Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes , 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation  to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set  used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density  were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region. The use of whole-genome sequence (WGS) data in genomic prediction is expected to be advantageous, since all or most of the causal mutations or single-nucleotide polymorphisms (SNPs) are putatively in strong linkage disequilibrium (LD) with causal mutations affecting the traits. This was confirmed in a simulation study , but in The poor performance of using WGS data could be due to several reasons, including the small number of sequenced animals , imputatIn recent years, the customized Hanwoo 50 k SNP Chip  has been the main technology used to calculate genomic breeding values (GEBV) in Hanwoo cattle. However, our previous study  showed tIn this study, imputed WGS data were partitioned into different genomic regions based on the functional annotation information, namely, intergenic (IGR), intron (ITR), REG, synonymous (SYN), and non-synonymous (NSY). Then, GWAS was carried out for each genomic region and the entire WGS data for the pre-selection of variants in a separate discovery dataset. Each set of pre-selected variants were added to the standard 50 k SNP array separately, and the predictive performance of each of these SNP sets was evaluated. We investigated whether the use of variants selected based on their functional annotation information especially those from the coding regions would improve the prediction accuracy compared to those that were selected from the WGS data. Moreover, we evaluated the different density of pre-selected variants to identify the optimum number of variants to be added to the standard 50 k set. The ultimate goal of this study was to improve the predictive performance of the customized Hanwoo 50 k SNP panel by incorporating highly predictive variants from the imputed sequence data for backfat thickness, carcass weight, longissimus muscle area, and marbling score carcass traits from a commercial Hanwoo population.2), and marbling score (MS: scored from 1 to 9). All animals were slaughtered at an average age of 30 months, and their carcass traits were measured in accordance with the guidelines proposed by the Korea Institute for Animal Production Quality Evaluation (KAPE). Ethics approval for this study was given by the Animal Care and Use Committee of the National Institute of Animal Science, Rural Development Administration, South Korea (2018–293). A detailed description of the data and other pertinent information were provided in the previous study of Data used in this study consisted of phenotypic and genotypic records of 16,892 Hanwoo cattle mostly composed of steers . These animals were born between April 2006 and June 2017 from around 4,000 farms in South Korea. Four routinely collected carcass traits were analyzed: backfat thickness (BFT in mm), carcass weight (CWT in kg), longissimus muscle area , sex, and slaughter age as a covariate; a is the vector of random additive genetic effects A is the pedigree-based relationship matrix of individuals; e is the vector of random residual effect I is an identity matrix. X and Z are indices matrices associating b and a to y.where, The genomic DNA for each animal was extracted from tissue samples using the DNeasy Blood and Tissue Kit . Samples were genotyped using the customized Hanwoo 50 k SNP Chip  according to the manufacturer’s instructions . The following threshold levels were applied for quality control using the PLINK software : SNPs wer2 value from Minimac3, which was the estimated value of the squared correlation between true and imputed genotypes. Similar to our previous studies (r2) lower than 0.60 were removed for further analysis. After removing those SNPs, the overall mean imputation accuracy was 0.87.A two-step imputation process of 50 k genotypes to WGS data was performed using Minimac3  after pr studies , SNPs wir2 > 0.95) in a 5000-kb sliding window with 100 variants were excluded using the same software. The number of variants annotated in different genomic regions before and after quality control and after LD pruning is shown in The imputed whole-genome variants were annotated based on the bovine genome assembly UMD 3.1 . SNPs wep-value from GWAS conducted on the discovery set . GWAS was performed both in WGS and each genomic region separately . Thus, we ran a total of seven GWAS for each trait using the mixed linear model based association analysis (MLMA) in the package GCTA ; αi is the size of the effect of the marker ; g is a vector of the GEBV of all individuals G is the marker-based GRM . Each set of pre-selected SNPs were added to the 50 k panel separately and tested for genomic prediction. Pre-selected SNPs that overlapped with those on the 50 k SNP chip was retained in the 50 k set and replaced by others to keep the same number of added SNPs.We selected 1,000, 3,000, 5,000, and 10,000 SNPs with the lowest The GEBVs of all genotyped individuals in the cross-validation dataset were predicted with the genomic best linear unbiased prediction (GBLUP) model using MTG2 . Genomicwhere the terms are as defined in the GWAS model above. Variance components were estimated with restricted maximum likelihood algorithm as employed in MTG2.A 10-fold cross-validation scheme was utilized to determine prediction accuracy. The cross-validation dataset  was randomly partitioned into 10 groups without overlapping of samples. In each cross-validation, one group (∼956) was treated as the validation set and the remaining groups were used as the training set . Phenotypes of animals in the validation set were assumed as unknown. The accuracy of genomic prediction was calculated as the correlation between the GEBV of the validation set and their corrected phenotypes, divided by the square root of heritability of the trait (estimated using a 50 K SNP data). Furthermore, the unbiasedness of genomic prediction was evaluated using the regression coefficient of corrected phenotypes on GEBV.Heritability estimates for carcass traits were obtained by fitting the different GRM constructed from various SNP sets as shown in The accuracy of genomic prediction using the regular 50 k SNP panel and 50 k with trait-specific pre-selected variants from WGS, IGR, ITR, REG, SYN, NSY, RSN, or RAN is shown in Using pre-selected variants from a specific genomic region based on their functional annotation did not show a clear advantage compared to using the whole genome. Hence, prediction accuracy based on 50 k + WGS remained the most accurate among SNP sets considered in this study. Nonetheless, prediction accuracies achieved by each of the genomic regions were substantially higher than those of randomly pre-selected SNPs from WGS for all traits. Moreover, we observed that the impact of genomic regions on the prediction accuracy differed between traits. The prediction accuracies based on 50 k + IGR were the most similar to those of 50 k + WGS for BFT, CWT, and LMA while 50 k + IGR, 50 k + ITR, 50 k + REG, or 50 k + RSN for MS. Meanwhile, 50 k plus the