The effect of cycles of genomic selection on the wheat (T. aestivum) genome

The effect of cycles of genomic selection on the wheat (T. aestivum) genome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of cycles of genomic selection on the wheat (T. aestivum) genome Nelly Arguello Blanco, Clay Sneller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1938161/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2023 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Genomic selection (GS) is widely used in plant breeders to shorten breeding cycles. Our objective was to assess the impact of rapid cycling GS on the wheat genome We used 3927 markers to genotype a training population (YTP) and individuals from five cycles (YC1-YC5) of GS for grain yield. We assessed changes of allele frequency, genetic distance, population structure, and linkage disequilibrium (LD). We found 27.3% of all markers had a significant allele frequency change by YC5, 18% experienced a significant change attributed to selection, and 9.3% had a significant change due to either drift or selection. A total of 725 of 3927 markers were fixed by YC5 with selection fixing 7.3% of the 725 markers. The genetic distance between cycles increased over time. The Fst value of 0.224 between YTP and YC5 indicates their relationship was low. The correlation between LD matrices and the number of LD blocks decreased over time. Overall, we found reduction in genetic diversity, increased genetic differentiation of cycles from the training population, and restructuring of the LD patterns over cycles. The accuracy of GS depends on the genomic similarity of the training population and the prediction populations. Our results show that similarity can decline rapidly over cycles of GS and seriously compromise the predictive ability of the YTP-based model. Our results support implementing a GS scheme where the training and prediction populations co-evolve instead of the use of a static training population. Wheat genomic selection breeding genome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key Message We documented changes in the wheat genome attributed to genomic selection including loss of diversity, and changes in population structure and linkage disequilibrium patterns. We discuss the implications to breeding. Introduction The goal in wheat ( Triticum aestivum L.) breeding is to accumulate favorable alleles by recombining the available genetic diversity in a gene pool into trait-enhanced inbred lines. These genotypes result from a breeding cycle involving crossing parents, inbreeding the progeny, and selecting the best progeny to release as a variety or to become parents of the next cycle. Many important wheat traits such as grain yield are inherited quantitatively. With relatively inexpensive genome-wide genotyping capacities, breeders are now employing genomic selection (GS) to improve the efficiency of breeding for quantitative traits and to improve genetic gain per unit of time. Genomic selection utilizes molecular markers and phenotyping information collected in a training population (TP) to model genomic estimated breeding values (GEBVs) of individuals related to the TP. Breeders can then select superior individuals based solely on their GEBVs (Meuwissen et al. 2001) to become parents of the next breeding cycle (Hoffstetter et al. 2016a), to advance lines through stages of trialing (Borrenpohl et al. 2020; Endelman et al. 2014), to predict line performance in untested environments (Huang et al. 2016; Huang et al. 2018), and to collaborate in multilocation testing and sharing of wheat lines across breeding programs with sparse testing (Atanda et al. 2021; Crespo-Herrera et al. 2021; Jarquin et al. 2020; Sneller et al. 2021). An advantage of GS over phenotypic selection (PS) is that GS can greatly reduce the duration of a breeding cycle and increase selection intensity. Because of the shorter cycle duration and higher selection intensity, GS can potentially change allele frequencies faster than PS (Jannink 2010; Makanjuola et al. 2020). Selection and drift are the main factors determining allele frequencies in closed breeding populations and both can lead to allele fixation and reduced genetic diversity. The dynamic of genetic diversity in wheat has mostly been studied in a historical and domestication context (Cabrera et al. 2014; Christiansen et al. 2002; Fu and Somers 2010; Reif et al. 2005; Roussel et al. 2005). Christiansen et al. (2002) used microsatellites to study the change in genetic diversity of spring wheat varieties released between 1900 and 1990s in the Nordic countries, and Roussel et al. (2005) studied microsatellites allelic diversity in 480 European wheat varieties developed between 1840 and 2000. These studies showed shifts in diversity, primarily attributable to the breeder's temporal needs and strategies for managing diversity. Fu and Somers (2010) studied microsatellite allele differences between populations with and without improvement for a given trait in wheat. Genotypes without improvement showed higher microsatellite diversity than wheat genotypes with improvement. The rate of increase or decrease in diversity was trait-dependent as selection for one trait could increase the allelic diversity, whereas selection for another trait could reduce allele diversity. Reif et al. (2005) compared microsatellite diversity in T. tauschii , landraces cultivars, and modern wheat cultivars. T. tauschii was more diverse than the landraces indicating a reduction in diversity during domestication. Landraces were more diverse than modern cultivars indicating a reduction of diversity through breeding. In addition, the comparisons within the modern cultivars showed less diversity when lines were closer in release date than lines farther apart (Reif et al. 2005) Few studies have researched genetic diversity in current breeding populations undergoing genomic selection. Jacobson et al. (2015) researched genetic variance in GS and PS selection bi-parental maize populations with different selection intensities. As selection intensity increased, genetic diversity decreased but remained unchanged with PS. Jacobson et al. (2015) considered this reduction in diversity minimal and that it had little impact on trait value. Long-term studies to monitor changes in allele frequency under selection are limited to maize ( Moose et al. (2004). The authors studied 49 markers in a population that had undergone forward and reverse selection for high and low oil content. Of the 49 markers studied, 59% showed significant differences in allele frequency, and 20% showed evidence of selection (Moose et al. 2004). Animal breeders have report realized genetic gain due to GS while also measuring the inbreeding rate per generation of breeding. Makanjuola et al. (2020) measured the rate of inbreeding in cattle before and after the implementation of GS. They showed that ten years of GS in cattle increased inbreeding by up to 2.06% per generation. This inbreeding rate was sufficient to elicit cattle breeders to develop strategies for better management of genetic resources. Scott et al. (2021) showed that the rate of inbreeding changed from 0.037% to 0.18% per year in Jersey bulls when using GS and from 0.037% (prior to GS) to 0.760% (after GS) per year in Holstein bulls. Aiming at better management of genetic diversity in animal breeding, Meuwissen et al. (2020) introduced the genomic optimal contribution selection method (GOC) that controls for inbreeding rate by limiting co-ancestry in crossing schemes. They found that using the same marker set to construct the identity-by-descent matrix and to estimate the GEBVs provided the highest genetic gain per unit of inbreeding. Meuwissen et al. (2020) suggested that GOC is a good method to obtain genetic gain while maintaining neutral alleles that may be needed for animal welfare and diversity for genetic gain (Meuwissen et al. 2020). Although the impact of GS on diversity was acknowledged more than 10 years ago (Jannink 2010), little has been reported on the actual impact of GS on genetic diversity and population structure. Rutkoski et al. (2015) measured the rate of inbreeding and genetic variance in populations used to improve quantitative resistance to stem rust of wheat. This study initiated with the same genetic pool (generation 0) and conducted one cycle of PS and two cycles of GS. The PS cycle increased inbreeding by 0.05 units per cycle, whereas GS increased inbreeding by 0.08 in cycle 1 and by 0.21 units in cycle 2, demonstrating that GS can reduce diversity faster than PS (Rutkoski et al. 2015). Allele frequencies within closed, finite populations can change due to mutation, selection, or drift. However, regardless of what causes the change, fixation of alleles reduces genetic diversity. Reduction in diversity can compromise the response to selection and reduce short and long-term genetic gains. In addition to reducing diversity, changes in allele frequencies can lead to genetic differentiation between the TP and the prediction population (PP). Changes in allele frequencies can lead to population differentiation and recombination could reduce the LD between markers and QTL. If later breeding cycles (PP cycles) have a different LD pattern than that of the TP then the accuracy of GS will be reduced (Wientjes et al. 2013). Research to overcome the challenge of reduced diversity has been conducted in plant breeding (Gaynor et al. 2017; Gorjanc et al. 2018; Gorjanc and Hickey 2018; Lado et al. 2017). Gorjanc et al. (2018) presented the optimal cross selection method (OCS) that is analogous to the GOC in animal breeding (Meuwissen et al. 2020). Both OCS and GOC limit population-wide inbreeding by penalizing crosses among closely related individuals. Gorjanc et al. (2018) also report that OCS could help maintain the prediction accuracy between the TP and the prediction populations (PP). The effect of rapid cycling with GS on the wheat genome is unknown. It is essential to assess the effect of rapid cycles of GS on genetic diversity, population structure, patterns of LD, and allele frequencies. Monitoring these population characteristics enables a) strategic planning of crosses, b) maintenance of trait genetic variation and response to selection, c) deciding when to utilize new genetic variation, d) retraining GS models for accurate predictions, and e) understanding alteration in the genome due to rapid cycling with GS. This research uses single nucleotide polymorphisms in a TP and five cycles of GS to accomplish the following objectives: 1) to quantify the magnitude of change in allele frequency, 2) to examine whether drift or selection drove the observed changes in allele frequency, and 3) to assess the impact of changes in allele frequency on genetic diversity, LD patterns, and population structure. Materials And Methods Training Population The training population (YTP) for this study was created for implementing GS for yield, fusarium head blight (FHB, caused by Fusarium graminearum ) index of infection, and quality traits. The YTP was previously described by Hoffstetter et al. (2016a). The YTP consists of 470 F 4 -derived soft red winter wheat breeding lines representing the diversity in 2010 of the breeding programs at The Ohio State University. The YTP resulted from 47 bi-parental crosses using 23 parents derived from seven wheat breeding programs in the United States. Training Population Phenotyping The phenotyping of the YTP has been described by Hoffstetter et al. (2016a). Briefly, the YTP was phenotyped for FHB index in inoculated and misted trials with three replications in Wooster, Ohio during the 2011 and 2012 growing seasons. Index of FHB was rated as percentage of all spikelets showing visual symptoms of infection. The YTP was assessed for grain yield in Custar, Fremont, and Wooster, Ohio, in 2010 and 2011. The grain yield experiment consisted of 13 augmented blocks with 37 YTP lines and three standard checks per block. Analysis of yield data indicated that the Custar and Fremont sites had little genotype x environment interaction between them, while the Wooster site was distinct from those sites (Hoffstetter et al. 2016b). Consequently, the Best Linear Unbiased Estimates (BLUES) for grain yield were obtained 1) over the Custar and Fremont sites, referred to as Northwest sites (NW), 2) over the Wooster sites, referred to as Wooster sites (WOO), and 2) overall sites (ALL). Genomic Selection Cycles Five breeding cycles of genomic selection for grain yield, abbreviated as YC1, YC2, YC3, YC4, and YC5 were conducted. For the first selection cycle (YC1), 71 crosses were made using 14 YTP lines as parents (Table 1). The F 1 seed produced by these crosses was sown in the greenhouse and self-pollinated to produce the F 2 seed. The F 2 seed was planted for a final YC1 population of 922 F 2 individuals (Table 1). Each F 2 plant was genotyped, and their GEBVs were estimated for yield in NW, WOO, and ALL (all sites combined) were estimated with the model trained with YTP data. While we originally planned to include quality traits and FHB resistance in the selection process, the final selection was based primarily on yield due to its importance in cultivar release. For each cycle, the F 2 plants were grouped based on their GEBVs for yield into priority groups 1, 2, and 3. Group-1 contained the most superior plants, and we crossed among them whenever flowering allowed. If a group-1 plant was ready for crossing when no other group-1 plant was ready, then the group-1 plant was crossed to a cross-ready group-2 or group-3 plant. We made a total of 78 crosses among the YC1 F 2 plants. The F 1 seed from the 78 crosses was planted to initiate YC2. Cycles YC2, YC3, YC4, and Y5 were conducted similarly. The number of F 2 plants, the times an F 2 was used as a parent, and the number of crosses per cycle is summarized in Table 1. The total time to conduct one GS cycle was 12 months and includes the time that takes to go from a cross to initiating a new cross from the preceding progeny. Genotyping and SNP Calling Leaf tissue of 2-week-old F 2 plants was harvested, lyophilized, and DNA extracted in a 96-well plate according to the DNeasy® 96 Plant Kit (Qiagen 2022) protocol. YTP RILs and YC F 2 individuals were genotyped in the wheat genetics laboratory at Kansas State University (KSU). KSU utilized the genotyping-by-sequencing (GBS) protocol for wheat developed by Poland et al. (2012). This GBS protocol consists of a) reduction of the wheat genome complexity by cutting it with restriction enzymes ( PstI and MspI ), b) ligation of the restriction pieces to adaptors, and c) amplification of fragments by polymerase chain reaction (PCR). SNPs were discovered and called with the TASSEL-GBS software, following the pipeline described in Glaubitz et al. (2014). The steps of the GBS pipeline include 1) identifying tags and taxa in the fastQ files, 2) aligning to the wheat reference genome to extract each tag's genomic position (mapping distances in base pairs), and 3) posterior variant detection (SNP calling). Marker Data Preparation Genotyping-by-sequencing produced 270,717 single nucleotide polymorphisms (SNP) across all the chromosomes of the wheat genome. We calculated the type, count, and frequency of alleles (major, minor, indels) and the proportion of missing and heterozygous data for each SNP in the YPT. Then, for each genotype in the YTP, we calculated the proportion of missing and heterozygous data. These summaries served as the reference for loci filtering in all cycles. Round 1 and 2 of Filtering For the first round of filtering, guided by the loci and genotype summaries, we remove 1) loci with > two alleles, 2) unmapped loci, 3) YTP lines with ≥ 50% missing data, 4) loci with missing data > 0.00, and 5) loci with <= 0.10 minor allele frequency (MAF). For the second filtering round, we merged YTP and YC cycles with only loci remaining from round 1 of filtering. Merged data became the master file to remove loci with > two alleles (appearing in YC cycles) and loci with missing data > 0.005 Round 3 and 4 of Filtering In the third round of filtering, executed with loci remaining after round 2 but with unmerged cycles, all YC1-YC5 genotypes with missing data > 0.05 were discarded. Finally, the fourth filtering round purged markers (using only the YTP), with heterozygosity ≥ 0.25, and then thinned by a minimal distance of 50 bp in adjacent loci. The YPT is a RIL population expected to be 100% homozygous at all loci. However, after the third filtering round, we found heterozygosity in the YTP ranging from 0.02 – 1.00, requiring a fourth filtering round for highly heterozygous loci. After four filtering steps, we retained a set of 3972 biallelic and mapped SNPs for all subsequent analyses in 6911 wheat genotypes (Table 1, Table 2). Data Analysis Genetic Drift Simulation We simulated the probability of obtaining a particular allele frequency in the YC5 due to drift using the genetic.drift function in the learnPopGen version 1.0.4 (Revell 2019) package in R version 4.2.1 (R Core Team 2022). Simulations were run for five mating cycles, assuming an effective population size of 20, and setting initial frequencies ranging from 0.50 – 0.99 in increments of 0.01 as these values represented all possible major allele frequencies in the training population, termed the TPMA. The simulation was run 100 times for each possible frequency to provide a variance (of the possible frequencies in the YC5 that could be produced by drift. This provided 50 estimates of , one for each frequency between 0.50 to 0.99. For each marker we did a one-tailed T-test to determine the probability of its observed value in the YC5 given its frequency in the YTP and the variance associated with that frequency. Regression Analysis of polymorphism information content (PIC) and Allele Frequency We estimated the PIC for all loci within each cycle as follows, where p i is the frequency of the i th allele of the j th marker in that cycle and n is the number of alleles at the j th marker (Botstein et al. 1980). We determined the PIC value and the frequency of the TPMA for all markers, in all cycles. We then regressed the allele frequencies and PIC values of each marker onto cycle numbers where the YTP was considered cycle 0. The regression analysis was performed using Proc Reg in SAS v9.4 Genetic Distance We estimated the genetic distance (GD) among individuals, within and between populations. The GD was estimated as the complement for a simple matching coefficient method (GD=1-SMC). The GD between the i th and jth individuals was calculated as: Where n 1,1 and n 0,0 Are the times the i th , and j th individuals share the same allele. And n 1 ,0 and n 0, 1 , are times individuals do not share the same allele. The distance between individuals was estimated within and between cycles. We used the dist.binary() (Dray and Dufour 2007) function in R version 4.2.1 (R Core Team 2022) to calculate the GD. Linkage Disequilibrium A matrix of LD between all pairs of SNPs was calculated for each cycle separately as: D is estimated between two loci ( A , B ), each with two alleles (A1 and A2, B1 and B2) producing A1B1 , A1B2 , A2B1 , A2B2 haplotypes with frequencies x 11, x 12, x 21, and x 22, respectively. Allele frequencies are equal to = x11 + x12 and = x 11 + x21 . We estimated the with LD() (Gregory Warnes 2021) function in the genetics package version 1.3.8.1.3 developed for R version 4.2.1 (R Core Team 2022). we conducted a Mantel test (Mantel 1967) to compare the LD matrices between every pair of LD matrices. We use the mantel() function from the package vegan developed in R (Oksanen et al. 2022; R Core Team 2022), with 9999 permutations to estimate a p-value. The Mantel test correlates variables between two matrices and the rows and columns are permuted to then estimate a p-value for the test. The LD blocks by chromosome were estimated using the function BigLD() from the package gpart version 3.15 (Kim et al. 2019) developed in R version 4.2.1 (R Core Team 2022). We defined an LD block as a group of SNPs with LD > 0.2. The LD blocks were only created for the YPT and the YC5. Population Structure The degree of differentiation between populations was estimated using the fixation index (F ST ) calculated as: Where a and b are the two populations, and p ajk represent the frequency of the j th allele of the k th SNP in population. These F ST values were calculated, between all pairs of cycles, with the function pairwise.neifs from the hierfstat package version 0.5-11 (Goudet 2005) coded in R version 4.2.1 (R Core Team 2022). To further study and visualize the structure among all populations, we conducted a discriminant analysis of the principal components (DAPC) (Jombart et al. 2010). The DAPC uses principal components scores to group observations with a discriminant analysis. We found the posterior probability of assigning an individual to one of six groups using the interactive function dapc (Jombart and Ahmed 2011) in R version 4.2.1 (R Core Team 2022). We first ran a principal component analysis of all genotyped individuals using all 3927 markers. For DAPC, we established six prior groups (YTP, YC1-YC5) and retained 17 principal components that explained 51% of the variance. The DAPC assigned posterior probabilities of belonging to one of the six cycles for each genotype. For example, a line in the YTP population is assigned six posterior membership probabilities (to YTP, YC1, YC2, YC3, YC4, and YC5). Association Analysis (AA) We conducted an association analysis (AA) for yield in NW, WOO, and ALL combined environments. We used GAPIT (Lipka et al. 2012) accounting for the population structure with principal components and kindship relationships. The AA was conducted with the 3972 markers. Genomic Estimated Breeding Values (GEBV) GEBVs were calculated for yield in NW, WOO, and ALL, with the ridge regression best linear unbiased predictor (rrBLUP) using the function mixed.solved (Endelman 2011) in the rrBLUP R package version 4.2.1 (R Core Team, 2020). Results Marker Data After four rounds of filtering, 3927 SNPs common across all cycles were used for analysis. The number of SNPs per chromosome ranged from 22 (4D) to 460 (2B) (Table 2). The SNP count in genome B was the highest (1878), followed by genome A (1596) and genome D (463). We did not impute missing values for the population genetics analyses (F ST , allele frequencies, GD, LD): missing marker data were imputed with the mean value for estimating GEBVs. Population and Genomic Estimated Breeding Values This research covered six years of implementing rapid cycling GS in The Ohio State University wheat breeding program. The first round of crosses occurred in 2011, with 3% of the YTP lines selected as parents based on GEBV and phenotypes for grain yield in NW and WO and FHB resistance (Table 1). Crosses were made, and F 2 plants were selected to be used as parents based on the GEBV for grain yield in NW and WOO in subsequent cycles. Grain yield in WOO was prioritized because it had the highest heritability (Hoffstetter et al. 2016a) and the greater variation for GEBV. The selection intensity in the subsequent cycles ranged from 3% to 8% (Table 1). The average GEBV for YLD increased over cycles (Table 3, Figure 1) while variance among the GEBVs decreased (Figure 2). The mean GEBV increased at a per cycle rate of 19 kg/ha for NW, 35 kg/ha for ALL sites, and 73 kg/ha for the WOO site. The largest change in mean GEBV was observed from YTP to YC1 in all sites. The GEBV variance, adjusted to that expected in the F 4 generation (e.g., 1.75 times greater than in the F 2 ), decreased over cycles for yield in NW and ALL (Figure 2). Variance for yield in WOO decreased from YTP to YC2, followed by an increase to YC5. Much of the reduction in variance occurred due to fewer individuals with low GEBVs (Figure 1). F 2 Individuals with yield GEBV greater than the maximum YTP GEBV were noticed by YC1 for yield over all environments, but not until later cycles in NW and WOO (Figure 1). Polymorphism information content (PIC) and genetic distance (GD) The PIC value for each of the 3927 SNP loci was estimated within the YTP and each cycle. The PIC is a characteristic for a marker and indicates how informative the marker is as well as its diversity. The PIC values ranged from 0.18 – 0.50 in the YTP and 0.00-0.50 in cycles (Figure 3A). The average PIC value decreased from 0.358 in the YTP to 0.227 in YC5. The minimum PIC value for a SNP in the YTP was 0.18, while PIC values of 0.0 (i.e., fixation) were observed in all subsequent GS cycles. We found a PIC value of zero for 76 and 616 loci in YC1 and YC5, respectively. We calculated the GD between individuals within and between cycles. The average GD increased slightly from the YTP to YC1 and declined slightly afterward (Figure 3B). When considering all possible comparisons with the YTP, the average GD between cycles was highest for YTP versus YC1 (0.562) and lowest for YTP versus YC4 (0.554) (Table 4). The average GD did not steadily change as cycles progressed; however, the minimum GD showed substantial change. For example, the minimum distance found in the YC1 was 0.157, whereas the YC5 comparison had a minimum difference of 0.318. In this study, the GD is an estimation of genetic diversity for a given individual versus every other individual. Population structure The F ST analysis showed differentiation of all cycles from the YTP with greater genetic differentiation with each subsequent cycle (Table 5, Figure 4A). For instance, the YC5 and YTP are five cycles apart and showed the highest F ST value (F ST = 0.224). There was an increase of 0.0456 F ST units per cycle difference, and the average F ST between consecutive cycles was 0.039. The first and second principal components obtained from an analysis using all 3927 markers explained 11.6 % and 4.2%, respectively, of the variation among all YTP and YC1-YC5 individuals. We conducted a discriminant analysis of the principal components (DAPC) to place lines into the prior (YTP, YC1 – YC5) grouping. For the DAPC, we retained 17 principal components, which cumulatively explained 51% of the variance and retained five discriminant functions ( N-1 , where N is the number of prior groups). The DAPC results showed the cycles becoming more distant from the YTP as the cycle number increased (Figure 5). More admixture is observed between cycles that are closer in number, than if cycles are far from each other. The admixture with the YTP reduces with the increase in GS cycles. For example, cycles YC1, YC2, and YC3 clustered closer to the YTP than cycles YC4 and YC5 (Figure 5). The DAPC assigns a posterior probability for an individual being in a prior group (YTP, YC1-YC5). We divided the posterior probabilities by quartiles (0.00 – 0.25, 0.26 – 0.50, 051 – 0.75, 0.76 – 1.00) and reported the frequency of individuals falling within each range (Table 6). For instance, we found that 80% of YTP individuals have between 0.00 – 0.25 posterior probability of belonging to the YC1 population. Linkage Disequilibrium We calculated a matrix of LD values among all SNP pairs within each cycle and performed a Mantel test to assess the correlation between the LD matrices. All pairs of matrices were significantly correlated at P <0.0001. The correlation of LD matrices decreased responding to their separation in time (Table 7, Figure 4B). The correlation value decreased by -0.057 for each unit of increase in the difference between cycle number. We use the YTP and YC5 LD matrices to extract LD blocks by population and chromosome. We defined a block as a group of SNPs with LD > 0.2. The YTP had 604 LD blocks, ranging from 2 - 51 loci per block. The LD blocks were dynamic, that is, some chromosomes would develop new LD blocks and others would lose LD blocks (Table 8). There was a 23% reduction in the number of LD blocks by YC5 compared to the YTP. The total number of blocks ranged from three (3A) and 67 (3B) in the YTP; and 13 (5D), 42 (3B) in the YC5 (Table 8). Regression Analysis of Allele Frequency We calculated the frequency of the major allele in the YTP (TPMA) in all cycles for all SNPs. We then calculated the correlation of the TPMA frequency between all cycles (Table 9). This correlation declines as cycles become more separated by cycle number with a slope of -0.106 per difference between cycle numbers (Figure 4C). The rate of change in allele frequency was estimated for each marker by regressing the frequency of the TPMA in each cycle onto the cycle number where the YTP was called cycle 0. The same regression was done using the PIC value of each marker. We declared an allele frequency change significant if the for the TPMA and PIC regressions both exceeded 0.70, and if both regression models had a probability < 0.05. A significant change was attributed to selection if the frequency in YC5 had a probability < 0.05 based on the distribution of possible allele frequencies generated by simulating drift. The TPMA and PIC regressions determined that 1074 (27.3%) SNPs had a significant allele frequency change by YC5 (Figure 6). These 1074 loci showed an average TPMA frequency in the YTP of 0.72, followed by YC1 of 0.65, YC2 of 0.59, YC3 of 0.58, YC4 of 0.57, and YC5 of 0.57. We deemed that 707 (18% of all loci) loci had a significant allele frequency change due to selection. An additional 367 (9.3%) loci had a significant change, but the cause was ambiguous as these changes could be due to drift or selection that did not meet our criteria. We considered alleles fixed if the TPMA frequency equaled 0 or 1 in YC5. A total of 725 loci (18.5%) had become fixed by YC5. Of these 53 became fixed due to selection and with the rest fixed due to drift or low selection pressure. Many loci (2853, 72.7%) across the wheat genome showed no significant change in the TPMA frequency, but 512 out of these loci became fixed by YC5. Genomewide Association Analysis We conducted genome-wide association analysis (GWAS) for yield in NW, WOO, and ALL. All 3972 SNPs and the yield data collected in the YTP were used for GWAS. We