The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes.We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees  from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests.OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes .We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3’ UTR Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.The online version of this article (doi:10.1186/s12877-016-0253-y) contains supplementary material, which is available to authorized users. At least 25 % of human lifespan is dictated by genetic factors, most of which is unknown [APOE) [FOXO1) [FOXO3) [IGF-1R) [TOMM40) [According to United Nations World Population Prospects 2012 revision ([TOMM40)  have allThus, we expect families clustered for long-lived healthy members , 12 to bThe LLFS , a multiIn this study, human lifespan and phenotypic traits related to healthy aging including survival, leukocyte telomere length, BMI, blood pressure, blood lipid levels, blood glucose and insulin levels, cognitive function, healthy aging index, as well as the ages of onset of stroke, coronary heart disease, congestive heart failure, cancer, and type 2 diabetes mellitus, were investigated.n = 810), was used for identifying variants related with survival.The lifespan in LLFS was estimated as the duration of survival free of death from any cause or from any one of a specified list of diseases including cardiovascular disease, congestive heart failure, cancer, stroke, and Type 2 diabetes. The LLFS proband generation, including probands and full and half siblings  as a subphenotype of longevity . Using a1c, blood glucose and insulin levels, leukocyte telomere length, blood lipid levels including total cholesterol, HDL cholesterol, LDL cholesterol and triglyceride, BMI, blood pressure, cognitive function, stroke, coronary heart disease, congestive heart failure, cancer, and type 2 diabetes.We looked for any possible association between our candidate genes related to healthy aging and various aging phenotypes including HbA1c was measured using identical ion exchange-based high performance liquid chromatography with the Tosoh 2.2 Plus and after 2007 with the Tosch G7 Glycohemoglobin Analyzer  at the Advanced Research and Diagnostics Laboratory, University of Minnesota. Fasting glucose was measured after an 8-h fast in serum by the Roche hexokinase method  on a Roche Modular P Chemistry Analyzer (Roche Diagnostics Corporation). Fasting insulin was measured after an 8-h fast in serum on a Roche Elecsys 2010 Analyzer (Roche Diagnostics Corporation) using a sandwich immunoassay method . Assays of average leukocyte telomere length were described by Lee et al. [bp =  + 3582. Fasting total cholesterol was measured after an 8-h fast in serum using a cholesterol oxidase method  on a Roche Modular P Chemistry Analyzer.As described previously , blood se et al. . Briefly2. Sitting systolic and diastolic blood pressure was obtained by the average of three measures using an automated blood pressure machine . Pulse Pressure was calculated as systolic blood pressure minus diastolic blood pressure. These results were corrected for individuals taking blood pressure modifying medication as previously published [Fasting HDL-cholesterol was measured after an 8-h fast directly in serum using the Roche HDL-Cholesterol 3rd generation direct method  on a Roche Modular P Chemistry Analyzer. Fasting triglycerides were measured after an 8-h fast in serum using Triglyceride GB reagent  on a Roche Modular P Chemistry Analyzer. LDL-cholesterol was calculated by the Friedewald equation using the measured results for total cholesterol, HDL-cholesterol, and triglycerides. This equation ) can be used to calculate LDL-cholesterol when triglycerides are less than 400 mg/dL. The corrected values for triglyceride and LDL cholesterol was calculated for individuals taking lipid lowering medications . BMI wasublished . Generalublished . Stroke www.cidr.jhmi.edu), and the resulting data has been submitted to dbGaP under accession number phs000397.v1.p1. Genome-wide SNP variants within targeted sequenced regions were used as controls for sequencing accuracy as described below.Illumina Human Omni 2.5 v1 was used to perform whole genome SNP genotyping on each study participant by CIDR  covered by baits in 6,966 distinct intervals.We have previously published the entire protocol for custom hybridization capture of multiplexed, indexed next generation sequencing , 27. BriSequencing was performed in the Genome Technology Access Center at Washington University using the HiSeq 2000 platform, generating 101 bp paired-end reads. GWA and sequencing information for the LLFS participants is available in dbGaP using accession number phs000397.v1.p1.Sequencing analysis for indexed captures of all LLFS participants followed previously reported methods . Briefly, raw sequence data was aligned against the human genome (hg19/NCBI 37.0) using Novoalign  and, from aligned reads, variants were called using samtools-0.1.18 mpileup at only the 2.5 Mb of target sequence within our custom hybridization array.The thresholds used for filtering called variants from sequencing data were determined by comparing common base positions shared with existing GWA data. For gene-level burden tests, variants were filtered for coverage <5-fold, quality score <45, call rate < 60 %, MAF > 0.01, Mendel errors, and nonfunctional variants. Single variants were filtered for coverage <5-fold, quality score <45, call rate < 60 %, and Mendel errors. For rare variants , these thresholds provided a sensitivity of ≥94.9 % and specificity of ≥99.9 % . Mendel probability of the trait score compared to the age/sex/birth-cohort matched in the Framingham Heart Study. Trait exceptionality scores for type 2 diabetes, heart disease, cancer, and stroke were calculated as the negative logarithm of the probability of the onset age of the disease, and compared to the distribution of the age of onset for each trait in the Framingham Heart Study. There are two additional components to the HAI, which are pulmonary vital capacity and serum creatinine. However, these were not available in Framingham Heart Study and were not included in our analyses.The LLFS cohort is enriched with longer-lived and healthier individuals than the general population, as well as the Framingham Heart Study. To account for this difference and increase our statistical power for detecting sequence variants associated with aging-related phenotypes, such as survival, BMI, blood pressure, blood lipid levels, blood glucose level, blood insulin level, cognitive function, type 2 diabetes, heart disease, cancer, and stroke are transformed to “trait exceptionality scores”. Higher trait exceptionality scores indicate that, for a given trait, an individual is significantly different from the reference population for the same trait, resulting in longer individual survival and/or better health. Using sex- and birth-year specific cohort life tables from the 2012 trustees report of the United States Social Security Administration, the trait exceptionality scores for survival were calculated for the oldest (proband) generation in LLFS, along with any blood relatives within the same generation regardless of age. These calculations are conditional on survival to age 40 and were determined as the negative logarithm of the probability of survival beyond age at last contact. Trait exceptionality scores for BMI, blood pressure, blood lipid levels, blood glucose level, blood insulin level, and cognitive function were also calculated as the negative logarithm of the 1c was adjusted for age, age2, age3, field center and the top twenty principal components (PCs); the standardized residuals from a stepwise covariate adjustment were used as final phenotype for the following association analyses. The leukocyte telomere length was transformed using an inverse normal function. This transformed trait was adjusted for covariates age, sex, education, field center, smoking, alcohol consumption, marital status, history of heart disease, and twenty PCs (PC8 was the only significant PC associated with leukocyte telomere length in the multivariate polygenic model). Mortality weighted healthy aging index were adjusted for age, sex, and 10 PCs and the residuals were used in the following analyses. The residuals of trait exceptionality scores adjusted for field centers and 20 PCs were used in the following analyses.To increase the power and reduce the confounder effects of our analyses, we performed the following covariates adjustment and transformation. HbAFamily relatedness was estimated based on pedigree structure using the “kinship” R package. Accounting for this relatedness within families as random effects, the additive genetic fixed effects of SNPs were analyzed using the linear mixed effects model implemented in the “lmekin” R packages , 22. Q-QX) and fit into a linear model. The kinship matrix was incorporated into the linear models to adjust for familial relatedness between subjects. Parameters in each model were estimated by the maximum likelihood algorithm and tested by the Wald test. Rather than using a fixed weight, the PWST method adaptively calculates the weights for individual variants from the observed genotype and phenotype data and then performs a permutation procedure for family data to avoid false positive inflation due to the over fitting of the adaptive score WSS.Currently un-weighted sum score (UWSS) , weighteth century birth cohorts (60–70 years) and the current life expectancy in both populations (~80 years).Our results included 4,217 LLFS subjects (mean of age 70.56 for men and 70.05 for women) with complete phenotypic and genotypic information. Individuals with inadequate GWA or sequencing coverage were excluded. The study participants tend to have healthier