pre-selected variants from SYN or NSY consistently yielded the lowest prediction accuracy among the genomic regions.Since the pre-selected variants differed among traits, we evaluated the predictive performance by combining such variants from all traits with 50 k genotypes. We only used the pre-selected variants from WGS since they improve the prediction accuracy more than those from a specific genomic region. The overlapping of pre-selected variants between traits is provided in In this study, we investigated the use of variants that were selected from imputed WGS data with the hope of improving the predictive performance of regular 50 k SNP panel for carcass traits in commercial Hanwoo population. We also evaluated the variants pre-selected based on their functional annotation information. The heritability estimates, genomic prediction accuracy, and unbiasedness based on 50 k plus pre-selected variants from different genomic regions  were compared to those of the standard 50 k genotypes for all traits.Heritability estimates based on standard 50 k genotypes for BFT (0.32), LMA (0.32), and MS (0.40) in this study were in agreement with those observed by The effect of adding pre-selected SNPs to the 50 k array on the heritability estimate varied among traits and SNP sets within traits. According to The prediction accuracy for all carcass traits increases with the addition of pre-selected SNPs to the 50 k panel. Previous works have also described that integrating these pre-selected markers derived from sequence data into a medium (50 k or 80 k) density panel data can improve the accuracy of genomic prediction . MoreoveCompared to using pre-selected SNPs from specific genomic regions, utilizing WGS data resulted in higher prediction accuracy for all traits of Hanwoo cattle. In commercial chicken population, Comparable with the findings of previous studies , some geIn this study, prediction bias was assessed as regression coefficients of corrected phenotypes on GEBV. Results showed that all regression coefficients were around 1 for any set of genotypes for all traits, indicating that our predictions were unbiased. Moreover, these findings suggest that prediction bias was not affected by the strategy used for pre-selecting variants or the density of the added variants. Here, we used a separate group of animals for the discovery set having low genetic relationships with those in the cross-validation set. Previous work reported an increased prediction bias due to the overlaps between the validation and discovery datasets .p-value of marker effects for the selection of sequence variants because setting a specific p-value threshold resulted in unequal numbers of selected variants between genomic regions, and a previous study indicated that the density of the SNP panel influences the accuracy of genomic prediction  on the average. Thus, we conclude that adding at least 3,000 SNPs to the Hanwoo 50 k SNP panel from each trait is sufficient enough to improve the prediction accuracy. This was further confirmed after evaluating the prediction accuracy of the combined pre-selected variants from whole-genome data from all traits wherein the prediction accuracies upon the addition of ∼12,000 pre-selected variants  were similar to those of 20,000 or 40,000 pre-selected variants for most of the traits  can be found below: The animal study was reviewed and approved by the Institutional Animal Care and Use Committee, National Institute of Animal Science, Rural Development Administration, Republic of Korea.DL conceived and supervised the study. DL, BL, and KS designed the study. SL, DH-S, and J-DO provided the data for analysis. KS and NA performed the imputation and annotation. WP, H-HC, and J-EP contributed to quality control of genotype data. BL and NA conducted the statistical analyses and drafted the manuscript. DL and KS edited and improved the manuscript. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Understanding the role of genetic factors in non-Mendelian traits characteristic for post-reproductive life, herein referred to as age-related traits, is lagged behind the understanding of the genetic architecture of Mendelian traits. This lag calls for new, more comprehensive approaches in the analyses of age-related traits leveraging their characteristic features. This paper discusses the role of the inherent heterogeneity in genetic predisposition to age-related traits and pleiotropy. It shows that the comprehensive analyses leveraging such heterogeneity can substantially increase the efficiency and accelerate the progress in uncovering genetic predisposition to such traits. Clustering of diseases and longevity in families and the estimates of heritability suggests that health, aging, and longevity can have a genetic component. The Human Genome Project was created to gain insights into the genetic architecture of human disease and related traits . CurrentThe progress in Mendelian genetics has been accelerated by large-scale genetic association studies. These studies follow the framework of medical genetics, assuming that there are genetic factors (mutations) directly affecting protein function and causing Mendelian traits . ImpliciIn contrast to the Mendelian traits, the age-related traits are complex phenotypes, which do not follow a clear pattern of Mendelian inheritance . GeneticGiven these complexities, genetic predisposition to age-related traits becomes inherently complex, even in genetically homogeneous populations, with critical roles of the interplay of genetic and non-genetic factors underlining, particularly, heterogeneity and pleiotropy ,18–22. TThis grant report provides short summaries of recent papers to illustrate substantial advantages of utilizing comprehensive approaches in the analyses of genetic predisposition to age-related traits. These approaches, implemented as a synthesis of different statistical methods, laid ground for the AG047310 grant focused on: (Aim 1) construction of age-related phenotypes; (Aim 2) identification of genetic predisposition to endophenotypes; (Aim 3) identification of genetic associations with risks of morbidity, disability, and mortality; (Aim 4) elucidating systemic role of the identified genetic variants in health, wellbeing, and survival, and (Aim 5) dissecting biological role of genes for the revealed single nucleotide polymorphisms (SNPs). The emphasis of the grant aims is on comprehensive strategies to dissect the role of the inherent heterogeneity in genetic predisposition to age-related traits and pleiotropy.The inherent heterogeneity in genetic predisposition to age-related traits suggests that differences in the associations across different population groups, even if they are genetically homogeneous, can be biologically motivated. Accordingly, unusual, even antagonistic, relationships between the same allele and the same trait in different population groups are biologically plausible ,30. IdenApoB) gene with several age-related traits in four independent studies, which included subjects of European ancestry from the Framingham Heart Study (FHS), the Atherosclerosis Risk in Communities (ARIC) Study, the Multi-Ethnic Study of Atherosclerosis (MESA), and the