focused on the results for the 1074 whose allele frequency was deemed to show significant change. Overall, we found 101 of these 1074 markers were significantly (P < 0.05) associated with at least one trait and 40 loci were significant for at least two traits. Two markers on chromosome 2B, three on 5A, and one on chromosome 6A were significant for yield in NW, WOO, and ALL environments. The allele frequency of these markers was not fixed by YC5, and these six markers were not deemed to be under selection. Discussion This study documents the effect of five GS cycles for grain yield on the wheat genome. Our analysis includes estimating the proportion of genome with a significant change in allele frequency, discerning between genetic drift and selection as the cause of those changes, and the change in the index of fixation (F ST ), diversity, and LD between the YTP and YC1-YC5 cycles. Additionally, we report the distribution of GEBVs for all cycles and connect changes to the value of alleles as determined by GWAS. Finite populations undergo selection, genetic drift, and recombination that can alter allele frequencies and the LD between markers and QTL (Flint-Garcia 2013; Fu 2015; Gorjanc et al. 2018; Hufford et al. 2019; Louwaars 2018). Plant breeders oscillate between 1) using current alleles of their elite pool to create diversity through recombination to 2) reducing diversity by selecting the most promising recombinants. Inexpensive genotyping can facilitate monitoring this creating/reducing dynamic. Selection inevitably causes changes in the genome, leading to loss of diversity and limiting long-term genetic gain though these changes may occur faster with GS than traditional phenotyping breeding. In the present study, five cycles of GS were completed in five years. The selection resulted in significant changes in alleles frequency compared to the TP, loss of diversity. fixation of alleles, increasing differentiation of the cycles from the TP, a general reduction in LD blocks, and increased differentiation of LD patterns over the cycles. We attributed these results to the selection intensity of 3% to 13% per cycle and to limited population size (n=445 to 1821) (Table 1) . The documented changes could have a significant effect on GS prediction accuracy. We used conservative criteria to determine if an allele frequency change over cycles was significant and whether the change was driven by selection or genetic drift. There was a significant change in allele frequency by cycle-5 for 27.3% of the loci relative to the YTP (Figure 6). Of the 1074 loci deemed to have a significant allele frequency change by cycle-5, 69% of the changes were attributed to the selection process, while the remaining significant changes could be due to either drift or selection. We found alleles fixed at 18.3% of all loci by cycle-5, indicating a loss of diversity. The percentage of all loci that became fixed and attributed to selection (1.3%) was much less than the percentage that became fixed due to other causes (98.7%). The fact that much of the fixation was not attributed to selection suggests that the genetic bottleneck caused by intense selection at each cycle was a primary cause of fixation. In addition, there was a decrease in the average PIC values and GD among individuals within a cycle as cycle number increased (Table 4, Figures 3A, 3B) indicating reduced diversity beyond the noted fixation. Research to overcome the challenge of reduced diversity from selection has been conducted in plant breeding (Gaynor et al. 2017; Gorjanc et al. 2018; Gorjanc and Hickey 2018; Lado et al. 2017). To conserve diversity in plant breeding, Gorjanc et al. (2018) presented the optimal cross selection method (OCS) and Gorjanc and Hickey (2018) developed AlphaMate to implement OCS. The OCS method is analogous to the genomic optimal contribution (GOC) selection method proposed for managing diversity in animal breeding (Meuwissen et al. 2020). Both OCS and GOC minimize population-wide inbreeding by penalizing crosses among closely related individuals. Gorjanc et al. (2018) also report that OCS could help maintain the prediction accuracy between the training population and the prediction populations. It would be interesting to simulate OCS in our populations to see how it would affect diversity and impact gains in GEBVs compared to our results. We cannot compare our results to genome changes that may occur using five cycles of PS as that assessment would take 35 years to complete (seven years per cycle of PS in out program). Rutkoski et al. (2015) compared GS with PS in wheat and showed that inbreeding increased faster with GS (0.08 per cycle) than with PS (0.05 per cycle), indicating that GS changed allele frequencies faster than PS. Results of an increase in inbreeding from GS are reported in animal breeding by Makanjuola et al. (2020). They investigated the changes in inbreeding, co-ancestry, and effective population size in cattle. They studied three periods that represent significant changes in breeding strategies: BLUPs (1990 – 1999), inbreeding control (2000 – 2009), and GS implementation (2009 – 2018). Makanjuola et al. (2020) report a rapid increase of inbreeding during GS implementation, and a steady rise in the BLUP and inbreeding control periods. The percentage increase of inbreeding reported for the three periods was 1.50% (BLUP), 0.50% (inbreeding control) and 1.95% (GS). We found that the genetic distance between cycles and the TP increased the further apart they were in time. The GD (Table 4) and Fst values (Table 5, Figure 3) between cycles also increased as cycle become further apart in time. This was supported by the DAPC analysis (Table 6, Figure 5) that showed individuals from five cycles of GS became more differentiated from the YTP as the cycle number increased. The greatest differentiation was between the YTP and YC5 where the Fst was 0.224 whereas the Fst between the YTP and YC1 was 0.059. Frankham et al. (2002) state that F ST values greater than 0.15 indicate significant differentiation in plants, while a value less than 0.05 indicate little differentiation. These results indicate the cycles had evolved to the point where they were not closely related to the YTP, and thus the YTP may not be predictive of genetic values in the later cycles. The reported change in LD patterns and number of LD blocks over five cycles of GS could impact the accuracy of GS predictions in later cycles (Tables 7 and 8). Prediction accuracy depends on maintaining the LD pattern between markers and QTL that existed in the TP over cycles of GS. Decay of this LD will reduce the accuracy of GS. We observed that the correlation of the LD matrices between cycles decreased at a rate of -0.056 as cycles became separated over time. This shows that the LD pattern between markers was changing. One can then assume that the LD between markers and QTL is also likely to be changing and that will affect the accuracy of GS. While we did observe an increase in GEBVs over the cycles (Figure 1), the predictions are based on the YTP data and assume that the marker-QTL associations of the YTP remain constant through the cycles. The change of LD over cycles indicates that a GS model based on the LD in the YTP may quickly lose predictive ability during rapid cycling. This study shows that GS can produce rapid changes in the genome and much of the change does not seem to result from selection per se . The changes seem very likely to compromise the predictive ability of a GS model in a relatively few number of cycles as well as reduce diversity. The accuracy of GS depends on a close genetic relationship of the TP and the PP. This relationship decayed quickly in this study. We cannot estimate the reduction of GS accuracy from the genome changes that we have documented, but our results serve as a reference to understand the impact of rapid cycling on the wheat genome and their implications for GS. Our results also show that a static TP may have a very limited useful lifespan over cycles of GS and is not desirable. Breeding schemes employing GS need to accommodate genome changes as well as short and long term breeding goals. Reducing diversity within a closed population is a goal of breeding as the selection process should fix favorable alleles and eliminate unfavorable ones. This is desirable in the short term but not the long term. Short term gains are of paramount importance in a competitive market. Our results indicate that the breeding scheme first outline by Gaynor et al. (2017) and supported by Gorjanc et al. 2018 is well suited to accommodate genome changes as well as short and long tern goals. They proposed coupling rapid cycling GS used in population improvement using GS in the product development phase of breeding. Here GS is executed in a dynamic breeding pipeline to identify new parents at the early stages of field testing using phenotypes, GEBVs, and OCS. This reduces the number years per cycle compared to selecting parent only after the later stages of testing (Borrenpohl et al. 2020; Gaynor et al. 2017; Gorjanc et al. 2018). At the same time GS is used in rapid cycling employing a GS model that is continually updated with data of the ongoing breeding pipeline. This can be viewed as “evolving-GS” where data from every new cohort of lines that enter the product development phase is also used to update the TP and the parental pool. The genomes of the population will be changing though TP and PP in this scheme can be viewed as evolving together in genetic space such that their genomes remain similar and GS accuracy can be maintained within the shifting window defined by the current TP and PP. The use of some common parents across cohorts will increase the relationship between the TP and PP and would allow data from past cohorts to be relevant to the current PP. This co-evolution of TP and PP is in stark contrast to the static TP such as our YTP. To some degree the genome changes we observed are inevitable consequences of the selection and drift that are inherent to all breeding and must be accommodated in a GS-based breeding scheme. A carefully constructed GS scheme can be implemented in an ongoing breeding pipeline where the genome information of the co-evolving TP and PPs maintains GS accuracy over many years of breeding. This would meet both the short and long term needs of a program. These schemes can be easily implemented by genotyping all lines that enter the product development phase and using data from the product development phase to update the TP every season to capture information on the changing genome and overcome some of the issues we report in this study. Declarations Acknowledgments: We wish to thank the many people who helped with this research including Cassi Sewell, Mao Huang, and Amber Hoffstetter. Funding: This project was supported by The National Institute of Food and Agriculture of the USDA, grant 20146701322419 Competing Interests: The authors have no financial or non-financial interests to disclose. Data Availability: Data is available from the corresponding author Author Contributions: NAB advanced material through the cycles, preformed data analyses, and drafted the manuscript; CHS designed crosses, organized data, suggested analyses, and drafted/edited the manuscript. 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Description YTP YC1 YC2 YC3 YC4 YC5 Population size 445 834 909 1821 1572 1330 Number of crosses made to create the cycle - 71 78 98 87 80 Number of parents used to create the cycle - 14 84 122 121 42 The cycle of the parents - YTP YC1 YC2 YC3 YC4 Generation genotyped RILs F 2 F 2 F 2 F 2 F 2 Mean heterozygosity 0.101 0.149 0.132 0.101 0.081 0.092 Mean genetic distance 0.547 0.548 0.527 0.493 0.462 0.461 Table 2 . Summary by wheat genome of the number of single nucleotide polymorphism (SNP) markers used in this study. Chromosome Genome A Genome B Genome D 1 149 161 134 2 239 460 49 3 239 378 33 4 182 145 22 5 259 237 63 6 193 226 84 7 335 271 68 Totals 1596 1878 453 Table 3 . Summary statistics of genomic estimated breeding values (GEBVs) for grain yield in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. Trait Cycle Mean Minimum Maximum Range Variance Skewness Yield, YTP -3 -198 210 408 6942 0.19 All environments YC1 107 -31 213 243 2832 -0.26 (kg/ha) YC2 121 -18 232 251 1597 -0.38 YC3 147 0 221 221 1550 -1.20 YC4 173 7.8 253 245 1918 -1.52 YC5 194 24 271 248 2065 -1.73 Yield YTP -4 -218 153 370 4689 -0.11 Northwest Ohio YC1 80 -58 158 216 1185 -0.58 (kg/ha) YC2 91 3 163 160 657 -0.23 YC3 93 4 167 170 559 -0.37 YC4 103 21 184 163 727 -0.10 YC5 116 15 188 173 758 -0.43 Yield YTP -5 -517 536 1053 39695 0.12 Wooster Ohio YC1 171 -158 479 637 19284 0.01 (kg/ha) YC2 212 -100 494 594 10792 -0.24 YC3 285 -142 513 655 12353 -1.00 YC4 341 -90 556 647 13166 -1.62 YC5 387 -125 578 704 15454 -1.98 Table 4 . Summary statistics of the genetic distance (GD) within and between the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. The GD was calculated as 1-simple matching coefficient using data from 3927 markers. Comparison Cycles Apart Minimum GD Mean GD Maximum GD Standard Deviation YTP YTP 0 0.11 0.55 0.67 0.05 YC1 1 0.16 0.56 0.67 0.04 YC2 2 0.28 0.56 0.67 0.04 YC3 3 0.27 0.56 0.67 0.05 YC4 4 0.29 0.55 0.68 0.05 YC5 5 0.32 0.56 0.67 0.04 YC1 YC1 0 0.07 0.55 0.66 0.06 YC2 1 0.18 0.54 0.66 0.05 YC3 2 0.17 0.53 0.66 0.05 YC4 3 0.27 0.53 0.65 0.05 YC5 4 0.33 0.54 0.65 0.05 YC2 YC2 0 0.14 0.53 0.64 0.05 YC3 1 0.15 0.52 0.66 0.05 YC4 2 0.23 0.51 0.64 0.05 YC5 3 0.32 0.52 0.64 0.04 YC3 YC3 0 0.12 0.49 0.66 0.06 YC4 1 0.11 0.48 0.66 0.06 YC5 2 0.26 0.49 0.65 0.05 YC4 YC4 0 0.07 0.46 0.65 0.07 YC5 1 0.13 0.47 0.64 0.06 YC5 YC5 0 0.09 0.46 0.64 0.07 Table 5 . Fixation index (Fst) between the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. YTP YC1 YC2 YC3 YC4 YC5 YTP 0.059 0.107 0.152 0.207 0.224 YC1 0.034 0.059 0.111 0.137 YC2 0.027 0.075 0.112 YC3 0.029 0.073 YC4 0.046 Table 6. Results from the discriminate analysis of the principal components (DAPC) performed with 3972 markers genotyped on all individuals in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. The table presents the percent of individuals from each prior group within each quartile of the posterior probability of membership for each group. For example, 70.6% of the individuals in the YTP had a > 0.75 posterior probability of being in YTP while only 3.3% of the YTP individuals had a >0.75 posterior probability of being in the YC1. Prior Posterior Total Quartile of posterior membership probability 0.00 - 0.25 0.26 - 0.50 0.51 - 0.75 0.76 - 1.00 YTP YTP 449 20.04 3.79 5.57 70.60 YC1 80.40 10.91 5.35 3.34 YC2 88.86 8.24 2.45 0.45 YC3 92.43 4.68 2.00 0.89 YC4 99.55 0.45 0.00 0.00 YC5 100.00 0.00 0.00 0.00 YC1 YTP 834 86.57 4.32 2.88 6.24 YC1 38.85 23.86 21.70 15.59 YC2 74.70 14.51 6.35 4.44 YC3 57.55 23.86 13.91 4.68 YC4 94.72 4.32 0.96 0.00 YC5 99.76 0.24 0.00 0.00 YC2 YTP 909 98.13 1.10 0.11 0.66 YC1 75.58 17.82 3.85 2.75 YC2 35.20 24.42 19.36 21.01 YC3 46.75 32.34 14.63 6.27 YC4 91.86 6.16 1.21 0.77 YC5 99.45 