profiles compared with other cohorts of adults. For these individuals, the mean score of healthy aging index (3.64 in men and women) and mortality weighted healthy aging index (3.24 in men and 3.03 in women) are low. The average age of the LLFS probands and their relatives (95.8 in men and 99.4 in women) is much higher than the cohort life expectancy of US and Danish (an LLFS study site) early 20n = 26,723) were rare, occurring at <1 % in the general population, and 12,303 (46.04 %) were singletons  with trait exceptionality for survival in long lived families. Intronic variants of OBFC1 genotyped by Illumina array were also queried, and two rare variants  were also found to have a significant association with survival  with telomere length (p = 0.038). Interestingly, one long-lived pedigree (pedID 25609942) with 14 family members was clustered for all three rare variants in seven family members, which suggests that multiple rare variants within OBFC1 might contribute to longer lifespan. The comparison of “survival” to OBFC1 genotype is shown in Additional file Because there are correlations between the target phenotypes, the likelihood of a single variant having a functional effect on a given phenotype was Bonferroni corrected by dividing 0.05 by the total number of variants queried without accounting for the number of analyzed phenotypes. This resulted in p-values of 7.56x10Linear mixed model based single variant testing is not a stable method for rare variants with <10 minor allele copies , which ihttp://genomics.senescence.info/longevity/) [www.genome.gov/gwastudies/). When considering survival and any single variant with a p-value of <0.05 and more than 10 allelic copies in the LLFS cohort, we replicated 76 variants within these 160 genes  with survival.Among the 439 candidate genes, 160 are well-known human longevity related genes based on LongevityMap (gevity/)  and A Cated gene –39, wereIGF1R  is well known to be associated with cell growth and various cancers [We found associations with phenotypes other than survival. One candidate gene,  cancers . As showCETP, APOC3, SYNE1 and TOMM40) were associated with blood lipid levels. The cholesterol ester transfer protein, CETP (located on chromosome 16q21), is essential for metabolism of plasma lipoproteins [CETP, one common intronic variant (rs1532625) [APOC3  is critical for triglyceride metabolism and a potential therapeutic target for metabolic syndrome [APOC3 was replicated. LDL cholesterol was associated with one synonymous variant in SYNE1  and two synonymous variants in TOMM40 . TOMM40 has been identified in genome-wide screens for dyslipidemia and carotid artery disease [TOMM40 rs10524523 polymorphism in combination with APOE alleles significantly influences late-onset Alzheimer’s disease and longevity [In addition, four genes (proteins . Three k1532625)  and two 1532625) , were resyndrome . One knosyndrome  in the U disease , 47. Morongevity . Comparip < 1.14x10−4 for significance and p < 1.0x10−3 would merely be suggestive. With respect to survival, none of the candidate genes passed this significance threshold. When comparing our association results for survival to published longevity related genes, 20 genes were replicated with p-value <0.05  within the same gene using the PWST  and famSGSK3B  achieved significance by PWSK. GSK3B, a serine-threonine kinase, has been associated with Alzheimer’s disease [NOTCH1 was significantly associated with diastolic blood pressure and TP53 with HDL (Table NOTCH1 (chromosome 9q34.3) is involved in a variety of developmental processes by controlling cell fate. In mice, NOTCH1 signaling is also required for vascular development [TP53 , is a well known tumor suppressor that regulates cell cycle and DNA repair. An inverse correlation of HDL cholesterol with cancer risk [TP53 with HDL cholesterol.We next performed PWSK and famSKAT analyses on our candidate genes to see if any gene harbored multiple putatively functional variants associated with the individual Healthy Aging Index (HAI) and those results are listed in Table  disease , 50 and DL Table . NOTCH1 elopment . TP53  by using linkage information in families with high LOD scores to inform targeted resequencing for rare variants. This experiment identified a familial variant in VEGFC that explained 23.8 % of phenotypic variance within a pedigree, but the same variant only described 0.1 % of the phenotypic variance in unrelated individuals [ADIPOQ  gene describing 63 % of the variance of plasma adiponectin levels, which are critical for glucose homeostasis [OBFC1, CTEP, ZNF562) found associated with longevity and healthy survival. Using a sequencing-based approach to identify rare variants within pedigrees associated with exceptional phenotypes. Of note, we identified one rare, novel variant in OBFC1 as well as two intronic variants from LLFS array data associated with survival. OBFC1 is involved in telomere maintenance; its role in promoting exceptionally healthy aging is intriguing given that this gene resides squarely within a family-based association peak for telomere length recently reported from the same LLFS families [OBFC1 variants, while 23 additional beneficial rare variants are clustered in two other exceptionally long-lived LLFS families.To take advantage of the unique family structure of the LLFS cohort, we have performed candidate gene resequencing to identify familial sequence variation that could explain multiple exceptional phenotypes contributing to long lifespan and healthy aging. Our results are limited by gaps in sequencing coverage of some genes due to non-uniform hybridization of baits across the entire cohort, which may mean an underestimate in the number of familial variants in these genes. This strategy was previously used to identify rare familial variants in ividuals . The sameostasis . Our resfamilies . The ideGSK3B, which is a kinase for over forty different proteins and plays key roles in numerous intracellular signaling pathways  [Because effect sizes from single rare variants are typically small, collapsing rare variants across larger loci (e.g. genes or pathways) can aggregate variants and highlight the importance of a larger genetic locus . By analoptosis)  was idenoptosis) .CETP, is a well-known regulator of HDL and has been associated with healthy aging in Ashkenazi Jewish [Another healthy aging candidate gene, i Jewish , 60 as wi Jewish . The negi Jewish . To undeUnder the Rare Variant/Complex Phenotype hypothesis, multiple rare variants have an aggregate effect on complex phenotypes or diseases, but identifying the genes and the relative contributions of the various sequence changes is difficult. By leveraging the pedigree structure and cohort size of the LLFS study, we demonstrate the utility of sequencing within pedigrees to identify inherited genetic variation influencing specific parameters of healthy aging. These data are limited by representing primarily genes that were already known to be associated with the various phenotypes being investigated. A larger survey of the genome in the LLFS cohort, especially the linkage peaks for multiple healthy aging phenotypes, will facilitate discovery of new genes and putative mechanisms for the genetic regulation of the complexities of aging.Each participant provided written, informed consent prior to inclusion in this study. This study is in compliance with the Helsinki Declaration and the results reported herein have been approved by the National Institute of Aging, the Human Research Protection Office of the coordinating center at Washington University under IRB#201106316, the University of Pittsburgh Institutional Review Board, the Boston University Office of the Institutional Review Board, the Columbia University Institutional Review Board and the Regional Scientific Ethical Committees for Southern Denmark.Not applicable.GWA and sequencing information for the LLFS participants is available in dbGaP using accession number phs000397.v1.p1.Additional file 1:Supplementary Materials. (PDF 1024 kb)


For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship.We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy.This alternative covariance estimator can be used to gain insight into trait-specific genetic heterogeneity by identifying relevant sub-populations that lack genetic correlation between them. Genetic correlation can be 0 between identified sub-populations by performing automatic selection of relevant sets of individuals to be included in the training population. It may also increase statistical power in GWAS.The online version of this article (doi:10.1186/s12711-015-0171-z) contains supplementary material, which is available to authorized users. Genomic prediction (GP) is now widely used in plants and animals. Numerous methods have been developed but a common approach is the GBLUP (genomic best linear unbiased prediction) model that uses markers to estimate the covariance between the performances of individuals. Similar models are also used for genome-wide association studies (GWAS) to control population structure and background effects in the detection of significant marker-trait associations e.g., . Many geIn plants, similar results have been reported. Windhausen et al.  found noG, is not always well estimated by the whole-genome kinship. The GBLUP estimate of G using If the covariance between individuals was well estimated by the current models, adding more individuals to the analysis should never be detrimental . Consequ∑jpj1-pj . \documed (REML) .G depends only on the causal loci [G will still be only estimated. As shown by Hill and Weir [G is not well estimated by If there are too few markers, this estimator is suboptimal and some shrinkage is needed to better estimate the whole-genome relationship –14. Two sal loci . As a coand Weir , G is noG is