Cardiovascular Health Study (CHS). We considered total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) as endophenotypes  and myostrengthened the beneficial association of rs693_A with MI in these studies. Also, we found that consistently with the adverse relationship between the rs693_A allele and TC, this allele was adversely associated with MI in CHS and MESA. However, despite this consistency, this relationship was not mediated by lipids. These antagonistic associations with MI were of about the same effect size but of opposite directions in ARIC/FHS and CHS/MESA. A meta-analysis of the results from all studies following the GWAS strategy showed a non-significant association of rs693_A with MI. In contrast, taking into account antagonistic effects, this association was strong and nearly genome-wide significant.Consistently with prior genome-wide association study (GWAS) meta-analyses –35, our These results caution against simplistic strategies for gaining profound insights into genetic predisposition to age-related traits.Another challenge is that genetic variants can show pleiotropy, which appears to be wide-spread in human disease and related traits . PleiotrThe analyses were performed in two stages. The first stage was designed to perform univariate GWAS by associating each SNP with each trait in each study separately. This analysis was enhanced by leveraging longitudinal information on traits. The univariate study-specific analysis provides the estimates of the effects and their significances, so for each SNP, we have a 20 × 5 table, which includes 20 estimates in each of 5 studies.The second stage was to combine these statistics and to perform comparative analysis. In , we seleA conventional meta-analysis across studies for each trait separately identified only 18 trait-associated SNPs. Of them, 16 SNPs replicated previously reported associations, primarily with lipids. This result is not surprising as the sample of 33,431 subjects is modest compared to the consortia-based GWAS.In contrast, our pleiotropic meta-analyses identified a large number of 124 SNPs with pleiotropic associations. The vast majority of these SNPs, 93% (115 of 124), were novel. They attained genome-wide significance by combining associations with multiple traits, which did not reach that significance in the univariate meta-analysis. Our analysis showed that the associations for 94% (108 of 115) novel pleiotropic SNPs, were strongly affected by the inherent heterogeneity in predisposition to age-related traits. This strong effect was seen as a phenomenon of antagonistic heterogeneity  when thThe results of our analyses strongly support the pivotal role of the inherent heterogeneity in genetic predisposition to age-related traits in pleiotropy. Dissecting such heterogeneity can substantially increase the efficiency of the analyses and provide novel insights into the genetic architecture of such traits.The progress in genetic association studies of various traits is mostly attributed to persistent efforts of big consortia on the national and international scale. Since the inception, however, the strategy of large-scale genetic association studies remains mainly unchanged regardless of the type of the analyzed traits. The age-related traits characteristic of health decline, the aging process, and longevity require more comprehensive methodology to gain insights into an inherent complexity of mechanisms involved in their genetic and non-genetic regulation. The main advantage of the strategy outlined in this grant report is its ability to improve the scientific understanding of genetic predisposition to age-related traits by dissecting substantial fraction of the inherently heterogeneous architecture of such traits. This improvement, however, comes with the price as implementation of such a strategy requires data over the humans’ life course and effort consuming analytical work. Nevertheless, this grant report shows highly promising potential of such comprehensive strategies in studies of genetic architecture of age-related traits and indicates that current discoveries from the large-scale studies are likely just “the tip of the iceberg”.


N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors . We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset ( High neuroticism is associated with increased psychiatric comorbidity, and there is a substantial overlap between neuroticism and a wide range of psychiatric disorders, particularly depression and anxiety6. The associated societal costs of neuroticism are substantial7, leading to increased use of both mental and physical health services due to poorer overall health and quality of life8.Neuroticism is a personality trait that is characterised by emotion dysregulation and negative affect. It has been thought to confer a general susceptibility to mental health problems, resulting in the frequent experience of negative emotions such as worry, sadness, self-consciousness, or anger13. In recent years, the genetic aetiology of neuroticism has been studied using large-scale genome-wide association studies (GWAS) which have uncovered more than a hundred genomic loci that point towards genes and pathways involved in brain functioning15.Twin studies have estimated the heritability of neuroticism to be around 40%, with the rest typically attributed to non-shared environmental factors23. Despite such studies consistently implicating environments that are shared within families, twin studies tend to assign very little or no proportion of variance to shared environmental factors:13 a phenomenon called the ‘the shared environment paradox’24.In the epidemiological literature, neuroticism and related phenotypes have been linked with a range of different environmental factors, with traumatic events, childhood maltreatment, and social support receiving the greatest attention25, a notion which has been related to the distinction between the ‘objective’ and ‘effective’ environments26. That is, while an environment may ‘objectively’ be shared between family members, their ‘effective’ environment, i.e., the environment as they experience it, is nevertheless unique; as is then also the resulting impact of that environment on each individual.It has been hypothesised that shared environments simply do not matter as much as do non-shared environments24.More recently, Uher and Zwicker proposed that the most parsimonious explanation for this shared environment paradox is the presence of gene-environment interactions (GxE). They argue that GxE would lead monozygotic twins to respond more similarly to shared environmental exposures than dizygotic twins and that GxE should therefore result in a substantial proportion of the shared environmental influences being wrongly attributed to genetic factors, causing an inflation of the heritability estimate instead30. Though neuroticism has traditionally been viewed as a relatively stable trait, a more dynamic aetiology has been proposed whereby it is continuously influenced by ongoing gene-environment interactions throughout the life span31.From a biological perspective, GxE can be seen as the process by which environmental influences are moderated by genetic factors (or vice versa). GxE has been speculated to play an integral role in the aetiology of psychiatric phenotypes for a long, as it provides an explanation for why some develop psychiatric symptoms after particular risk exposures while others do not36. It is only quite recently that the available data and computational resources have begun to allow for the conduction of GWEIS, but as interactions may require larger sample sizes to detect effects of similar magnitude as main effects, sample size requirements may be even greater for GWEIS than for GWAS38.To date, however, there have been few truly genome-wide GxE studies (GWEIS) of psychiatric phenotypes, and the majority of molecular GxE research has been limited to candidate genes40. While these two-stage approaches could potentially yield more significant SNPs, individual SNP effects are unlikely to yield insight into the higher-order biological mechanisms underlying GxE (as is the case for GWAS41), and the lack of genome-wide GxE data limits the opportunity for follow-up analyses such as gene-set analysis, which could elucidate the function of GxE effects42. In addition, since interacting SNPs may not display strong main effects, this approach could also lead to potential key interactions going undetected43. Another option may be to model interactions of individual variants with multiple environments simultaneously44, though this is also at the cost of environmental specificity which could complicate the interpretation of any functional follow-up analysis.To overcome this, some have reduced the multiple testing burden by pre-selecting variants based on main effects from GWAS45, or by modelling interactions with polygenic risk scores constructed using SNP main effects from GWAS49. But while such approaches may indicate the presence of GxE, they cannot determine which SNPs or genes are driving the interactions. For the purposes of gaining relevant biological information from the GxE analyses, we, therefore, considered GWEIS to be the most suitable approach.Alternatively, global GxE effects across the entire genome may be investigated by estimating the proportion of variance explained by GxE effects50. While this can be resolved with the use of heteroscedasticity consistent, or so-called robust, standard errors52, these are not currently available in software optimised for large-scale genetic analysis like PLINK53, and researchers have had to implement this them themselves54. Interaction effects may also be confounded by covariate-SNP and covariate-environment interaction effects unless these are accounted for55, but doing so can dramatically increase the number of variables analysed and add further computational constraints to this already intensive analysis.Beyond issues with power, GWEIS requires particular consideration regarding the control of error rate inflation, as it is particularly vulnerable to the effects of heteroscedastic residuals34. These studies have found few significant interactions, though only one of these studies featured a sample size close to 100,000 individuals . As such, it is evident that there is a substantial gap in the available genome-wide evidence for GxE in mental health phenotypes in general, including neuroticism for which there are currently none.To our knowledge, there have only been three GWEIS of psychiatric phenotypes to date, all of which have focused on depressive symptoms and used some composite measure of stressful life events as environment56 to perform a series of GWEIS for neuroticism, with a total of 25 broadly defined environmental variables  for each environment and used these to predict neuroticism in an independent subset of the UKB sample .Given that conceptually meaningful interaction effects may not be evident on the level of individual SNPs, whose effects are likely small in magnitude, we sought to elucidate relevant biological mechanisms that might govern GxE by testing whether single SNP-environment interaction effects were over-represented within particular genes, tissues, or gene-sets. We also evaluated the predictive ability of SNP-interaction effects across the genome by constructing interaction-based polygenic scores  rather than pure GxE . Though our rationale for including these is that any potential interactions, be it GxE or GxG, may nonetheless highlight relevant biological mechanisms that contribute to neuroticism. For reasons of convenience, we chose to retain the general term ‘GxE’ throughout the paper, but acknowledge that the term ‘Gene x Trait’ interaction is more suitable.The selection of environmental variables was based on the epidemiological literature, and consisted primarily of variables relating to trauma and social support, but also to physical health, socioeconomic status, cognitive function, sleep, and substance use : ‘Heteroscedasticity and Spurious Inflation of GWEIS Test Statistics’ for comparison with traditional, model-based standard errors). In order to account for any potentially confounding covariate interactions55, we also included covariate-SNP and covariate-environment interaction effects in the model in addition to covariate main effects (see “Methods” section).Due to the risk of inflation of the GWEIS test statistics that were mentioned previouslyN = 84,711–313,339; Table r2 < 0.8) for 7 environments that showed interaction effects at the standard genome-wide significance threshold of p < 5e−8 ; an SNP which was also suggestively significant for ‘childhood physical abuse’ (p = 6.93e−7).We analysed the single SNP-by-environment interactions between each of the 25 environments (−8 Table . Of thesp < 5e−8), we did not find any evidence of a significant main effect in the GWAS , for which 103 independent significant SNPs were detected. For the 8 SNPs that showed significant interactions at the standard genome-wide significance threshold . From these analyses, we found a total of 10 genes from 7 environments that reached standard genome-wide significance, correcting only for the number of genes analysed ; Table p < 2.52e−6/25).We thus performed 25 gene-based tests for 19,831 protein-coding genes in MAGMA using the interaction p = 1e−4; Table Similar to the SNP-level results, the concordance between suggestive main and interaction effects on the gene-level was low, and only one gene  reached suggestive significance in the main effects gene analysis for neuroticism (In order to determine whether the most strongly associated genes for any environment (including sub-significant ones) tended to be overrepresented within particular pathways, cellular locations, or implicated in particular tissue-specific gene expression patterns, we performed competitive gene-set and gene-property analyses in MAGMA using the results from the 25 GWEIS-based gene analyses as input. These analyses concerned 7426 gene sets (MSigDB) and 53 tissues .p-value threshold of 6.85e−6 (.05/(7246 + 53)), 12 gene sets from 7 environments were significant (Table p < 6.85e-6/25): ‘nucleotide transmembrane transporter activity’ , and ‘glucose binding’ (insomnia).At a 6 (.05/726 + 53), nt Table –3y. Of tAgain, none of the interacting gene-sets showed evidence of a significant, or even suggestively significant, the