0.11 0.22 0.22 YC3 YTP 1821 98.13 0.66 0.33 0.88 YC1 84.95 7.80 5.33 1.92 YC2 80.40 8.24 4.17 7.19 YC3 31.69 22.41 18.34 27.57 YC4 75.34 9.06 9.12 6.48 YC5 94.40 2.03 2.47 1.10 YC4 YTP 1572 99.11 0.00 0.19 0.70 YC1 96.31 2.86 0.76 0.06 YC2 95.99 2.29 1.72 0.00 YC3 70.93 16.79 8.72 3.56 YC4 25.19 16.67 18.07 40.08 YC5 81.04 6.11 3.82 9.03 YC5 YTP 1330 100.00 0.00 0.00 0.00 YC1 100.00 0.00 0.00 0.00 YC2 100.00 0.00 0.00 0.00 YC3 96.77 2.93 0.30 0.00 YC4 68.35 13.46 14.14 4.06 YC5 13.53 8.35 10.90 67.22 Table 7 . Mantel statistic between the matrices of marker linkage disequilibrium of the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. All values are significant at p < 0.0001. YTP YC1 YC2 YC3 YC4 YC5 YTP 0.833 0.883 0.851 0.772 0.668 YC1 0.884 0.859 0.756 0.652 YC2 0.925 0.801 0.686 YC3 0.864 0.864 YC4 0.879 YC5 Table 8 . Summary of linkage disequilibrium blocks in the training population (YTP) and in the fifth cycle of genomic selection (YC5) in winter wheat. The YTP column indicates the number of LD blocks present in the training population. The YC5 column indicates the number of LD blocks present in the cycle 5 of genomic selection. The difference is the number of YC5 LD blocks minus the number of YTP LD blocks. Chromosome Total Loci YTP YC5 Difference (YC5-YTP) 1A 149 27 13 -14 2A 239 44 18 -26 3A 239 34 38 4 4A 182 32 5 -27 5A 259 36 6 -30 6A 193 31 10 -21 7A 335 51 9 -42 1B 161 23 5 -18 2B 460 28 5 -23 3B 378 67 25 -42 4B 145 28 14 -14 5B 237 40 7 -33 6B 226 47 22 -25 7B 271 56 23 -33 1D 134 18 9 -9 2D 49 7 3 -4 3D 33 3 7 4 4D 22 4 1 -3 5D 63 8 13 5 6D 84 16 2 -14 7D 68 5 9 4 Table 9 . Correlation of the allele frequencies of 3927 markers in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. YTP YC1 YC2 YC3 YC4 YC5 YTP 0.642 0.557 0.493 0.429 0.395 YC1 0.899 0.880 0.811 0.760 YC2 0.944 0.881 0.815 YC3 0.961 0.893 YC4 0.941 Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2023 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Minor revisions 05 Sep, 2022 Reviewers agreed at journal 14 Aug, 2022 Reviewers invited by journal 13 Aug, 2022 Editor assigned by journal 11 Aug, 2022 First submitted to journal 07 Aug, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1938161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":128729574,"identity":"cc6dbbe7-1fdb-41e3-b068-b7c48950854d","order_by":0,"name":"Nelly Arguello Blanco","email":"","orcid":"","institution":"The Ohio State University OARDC: The Ohio State University Ohio Agricultural Research and Development Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nelly","middleName":"Arguello","lastName":"Blanco","suffix":""},{"id":128729575,"identity":"7512d2ed-8fed-4b2b-8669-062e86c3b579","order_by":1,"name":"Clay Sneller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYHACxgMPgKR9ewOE10CMngMJQMKA5wDJWiQSiNQiP7v5wIGEinsM5pKPD3/mYbCR3XCAgBaDO8cSDiScKWawnJ2WJs3DkGZMWItEjsGBxDago27nmDHzMBxOJKhFfkb+hwOJ/4Babp4xBjrsP2EtDDdyGA4kNiQwGNzgMQA67ABhLQY30gwOJBxL4JHsSUuTnGOQbDyTsMOSHz74UJMgx89++PCHNxV2sn0EHQYFPFBLiVQ+CkbBKBgFowA/AAAe/UU4DO9aDAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4195-4743","institution":"The Ohio State University OARDC: The Ohio State University Ohio Agricultural Research and Development Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Clay","middleName":"","lastName":"Sneller","suffix":""}],"badges":[],"createdAt":"2022-08-07 13:34:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1938161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1938161/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-023-04279-0","type":"published","date":"2023-03-23T20:06:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25419758,"identity":"d25958a2-3327-413e-9166-2d9ce6015b75","added_by":"auto","created_at":"2022-08-19 16:01:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44494,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the genomic estimated breeding values for individuals in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat.\u0026nbsp;Traits are yield over all environments (YLDALL), yield in Northwest Ohio (YLDNW), yield in Wooster Ohio (YLDWOO), and Fusarium Head Blight index (FHB).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/99d9cc7f1c6942b0669106a9.jpg"},{"id":25419756,"identity":"af629757-507e-43ac-adf5-6ba2a2da8ea8","added_by":"auto","created_at":"2022-08-19 16:01:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35046,"visible":true,"origin":"","legend":"\u003cp\u003eVariance of the genomic estimated breeding values (GEBV) for grain yield for individuals in the training population (YTP) and each of five cycles (YC1-YC5) of genomic selection in winter wheat. Yield was estimated over all environments (YLDALL), over Northwest Ohio environments (YLDNW), or over Wooster Ohio environments (YLDWOO).\u0026nbsp;The YTP was comprised of F\u003csub\u003e4\u003c/sub\u003e derived lines, and the cycles were comprised of F\u003csub\u003e2\u003c/sub\u003e individuals.\u0026nbsp;The variance of GEBVs in the cycles was multiplied by 1.75 to equate it to the variance expected among of F\u003csub\u003e4\u003c/sub\u003e individuals.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/4f348b5703c1420705e65f08.jpg"},{"id":25420491,"identity":"71149ce9-91f4-44a4-8d3d-e53051afc6bf","added_by":"auto","created_at":"2022-08-19 16:06:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54443,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of A) polymorphism information content for 3927 markers scored in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat, and B) genetic distance (1-simple matching coefficient) among individuals in the training YTP and YC1-YC5\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/0228bf370bfd4db6f34ddf4d.jpg"},{"id":25419753,"identity":"7ccf44cb-a633-4518-b121-d8852bc18160","added_by":"auto","created_at":"2022-08-19 16:01:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47390,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference of A) fixation index (F\u003csub\u003eST\u003c/sub\u003e), B) Mantel statistic of the linkage disequilibrium matrices and C) the correlation of allele frequencies when comparing values between the between the training population and the five cycles of genomic selection in winter wheat.\u0026nbsp;The X axis is the difference between cycle number.\u0026nbsp;For example, the difference in cycle number when comparing the training population (assigned as cycle 0) to cycle 1 is 1, as is the difference between cycles 4 and 5, while the difference between comparison of the training population and cycle 5 is 5.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/80a91aa94fa858ac2b519bd4.jpg"},{"id":25420492,"identity":"ce332523-d3e3-49dd-b0e8-eb0b53eefa9e","added_by":"auto","created_at":"2022-08-19 16:06:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63087,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the scores from the discriminate analysis of the principal component (DAPC) of all individuals in the training population (YTP) and each cycle of genomic selection (YC1-YC5).\u0026nbsp;The DAPC was conducted using the first 17 principal components from the principal component analysis of 3927 markers.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/a55a8d739733ed317270e73f.jpg"},{"id":25419754,"identity":"0ba2c195-9002-44af-9889-a5623e97dafb","added_by":"auto","created_at":"2022-08-19 16:01:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46479,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of 3927 wheat markers as to whether they 1) had a significant change in allele frequency over five cycles of genomic selection, 2) whether the change for markers with a significant change could be attributed to selection, and 3) whether the markers become fixed or not.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/586d43751b4595080270e8c6.jpg"},{"id":44723193,"identity":"288d86e8-5748-4fc4-9eec-396208bab48e","added_by":"auto","created_at":"2023-10-16 20:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":624770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1938161/v1/43e8ff3e-8988-41bc-ac4c-b1ba487d69b8.pdf"}],"financialInterests":"","formattedTitle":"The effect of cycles of genomic selection on the wheat (T. aestivum) genome","fulltext":[{"header":"Key Message","content":"\u003cp\u003eWe documented changes in the wheat genome attributed to genomic selection including loss of diversity, and changes in population structure and linkage disequilibrium patterns. We discuss the implications to breeding.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe goal in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) breeding is to accumulate favorable alleles by recombining the available genetic diversity in a gene pool into trait-enhanced inbred lines. \u0026nbsp;These genotypes result from a breeding cycle involving crossing parents, inbreeding the progeny, and selecting the best progeny to release as a variety or to become parents of the next cycle. \u0026nbsp;Many important wheat \u0026nbsp;traits such as grain yield are inherited quantitatively. With relatively inexpensive genome-wide genotyping capacities, breeders are now employing genomic selection (GS) to improve the efficiency of breeding for quantitative traits and to improve genetic gain per unit of time. Genomic selection utilizes molecular markers and phenotyping information collected in a training population (TP) to model genomic estimated breeding values (GEBVs) of individuals related to the TP. \u0026nbsp;Breeders can then select superior individuals based solely on their GEBVs (Meuwissen et al. 2001) to become parents of the next breeding cycle (Hoffstetter et al. 2016a), to advance lines through stages of trialing (Borrenpohl et al. 2020; Endelman et al. 2014), to predict line performance in untested environments (Huang et al. 2016; Huang et al. 2018), and to collaborate in multilocation testing and sharing of wheat lines across breeding programs with sparse testing (Atanda et al. 2021; Crespo-Herrera et al. 2021; Jarquin et al. 2020; Sneller et al. 2021).\u003c/p\u003e\n\u003cp\u003eAn advantage of GS over phenotypic selection (PS) is that GS can greatly reduce the duration of a breeding cycle and increase selection intensity. \u0026nbsp;Because of the shorter cycle duration and higher selection intensity, GS can potentially change allele frequencies faster than PS (Jannink 2010; Makanjuola et al. 2020). \u0026nbsp;Selection and drift are the main factors determining allele frequencies in closed breeding populations and both can lead to allele fixation and reduced genetic diversity. The dynamic of genetic diversity in wheat has mostly been studied in a historical and domestication context (Cabrera et al. 2014; Christiansen et al. 2002; Fu and Somers 2010; Reif et al. 2005; Roussel et al. 2005). \u0026nbsp;Christiansen et al. (2002) used microsatellites to study the change in genetic diversity of spring wheat varieties released between 1900 and 1990s in the Nordic countries, and Roussel et al. (2005) studied microsatellites allelic diversity in 480 European wheat varieties developed between 1840 and 2000. \u0026nbsp;These studies showed shifts in diversity, primarily attributable to the breeder\u0026apos;s temporal needs and strategies for managing diversity. \u0026nbsp;Fu and Somers (2010) studied microsatellite allele differences between populations with and without improvement for a given trait in wheat. \u0026nbsp;Genotypes without improvement showed higher microsatellite diversity than wheat genotypes with improvement. The rate of increase or decrease in diversity was trait-dependent as selection for one trait could increase the allelic diversity, whereas selection for another trait could reduce allele diversity. \u0026nbsp;Reif et al. (2005) compared microsatellite diversity in \u003cem\u003eT. tauschii\u003c/em\u003e, landraces cultivars, and modern wheat cultivars. \u0026nbsp;\u003cem\u003eT. tauschii\u0026nbsp;\u003c/em\u003ewas more diverse than the landraces indicating a reduction in diversity during domestication. Landraces were more diverse than modern cultivars indicating a reduction of diversity through breeding. \u0026nbsp;In addition, the comparisons within the modern cultivars showed less diversity when lines were closer in release date than lines farther apart (Reif et al. 2005)\u003c/p\u003e\n\u003cp\u003eFew studies have researched genetic diversity in current breeding populations undergoing genomic selection. \u0026nbsp;Jacobson et al. (2015) researched genetic variance in GS and PS selection bi-parental maize populations with different selection intensities. \u0026nbsp;As selection intensity increased, genetic diversity decreased but \u0026nbsp;remained unchanged with PS. \u0026nbsp;Jacobson et al. (2015) considered this reduction in diversity minimal and that it had little impact on trait value. Long-term studies to monitor changes in allele frequency under selection are limited to maize ( Moose et al. (2004). \u0026nbsp;The authors studied 49 markers in a population that had undergone forward and reverse selection for high and low oil content. Of the 49 markers studied, 59% showed significant differences in allele frequency, and 20% showed evidence of selection (Moose et al. 2004).\u003c/p\u003e\n\u003cp\u003eAnimal breeders have report realized genetic gain due to GS while also measuring the inbreeding rate per generation of breeding. \u0026nbsp;Makanjuola et al. (2020) measured the rate of inbreeding in cattle before and after the implementation of GS. \u0026nbsp;They showed that ten years of GS in cattle increased inbreeding by up to 2.06% per generation. This inbreeding rate was sufficient to elicit cattle breeders to develop strategies for better management of genetic resources. \u0026nbsp;Scott et al. (2021) showed that the rate of inbreeding changed from 0.037% to 0.18% per year in Jersey bulls when using GS and from 0.037% (prior to GS) to 0.760% (after GS) per year in Holstein bulls. \u0026nbsp; Aiming at better management of genetic diversity in animal breeding, Meuwissen et al. (2020) introduced the genomic optimal contribution selection method (GOC) that controls for inbreeding rate by limiting co-ancestry in crossing schemes. They found that using the same marker set to construct the identity-by-descent matrix and to estimate the GEBVs provided the highest genetic gain per unit of inbreeding. Meuwissen et al. (2020) suggested that GOC is a good method to obtain genetic gain while maintaining \u0026nbsp;neutral alleles that may be needed for animal welfare and diversity for genetic gain (Meuwissen et al. 