a critical issue for the design of training populations, for increased prediction accuracy and for increased power of the detection of marker-trait associations. De los Campos et al. [This discussion makes clear that improved estimation of s et al. , showed iewed in . A largensidered .Our first objective was to propose a multiple kernel model to study the problem of genetic heterogeneity without pedigree or a priori knowledge about subpopulations. The second objective was to compare the new model to other available kernels using empirical data.A basic mixed model for genetic evaluation or GWAS can be written as:mentclass2pt{minimre known  which isDe los Campos et al.  showed tU such that Another common family of kernels is the Gaussian family. In that case, learning . For fulcument}S . A Taylocument}D . This shcal data . For outWhen the number of markers is small compared to the number of individuals, Endelman et al.  have shomentclasspt{minimaSimilarly, Goddard et al.  argued tIn the GWAS context, another kernel function was proposed by  and termUP model . HoweverAll the kernels presented above have a common limitation: they do not allow for genetic heterogeneity. For example, they cannot describe well a situation where two populations have completely different additive genetic architectures. Such a case would be best described by a block diagonal covariance structure. Recently, , 23 propposed by .This means that the proposed K-kernels allow in one extreme case, We used several publicly available datasets representative of plant and animal breeding data, which are summarized in Table k ranging from 1 to 15 for K-kernel, from 5 up to the number of individuals by steps of 5 for the C-kernel, and For each dataset and trait, GBLUP, Gaussian kernel, C-kernel and K-kernel models were fitted to all the data available using the R package rrBLUP . For theested by . The kerThe GBLUP kernel is nested within the Gaussian, K- and C-kernels. For inbred individuals, if ervative . The impTo select the most predictive model, the AIC (Akaike information criterion) was used to compare kernels along with cross-validated prediction accuracy. The AIC quantifies the goodness of prediction rather than goodness of fit . The numFor the rice  and maizmentclass2pt{minimTo assess the statistical power of GWAS with the different kernels, we used the simulation scheme of  which prribed in . StatistAll computations were implemented using R 2.15.3 .The empirical null distributions of the log-likelihood ratio test for each trait and dataset allowed the verification of the test assumptions and the control of type I errors based on the observed 95 % quartile. For the Gaussian kernel, the mean of the 95 % quartile of the simulated likelihood ratio test statistics was equal to 2.58 with a minimum of 0 and a maximum of 3.84 across traits and datasets, which was close to the expected value of 3.84 for the 95 % quartile of the Chi squared distribution with one degree of freedom . Thus, test assumptions were correct for the Gaussian kernel. For the K-kernel, the mean was equal to 4.70 with a minimum of 3.55 and a maximum of 5.74 when the 95 % quartile of the Chi squared distribution with two degrees of freedom  was equal to 5.99. Thus, test assumptions were correct for the K-kernel. For the C-kernel although it requires only one additional parameter, the mean was equal to 4.71 with a minimum of 3.2 and a maximum of 5.94 when a value of 3.84 for the 95 % quartile is expected. Thus, test assumptions were incorrect for the C-kernel. In the following, for the C-kernel, the likelihood ratio test was corrected to account for two parameters to better control for false positives. The AIC of the C-kernel was also corrected to account for two parameters instead of one.P value, corresponding to a high negative log of P-value indicates that an alternative kernel describes the genetic architecture significantly better than GBLUP. This is the case for a number of traits, even after Bonferroni correction. The C-kernel performed well on the CIMMYT wheat dataset that is characterized by a small number of markers and on the rice dataset , which suggests that there is some overlap in the additional information that the two kernels capture. The other pairwise correlations were not significant.The log P-value of the Gaussian and K-kernel were significantly correlated across traits and datasets .Statistical power was calculated as the percentage of true QTN detected at the significance threshold of 0.01 based on a sample of 5000 markers taken successively as true QTN using the same test settings as previously. On average, for the maize panel Table  there waThe gain in power was similar on average for different true QTN sizes but large differences were observed between traits in the rice panel. For a few traits, gains in power were above 10 % Fig. .With theWith the Gaussian kernel and K-kernel Fig. a, b, a s−6 in both cases) but not for the C-kernel (non-significant correlation). This provides further evidence that both the Gaussian kernel and K-kernel are not overfitting the data. With leave-one-cluster-out cross-validation, accuracies were on average much lower, especially for the most diverse datasets. The correlation between the AIC reduction and the observed gain in accuracy was less clear but still significant for the Gaussian kernel and K-kernel . The correlation was not significantly different from 0 for the C-kernel .Similarly, if an alternative kernel better describes the true genetic architecture, it should also increase prediction accuracy in cross-validation. Figure The results of the likelihood ratio tests support the hypothesis that alternative covariance estimators better describe the true genetic architecture of the trait. The tests results are supported by simulations of the null-distribution of the test statistics. The fact that this new covariance estimator better describes the true genetic architecture of the trait in some cases is evidence of significant heterogeneity of the trait architecture. The new model provides a way to investigate genetic heterogeneity and find the relevant scale to apply genomic selection or detection of marker-trait associations. The increase in GWAS power and stratified cross-validation accuracy are evidence that the alternative covariance estimators are not overfitting the data and, thus, further support the findings on genetic heterogeneity.There are several non-exclusive explanations for the significantly improved fit to the data with alternative covariance estimators. The most obvious one is marker density, but most datasets used here have a high marker density. Another explanation is the presence of non-additive effects. Improved fit to the data with the Gaussian kernel compared to GBLUP can be explained by epistatic interactions in inbred individuals  but thisGWAS with a mixed model is standard practice but there are few published reports of alternative kernels to control background effects. Stich et al.  tested aFor the C-kernel, results suggest that the reduction in AIC is not a good predictor of an increase in power. This result contrasts with that reported in  for whicFor genomic prediction, the K-kernel or Gaussian kernel provided a small increase in accuracy in stratified cross-validation depending on the trait. Figure Results of the leave-one-cluster-out cross-validation were poor, with none of the alternative kernels performing better than GBLUP. This might be expected because those kernels make additional hypotheses on relationships compared to GBLUP. If a cluster is fully removed from the training set, the model cannot reliably identify those parameters on the relationship between the training and validation sets. However, it might prove useful in across-breed predictions, if training data are available on both breeds and the focus is on prediction accuracy in a given breed. In that context, training data in the breed of interest would provide information on how informative the other breed is for the validation set.h = 1, it is beneficial to analyze the data in distinct sets. Otherwise, the data can be analyzed together, but the K-kernel allows the less related individuals to contribute less information to the prediction than GBLUP. The K-kernel can be used to identify an optimal training set as well as provide a way to better use distantly related individuals for training. This model is a refinement of the multi-trait model proposed by [Results from , 32 suggposed by , 23 that