main effect in neuroticism Table  and 3z.GxE)—taking the sum of effect alleles weighted by the interaction beta and the environment—and used these to model neuroticism in an independent subset of the UKB sample . An alternative to GWEIS mentioned earlier is to model the interaction between a traditional main effect PRS from GWAS and an environmental variable of interest (henceforth: iPRSG). Since the iPRSG is more widely accessible than the iPRSGxE (as it does not require existing GWEISs), we also computed iPRSGs for each environment as a comparison.To evaluate the predictive accuracy of our SNP-environment interactions, we constructed interaction-based polygenic risk scores . This was done using the anova function in R.GxE nor the iPRSG provided a significant increase in model fit  above that of the covariates only model, with the attributable variance reaching a maximum of .04% for the iPRSGxEs and .03% for the iPRSGs .For any of the 25 environments, neither the iPRSWe, therefore, conclude that based on the environments and sample population analysed here, there is currently limited evidence that genome-wide GxE effects in the form of iPRSs can improve prediction accuracy in neuroticism beyond what can already be achieved using SNP and environment main effects.In this study, we have investigated genome-wide gene-environment interactions in neuroticism across a total of 25 different environmental variables previously associated with mental health outcomes. From all SNP, gene, and gene-set based analyses, we detected one SNP (rs115385310 for ‘felt hated as a child’) and two gene-sets  that survived Bonferroni-correction for the number of environments analysed.Although multiple interactions were found at standard genome-wide significance thresholds , they were substantially fewer than that detected in a traditional GWAS on neuroticism, in which we identified just over 100 independent significant SNPs. This is in line with the notion that the power to detect interactions is lower than that of main effects, and suggests that even larger data sets will be required before we can uncover a more considerable fraction of relevant interactions. The lack of predictive value for interaction-based polygenic risk scores (iPRSs) echoed this further.41), the genome-wide nature of GWEIS is vital as it allows for follow-up analyses, such as gene-set analysis, which can elucidate the function of GxE effects.A GWEIS analysis will naturally suffer more from an increased multiple testing burden compared to, for example, two-stage GxE approaches which pre-select genetic variants based on their observed main effects. In this study, however, we found that none of the interacting SNPs identified at standard genome-wide significance thresholds  showed any evidence of even suggestive main effects in the GWAS—the same was largely true the gene and gene-set level results—implying that preselection based on main effects could result in key interactions being overlooked. In addition, as individual SNP interaction effects might themselves not yield notable insight into the biological mechanisms that govern GxE . While a systematic investigation of shared GxE effects was not conducted here due to the lack of power even when not correcting for the environments, we hope that our results may prove useful for researchers conducting similar studies in the future, for example, as a basis for replication or meta-analysis. As increased sample sizes lead to the detection of more reliable SNP-environment interactions, we expect that results from GWEIS and related functional follow-up analyses will become valuable for our understanding of the biological mechanisms that underlie GxE.8 and widespread links with several clinical psychiatric disorders6. Although evidence suggests that neuroticism is more dynamic than traditionally thought31, as a personality trait, it may nonetheless be more stable than some clinical phenotypes, such as depression or alcohol use disorder66, and could also be comparatively less sensitive to GxE. In addition, it should be noted that the UKB sample analysed here consists of a relatively older population, and since the influence of GxE may be more pronounced at an earlier stage of development67, the age of this sample might have affected our power to detect certain GxE effects.In this study, we selected neuroticism as our phenotype of interest due to its significant public health impactHere, we chose to model the relationships between all variables as linear , but there is a possibility that some interactions may have a more complex, non-linear form. For instance, for the ordinal environmental variable ‘physical assault’ we now assumed that having been assaulted recently versus in the past results in a similar change in the SNP effect as having been assaulted in the past compared to never. While this may not be a fully accurate representation of the data, we expected that the increased multiple testing resulting from analysing the levels of each ordinal variable separately would nevertheless have had a more severe impact on power.61. Although there have been efforts to distinguish GxG from GxE in the twin modelling literature68, doing so in this setting is not uncomplicated, and simply conditioning on heritable components could induce collider bias69.Finally, we wish to reiterate a key limitation regarding the interpretation of our results in the context of heritable environments. The environmental component in GxE is sometimes seen as an independent force that regulates the penetrance of genetic effects (or vice versa), while in practice, any environmental measure obtained in a cross-sectional design is unlikely to be free from genetic influenceIn this study, we chose to be particularly lenient with what we considered ‘environment’ in favour of covering as broad a range of relevant variables as possible. Based on these results alone, it is therefore not possible to determine whether any interaction detected here represents one with the environmental components directly (GxE) or with some heritable component thereof (GxG). If well-powered, however, we argue that GWEISs of heritable environments are still useful as they could elucidate important sources of aetiological heterogeneity which can be followed up in greater depth using experimental or more controlled observational designs in the future.Representing the largest effort of its kind to date, we used a total of 25 environmental variables to investigate gene-environment interactions in neuroticism. Although power is low compared to GWAS, we detected one variant and two gene sets that showed significant interaction after correction for the number of environments analysed. Larger sample sizes are, however, needed to obtain more reliable estimates of relevant SNP-environment interaction effects, which will be required in order to understand the molecular mechanisms that govern gene-environment interactions in neuroticism.56 , and this study was conducted under the UK Biobank application 16406. Data collection, primary quality control, and imputation of the genotype data were performed by the UK Biobank itself, the full details of which have been described elsewhere70. We