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Although the impact of GS on diversity was acknowledged more than 10 years ago (Jannink 2010), little has been reported on the actual impact of GS on genetic diversity and population structure. \u0026nbsp;Rutkoski et al. (2015) measured the rate of inbreeding and genetic variance in populations used to improve quantitative resistance to stem rust of wheat. This study initiated with the same genetic pool (generation 0) and conducted one cycle of PS and two cycles of GS. \u0026nbsp;The PS cycle increased inbreeding by 0.05 units per cycle, whereas GS increased inbreeding by \u0026nbsp;0.08 in cycle 1 and by 0.21 units in cycle 2, demonstrating that GS can reduce diversity faster than PS (Rutkoski et al. 2015). \u0026nbsp;Allele frequencies within closed, finite populations can change due to mutation, selection, or drift. \u0026nbsp;However, regardless of what causes the change, fixation of alleles reduces genetic diversity. \u0026nbsp;Reduction in diversity can compromise the response to selection and reduce short and long-term genetic gains. \u0026nbsp;In addition to reducing diversity, changes in allele frequencies can lead to genetic differentiation between the TP and the prediction population (PP). Changes in allele frequencies can lead to population differentiation and recombination could reduce the LD between markers and QTL. \u0026nbsp;If later breeding cycles (PP cycles) have a different LD pattern than that of the TP then the accuracy of GS will be reduced (Wientjes et al. 2013).\u003c/p\u003e\n\u003cp\u003eResearch to overcome the challenge of reduced diversity has been conducted in plant breeding (Gaynor et al. 2017; Gorjanc et al. 2018; Gorjanc and Hickey 2018; Lado et al. 2017). \u0026nbsp;Gorjanc et al. (2018) presented the optimal cross selection method (OCS) that is analogous to the GOC in animal breeding (Meuwissen et al. 2020). Both OCS and GOC limit population-wide inbreeding by penalizing crosses among closely related individuals. \u0026nbsp;Gorjanc et al. (2018) also report that OCS could help maintain the prediction accuracy between the TP and the prediction populations (PP).\u003c/p\u003e\n\u003cp\u003eThe effect of rapid cycling with GS on the wheat genome is unknown. \u0026nbsp;It is essential to assess the effect of rapid cycles of GS on genetic diversity, population structure, patterns of \u0026nbsp;LD, and allele frequencies. \u0026nbsp;Monitoring these population characteristics enables a) strategic planning of crosses, b) maintenance of trait genetic variation and response to selection, c) deciding when to utilize new genetic variation, d) retraining GS models for accurate predictions, and e) understanding alteration in the genome due to rapid cycling with GS. \u0026nbsp;This research uses single nucleotide polymorphisms in a TP and five cycles of GS to accomplish the following objectives: 1) to quantify the magnitude of change in allele frequency, 2) to examine whether drift or selection drove the observed changes in allele frequency, and 3) to assess the impact of changes in allele frequency on genetic diversity, LD patterns, and population structure.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Materials And Methods","content":"\u003ch2\u003eTraining Population\u003c/h2\u003e\n\u003cp\u003eThe training population (YTP) for this study was created for implementing GS for yield, fusarium head blight (FHB, caused by \u003cem\u003eFusarium graminearum\u003c/em\u003e) index of infection, and quality traits. \u0026nbsp;The YTP was previously described by Hoffstetter et al. (2016a). \u0026nbsp;The YTP consists of 470 F\u003csub\u003e4\u003c/sub\u003e-derived soft red winter wheat breeding lines representing the diversity in 2010 of the breeding programs at The Ohio State University. \u0026nbsp; The YTP resulted from 47 bi-parental crosses using 23 parents derived from seven wheat breeding programs in the United States.\u003c/p\u003e\n\u003ch2\u003eTraining Population Phenotyping\u003c/h2\u003e\n\u003cp\u003eThe phenotyping of the YTP has been described by Hoffstetter et al. (2016a). \u0026nbsp;Briefly, the YTP was phenotyped for FHB index in inoculated and misted trials with three replications in Wooster, Ohio during the 2011 and 2012 growing seasons. \u0026nbsp;Index of FHB was rated as percentage of all spikelets showing visual symptoms of infection. \u0026nbsp;The YTP was assessed for grain yield in Custar, Fremont, and Wooster, Ohio, in 2010 and 2011. \u0026nbsp;The grain yield experiment consisted of 13 augmented blocks with 37 YTP lines and three standard checks per block. \u0026nbsp;Analysis of yield data indicated that the Custar and Fremont sites had little genotype x environment interaction between them, while the Wooster site was distinct from those sites (Hoffstetter et al. 2016b). \u0026nbsp;Consequently, the Best Linear Unbiased Estimates (BLUES) for grain yield were obtained 1) over the Custar and Fremont sites, referred to as Northwest sites (NW), 2) over the Wooster sites, referred to as Wooster sites (WOO), and 2) overall sites (ALL).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGenomic Selection Cycles\u003c/h2\u003e\n\u003cp\u003eFive breeding cycles of genomic selection for grain yield, abbreviated as YC1, YC2, YC3, YC4, and YC5 were conducted. \u0026nbsp;For the first selection cycle (YC1), 71 crosses were made using 14 YTP lines as parents (Table 1). \u0026nbsp;The F\u003csub\u003e1\u003c/sub\u003e seed produced by these crosses was sown in the greenhouse and self-pollinated to produce the F\u003csub\u003e2\u003c/sub\u003e seed. \u0026nbsp;The F\u003csub\u003e2\u003c/sub\u003e seed was planted for a final YC1 population of 922 F\u003csub\u003e2\u003c/sub\u003e individuals (Table 1). \u0026nbsp;Each F\u003csub\u003e2\u003c/sub\u003e plant was genotyped, and their GEBVs were estimated for yield in NW, WOO, and ALL (all sites combined) were estimated with the model trained with YTP data. \u0026nbsp;While we originally planned to include quality traits and FHB resistance in the selection process, the final selection was based primarily on yield due to its importance in cultivar release. \u0026nbsp;For each cycle, the F\u003csub\u003e2\u003c/sub\u003e plants were grouped based on their GEBVs for yield into priority groups 1, 2, and 3. \u0026nbsp;Group-1 contained the most superior plants, and we crossed among them whenever flowering allowed. \u0026nbsp;If a group-1 plant was ready for crossing when no other group-1 plant was ready, then the group-1 plant was crossed to a cross-ready group-2 or group-3 plant. \u0026nbsp;We made a total of 78 crosses among the YC1 F\u003csub\u003e2\u003c/sub\u003e plants. \u0026nbsp;The F\u003csub\u003e1\u003c/sub\u003e seed from the 78 crosses was planted to initiate YC2. \u0026nbsp;Cycles YC2, YC3, YC4, and Y5 were conducted similarly. \u0026nbsp; The number of F\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eplants, the times an F\u003csub\u003e2\u003c/sub\u003e was used as a parent, and the number of crosses per cycle is summarized in Table 1. The total time to conduct one GS cycle was 12 months and includes the time that takes to go from a cross to initiating a new cross from the preceding progeny.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGenotyping and SNP Calling\u003c/h2\u003e\n\u003cp\u003eLeaf tissue of 2-week-old F\u003csub\u003e2\u003c/sub\u003e plants was harvested, lyophilized, and DNA extracted in a 96-well plate according to the DNeasy\u0026reg; 96 Plant Kit (Qiagen 2022) protocol. \u0026nbsp;YTP RILs and YC F\u003csub\u003e2\u003c/sub\u003e individuals were genotyped in the wheat genetics laboratory at Kansas State University (KSU). \u0026nbsp;KSU utilized the genotyping-by-sequencing (GBS) protocol for wheat developed by Poland et al. (2012). \u0026nbsp;This GBS protocol consists of a) reduction of the wheat genome complexity by cutting it with restriction enzymes (\u003cem\u003ePstI\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;MspI\u003c/em\u003e), b) ligation of the restriction pieces to adaptors, and c) amplification of fragments by polymerase chain reaction (PCR). SNPs were discovered and called with the TASSEL-GBS software, following the pipeline described in Glaubitz et al. (2014). \u0026nbsp;The steps of the GBS pipeline include 1) identifying tags and taxa in the fastQ files, 2) aligning to the wheat reference genome to extract each tag\u0026apos;s genomic position (mapping distances in base pairs), and 3) posterior variant detection (SNP calling).\u003c/p\u003e\n\u003ch3\u003eMarker Data Preparation\u003c/h3\u003e\n\u003cp\u003eGenotyping-by-sequencing produced 270,717 single nucleotide polymorphisms (SNP) across all the chromosomes of the wheat genome. \u0026nbsp;We calculated the type, count, and frequency of alleles (major, minor, indels) and the proportion of missing and heterozygous data for each SNP in the YPT. \u0026nbsp; Then, for each genotype in the YTP, we calculated the proportion of missing and heterozygous data. \u0026nbsp;These summaries served as the reference for loci filtering in all cycles.\u003c/p\u003e\n\u003ch4\u003eRound 1 and 2 of Filtering\u003c/h4\u003e\n\u003cp\u003eFor the first round of filtering, guided by the loci and genotype summaries, we remove 1) loci with \u0026gt; two alleles, 2) unmapped loci, 3) YTP lines with \u0026ge; 50% missing data, 4) loci with missing data \u0026gt; 0.00, and 5) loci with \u0026lt;= 0.10 minor allele frequency (MAF). \u0026nbsp;For the second filtering round, we merged YTP and YC cycles with only loci remaining from round 1 of filtering. \u0026nbsp;Merged data became the master file to remove loci with \u0026gt; two alleles (appearing in YC cycles) and loci with missing data \u0026gt; 0.005\u003c/p\u003e\n\u003ch4\u003eRound 3 and 4 of Filtering\u003c/h4\u003e\n\u003cp\u003eIn the third round of filtering, executed with loci remaining after round 2 but with unmerged cycles, all YC1-YC5 genotypes with missing data \u0026gt; 0.05 were discarded. \u0026nbsp; Finally, the fourth filtering round purged markers (using only the YTP), with heterozygosity \u0026ge; 0.25, and then thinned by a minimal distance of 50 bp in adjacent loci. \u0026nbsp;The YPT is a RIL population expected to be 100% homozygous at all loci. \u0026nbsp; However, after the third filtering round, we found heterozygosity in the YTP ranging from 0.02 \u0026ndash; 1.00, requiring a fourth filtering round for highly heterozygous loci. \u0026nbsp;After four filtering steps, we retained a set of 3972 biallelic and mapped SNPs for all subsequent analyses in 6911 wheat genotypes (Table 1, Table 2).\u003c/p\u003e"},{"header":"Data Analysis","content":"\u003ch2\u003eGenetic Drift Simulation\u003c/h2\u003e\n\u003cp\u003eWe simulated the probability of obtaining a particular allele frequency in the YC5 due to drift using the \u003cem\u003egenetic.drift\u003c/em\u003e\u0026nbsp; function in the learnPopGen version 1.0.4 (Revell 2019) package in \u0026nbsp;R version 4.2.1 (R Core Team 2022). \u0026nbsp;Simulations were run for five mating cycles, assuming an effective population size of 20, and setting initial frequencies ranging from 0.50 \u0026ndash; 0.99 in increments of 0.01 as these values represented all possible major allele frequencies in the training population, termed the TPMA. \u0026nbsp;The simulation was run 100 times for each possible frequency to provide a variance (of the possible frequencies in the YC5 that could be produced by drift. \u0026nbsp;This provided 50 estimates of \u0026nbsp; , one for each frequency between 0.50 to 0.99. \u0026nbsp; For each marker we did a one-tailed T-test to determine the probability of its observed value in the YC5 given its frequency in the YTP and the variance associated with that frequency.\u003c/p\u003e\n\u003cp\u003eRegression Analysis of polymorphism information content (PIC) and Allele Frequency\u003c/p\u003e\n\u003cp\u003eWe estimated the PIC for all loci within each cycle as follows,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere p\u003cem\u003e\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the frequency of the\u003cem\u003e\u0026nbsp;i\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e allele of the \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e marker in that cycle and \u003cem\u003en\u003c/em\u003e is the number of alleles at the \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e marker (Botstein et al. 1980). \u0026nbsp;We determined the PIC value and the frequency of the TPMA for all markers, in all cycles. We then regressed the allele frequencies and PIC values of each marker onto cycle numbers where the YTP was considered cycle 0. \u0026nbsp;The regression analysis was performed using Proc Reg in SAS v9.4\u003c/p\u003e\n\u003ch2\u003eGenetic Distance\u003c/h2\u003e\n\u003cp\u003eWe estimated the genetic distance (GD) among individuals, within and between populations. The GD was estimated as the complement for a simple matching coefficient method (GD=1-SMC). \u0026nbsp; The GD between the i\u003csup\u003eth\u003c/sup\u003e and jth individuals was calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003cem style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003en\u003csub\u003e1,1\u003c/sub\u003e\u003c/em\u003e\u0026nbsp; and \u0026nbsp;\u003cem style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003en\u003csub\u003e0,0\u003c/sub\u003e\u003c/em\u003e\u0026nbsp; Are the times the i\u003csup\u003eth\u003c/sup\u003e, and j\u003csup\u003eth\u003c/sup\u003e individuals share the same allele. And \u003cem style='color: rgb(0, 0, 0); font-family: \"Times New Roman\"; font-size: medium; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;'\u003en\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e,0\u003c/sub\u003e\u003c/em\u003e\u0026nbsp; and \u0026nbsp;\u003cem\u003en\u003csub\u003e0,\u003c/sub\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e , are times individuals do not share the same allele. \u0026nbsp; The distance between individuals was estimated within and between cycles. We used the \u003cem\u003edist.binary()\u0026nbsp;\u003c/em\u003e(Dray and Dufour 2007) function in R version 4.2.1 (R Core Team 2022) to calculate the GD.\u003c/p\u003e\n\u003ch2\u003eLinkage Disequilibrium\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eA matrix of LD between all pairs of SNPs was calculated for each cycle separately as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eD is estimated between two loci (\u003cem\u003eA\u003c/em\u003e, \u003cem\u003eB\u003c/em\u003e), each with two alleles (A1 and A2, B1 and B2) producing \u003cem\u003eA1B1\u003c/em\u003e, \u003cem\u003eA1B2\u003c/em\u003e, \u003cem\u003eA2B1\u003c/em\u003e, \u003cem\u003eA2B2\u0026nbsp;\u003c/em\u003ehaplotypes with frequencies \u003cem\u003ex\u003c/em\u003e11, \u003cem\u003ex\u003c/em\u003e12, \u003cem\u003ex\u003c/em\u003e21, and \u003cem\u003ex\u003c/em\u003e22, respectively. \u0026nbsp;Allele frequencies are equal to \u0026nbsp; = \u003cem\u003ex11 + x12\u003c/em\u003e and \u0026nbsp;= x\u003cem\u003e11 + x21\u003c/em\u003e. We estimated the \u0026nbsp;with \u0026nbsp;\u003cem\u003eLD()\u0026nbsp;\u003c/em\u003e(Gregory Warnes 2021) function in the genetics package version 1.3.8.1.3 developed for R version 4.2.1 (R Core Team 2022). we conducted a Mantel test (Mantel 1967) to compare the LD matrices between every pair of LD matrices. \u0026nbsp;We use the \u003cem\u003emantel()\u003c/em\u003e function from the package vegan developed in R (Oksanen et al. 2022; R Core Team 2022), with 9999 permutations to estimate a p-value. \u0026nbsp;The Mantel test correlates variables between two matrices and the rows and columns are permuted to then estimate a p-value for the test. The LD blocks by chromosome were estimated using the function \u003cem\u003eBigLD()\u003c/em\u003e from the package gpart version 3.15\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Kim et al. 2019) developed in R version 4.2.1 (R Core Team 2022). We defined an LD block as a group of SNPs with LD \u0026gt; 0.2. The LD blocks were only created for the YPT and the YC5.