The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r2 = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies , and differentiation between the four synthetic populations was low (FST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S Genomic selection (GS) arose from the combination of new high-throughput marker technologies and new statistical methods that allow the analysis of the genetic architecture of complex traits in the framework of infinitesimal model effects. GS differs significantly from conventional marker assisted selection as it makes it possible to select individuals without prior knowledge of an association between the markers and the trait of interest. GS refers to methods that use genome-wide markers to predict an individual’s genomic value with enough accuracy to allow the selection of individuals based on that prediction alone. GS consists of three steps: (i) inference of the genotype-phenotype relationships in a training population, (ii) prediction of genetic value based on marker genotypes or genomic estimated breeding values (GEBV) in a target population and (iii) selection of individuals based on their GEBV . As GS dSeveral statistical prediction models have been developed that differ in the assumptions they make about the effects of markers and the variance of such effects across the genome: random effects drawn from a normal distribution with equal variance for all markers (ridge regression best linear unbiased prediction), random effects drawn for each marker from a normal distribution with its own variance (Bayes A), or the probability that the marker has no effect at all (Bayes B). Semi-parametric and non-parametric regressions have been developed to map genotypes to phenotypes for traits with non-additive genetic architecture . HoweverTo date, research on GS has mainly focussed on livestock breeding. Its use in plant breeding schemes began  with invOryza sativa). A simulation study comparing the accuracy of nine GS methods in predicting eight traits in a collection of 110 Asian cultivars [GS is rather new in rice  endowed with sufficient genetic variability for future selection. The RS scheme includes three stages conducted recurrently: (i) evaluation of individuals derived from a population Pn, (ii) selection with mild pressure of the best individuals to gradually increase the frequency of favourable alleles at loci involved in complex traits (additive and epistasis effects) [n+1 with an improved mean. This scheme is facilitated by the presence of a recessive nuclear male-sterility (ms-IR36) gene located on chromosome 2, segregating within the population [Since 1992, CIAT  and Cirad  have developed a rice breeding program for Latin American and the Caribbean based on the improvement of synthetic populations through recurrent selection (RS) . The proeffects) , (iii) ipulation . At the 2:4 lines extracted from our upland rice synthetic population.Here, we present the results of a large array of cross validation experiments exploring the effects of the size of the training population, the density of markers in relation with linkage disequilibrium (LD) and minor allele frequency (MAF), the relatedness between the training and the breeding population, the heritability of the target traits and the type of prediction model on the accuracy of GEBV prediction using phenotypic and genotypic data on 343 S0 plants with a heterozygous genotype at the male sterility locus ms-IR36 [0 were advanced to S1:2 through single seed descent while marker assisted selection was performed at the ms-IR36 locus. For each S1:2 one homozygote fertile plant was advanced to S2:3 for DNA extraction and to S2:4 for phenotyping, using a bulk breeding procedure.Numerous populations have been developed by Cirad and CIAT for the purpose of breeding upland rice varieties for Latin America and the Caribbean. Four populations that underwent more than 10 cycles of RS with very mild selection for yield under favourable upland conditions form the base of the CIAT upland rice-breeding program. To test the effect of structure in GS, the four populations were combined to build a training population (TP). One hundred S ms-IR36  were ext ms-IR36 . The 4002:4 lines  with three standard checks  and 10 additional entries to complete the design. The experiment began with dry direct seeding. Each experimental plot comprised two 3 m-long rows with 26 cm between row spacing. Seed density was one gram of seed per meter.The phenotyping experiment took place in the Colombia Agricultural Research Corporation (Spanish acronym CORPOICA) Center (La Libertad) located in Villavicencio, Meta, Colombia  during the 2013 rainy season. No specific permission was required for phenotyping at the CORPOICA facility as an existing agreement links CIAT and CORPOICA to carry-on field trials at the La Libertad location (convenio de accuerdo CIAT-CORPOICA # 20130181 del 15 de Mayo de 2013). Furthermore, the field studies did not involve endangered or protected species. The field used for evaluation is typical of the savannah conditions, with acid soils (pH < 5.0) and high aluminium content (> 75 ppm). The field evaluation was conducted under standard agricultural practices. The experimental design was an alpha lattice with 21 blocks of 17 plots for each of the two complete replicates. It comprised 346 SThe target traits were days to flowering (FL), plant height at maturity (PH), weight of an individual panicle (PW) and grain yield (YLD), assumed to represent together a wide range of heritability and genetic architecture. FL was recorded as the date when 50% of the plants in the plot had flowered. PH was the average height (cm) of five plants in the plot. Five panicles were collected along a distance of one meter in the row, weighed and averaged to obtain the individual PW (g). Grains from these five panicles were added to the grains collected from plants along the remainder of the one-meter length to give YLD expressed in g/m.http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_mixed_sect027.htm). This diagnostic procedure led to the elimination of two elementary data. The selection of entries with both phenotypic data and genotypic data lead to a final number of 343 S2:4 lines, which were used to develop the prediction model.First, a diagnostic module was run using the “influence” option to detect outliers among the individual observations. It included the “iter =“ sub-option that updates both fixed effects and covariance parameters by refitting the mixed model when an observation is deleted. (ijk = μ + gi + Rj + bk(j) + eijk in which Yijk was the phenotype, μ the overall mean, gi was the genotype effect considered as random, Rj was the replicate effect considered as fixed, bk(j) was the random effect of the block within a replicate, and eijk was the residual considered as a random effect.For each trait, experimental data were analyzed using the SAS 9.3 MIXED procedure  to estimhttp://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE as "SEPANG dataset".Variance components were obtained, and a BLUP value was extracted for each line to be used in the GS models. The phenotypic BLUP x accession matrix is available for download at 2:4 families) and the phenotypic variance For each trait, narrow sense heritability was calculated using the ratio Multi-dimensional analysis of phenotypic data by factorial discriminant analysis (FDA) was performed and pairwise Fisher distance between subpopulations was calculated using the XLSTAT package .2:3 plants following the MATAB method described in [2:3 lines. The average rate of missing data among the 8,385 SNP was 3.2%. Genotypic profiles for these missing data were estimated using Beagle v3.3 through the open source Synbreed package available from the R website (http://cran.r-project.org/web/packages/synbreed/index.html). Beagle uses a hidden Markov model to infer haplotypes and imputes sporadic missing data on large scale phase known or unknown genotype data sets [http://tropgenedb.cirad.fr/tropgene/JSP/interface.jsp?module=RICE as "SEPANG dataset".Genomic DNA was extracted from bulked leaf tissue from 15 Sribed in  and diluribed in . The proata sets . The imp2:3 lines into four subpopulations corresponding to PCT4-C0, PCT4-C1, PCT4-C2 and PCT11-C1 was investigated by FDA [2:3 lines on the 10 first axes of a principal components analysis, using the genotype on the 3,675 SNP loci with no missing data.The structuring of the 343 Sd by FDA . The anaFST test that estimates genetic differentiation based on allele frequency [FST statistics were calculated over the genome using 3,675 markers with no missing data with Arlequin software [Genetic variation between subpopulations was estimated using a pairwise requency . The FSsoftware .Ne was estimated for each of the four subpopulations as well as for the whole population, from genotypes at 3,675 SNP loci. Ne was calculated using the linkage disequilibrium method of Waples and Do [s and Do , re-impls and Do .ij = n/8,336; n being the number of SNPs that differ between line i and line j), which was converted into a similarity matrix by subtracting all values from 2, then scaled to rank values from 0 to 2, using the Synbreed package [A marker-based kinship matrix was generated using the 8,336 SNP available after imputation. Kinship estimates were obtained by calculating a distance matrix  was used for the validation of a prediction model developed on (k-1)/k of the population.The effect of the size of the training population (TP) was analyzed by varying the fraction (k) of the population used for the model calibration with respect to the fraction used for model validation (VP). Three fractions were considered, k = 3, 6 and 9, where 1/k of the total population . Markers were first retained on the basis of MAF threshold and second on the basis of pairwise r2 values with all other markers on the carrier chromosome. MAF threshold values were selected to assess the impact of including less frequent alleles in the prediction model, while the LD threshold values were selected to assess the impact of redundancy caused by linked loci.For the main cross validation experiment, incidence matrices were built to evaluate the effects of MAF, LD and the resulting number of markers. Nine sets of SNP markers were selected by combining three threshold values of MAF  with three threshold values of LD . The 35 sets of SNP markers were used for cross validation with the ridge regression (RR-BLUP) prediction method (see below) with k = 3-fold cross validation, for each of the four traits considered.In a supplementary set of cross validation experiments, the effects of LD and MAF on the accuracy of predictions were further analyzed by combining five threshold values of MAF  with seven threshold values of LD  random assignment, where the 343 S2:4 lines were considered as being derived from a single population, i.e. 2/3 being randomly assigned to the TP and 1/3 to the VP; (ii) balanced assignment based on the stratification of the 343 S2:4 lines into four subpopulations , where each subpopulation contributed 2/3 to the TP and 1/3 to the VP; (iii) unbalanced assignment where, in