applied further quality control in order to ensure the inclusion only of high-quality variants. This entailed filtering SNPs with a minimum info score of .9 (HRC panel imputed), maximum missingness of 5%, and a minor allele frequency of at least 1%, resulting in a total of 8,614,007 SNPs for the analysis.All genotype and phenotype data were obtained from the UK Biobank71) were included as covariates in all analyses to control for population stratification. To ensure that the selection of SNPs remained constant across environments, quality control and filtering were performed on the full subset of individuals with complete neuroticism data (see below), and it is, therefore, possible that exact minor allele frequencies and call rates may vary slightly between the sample subsets for each environment.We used only European, unrelated samples with concordant sex (see Suppl. Info (A): UK Biobank Sample Information and Quality Control). Thirty principal components (computed with FlashPCA72), which contains 12 dichotomous items asking participants to indicate whether they agree with statements such as “Do you worry too long after an embarrassing experience?”, or “Do you ever feel ‘just miserable' for no reason?”. An individual’s level of neuroticism was quantified as the sum of items with which they agreed, ranging from 0 and 12. We included only individuals who had provided complete responses to all items , resulting in 313,467 samples. To ensure that neuroticism and each environment had been measured simultaneously, we used data collected from the first visit only.Neuroticism was measured using the Eysenck Personality Questionnaire . We gathered all available variables that related to any of these categories, limiting the final selection to a subset of 25. We selected variables as such that there was at least one variable from each category, then giving preference to those with larger total sample sizes and less skew in relation to the remaining variables. Given their central role in the literature, we prioritised a wider selection of items related to trauma and social support but sought to include at least one item related to all other domains. Here, we refer to these variables as ‘environments’ as that is their role in the current analyses while acknowledging that many of the selected environments have a (sometimes considerable) heritable component.We considered broadly as ‘environment’ a wide range of variables available from the UKB Biobank that have been associated with neuroticism and related mental health phenotypes in the literature. This included primarily those relating to trauma exposureThe majority of environments were ordinal, consisting of responses such as ‘never true’ to ‘very often true’, or ‘never or almost never’ to ‘almost daily’ . As have been shown previously54, which performs a joint test of SNP and SNP-environment interaction effects (https://epstein-software.github.io/robust-joint-interaction). Beyond run-time optimisation, we computed p-values for the gene-environment interaction , and included covariate-SNP and covariate-environment interactions in addition to covariate main effects. As has been shown55, covariate main effects alone do not effectively control for potentially confounding interactions of the covariate with the SNP or the environment, and unless controlled for, such interactions may be captured in the SNP-environment interaction term. We thus implemented the following linear regression model for every SNP and environment:Yi represents the phenotype measure for any individual i, Gi the SNP allele count, and Ei the environmental measure. Ci is a k × 1 vector of covariates, with k equalling the total number of covariates, and ϵi the residual, and ′ denotes the transpose. The intercept (β0) and betas for the SNP (βG), environment (βE), and SNP-environment interaction term (βGxE) are all scalars, while the betas for the covariate-environment (βCxE) and covariate-SNP (βCxG) interactions are k × 1 vectors. The parameter of interest here is βGxE: the beta for the SNP-environment interaction.Our script is an adaptation of a PLINK R plugin originally developed by Almli et al.N > 10,000. As recommendations or standards regarding the number of PCs that should be included typically concern main effects analyses, we could not exclude the possibility that potentially more complex confounding effects of ancestry might arise when analysing interactions, and therefore chose a more cautious approach of including as many as 30 PCs.As covariates, we included age, sex, 30 PCs, and all assessment centres with For the analysis, PLINK formatted genotype data was read into R (v3.2.1) using the read.plink function from the snpStats package (see the Suppl. Info (B)–Analysis script for the full R script). As per the snpStats default settings, autosomal SNPs were coded as 0, 1, and 2, representing the homozygous minor, heterozygous, or homozygous major genotypes, respectively. On the X chromosome, male genotypes were coded as 0 and 2, representing single copies of the minor or major alleles.53 using the same set of covariates as in the GWEIS: one using the full neuroticism sample , done with the purpose of determining whether interacting SNPs, genes, or gene-sets displayed any main effects on neuroticism, and one that excluded a test set of 10,000 individuals done for the purpose of constructing a main effect polygenic risk score.We conducted two GWASs of neuroticism in PLINK v.2.042 using the p-values from the GWEIS as input. Gene locations for 20,260 protein-coding genes were obtained from Ensembl , of which 19,831 contained at least one SNP in our data. To allow the inclusion of nearby, potentially regulatory SNPs, we used windows of 2 kb upstream and 1 kb downstream of the transcription start and stop sites, respectively. For computational efficiency, a random subset of 10,000 individuals from the UKB data set was used as a reference for the estimation of LD.To investigate whether SNP-environment interaction signals tended to congregate within genic regions, we performed genome-wide gene analyses with MAGMA (v1.07b)p-value corrected for gene size. In essence, the ‘multi model’ applies both the ‘mean’ and ‘top’ models and selects the one with the best fit.As an aggregation method for the SNP effects, we employed the ‘multi model’ which is a hybrid between the commonly used ‘mean model’, which simply averages the SNP effects across the gene, and the ’top model’, which uses the lowest SNP 42. A total of 7246 gene set definitions were obtained from MsigDB (v6.2), including gene ontology (GO) terms, cellular locations, and biological pathways from multiple sources . These were analysed in a competitive framework (as is the default in MAGMA), testing whether the average association with genes within a gene set is greater than that of genes outside the gene set, while correcting for LD.Competitive gene-set and gene-property analyses were performed for all GWEISs and the GWAS using MAGMA (v1.07b)To test for tissue specificity of associated genes, we used the recently implemented conditional gene property analysis in MAGMA. In this framework, any given tissue can be conceptualised as a gene-set, where gene mRNA