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePopulation Structure\u003c/h2\u003e\n\u003cp\u003eThe degree of differentiation between populations was estimated using the fixation index (F\u003csub\u003eST\u003c/sub\u003e) calculated as:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003ea\u003c/em\u003e and b are the two populations, and \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eajk\u003c/em\u003e\u0026nbsp;\u003c/sub\u003erepresent the frequency of the j\u003csup\u003eth\u003c/sup\u003e allele of the k\u003csup\u003eth\u003c/sup\u003e SNP in population. \u0026nbsp;These F\u003csub\u003eST\u003c/sub\u003e values were calculated, between all pairs of cycles, with the function \u003cem\u003epairwise.neifs\u003c/em\u003e from the hierfstat package version 0.5-11 (Goudet 2005) coded in R version 4.2.1 (R Core Team 2022). \u0026nbsp;To further study and visualize the structure among all populations, we conducted a discriminant analysis of the principal components (DAPC) (Jombart et al. 2010). \u0026nbsp;The DAPC uses principal components scores to group observations with a discriminant analysis. \u0026nbsp;We found the posterior probability of assigning an individual to one of six groups using the interactive function \u003cem\u003edapc\u003c/em\u003e (Jombart and Ahmed 2011) in R version 4.2.1 (R Core Team 2022). We first ran a principal component analysis of all genotyped individuals using all 3927 markers. \u0026nbsp; For DAPC, we established six prior groups (YTP, YC1-YC5) and retained 17 principal components that explained 51% of the variance. \u0026nbsp;The DAPC assigned posterior probabilities of belonging to one of the six cycles for each genotype. \u0026nbsp;For example, a line in the YTP population is assigned six posterior membership probabilities (to YTP, YC1, YC2, YC3, YC4, and YC5).\u003c/p\u003e\n\u003ch2\u003eAssociation Analysis (AA)\u003c/h2\u003e\n\u003cp\u003eWe conducted an association analysis (AA) for yield in NW, WOO, and ALL combined environments. \u0026nbsp;We used \u003cem\u003eGAPIT\u0026nbsp;\u003c/em\u003e(Lipka et al. 2012) accounting for the population structure with principal components and kindship relationships. The AA was conducted with the 3972 markers.\u003c/p\u003e\n\u003ch2\u003eGenomic Estimated Breeding Values (GEBV)\u003c/h2\u003e\n\u003cp\u003eGEBVs were calculated for yield in NW, WOO, and ALL, with the ridge regression best linear unbiased predictor (rrBLUP) using the function \u003cem\u003emixed.solved\u0026nbsp;\u003c/em\u003e(Endelman 2011) in the rrBLUP R package version 4.2.1 (R Core Team, 2020).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eMarker Data\u003c/h2\u003e\n\u003cp\u003eAfter four rounds of filtering, 3927 SNPs common across all cycles were used for analysis. \u0026nbsp;The number of SNPs per chromosome ranged from 22 (4D) to 460 (2B) (Table 2). \u0026nbsp;The SNP count in genome B was the highest (1878), followed by genome A (1596) and genome D (463). \u0026nbsp;We did not impute missing values for the population genetics analyses (F\u003csub\u003eST\u003c/sub\u003e, allele frequencies, GD, LD): missing marker data were imputed with the mean value for estimating GEBVs.\u003c/p\u003e\n\u003ch2\u003ePopulation and Genomic Estimated Breeding Values\u003c/h2\u003e\n\u003cp\u003eThis research covered six years of implementing rapid cycling GS in The Ohio State University wheat breeding program. The first round of crosses occurred in 2011, with 3% of the YTP lines selected as parents based on GEBV and phenotypes for grain yield in NW and WO and FHB resistance (Table 1). \u0026nbsp;Crosses were made, and F\u003csub\u003e2\u003c/sub\u003e plants were selected to be used as parents based on the GEBV for grain yield in NW and WOO in subsequent cycles. \u0026nbsp;Grain yield in WOO was prioritized because it had the highest heritability (Hoffstetter et al. 2016a) and the greater variation for GEBV. \u0026nbsp;The selection intensity in the subsequent cycles ranged from 3% to 8% (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average GEBV for YLD increased over cycles (Table 3, Figure 1) while variance among the GEBVs decreased (Figure 2). \u0026nbsp;The mean GEBV increased at a per cycle rate of 19 kg/ha for NW, 35 kg/ha for ALL sites, and 73 kg/ha for the WOO site. \u0026nbsp;The largest change in mean GEBV was observed from YTP to YC1 in all sites. \u0026nbsp;The GEBV variance, adjusted to that expected in the F\u003csub\u003e4\u003c/sub\u003e generation (e.g., 1.75 times greater than in the F\u003csub\u003e2\u003c/sub\u003e), decreased over cycles for yield in NW and ALL (Figure 2). \u0026nbsp;Variance for yield in WOO decreased from YTP to YC2, followed by an increase to YC5. Much of the reduction in variance occurred due to fewer individuals with low GEBVs (Figure 1). \u0026nbsp;F\u003csub\u003e2\u003c/sub\u003e Individuals with yield GEBV greater than the maximum YTP GEBV were noticed by YC1 for yield over all environments, but not until later cycles in NW and WOO (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePolymorphism information content (PIC) and genetic distance (GD)\u003c/h2\u003e\n\u003cp\u003eThe PIC value for each of the 3927 SNP loci was estimated within the YTP and each cycle. The PIC is a characteristic for a marker and indicates how informative the marker is as well as its diversity. The PIC values ranged from 0.18 \u0026ndash; 0.50 in the YTP and 0.00-0.50 in cycles (Figure 3A). \u0026nbsp;The average PIC value decreased from 0.358 in the YTP to 0.227 in YC5. \u0026nbsp;The minimum PIC value for a SNP in the YTP was 0.18, while PIC values of 0.0 (i.e., fixation) were observed in all subsequent GS cycles. We found a PIC value of zero for 76 and 616 loci in YC1 and YC5, respectively. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe calculated the GD between individuals within and between cycles. \u0026nbsp; The average GD increased slightly from the YTP to YC1 and declined slightly afterward (Figure 3B). \u0026nbsp;When considering all possible comparisons with the YTP, the average GD between cycles was highest for YTP versus YC1 (0.562) and lowest for YTP versus YC4 (0.554) (Table 4). \u0026nbsp;The average GD did not steadily change as cycles progressed; however, the minimum GD showed substantial change. \u0026nbsp;For example, the minimum distance found in the YC1 was 0.157, whereas the YC5 comparison had a minimum difference of 0.318. \u0026nbsp;In this study, the GD is an estimation of genetic diversity for a given individual versus every other individual.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003ePopulation structure\u003c/h2\u003e\n\u003cp\u003eThe F\u003csub\u003eST\u003c/sub\u003e analysis showed differentiation of all cycles from the YTP with greater genetic differentiation with each subsequent cycle (Table 5, Figure 4A). For instance, the YC5 and YTP are five cycles apart and showed the highest F\u003csub\u003eST\u003c/sub\u003e value (F\u003csub\u003eST\u003c/sub\u003e = 0.224). There was an increase of 0.0456 F\u003csub\u003eST\u003c/sub\u003e units per cycle difference, and the average F\u003csub\u003eST\u003c/sub\u003e between consecutive cycles was 0.039. \u0026nbsp;The first and second principal components obtained from an analysis using all 3927 markers explained 11.6 % and 4.2%, respectively, of the variation among all YTP and YC1-YC5 individuals. \u0026nbsp;We conducted a discriminant analysis of the principal components (DAPC) to place lines into the prior (YTP, YC1 \u0026ndash; YC5) grouping. \u0026nbsp;For the DAPC, we retained 17 principal components, which cumulatively explained 51% of the variance and retained five discriminant functions (\u003cem\u003eN-1\u003c/em\u003e, where N is the number of prior groups). \u0026nbsp; The DAPC results showed the cycles becoming more distant from the YTP as the cycle number increased (Figure 5). More admixture is observed between cycles that are closer in number, than if cycles are far from each other. The admixture with the YTP reduces with the increase in GS cycles. \u0026nbsp; For example, cycles YC1, YC2, and YC3 clustered closer to the YTP than cycles YC4 and YC5 (Figure 5). \u0026nbsp;The DAPC assigns a posterior probability for an individual being in a prior group (YTP, YC1-YC5). We divided the posterior probabilities by quartiles (0.00 \u0026ndash; 0.25, 0.26 \u0026ndash; 0.50, 051 \u0026ndash; 0.75, 0.76 \u0026ndash; 1.00) and reported the frequency of individuals falling within each range (Table 6). \u0026nbsp;For instance, we found that 80% of YTP individuals have between 0.00 \u0026ndash; 0.25 posterior probability of belonging to the YC1 population.\u003c/p\u003e\n\u003ch2\u003eLinkage Disequilibrium\u003c/h2\u003e\n\u003cp\u003eWe calculated a matrix of LD values among all SNP pairs within each cycle and performed a Mantel test to assess the correlation between the LD matrices. \u0026nbsp;All pairs of matrices were significantly correlated at P \u0026lt;0.0001. \u0026nbsp; The correlation of LD matrices decreased responding to their separation in time (Table 7, Figure 4B). \u0026nbsp;The correlation value decreased by -0.057 for each unit of increase in the difference between cycle number. \u0026nbsp;We use the YTP and YC5 LD matrices to extract LD blocks by population and chromosome. We defined a block as a group of SNPs with LD \u0026gt; 0.2. The YTP had 604 LD blocks, ranging from 2 - 51 loci per block. \u0026nbsp; The LD blocks were dynamic, that is, some chromosomes would develop new LD blocks and others would lose LD blocks (Table 8). There was a 23% reduction in the number of LD blocks by YC5 compared to the YTP. \u0026nbsp;The total number of blocks ranged from three (3A) and 67 (3B) in the YTP; and 13 (5D), 42 (3B) in the YC5 (Table 8).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eRegression Analysis of Allele Frequency\u003c/h2\u003e\n\u003cp\u003eWe calculated the frequency of the major allele in the YTP (TPMA) in all cycles for all SNPs. We then calculated the correlation of the TPMA frequency between all cycles (Table 9). \u0026nbsp; This correlation declines as cycles become more separated by cycle number with a slope of -0.106 per difference between cycle numbers (Figure 4C). \u0026nbsp; The rate of change in allele frequency was estimated for each marker by regressing the frequency of the TPMA in each cycle onto the cycle number where the YTP was called cycle 0. \u0026nbsp;The same regression was done using the PIC value of each marker. \u0026nbsp;We declared an allele frequency change significant if the\u0026nbsp;\u0026nbsp;for the TPMA and PIC regressions both exceeded 0.70, and if both regression models had a probability \u0026lt; 0.05. A significant change was attributed to selection if the frequency in YC5 had a probability \u0026lt; 0.05 based on the distribution of possible allele frequencies generated by simulating drift. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe TPMA and PIC regressions determined that 1074 (27.3%) SNPs had a significant allele frequency change by YC5 (Figure 6). \u0026nbsp;These 1074 loci showed an average TPMA frequency in the YTP of 0.72, followed by YC1 of 0.65, YC2 of 0.59, YC3 of 0.58, YC4 of 0.57, and YC5 of 0.57. \u0026nbsp;We deemed that 707 (18% of all loci) loci had a significant allele frequency change due to selection. An additional 367 (9.3%) loci had a significant change, but the cause was ambiguous as these changes could be due to drift or selection that did not meet our criteria. We considered alleles fixed if the TPMA frequency equaled 0 or 1 in YC5. A total of 725 loci (18.5%) had become fixed by YC5. \u0026nbsp; Of these 53 became fixed due to selection and with the rest fixed due to drift or low selection pressure. \u0026nbsp;Many loci (2853, 72.7%) across the wheat genome showed no significant change in the TPMA frequency, but 512 out of these loci became fixed by YC5.\u003c/p\u003e\n\u003ch2\u003eGenomewide Association Analysis\u003c/h2\u003e\n\u003cp\u003eWe conducted genome-wide association analysis (GWAS) for yield in NW, WOO, and ALL. All 3972 SNPs and the yield data collected in the YTP were used for GWAS. \u0026nbsp;We focused on the results for the 1074 whose allele frequency was deemed to show significant change. \u0026nbsp;Overall, we found 101 of these 1074 markers were significantly (P \u0026lt; 0.05) associated with at least one trait and 40 loci were significant for at least two traits. Two markers on chromosome 2B, three on 5A, and one on chromosome 6A were significant for yield in NW, WOO, and ALL environments. The allele frequency of these markers was not fixed by YC5, and these six markers were not deemed to be under selection.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study documents the effect of five GS cycles for grain yield on the wheat genome. \u0026nbsp;Our analysis includes estimating the proportion of genome with a significant change in allele frequency, discerning between genetic drift and selection as the cause of those changes, and the change in the index of fixation (F\u003csub\u003eST\u003c/sub\u003e), diversity, and LD between the YTP and YC1-YC5 cycles. Additionally, we report the distribution of GEBVs for all cycles and connect changes to the value of alleles as determined by GWAS.\u003c/p\u003e\n\u003cp\u003eFinite populations undergo selection, genetic drift, and recombination that can alter allele frequencies and the LD between markers and QTL (Flint-Garcia 2013; Fu 2015; Gorjanc et al. 2018; Hufford et al. 2019; Louwaars 2018). Plant breeders oscillate between 1) using current alleles of their elite pool to create diversity through recombination to 2) reducing diversity by selecting the most promising recombinants. Inexpensive genotyping can facilitate monitoring this creating/reducing dynamic. Selection inevitably causes changes in the genome, leading to loss of diversity and limiting long-term genetic gain though these changes may occur faster with GS than traditional phenotyping breeding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, five cycles of GS were completed in five years. \u0026nbsp; The selection resulted in significant changes in alleles frequency compared to the TP, loss of diversity. fixation of alleles, increasing differentiation of the cycles from the TP, a general reduction in LD blocks, and increased differentiation of LD patterns over the cycles. \u0026nbsp;We attributed these results to the selection intensity of 3% to 13% per cycle and to limited population size (n=445 to 1821) (Table 1) . \u0026nbsp;The documented changes could have a significant effect on GS prediction accuracy.