turn, all lines from three subpopulations were used as TP and all lines from the subpopulation that was left out were used as the VP. These six case studies were tested for accuracy using the RR-BLUP method for the four traits and 35 incidence matrices.The effect of varying degrees of relationship between the TP and VP was investigated in a set of cross validation experiments by comparing the accuracies of prediction obtained under three strategies for the assignment of the S2:4 families. This was a reasonable approximation in the context of population improvement with mild selection pressure for the traits of interest. Parametric whole genome regression methods are widely used to handle these additive genetic models. These methods are based on well-established theories and to some extent apply the regularization technique to resolve the p >> n problem. The parametric whole genome regression methods differ considerably in the way the regularization parameter λ is obtained and used in calculations [http://www.r-project.org/) to perform whole genome regression.As a first step in exploring the potential of genomic selection in our rice population breeding program, we assumed a strictly additive genetic model among the Sulations . SeveralFive methods were applied (three frequentist and two Bayesian) to test the different hypotheses concerning the marker effects. The frequentist methods were the genomic best linear unbiased prediction (G-BLUP), the ridge regression best linear unbiased prediction (RR-BLUP), and the least absolute shrinkage and selection operator (LASSO). The Bayesian methods were Bayesian ridge regression (BRR) and Bayesian LASSO (BL). In G-BLUP, the first step consisted in using the incidence matrix X linking markers to individuals to estimate the genomic relationship matrix (G), a matrix of covariance between individuals based on observed similarity at the level of the markers . A mixed2:4 lines was partitioned randomly into k = 3, 6 and 9 folds. For each combination of the prediction method, population parameter and phenotypic trait investigated, a model was calibrated using The set of 343 SThe first cross validation experiment produced up to 55,296 correlations, resulting from 540 scenarios , with 100 to 108 tests for each scenario. These correlations (r) were then normalized, using a transformation function as proposed by Fisher  applyingp < 0.01) for PH and PW and a significant effect (p < 0.05) for FL and YLD. The replicate and block effects were highly significant for all traits. The results of ANOVA on the transformed variables were identical to those on non-transformed data. Consequently BLUP for individual S2:4 lines were calculated using untransformed data. Narrow sense heritability was high for FL and PH, moderate for YLD and low for PW  between all pairs of subpopulations except PCT4-C2 and PCT11-C1  / 4) shows a nearly U-shape distribution , with a median value of 4.8% and a maximum of 22%. The distribution of observed heterozygous loci (Ho) varied among the 12 rice chromosomes  and the ribution . For allribution .Ne varied among the four subpopulations, from 28 for PCT4-C1 to 53 for PCT11-C1  between all pairs of subpopulations  with BL, k = 9, r2 ≤ 1 and MAF ≥ 5%, for YLD (AA = 0.309) with BL, k = 9, r2 ≤ 0.9 and MAF ≥ 5%, and for FL (AA = 0.295) with LASSO, k = 6, r2 ≤ 0.9 and MAF ≥ 5%  values obtained differed greatly between traits, k-fold ratio, incidence matrices and regression methods . OverallMAF ≥ 5% .2 = 0.56) but that a high proportion of variation remained unexplained (CV = 39.6%). Under the second model, considering the main effects of controlled factors (13 degrees of freedom), ANOVA revealed very highly significant (p < 0.0001) effect of all factors, explaining 52% of total variation. The third ANOVA, which considered both main factors and all first-order interactions (79 degrees of freedom), showed that the interactions as a whole marginally improved the model (R2 = 0.55), although six of them proved to be very highly significant (p < 0.0001). Thus, when comparing R2 in model 1 and model 3, higher order interactions should explain even less variation.For the first cross validation experiment, we used a factorial design to compare the 540 scenarios resulting from a combination of traits, methods, MAF, LD and k-fold ratio. This design made it possible to identify the effects of controlled factors and their interactions. Each single correlation between GEBVs and BLUP values was transformed as recommended by Fisher  to normaAA values differed significantly among the five methods of prediction implemented. The AA was the highest for BL (AA = 0.312) on average and lowest for LASSO (AA = 0.265) . The AA 2 values  differences among AA were observed for the four phenotypic traits characterized by different narrow sense heritability (2 = 0.86) to AA = 0.489 for the trait with the second highest heritability . YLD and PW had an intermediate level of AA  despite some difference in heritability .Highly significant  was detected between LD and phenotypic trait, which was not observed between MAF and traits, while both LD and MAF interacted significantly with the prediction methods. For all traits, prediction accuracy decreased significantly when no selection was made on LD (r2 ≤ 1) , with the accuracy achieved using between 100 and 7,200 randomly sampled markers. This second set of cross validation experiments was performed with the RR-BLUP prediction model with k = 3-fold cross validation. Results for k = 3-fold cross validation and 15 incidence matrices resulting from the combination of five MAF thresholds and three LD thresholds are presented for FL and PH  . For all(r2 ≤ 1) . The nonL and PH . For bot2 ≤ 0.4 to r2 ≤ 1, with an increment of 0.1) tested under the RR-BLUP prediction model with k = 3-fold cross validation  when the TP comprised all the lines of three of the subpopulations and the VP comprised all the lines of the fourth subpopulation. In this sampling scenario, the lowest AA was observed when validation was performed using the PCT4-C0, PCT4-C1 or PCT4-C2 subpopulation, AA = 0.224, AA = 0.227 and AA = 0.231, respectively, and the AA was significantly higher (AA = 0.243) when PCT11-C1 was used as the VP  and in the indica population (h2 = 0.30 and 0.35 according to the season).The accuracy of genomic prediction in crop species obtained through cross validation using empirical data varies greatly. Most variation is attributed to the population structure and size, marker density and the genetic architecture of the traits. Generally, the accuracies of GEBV obtained in our study were lower than, or similar to, those reported in the rice literature. For instance, similar to our results, the accuracy of prediction for flowering in the ged 0.77  versus 0pulation . Convers2:4 lines for TP and VP during the cross validation process.The results we obtained with the different regression methods differed significantly: the BL prediction model was slightly better (AA = 0.312), the BRR, G-BLUP and RR-BLUP methods resembled each other and intermediate, and the LASSO method produced lower AA values (AA = 0.265). Interactions between methods and traits were significant, mainly because LASSO produced a high AA for FL. The higher AA provided by LASSO compared to BLUP methods was the consequence of fitting a reduced number of explanatory variables to the model, setting some markers to exactly zero effect, thereby making it possible to avoid model over-parameterization for a trait controlled by a few major loci. As a matter of fact, looking at the effect of the markers for FL, in our population, a single locus with a strong effect was detected (data not shown). Similar results have been reported in the GS of fusiform rust resistance in loblolly pine, a trait controlled by a few genes with large effects  thus hig2:3 lines exhibited a mild structure due to the breeding history of its four component subpopulations. GS was more accurate when the training set was more closely related to the selection candidates, i.e. when the TP and VP were made of a random set of all four subpopulations or from an equal proportion of each subpopulation. A similar effect of the composition of the TP has been reported in GS experiments using the rice diversity panel of 413 accessions. Accuracy within indica and japonica subpopulations was much greater than across the whole population due to a strong population structure accounting for 58.7% of overall molecular variance [As expected, our synthetic population of 343 Svariance . The aut2:3 lines, the short distance LD (r2 = 0.59 for distances of 0–25 kb) was similar to the one observed in a reference panel of 168 accessions representing the diversity of the tropical japonica group of O. sativa (to which our synthetic population belongs) genotyped in an identical GBS experiment [japonica backgrounds, which vary between 150 and 180 kb. Given these LD values, the marker density of 1 SNP per 45 kb we achieved is reasonably good coverage for the purpose of genomic GEBV prediction. In similar GS studies in diverse rice populations, the authors recommended the use of 6 and 7 k SNPs [The prequirement for high accuracy in GS is that the markers and QTLs are in strong linkage disequilibrium. When no prior information is available on the number and position of the QTLs, LD between SNPs can be used as a surrogate to evaluate the extent of LD in the population of interest. For quantitative traits controlled by many genes, in a multi-SNP effect detection approach, sampling the whole genome would ensure that the distribution of markers is in agreement with the LD of the population. In our population of 343 Speriment . Howeverperiment ,51–53 in7 k SNPs .0 –S2) performed through single seed descent (SSD). High LD due to recent recombination events can reduce the