expression levels represent the continuous gene-set membership for any given gene, with its mean gene expression level across tissues included as a covariate. For this analysis, we used the mean log-transformed gene mRNA expression profiles in 53 different tissues obtained from GTEx (v7).GxE) using the SNP-environment interaction effects from each of the 25 GWEISs. As a comparison, we created alternative iPRSs representing the interactions between a standard main effect PRS and each environment (iPRSG). We evaluated the predictive accuracy of all iPRSs  against that of a traditional main effect PRS.In order to evaluate the predictive ability of our GWEIS results, we constructed interaction-based polygenic risk scores , and used these SNPs to construct PRSs for every individual in the hold-out sample.To obtain GWEIS and GWAS effect sizes for these PRS analyses, we excluded a hold-out sample of 10,000 individuals (to be used for prediction) and re-analysed all main and interaction effects as described previously. For each analysis, we extracted the independent significant SNPs using clumping in PLINKi, with Gij their genotype value for SNP j (k being the number of SNPs used), and E, the iPRSG score was computed as GxE as j.The different PRS scores were defined as follows. The standard main effect PRS was computed as GxE here may be to include the SNP main effect from the interaction analyses in the iPRSGxE itself, i.e., PRSi as a covariate to account for the genetic main effect.A possible alternative to how we computed the iPRSp-value thresholds . For each PRS score, we then fit a linear regression in the hold-out sample with neuroticism as an outcome, with the PRS score and a set of covariates as predictors. An estimate of the predictive ability of the PRS score was then computed as the difference between the adjusted r2 for this model and the corresponding covariate-only model. Here, we chose to use the adjusted r2, rather than the full r2, as this provides an unbiased estimation of the population explained variance in models with multiple predictors. For the main effect PRS, as well as for both the iPRSGxE and iPRSG for each environment, we selected the PRS based on the p-value threshold for which the predictive ability was greatest.The PRS scores were constructed using SNPs significant at different Yi the neuroticism score for any one individual i in the holdout sample, Ci the 1 × k vector of base covariates , β0 the intercept, βC the covariate effect sizes, and ϵi the residual. The full model including the PRS is then PRSi representing the main effect PRS for that individual, and βPRS the beta coefficient for the PRS on neuroticism in the hold-out sample.As covariates, we used the same base covariates as in the GWEIS/GWAS analyses . For the traditional PRS, the covariates only model was GxE and iPRSG scores, however, we also included the relevant environment and the main effect PRS as covariates, as well interaction between these and the base covariates (similar to the GWEIS setup). Thus, the covariate only model used for any iPRS with environment E is:For the iPRSi and Ei representing the traditional main effect PRS and the environment, respectively (with βPRS and βE their effect on the neuroticism), and βPxC), and βExC). The full model for any iPRS would then also contain the term for the iPRS and its effect on neuroticism in addition to all variables in the null model, i.e.:with PRSThe reason why we include the main effect PRS derived from the GWAS as a representation of the SNP main effects, rather than simply a PRS constructed from the SNP main effect from the GWEIS, is because the GWEIS PRSs will have been pruned based on the interaction effects, and will thus underestimate the total amount of variance contributed by SNP main effects across the genome. Since we are specifically interested in how much the iPRSs contribute above and beyond what can be obtained using a simple main effect PRS from GWAS, the SNP main effects as obtained in the GWEIS would not have been appropriate.Suppl. FiguresSuppl. Info (A)Suppl. Info (B) - Analysis ScriptSuppl. Tables 1a-1z (genes)Suppl. Tables 2a-2z (gene sets)Suppl. Tables 3a-3z (tissues)


Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 spring barley and 1,325 winter wheat breeding lines from a commercial breeding program were genotyped with the Illumina 9 K barley or 15 K wheat SNP-chip, and phenotyped in multiple years and locations. For GWAS, in spring barley, a QTL on chr. 4H associated with powdery mildew and ramularia resistance were found. There were several SNPs on chr. 4H showing genome-wide significance with yield traits. In winter wheat, GWAS identified two SNPs on chr. 6A, and one SNP on chr. 1B, significantly associated with quality trait moisture, as well as one SNP located on chr. 5B associated with starch content in the seeds. The significant SNPs identified by multiple trait GWAS were generally the same as those found in single trait GWAS. GWAS including genotype-location information in the model identified significant SNPs in each tested location, which were not found previously when including all locations in the GWAS. For GP, in spring barley, GP using the Bayesian Power Lasso model had higher accuracy than ridge regression BLUP in powdery mildew and yield traits, whereas the prediction accuracies were similar using Bayesian Power Lasso model and rrBLUP for yield traits in winter wheat. Triticum aestivum L.) and barley (Hordeum vulgare L.) are major cereal crops worldwide, ranking as the second and fourth in total grain production. The global wheat and barley production amounted to 736 and 146 million metric tons in 2016, with 21% and 41% contributed by European countries, respectiely1. Due to their economic role worldwide, the advancement of economically important traits of wheat and barley is indispensable, in order to meet the food demands of growing human population.Both wheat  mapping using hundreds of thousands of genetic polymorphisms throughout the entire genome, are now available. For example, there are several SNP arrays with the varying number of genetic variants customized for both barley and wheat9.Recent developments in genome sequencing technologies allow plant breeders to characterize the genetic architecture of economically important cereal crops. The reference genomes are now available in both spring barley and winter wheat11. This severely limits the application of traditional molecular-assisted selection in cereal crops.Traditionally, the molecular-assisted breeding, using molecular biology techniques, such as gene identification and gene functional characterization, to explain the genetic mechanism of economically important traits has been used in crops. However, several studies evidenced that most traits of commercial interest in wheat and barley are highly polygenic, with many QTLs that each only account for a small proportion of total genetic variances13. Therefore, GWAS has become a better choice to identify the genetic variants or QTLs associated with phenotypes of interest. By using GWAS, numerous putative genetic regions associated with important economic traits and diseases have been documented in plants15. However, GWAS