\u003c/p\u003e\n\u003cp\u003eWe used conservative criteria to determine if an allele frequency change over cycles was significant and whether the change was driven by selection or genetic drift. \u0026nbsp;There was a significant change in allele frequency by cycle-5 for 27.3% of the loci relative to the YTP (Figure 6). \u0026nbsp;Of the 1074 loci deemed to have a significant allele frequency change by cycle-5, 69% of the changes were attributed to the selection process, while the remaining significant changes could be due to either drift or selection. \u0026nbsp;We found alleles fixed at 18.3% of all loci by cycle-5, indicating a loss of diversity. \u0026nbsp;The percentage of all loci that became fixed and attributed to selection (1.3%) was much less than the percentage that became fixed due to other causes (98.7%). \u0026nbsp;The fact that much of the fixation was not attributed to selection suggests that the genetic bottleneck caused by intense selection at each cycle was a primary cause of fixation. \u0026nbsp;In addition, there was a decrease in the average PIC values and GD among individuals within a cycle as cycle number increased (Table 4, Figures 3A, 3B) indicating reduced diversity beyond the noted fixation. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch to overcome the challenge of reduced diversity from selection has been conducted in plant breeding (Gaynor et al. 2017; Gorjanc et al. 2018; Gorjanc and Hickey 2018; Lado et al. 2017). \u0026nbsp;To conserve diversity in plant breeding, Gorjanc et al. (2018) presented the optimal cross selection method (OCS) and Gorjanc and Hickey (2018) developed AlphaMate to implement OCS. \u0026nbsp;The OCS method is analogous to the genomic optimal contribution (GOC) selection method proposed for managing diversity in animal breeding (Meuwissen et al. 2020). Both OCS and GOC minimize population-wide inbreeding by penalizing crosses among closely related individuals. \u0026nbsp;Gorjanc et al. (2018) also report that OCS could help maintain the prediction accuracy between the training population and the prediction populations. It would be interesting to simulate OCS in our populations to see how it would affect diversity and impact gains in GEBVs compared to our results. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe cannot compare our results to genome changes that may occur using five cycles of PS as that assessment would take 35 years to complete (seven years per cycle of PS in out program). \u0026nbsp;Rutkoski et al. (2015) compared GS with PS in wheat and showed that inbreeding increased faster with GS (0.08 per cycle) than with PS (0.05 per cycle), indicating that GS changed allele frequencies faster than PS. \u0026nbsp;Results of an increase in inbreeding from GS are reported in animal breeding by Makanjuola et al. (2020). \u0026nbsp;They investigated the changes in inbreeding, co-ancestry, and effective population size in cattle. They studied three periods that represent significant changes in breeding strategies: BLUPs (1990 \u0026ndash; 1999), inbreeding control (2000 \u0026ndash; 2009), and GS implementation (2009 \u0026ndash; 2018). Makanjuola et al. (2020) report a rapid increase of inbreeding during GS implementation, and a steady rise in the BLUP and inbreeding control periods. The percentage increase of inbreeding reported for the three periods was 1.50% (BLUP), 0.50% (inbreeding control) and 1.95% (GS).\u003c/p\u003e\n\u003cp\u003eWe found that the genetic distance between cycles and the TP increased the further apart they were in time. \u0026nbsp;The GD (Table 4) and Fst values (Table 5, Figure 3) between cycles also increased as cycle become further apart in time. \u0026nbsp;This was supported by the DAPC analysis (Table 6, Figure 5) that showed individuals from five cycles of GS became more differentiated from the YTP as the cycle number increased. \u0026nbsp;The greatest differentiation was between the YTP and YC5 where the Fst was 0.224 whereas the Fst between the YTP and YC1 was 0.059. \u0026nbsp;Frankham et al. (2002) state that F\u003csub\u003eST\u003c/sub\u003e values greater than 0.15 indicate significant differentiation in plants, while a value less than 0.05 indicate little differentiation. \u0026nbsp;These results indicate the cycles had evolved to the point where they were not closely related to the YTP, and thus the YTP may not be predictive of genetic values in the later cycles. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reported change in LD patterns and number of LD blocks over five cycles of GS could impact the accuracy of GS predictions in later cycles (Tables 7 and 8). \u0026nbsp;Prediction accuracy depends on maintaining the LD pattern between markers and QTL that existed in the TP over cycles of GS. Decay of this LD will reduce the accuracy of GS. We observed that the correlation of the LD matrices between cycles decreased at a rate of -0.056 as cycles became separated over time. \u0026nbsp;This shows that the LD pattern between markers was changing. \u0026nbsp;One can then assume that the LD between markers and QTL is also likely to be changing and that will affect the accuracy of GS. \u0026nbsp;While we did observe an increase in GEBVs over the cycles (Figure 1), the predictions are based on the YTP data and assume that the marker-QTL associations of the YTP remain constant through the cycles. \u0026nbsp;The change of LD over cycles indicates that a GS model based on the LD in the YTP may quickly lose predictive ability during rapid cycling. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study shows that GS can produce rapid changes in the genome and much of the change does not seem to result from selection \u003cem\u003eper se\u003c/em\u003e. \u0026nbsp; The changes seem very likely to compromise the predictive ability of a GS model in a relatively few number of cycles as well as reduce diversity. \u0026nbsp; The accuracy of GS depends on a close genetic relationship of the TP and the PP. \u0026nbsp; This relationship decayed quickly in this study. We cannot estimate the reduction of GS accuracy from the genome changes that we have documented, \u0026nbsp;but our results serve as a reference to understand the impact of rapid cycling on the wheat genome and their implications for GS. \u0026nbsp;Our results also show that a static TP may have a very limited useful lifespan over cycles of GS and is not desirable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBreeding schemes employing GS need to accommodate genome changes as well as short and long term breeding goals. \u0026nbsp;Reducing diversity within a closed population is a goal of breeding as the selection process should fix favorable alleles and eliminate unfavorable ones. \u0026nbsp;This is desirable in the short term but not the long term. \u0026nbsp;Short term gains are of paramount importance in a competitive market. \u0026nbsp; Our results indicate that the breeding scheme first outline by Gaynor et al. (2017) and supported by Gorjanc et al. 2018 is well suited to accommodate genome changes as well as short and long tern goals. \u0026nbsp; They proposed coupling rapid cycling GS used in population improvement using GS in the product development phase of breeding. Here GS is executed in a dynamic breeding pipeline to identify new parents at the early stages of field testing using phenotypes, GEBVs, and OCS. \u0026nbsp; This reduces the number years per cycle compared to selecting parent only after the later stages of testing (Borrenpohl et al. 2020; Gaynor et al. 2017; Gorjanc et al. 2018). \u0026nbsp;At the same time GS is used in rapid cycling employing a GS model that is continually updated with data of the ongoing breeding pipeline. \u0026nbsp;This can be viewed as \u0026ldquo;evolving-GS\u0026rdquo; where data from every new cohort of lines that enter the product development phase is also used to update the TP and the parental pool. \u0026nbsp;The genomes of the population will be changing though TP and PP in this scheme can be viewed as evolving together in genetic space such that their genomes remain similar and GS accuracy can be maintained within the shifting window defined by the current TP and PP. \u0026nbsp;The use of some common parents across cohorts will increase the relationship between the TP and PP and would allow data from past cohorts to be relevant to the current PP. \u0026nbsp; This co-evolution of TP and PP is in stark contrast to the static TP such as our YTP. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo some degree the genome changes we observed are inevitable consequences of the selection and drift that are inherent to all breeding and must be accommodated in a GS-based breeding scheme. \u0026nbsp;A carefully constructed \u0026nbsp;GS scheme can be implemented in an ongoing breeding pipeline where the genome information of the co-evolving TP and PPs maintains GS accuracy over many years of breeding. \u0026nbsp;This would meet both the short and long term needs of a program. \u0026nbsp;These schemes can be easily implemented by genotyping all lines that enter the product development phase and using data from the product development phase to update the TP every season to capture information on the changing genome and overcome some of the issues we report in this study. \u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We wish to thank the many people who helped with this research including Cassi Sewell, Mao Huang, and Amber Hoffstetter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This project was supported by The National Institute of Food and Agriculture of the USDA, grant 20146701322419\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors have no financial or non-financial interests to disclose. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e Data is available from the corresponding author\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e NAB advanced material through the cycles, preformed data analyses, and drafted the manuscript; CHS designed crosses, organized data, suggested analyses, and drafted/edited the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAtanda SA, Olsen M, Crossa J, Burgue\u0026ntilde;o J, Rincent R, Dzidzienyo D, Beyene Y, Gowda M, Dreher K, Boddupalli PM, Tongoona P, Danquah EY, Olaoye G, Robbins KR (2021) Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage. 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G3 (Bethesda) 6:2919-2928\u003c/li\u003e\n \u003cli\u003eHoffstetter A, Cabrera A, Sneller C (2016b) Identifying Quantitative Trait Loci for Economic Traits in an Elite Soft Red Winter Wheat Population. Crop Science 56:547-558\u003c/li\u003e\n \u003cli\u003eHuang M, Cabrera A, Hoffstetter A, Griffey C, Van Sanford D, Costa J, McKendry A, Chao S, Sneller C (2016) Genomic selection for wheat traits and trait stability. TAG Theoretical and applied genetics Theoretische und angewandte Genetik 129:1697-1710\u003c/li\u003e\n \u003cli\u003eHuang M, Ward B, Griffey C, Van Sanford D, McKendry A, Brown-Guedira G, Tyagi P, Sneller C (2018) The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits 58:2274-2288\u003c/li\u003e\n \u003cli\u003eHufford MB, Berny Mier YTJC, Gepts P (2019) Crop Biodiversity: An Unfinished Magnum Opus of Nature. Annu Rev Plant Biol 70:727-751\u003c/li\u003e\n \u003cli\u003eJacobson A, Lian L, Zhong S, Bernardo R (2015) Minimal Loss of Genetic Diversity after Genomewide Selection within Biparental Maize Populations 55:783-789\u003c/li\u003e\n \u003cli\u003eJannink J-L (2010) Dynamics of long-term genomic selection. Genetics Selection Evolution 42:35\u003c/li\u003e\n \u003cli\u003eJarquin D, Howard R, Crossa J, Beyene Y, Gowda M, Martini JWR, Covarrubias Pazaran G, Burgue\u0026ntilde;o J, Pacheco A, Grondona M, Wimmer V, Prasanna BM (2020) Genomic Prediction Enhanced Sparse Testing for Multi-environment Trials. G3 Genes|Genomes|Genetics 10:2725-2739\u003c/li\u003e\n \u003cli\u003eJombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:3070-3071\u003c/li\u003e\n \u003cli\u003eJombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11:94\u003c/li\u003e\n \u003cli\u003eKim SA, Brossard M, Roshandel D, Paterson AD, Bull SB, Yoo YJ (2019) gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks. Bioinformatics 35:4419-4421\u003c/li\u003e\n \u003cli\u003eLado B, Battenfield S, Guzm\u0026aacute;n C, Quincke M, Singh RP, Dreisigacker S, Pe\u0026ntilde;a RJ, Fritz A, Silva P, Poland J, Guti\u0026eacute;rrez L (2017) Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs. The plant genome 10\u003c/li\u003e\n \u003cli\u003eLipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397-2399\u003c/li\u003e\n \u003cli\u003eLouwaars NP (2018) Plant breeding and diversity: A troubled relationship? Euphytica 214:114\u003c/li\u003e\n \u003cli\u003eMakanjuola BO, Miglior F, Abdalla EA, Maltecca C, Schenkel FS, Baes CF (2020) Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations. Journal of Dairy Science 103:5183-5199\u003c/li\u003e\n \u003cli\u003eMantel N (1967) The Detection of Disease Clustering and a Generalized Regression Approach. Cancer Research 27:209-220\u003c/li\u003e\n \u003cli\u003eMeuwissen T, Hayes B, Goddard M (2013) Accelerating Improvement of Livestock with Genomic Selection. In: Lewin HA, Roberts RM (eds) Annual Review of Animal Biosciences, Vol 1, pp 221-237\u003c/li\u003e\n \u003cli\u003eMeuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819-1829\u003c/li\u003e\n \u003cli\u003eMeuwissen THE, Sonesson AK, Gebregiwergis G, Woolliams JA (2020) Management of Genetic Diversity in the Era of Genomics. Frontiers in genetics 11\u003c/li\u003e\n \u003cli\u003eMoose SP, Dudley JW, Rocheford TR (2004) Maize selection passes the century mark: a unique resource for 21st century genomics. Trends in plant science 9:358-364\u003c/li\u003e\n \u003cli\u003eOksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, R. B. O\u0026apos;Hara G, Simpson L, Solymos P, Stevens MHH, Wagner ESaH (2022) vegan: Community Ecology Package. R package \u0026nbsp; version 2.5-7.\u003c/li\u003e\n \u003cli\u003ePoland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach. PLOS ONE 7:e32253\u003c/li\u003e\n \u003cli\u003eQiagen (2022) DNeasy 96 Plant Kit Quick-Start Protocol.