accuracy of GEBV in cross validation experiments, as it is distributed within and not across the progenies. Likewise, it can rapidity reduce the accuracy of GEBV across breeding cycles, as it is highly susceptible to meiosis effects.High long range LD can emerge from co-selection of favorable mutations in some regions of the genome conferring an adaptive advantage. However, in our case it was most probably due to the random associations of distant loci during the two-generation advance . As suggested in other studies, the number of markers does not need to be high to achieve good genomic prediction, and best accuracy was achieved with no more than 7,142 well distributed SNPs (~ 1 SNP every 0.2 cM) in the irrigated rice population of breeding lines [Ne/morgan for an effective population size of 100 with 1,000 phenotypes and a heritability of 0.5. Understandably, this simulation depends on the genetic model of the trait concerned, and requires adjustment of the prior distribution to the true distribution of the QTL effects. In our case, with a map of 18 morgans (www.mapdisto.free.fr/), Ne ~ 50 implies that about 3,600 SNPs would be needed under an infinitesimal model with additive effects and under the assumption of evenly distributed QTLs on the chromosomes. For PH, PW and YLD, the greatest accuracies with the RR-BLUP method were achieved with matrix sizes ranging from 4,011 to 5,148 SNPs. Conversely, for FL, the greatest accuracies were achieved with a matrix size of 1,758 SNP, suggesting that the assumptions presented in the aforementioned simulation study do not apply to this trait. The low accuracy of the prediction of the highly heritable FL trait using the largest SNP matrices may result from high collinearity (the above mentioned long range LD) between markers and associated numerical instability.In our cross validation experiment, the accuracy of genomic prediction for PH, PW and YLD was affected by the choice of markers based on LD, and significantly higher accuracy was achieved for FL when the SNPs were selected with a LD threshold of rify QTLs . Indeed,ng lines . Based ong lines  showed t2:3 lines exhibited a number of polymorphic loci similar to the number observed in the reference tropical japonica panel [in silico simulation of base populations that are needed to help deal with high dimensional problems, to explore experimental designs for GS, or to test alternative hypotheses. Our synthetic population meets these conditions since the U-shaped distribution of MAF can be interpreted as a population in mutation-drift equilibrium [Our interest in accounting for MAF in GS was motivated by the need to determine a reasonable MAF threshold to be used in the predictions. Our population of 343 Sca panel  but the ca panel  showed tilibrium .1 instead of S2). Second, the accuracy of GEBV can be further improved through more favourable germplasm development options, such as switching from single seed descent to a bulk method when advancing from generation S0 to S2. To further explore the potential of GS, we are currently developing a more analytical approach using simulation tools for the development of the training population.Here we evaluated the effect of different parameters  on the accuracy of GEBV, in the context of rice population improvement through recurrent selection. Although a rather low level of accuracy of GEBVs was observed in any combination of levels of the different parameters, this first study does not call into question the potential of genomic selection (GS) to increase genetic gains in the context of this breeding scheme. First, because the observed accuracy of GEBV was of the same magnitude as the heritability of complex traits such as grain yield, and GS can potentially accelerate genetic gain by increasing selection intensity (provided genotyping of a large number of entries is possible) and by shortening the selection cycle with genomic predictions approximating future pure lines performances in an earlier generation Click here for additional data file.S2 FigThe U shape can be approximated by a beta distribution with shape parameters  = .(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig2: linkage disequilibrium; a, b, c, d and e: minor allele frequency (MAF) thresholds of ≥ 0%, ≥ 2.5%, ≥ 5%, ≥ 7.5% and ≥ 10%.The prediction method was RR-BLUP with k = 3-fold cross validation; r(TIFF)Click here for additional data file.S7 Fig2: linkage disequilibrium; a, b, c, d and e: minor allele frequency (MAF) thresholds of ≥ 0%, ≥ 2.5%, ≥ 5%, ≥ 7.5% and ≥ 10%.The prediction method was RR-BLUP with k = 3-fold cross validation; r(TIFF)Click here for additional data file.S8 Fig2: linkage disequilibrium; a, b, c, d and e: minor allele frequency (MAF) thresholds of ≥ 0%, ≥ 2.5%, ≥ 5%, ≥ 7.5% and ≥ 10%.The prediction method was RR-BLUP with k = 3-fold cross validation; r(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableMean of correlations (standard deviation) across the 100 cross validation replicates(XLSX)Click here for additional data file.S4 TableDifferent letters indicate significantly different least square means LSMeans.(XLSX)Click here for additional data file.S5 TableDifferent letters indicate significantly different LSMeans.(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Candidate loci were detected that putatively contribute to adaptation to abiotic stresses. The identification of potentially adaptive variants in this ex situ collection may permit a more targeted use of germplasm collections.Natural populations across a species range demonstrate population structure owing to neutral processes such as localized origins of mutations and migration limitations. Selection also acts on a subset of loci, contributing to local adaptation. An understanding of the genetic basis of adaptation to local environmental conditions is a fundamental goal in basic biological research. When applied to crop wild relatives, this same research provides the opportunity to identify adaptive genetic variation that may be used to breed for crops better adapted to novel or changing environments. The present study explores an  Local adaptation has been a topic of research for more than a century and a half. FST . Screening an entire germplasm collection for agronomically important traits is impractical for individual breeding programs. Environmental association within ex situ germplasm collections provides a path to targeted use of crop wild relatives.The potential to identify genetic variants that contribute to local adaptation in crop wild relatives has important agricultural implications. Crop wild relatives are often a source of novel variation for plant breeding . We also excluded eight accessions from Taiwan and Northern Russia that were geographic outliers. Ambiguous and heterozygous SNP calls were treated as missing data due to the low outcrossing rate (∼3%) in G. soja , where the SNP query sequences were aligned with Bowtie 2   in permilles] at a resolution of 30 arcseconds (Table S3). We grouped soil data into two classes: topsoil (from 0 cm to 30 cm), and subsoil (from 30 cm to 200 cm), resulting in 14 soil variables. Classes were created by averaging the appropriate depths from the six depths available in the ISRIC database: 2.5 cm, 10 cm, 22.5 cm, 45 cm, 80 cm, and 150 cm (Table S4). Boxplots for each scaled bioclimatic and biophysical variable were created based on G. soja localities to examine the distribution for each variable .The latitude and longitude coordinates of d 150 cm . PearsonK), ranging from 2 to 5, was explored using a model with uncorrelated allele frequencies and no admixture between clusters, parameters that reflect a high degree of observed allele frequency differentiation among populations, and no prior evidence of admixture in G. soja. Runs for each K value were replicated 10 times, with 10,000 burn-in steps, and recorded for 10,000 subsequent steps. STRUCTURE assignments were visualized with the CLUMPPAK server   algorithm implemented in STRUCTURE (FST was estimated among populations identified through genetic assignment (STRUCTURE). Theta (Ɵ), the variance-based FST estimate of FST was averaged in sliding windows, with a window size of 5, and a step of 3 SNPs. A Mantel test was conducted to explore isolation by distance, utilizing great circle distance between geographic locations, and pairwise genetic distance using the ‘vegan’ package in R  in the sample was calculated with the ‘LDheatmap’ package in R  was calcQ-matrix from STRUCTURE to control for population structure, a model using a kinship matrix (K-matrix) based on pairwise Manhattan distance among individuals at all SNPs, a model using both a K-matrix and a Q-matrix, and models that integrate latitude or latitude and longitude as covariates. Quantile-Quantile (qq) plots were examined for each model, and the genomic inflation parameter lambda (Λ) .Mixed-model association as implemented in Tassel (5.0v) (mbda (Λ) , a metrip-values was used to identify candidates for each environmental association, resulting in three significant marker associations per environmental variable. This threshold was chosen to focus the analysis on a minimum number of large effect variants, and to limit false positives. The qqman R package  .The scripts and most of the input files used to filter SNPs, run STRUCTURE, calculate G. soja accessions for analysis from the GRIN collection. These accessions came from 273 unique sampling locations . The first principal component (PC) explained 5.1% of the variation, and primarily separated samples on an east–west gradient. The second PC explained 2.7% of the variation, and a largely north–south gradient. A Mantel test identified isolation by distance in our sample .The filtering criteria resulted in 533 ocations . Each acGlycine soja had a mean pairwise similarity of ∼70% across all samples . The Mainland North population was an outlier in terms of mean pairwise similarity within the three clusters, and contained a smaller number of segregating sites . The Island cluster had the highest private allele richness (corrected for sample size), followed by the Mainland South, then Mainland North . Across the range of G. soja, the northwestern region is colder and drier . The soils data indicate that the portion of the range in Japan has lower organic matter, higher sand content, and more variable pH than the mainland . A PCA of the bioclimatic and biophysical variables recapitulates the geography . The first four PCs explained 86.3% of the variation. Specifically, the first PC was associated with temperature, the second PC with precipitation seasonality (coefficient of variation in yearly precipitation), the third PC precipitation/soil, and the fourth PC with soil. Pearson correlations between all bioclimatic and biophysical variables (Table S4) showed high correlation between topsoil and subsoil (> 0.99); temperature of adjacent months (> 0.91); and spring, summer, and fall precipitation. While precipitation in July and August was highly correlated (0.86), they had low correlation with adjacent months .Most of the 82 environmental variables showed a high degree of variation across the 273 sampled locations . The final model used the bioclimatic/biophysical variable as the response, and genotype as a fixed effect, the K-matrix as a random effect, and latitude as a covariate . This was the simplest model (fewest covariates), with a Λ near 1, indicating minimal inflation of p-values . The use of latitude, or latitude and longitude, as covariates resulted in nearly the same Λ.Environmental association mapping using mixed models that incorporated the p-values from mixed model analyses are reported in Table S5 . As expected with a marker set this size, typical thresholds of 0.01 Benjamini-Hochberg FDR-value, or 0.001 p-value, resulted in thousands of markers below the significance threshold . These thresholds were therefore deemed insufficient for extracting only major loci. Instead, the threshold of 0.01% for each association was investigated, corresponding to the three strongest marker associations for each bioclimatic and biophysical variable. At this significance level, 110 unique SNPs were associated with at least one bioclimatic or biophysical variable (Table S7). The p-value for these significant SNPs ranged from 4.05E–4 to 5.69E–9, with an average of 2.98E–5. We examined GO terms and the putative function of Arabidopsis thaliana orthologs for genes within 34 kb  of these significant markers. As expected, several patterns arose corresponding to correlated environmental variables (Table S4), and major contributors to the environmental PCA .This model was applied across all 82 environmental variables. All p = 1.47E–6 and 6.78E–6) occurring less than 5 kb downstream of Glyma.08g298200 , individuals with the nonreference allele occur in environments that are on average ∼2° warmer in the wettest quarter than those with the reference variant .Mean Temperature Wettest Quarter was a major contributor to PC1 of the environmental PCA. Mixed model association of this variable identified an association on chromosome 8 with two SNPs . Many of these bioclimatic variables were major contributors to PC1 (temperature) in the environmental PCA. The Arabidopsis ortholog for the nearest gene, Glyma.16g017600, was TMP14, which encodes the P subunit of Photosystem I .Often, the same genetic variants were found associated with more temperature variables from adjacent months. An example is SNP ‘BARC_1.01_Gm16_1552499_A_G,’ significant in 11 associations with summer temperature variables . This SNP was significant in both July Precipitation, and Precipitation Wettest Quarter. This SNP occurs within Glyma.08g028200 . The Arabidopsis ortholog for this gene is PECT1, shown to be involved in respiration capacity in leaves . This is not merely a result of population structure, as the accessions with the nonreference variant are widely dispersed  variable was an association between SNP ‘BARC_1.01_Gm14_23750665_G_A,’ on chromosome 14, and Percent Sand Subsoil . This maispersed .Table S7, or Figure S12 for Manhattan plots of the mixed model association results for each of the bioclimatic and biophysical variables.For a complete list of significant markers see STF outlier analyses were used to identify allele frequencies that could indicate the action of selection at a locus. SPA outliers tended to divide the sample along the same axes identified in genetic assignment and PCA analyses. SPA selection scores were positively correlated with STF . The 99.9% outliers from SPA (Table S8) and STF (Table S9) overlapped at two SNPs (9% of outliers) .SPA and utliers) . One of utliers)  with Arautliers) . SPA outG. soja in East Asia identified three primary clusters: Mainland North, Mainland South, and Island. The Mantel test identified isolation by distance, a result consistent with previous observations of differentiation in G. soja based on a latitude gradient and flowering time , and PI479752 (collected in China but does not have longitude and latitude) , which may be relevant in the response to iron deficiency chlorosis (IDC). IDC is not necessarily a shortage of iron in the soil, but rather the inability of the plant to uptake iron under certain conditions , which could immediately be applied to a validation population, and implemented in a breeding program.These findings in elatives . This unWhile these results are promising, a number of limitations and biases with these data and approaches need to be considered. The limitations include the resolution of the bioclimatic and biophysical data, the assumptions made in treating environmental variables as a phenotype, and using an association mapping framework.The data in the databases are limited to a specific resolution. That is, points a certain distance apart are indistinguishable by their bioclimatic or biophysical variables. This amounts to “phenotyping error” in our approach, and reduces the power of our tests. This is particularly true for biophysical (soil) variables, which may vary over much finer scales than the resolution of the database , our approach is more likely to identify it as significant than if it were neutral in the second environment  .Lastly, because we are taking an association mapping approach, the limitations of association analyses apply. The variants we identify are not assumed to be causative, but are presumed to be in LD with a causative variant. Limited marker density can increase the false negative rate as there is limited power to detect all but the strongest signals of selection . BecauseG. soja. Genetic variation associated with the environmental variation across the native range of G. soja was identified. We focus on the identification of loci that could contribute to abiotic stress tolerance. Genome scans for environmental association provide an opportunity to categorize and evaluate the utility of genetic variants in germplasm collections, complimenting recent initiatives to categorize and evaluate international germplasm collections  were used to explore the intersection of bioclimatic, biophysical, and genetic components in the important soybean crop wild relative,


Yield stability in rye is controlled by many QTLs with small effects. This is in contrast to phenotypic stability of several quality traits, which are influenced by some large-effect QTLs. Improving phenotypic stability of crops is pivotal for coping with the detrimental impacts of climate change. The goal of this study was to gain first insights into the genetic architecture of phenotypic stability in cereals. To this end, we determined grain yield, thousand kernel weight, test weight, falling number, and both protein and soluble pentosan content for two large bi-parental rye populations connected through one common parent and grown in multi-environmental field trials involving more than 15 000 yield plots. Based on these extensive phenotypic data, we calculated parameters for static and dynamic phenotypic stability of the different traits and applied linkage mapping using whole-genome molecular marker profiles. While we observed an absence of large-effect quantitative trait loci (QTLs) underlying yield stability, large and stable QTLs were found for phenotypic stability of test weight, soluble pentosan content, and falling number. Applying genome-wide selection, which in contrast to marker-assisted selection also takes into account loci with small-effect sizes, considerably increased the accuracy of prediction of phenotypic stability for all traits by exploiting both genetic relatedness and linkage between single-nucleotide polymorphisms and QTLs. We conclude that breeding for crop phenotypic stability can be improved in related populations using genomic selection approaches established upon extensive phenotypic data. The primary goal in plant breeding is to identify high-yielding genotypes combining excellent quality with pronounced resistance to abiotic and biotic stresses. With stress levels increasing due to climate change , enhanceLarge-scale phenotyping is required to precisely determine yield stability, with estimates for the number of required environments ranging from 10  to 200 . As phenIn Central Europe, rye is commonly improved by hybrid breeding based on a cytoplasmic male sterility system . Importa3:4 rye test-cross populations connected through one common parent, each comprising 220 lines, for grain yield, thousand kernel weight, test weight, falling number, and both protein and pentosan content in up to 16 environments, including drought-stress environments  inbred lines  as parents for this study. Two segregating F3:4 populations, POP-A (Lo90-N × Lo115-N) and POP-B (Lo115-N × Lo117-N), were generated. From each population, we randomly selected 220 F3:4 progenies, which were used to pollinate a common cytoplasmic male sterile (CMS) single-cross tester.The plant material and field experiments were described in detail by –1), thousand kernel weight (g), test weight (g), falling number (s), protein content (%), and soluble pentosan content (%) were obtained as outlined in detail by The test-cross families were evaluated in two years (2010 and 2011) at five locations  with different water regimes . The respective location × year × irrigation level combinations were denoted as environments throughout the study. The two test-cross populations were evaluated in each environment using an incomplete 24×10 alpha design with two replications. Data for grain yield (dt haBest linear unbiased estimates (BLUEs) for test-cross progenies across environments were determined by the restricted maximum likelihood method using ASReml version 3.0 , µ represents the overall mean, G refers to a design matrix of genotypes, αG is an N-vector of the genotype effects with length N equal to the number of genotypes, R corresponds to a design matrix for replications, αR makes reference to a vector of the replication effects, B stands for a design matrix of the blocks, αB refers to a vector of the block effects, and e is a residual term. Genotype effects were treated as fixed while the other factors included in model (1) were treated as random. BLUEs of test-cross progenies within each environment were estimated in step one. BLUEs of test-cross progenies across different environments were estimated in step two as follows:where yEnvs refers to the BLUEs across all the environments, 1k is a vector with the length k equal to the number of genotypes multiplied by the number of environments, E denotes a design matrix of environments, αEnv represents a vector of environment effects, F is a design matrix of genotype × environment interactions, αF represents a vector of interaction effects, and e a residual term. We kept genotype effects fixed, and treated both environment and interaction effects as random. Cluster analysis of environments was performed based on 1 minus the correlation coefficient of BLUEs of genotypes among all pairs of environments. Heritability was estimated on an entry-mean basis (h2) as outlined in detail elsewhere , 732 single-nucleotide polymorphisms (SNPs), and 900 diversity array technology (DArT) markers. Details of the marker data used in our study have been outlined elsewhere (Supplementary Table S1) and were used in combination with the environmental variance (Var), regression coefficient (β), and logarithmic transformations (base-e logs) of deviation variance (Devln) in classical bi-parental linkage mapping. The conditional probabilities of QTL genotypes have been estimated with the software package PlabQTL   and e corresponds to the residual term of the model. The relevant parameters for RR-BLUP were estimated based on the mixed model equations outlined by where y is the vector of the estimated phenotypic stability parameters, 1rMP) between an observed phenotypic stability parameter and its predicted values based on the determined marker effects were calculated. Accuracy of prediction was estimated by standardizing with the square root of the heritability, rg = rMP / h  larger than zero . The clustering of locations was not stable across years and also differed between both populations, clearly indicating that subgrouping of locations in homogeneous sets is impossible. Consequently, the large-scale phenotyping in diverse environments conducted in our study provides a unique data base in order to study the genetics of phenotypic stability across location, year, and water availability.We evaluated 440 Fhan zero . The divhan zero  as well σG2 significantly (P < 0.01) larger than zero  larger than zero for all traits and exceeded twice the genotypic variances in the case of grain yield. The broad variation observed for best linear unbiased estimates of the 440 F3:4 test-cross progenies (Supplementary Table S4) resulted in high heritabilities on an entry-mean basis (h2) for the six traits under consideration.For the six traits and both populations, we also consistently observed genotypic variance components han zero . The varIn contrast, heritabilities estimated for the three phenotypic stability parameters, Var and β for static and Dev for dynamic stability, were substantially smaller . MoreoveP < 0.05) but low correlation coefficients in the undesired direction. In contrast, for 19% of the observations we found significant (P < 0.05) correlations in the desired direction, while for 56% of the cases correlation coefficients were not significantly (P > 0.05) different from zero. Consequently, breeding rye hybrids combining excellent trait performance with high phenotypic stability is impaired by contrasting pleiotropic effects or linkage among favourable alleles in repulsion phase only in the minority of cases.A rye ideotype would combine excellent trait performance with high phenotypic stability. As these two features have previously been discussed as mutually exclusive, we inspected the associations between trait performance and the three phenotypic stability parameters . For 25%We performed linkage mapping in combination with 5-fold cross validations and observed very low accuracies of prediction of dynamic phenotypic stability Dev with a maximum value amounting to 0.13 for thousand kernel weight . In contApplying genomic selection instead of marker-assisted selection led to considerably increased accuracies of prediction of phenotypic stability for all traits under consideration (Tablse 4 and 5). We found higher accuracies of prediction for POP-B as compared to POP-A for all phenotypic stability parameters and traits, except for the deviation variance Dev of falling number and protein content . This diSupplementary Figure S1) with the main goal being to gain initial insights into the genotype–phenotype map of phenotypic stability of several important agronomic and quality traits in rye.Only a very limited number of studies have so far examined the genetic architecture of phenotypic stability in plants  and, henFollowing previous suggestions (b). The slightly higher heritability for thousand kernel weight stability as compared to grain yield stability . Thus, establishment of a diverse training population for genomic selection, which is well characterized for yield stability, is very challenging and requires huge investments in multi-environmental field trials.The absence of stable QTLs for yield stability and its component thousand kernel weight stability in our study , and theFor rye, the ideotype with respect to quality traits depends mainly on the intended end use . Test weDynamic phenotypic stability for test weight, falling number and soluble pentosan content is characterized by low heritability estimates, with an average value of 0.1 . Taking P values of 0.88 and 0.64 for β in POP-A and POP-B, respectively; P values of 0.45 and 0.64 for Var in POP-A and POP-B, separately). Consequently, marker-assisted selection for static stability of falling number based on the QTL located on chromosome 6 does not impair the selection of varieties with high falling number.The static stability for falling number in POP-B was significantly correlated with falling number values in an undesired direction . In lineBeyond the prospect of marker-assisted selection within these two connected bi-parental populations, genomic selection holds even greater potential for improving the static phenotypic stability of test weight, falling number, and soluble pentosan content . IntegraPhenotypic stability of varieties is pivotal for combating climate change-related challenges. Our findings reveal that large-scale multi-environmental phenotyping is needed to efficiently select for enhanced phenotypic stability. Genome-assisted breeding approaches offer the potential for building prediction models upon such extensive phenotypic data, facilitating an economic improvement of phenotypic stability in crops.JXB online.Supplementary data can be found at Supplementary Table S1. Conditional probabilities of QTL genotypes of population Lo115-N × Lo117-N and population Lo115-N × Lo90-N.Supplementary Table S2. Marker data of 220 F3:4 test-cross progenies of segregating rye population Lo115-N × Lo90-N used for genomic selection.Supplementary Table S3. Marker data of 220 F3:4 test-cross progenies of segregating rye population Lo115-N × Lo117-N used for genomic selection.Supplementary Table S4. BLUEs for test-cross progenies across environments and estimates of phenotypic stability parameters for six traits in population Lo115-N × Lo90-N and Lo115-N × Lo117-N.Supplementary Figure S1. Dendrogram of cluster analysis including 15 environments for thousand kernel weight (TKW), 16 environments for test weight (TW), 11 environments for falling number (FN), and 10 environments for protein content (PC) and soluble pentosan content (SPC) for POP-A and POP-B.Supplementary Figure S2. Predicted heritability of the three stability parameters Dev, β, and Var for the quantitative traits grain yield (GY) and thousand kernel weight (TKW), and for the quality traits test weight (TW), falling number (FN), protein content (PC), and soluble pentosan content (SPC) based on an increasing number of test environments in POP-A and POP-B.German Federal Ministry of Food and Agriculture via the ‘Fachagentur Nachwachsende Rohstoffe e.V.’, Gülzow, Germany (Grant ID: 22021711).This research was conducted within the project ‘Erweiterung der genetischen Basis von Hybridroggen für Korn- und Biomasseleistung sowie Trockenheitstoleranz mittels Mehrlinienkartierung und DH-Technik’ financially supported by the