) infer or determine haplotypes relative to the plurality of genomically broad markers for the first breeding partner and the second breeding partner; (d) simulate a cross between the first breeding partner and the second breeding partner to produce an offspring generation, with each member of the offspring generation comprising a simulated genotype;(e) calculate a genetic potential value for the offspring generation, where the genetic potential value for the offspring generation is the average of the genomic breeding values ​​of the simulated genotypes of the offspring generation member; (f) repeat steps (b)-(e) one or more times, wherein in each iteration of step (b), selection comprises selecting a different first breeding partner, a different second breeding partner, or both from the breeding population; (g) rank each simulated cross from step (d) based on the genetic potential values ​​calculated in step (e);(e) and (h) select one or more breeding pairs based on the ranking from step (g), wherein the crossing of the breeding pair selected in step (g) is predicted to generate offspring with greater genetic gain, and further, wherein the inference step, the simulation step, or both are performed by a suitably programmed computer, wherein the offspring derived from it comprise a transgene.

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