\u003c/li\u003e\n \u003cli\u003eR Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria\u003c/li\u003e\n \u003cli\u003eReif JC, Zhang P, Dreisigacker S, Warburton ML, van Ginkel M, Hoisington D, Bohn M, Melchinger AE (2005) Wheat genetic diversity trends during domestication and breeding. TAG Theoretical and applied genetics Theoretische und angewandte Genetik 110:859-864\u003c/li\u003e\n \u003cli\u003eRevell LJ (2019) learnPopGen: An R package for population genetic simulation and numerical analysis. Ecology and Evolution 9:7896-7902\u003c/li\u003e\n \u003cli\u003eRoussel V, Leisova L, Exbrayat F, Stehno Z, Balfourier F (2005) SSR allelic diversity changes in 480 European bread wheat varieties released from 1840 to 2000. TAG Theoretical and applied genetics Theoretische und angewandte Genetik 111:162-170\u003c/li\u003e\n \u003cli\u003eRutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Genetic Gain from Phenotypic and Genomic Selection for Quantitative Resistance to Stem Rust of Wheat. The plant genome 8:plantgenome2014.2010.0074\u003c/li\u003e\n \u003cli\u003eScott BA, Haile-Mariam M, Cocks BG, Pryce JE (2021) How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls. Journal of Dairy Science 104:11832-11849\u003c/li\u003e\n \u003cli\u003eSneller C, Ignacio C, Ward B, Rutkoski J, Mohammadi M (2021) Using Genomic Selection to Leverage Resources among Breeding Programs: Consortium-Based Breeding. Agronomy 11:1555\u003c/li\u003e\n \u003cli\u003eWientjes YCJ, Veerkamp RF, Calus MPL (2013) The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction. Genetics 193:621-+\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable \u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u0026nbsp; Description of the training population (YTP) and each of the five cycles (YC1 \u0026ndash; YC5) resulting from use of genomic selection in winter wheat. The YTP is composed of F4-derived recombinant inbred lines while the YC1-YC5 are composed of F\u003csub\u003e2\u003c/sub\u003e plants. Mean heterozygosity is Fst while genetic distance is calculated as 1-simple matching coefficient.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.958333333333336%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"48.958333333333336%\"\u003e\n \u003cp\u003ePopulation size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.375%\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.958333333333336%\"\u003e\n \u003cp\u003eNumber of crosses made to create the cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.958333333333336%\"\u003e\n \u003cp\u003eNumber of parents used to create the cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.958333333333336%\"\u003e\n \u003cp\u003eThe cycle of the parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.958333333333336%\"\u003e\n \u003cp\u003eGeneration genotyped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;RILs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"48.958333333333336%\"\u003e\n \u003cp\u003eMean heterozygosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.375%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"48.958333333333336%\"\u003e\n \u003cp\u003eMean genetic distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.375%\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e. \u0026nbsp;Summary by wheat genome of the number of single nucleotide polymorphism (SNP) markers used in this study.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"62%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eGenome A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eGenome B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003eGenome D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"30.303030303030305%\"\u003e\n \u003cp\u003eTotals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e1596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e1878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e3\u003c/strong\u003e. \u0026nbsp;Summary statistics of genomic estimated breeding values (GEBVs) for grain yield in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;Trait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eCycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eYield,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e6942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eAll environments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;(kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e4689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eNorthwest Ohio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;(kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e39695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003eWooster Ohio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e19284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;(kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e10792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e12353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e13166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.458333333333334%\"\u003e\n \u003cp\u003e15454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.5%\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e4\u003c/strong\u003e. \u0026nbsp;Summary statistics of the genetic distance (GD) within and between the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. \u0026nbsp;The GD was calculated as 1-simple matching coefficient using data from 3927 markers.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"55%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"23.958333333333332%\"\u003e\u003cbr\u003e \u0026nbsp;\u003cp\u003eComparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003eCycles\u003c/p\u003e\n \u003cp\u003eApart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003eDeviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e5\u003c/strong\u003e. \u0026nbsp;Fixation index (Fst) between the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"65%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.578947368421053%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.68421052631579%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e6.\u003c/strong\u003e\u0026nbsp; Results from the discriminate analysis of the principal components (DAPC) performed with 3972 markers genotyped on all individuals in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. \u0026nbsp;The table presents the percent of individuals from each prior group within each quartile of the posterior probability of membership for each group. \u0026nbsp;For example, 70.6% of the individuals in the YTP had a \u0026gt; 0.75 posterior probability of being in YTP while only 3.3% of the YTP individuals had a \u0026gt;0.75 posterior probability of being in the YC1. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"8.52575488454707%\"\u003e\n \u003cp\u003ePrior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"15.097690941385435%\"\u003e\n \u003cp\u003ePosterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"9.058614564831261%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"67.31793960923623%\"\u003e\n \u003cp\u003eQuartile of posterior membership probability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.00 - 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.26 - 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.51 - 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.76 - 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e20.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e70.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e80.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e88.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e92.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e99.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e86.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e38.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e23.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e74.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e14.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e57.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e23.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e94.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e99.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e98.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e75.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e35.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e24.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e19.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e21.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e46.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e32.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e91.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e99.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e1821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e98.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e84.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e80.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e31.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e22.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e18.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e27.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e75.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n 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width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e1572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e99.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e96.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e95.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e70.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e16.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e25.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e40.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e81.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.070921985815604%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"9.042553191489361%\"\u003e\n \u003cp\u003e1330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.843971631205672%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e96.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e68.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e13.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.27956989247312%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e13.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"20.43010752688172%\"\u003e\n \u003cp\u003e67.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e7\u003c/strong\u003e. \u0026nbsp;Mantel statistic between the matrices of marker linkage disequilibrium of the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat. \u0026nbsp;All values are significant at p \u0026lt; 0.0001.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"72%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.851\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.772\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.668\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e8\u003c/strong\u003e. Summary of linkage disequilibrium blocks in the training population (YTP) and in the fifth cycle of genomic selection (YC5) in winter wheat. \u0026nbsp;The YTP column indicates the number of LD blocks present in the training population. The YC5 column indicates the number of LD blocks present in the cycle 5 of genomic selection. The difference is the number of YC5 LD blocks minus the number of YTP LD blocks. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"67%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eTotal Loci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003eDifference (YC5-YTP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e4D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable \u003cstrong\u003e9\u003c/strong\u003e. \u0026nbsp;Correlation of the allele frequencies of 3927 markers in the training population (YTP) and each cycle of genomic selection (YC1-YC5) in winter wheat.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"74%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003eYC5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eYTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.244897959183673%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n 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valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eYC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eYC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eYC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.26530612244898%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Wheat, genomic selection, breeding, genome","lastPublishedDoi":"10.21203/rs.3.rs-1938161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1938161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Genomic selection (GS) is widely used in plant breeders to shorten breeding cycles. Our objective was to assess the impact of rapid cycling GS on the wheat genome We used 3927 markers to genotype a training population (YTP) and individuals from five cycles (YC1-YC5) of GS for grain yield. We assessed changes of allele frequency, genetic distance, population structure, and linkage disequilibrium (LD). We found 27.3% of all markers had a significant allele frequency change by YC5, 18% experienced a significant change attributed to selection, and 9.3% had a significant change due to either drift or selection. A total of 725 of 3927 markers were fixed by YC5 with selection fixing 7.3% of the 725 markers. The genetic distance between cycles increased over time. The Fst value of 0.224 between YTP and YC5 indicates their relationship was low. The correlation between LD matrices and the number of LD blocks decreased over time. Overall, we found reduction in genetic diversity, increased genetic differentiation of cycles from the training population, and restructuring of the LD patterns over cycles. The accuracy of GS depends on the genomic similarity of the training population and the prediction populations. Our results show that similarity can decline rapidly over cycles of GS and seriously compromise the predictive ability of the YTP-based model. Our results support implementing a GS scheme where the training and prediction populations co-evolve instead of the use of a static training population.","manuscriptTitle":"The effect of cycles of genomic selection on the wheat (T. aestivum) genome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-19 16:01:00","doi":"10.21203/rs.3.rs-1938161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2022-09-05T10:01:03+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-08-14T11:44:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-13T20:39:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-11T04:54:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2022-08-07T09:33:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"11147b36-2adc-48a1-b6b7-9ffefe99644e","owner":[],"postedDate":"August 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T20:10:41+00:00","versionOfRecord":{"articleIdentity":"rs-1938161","link":"https://doi.org/10.1007/s00122-023-04279-0","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2023-03-23 20:06:43","publishedOnDateReadable":"March 23rd, 2023"},"versionCreatedAt":"2022-08-19 16:01:00","video":"","vorDoi":"10.1007/s00122-023-04279-0","vorDoiUrl":"https://doi.org/10.1007/s00122-023-04279-0","workflowStages":[]},"version":"v1","identity":"rs-1938161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1938161","identity":"rs-1938161","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}