methods are usually implemented using single-trait-based studies, the multivariate trait records are compressed to single score (such as selection index), which may limit the detection of pleiotropic genetic variants across traits16. Besides, genotype by environment (GxE) effects, such as the interaction between genotype and location, usually have considerably influenced grain yield performance in field experiments. Several studies included GxE effects to characterize the GxE, and to increase the prediction accuracy of genomic prediction in plant breeding. However, relatively few studies characterized the effect between genotypes and locations using GWAS model to evaluate the SNP effects in different locations18.Due to the SNP microarray becoming commercially available, and the genotyping cost is continually decreasing, the genome-assisted breeding has become an applicable method to advance the efficiency of selection. Genes or QTLs with large effects can be mapped using linkage mapping or genome-wide association studies (GWAS), which are mainly based on LD between the genetic marker and causal mutation. Compared with linkage mapping, GWAS can account for higher allelic diversity at the corresponding loci, and can exploit ancestral recombination events in a population or species21. The concept of GS is to utilize a large set of, usually anonymous markers, spread over the whole genome such that every QTL are in linkage disequilibrium (LD) with at least one marker. By accumulating effects of markers or haplotypes, this approach allows plant breeders to assess the genomic estimated breeding values (GEBVs) of breeding lines at a stage in plant breeding programs using early generations of the plant breeding program. This is especially advantageous for traits that cannot be assessed on single or few plants, and for traits that are difficult to measure. Several reviews on GS in plant breeding has been reported23. However, how to advance the accuracy of GEBVs prediction to select the elite breeding lines is still an important topic for plant breeders.The genomic-assisted breeding, such as genomic selection (GS), has been extensively implemented in animal and plant breeding24. For rrBLUP, the GEBVs of breeding lines can be estimated as the sum of all predicted additive marker effects. The marker effects are usually assumed to have a normal distribution with the same variance for all marker effects. The marker effects are equally shrunken towards to a small number, which closes to but not zero, to fit with the total genetic variance, and to avoid over-fit in the model when including large amounts of genetic markers in the analysis. This assumption may lead to markers with large effect can be shrunken too much, but small marker effects are not shrunken enough. A Bayesian model provides an alternative way to adjust the prior distribution of marker effects. Rather than setting as normal distribution as the rrBLUP model, the Bayesian models can assign a distribution so that markers with large effects are shrunken less, and small marker effects are shrunken more. As a result, compared with Bayesian model, rrBLUP is likely not the ideal method to evaluate marker effects when some markers have large effects while most of them have small effects. However, drawbacks of Bayesian model can be very tedious computations, and in some cases the prediction accuracy is not significantly more accurate than predictions using rrBLUP, when the genomic relationships between individuals in the training population are high, or the genomic relationship between training population and validation population is close25.Several statistical approaches have been proposed for efficient prediction of genomic breeding values in genomic-assisted breeding programs. For instance, ridge regression BLUP model (rrBLUP), also called as SNP-BLUP model, is a commonly employed model in genomic evaluationThe aims of this study were: (1) to identify genetic loci associated with grain yield traits, quality traits, and disease-resistance traits by using single trait and multiple trait GWAS; (2) to include the genotype-by-location information in the GWAS model to evaluate the marker effects toward grain yield performance in winter wheat tested in three different experimental locations and (3) to develop models for genomic prediction of traits of interest using rrBLUP and Bayesian Power Lasso model.27) using similar dataset also lead to the same result for both crops27. The square root of narrow-sense plot genomic heritabilities for all traits are given in Fig. General descriptive statistics of the traits analyzed are detailed in Table GWAS were performed using single marker regression to estimate SNP effects for each trait. The family structure was taken into account in the model using the genomic relationship matrix based on all validated genetic markers in both species, respectively. The Manhattan plots of univariate GWAS (or named as single trait GWAS) and multivariate GWAS (or named as multiple trait GWAS) for tested traits are given in Fig. 10 was close to expected −log10, but in the tail of the distribution dots were deviated from observed value in most cases indicating that significant marker effects were found. Also, for traits, such as moisture and protein content in winter wheat, p-values were slightly inflated (See Q-Q plots in Supplementary Information).In general, the Q-Q plot for each analysis showed that the observed −logFor barley, most genome-wide significant markers were mapped on chromosome 4H, and some significant markers were at unknown genome position. For powdery mildew, seven markers on chromosome 4H and one unmapped marker passed the genome-wide significance threshold (P < 0.05), explaining 8.9% of the total genetic variance. For ramularia, there were two markers on chromosome 4H and one with unknown position showed genome-wide significance, explaining only 0.9% of the total genetic variance. The multiple trait GWAS jointly analyzing powdery mildew and ramularia showed that the results were consistent with the univariate GWAS, as the same significant markers were identified on chromosome 4H and one marker with unknown position Fig.  and threFor protein content and Zeleny value in winter wheat, the most significant SNPs were found on chromosome 6A, but no genome-wide significant SNPs were associated with any of the traits. This was also observed from the multivariate GWAS results Fig. . For moi10) when the adjusted GRM method (25 (Table 10) in both location 1 and location 2. And, two markers on chromosome 5B (marker name: geSNP1 and geSNP3) were QTLs, but these two SNPs only surpassed the genome-wide significance level (P < 0.05) in location 1, not for location 2 and location 3. In general, the GWAS model including genotype-location information showed that some significant markers were location-specific, and usually significantly associated with one location only.To identify SNPs that are significant only at a certain location in yield in winter wheat, the GWAS model including genotype-location information was developed to estimate the SNP effects in three test locations, respectively. Overall, the Manhattan plots showed that the SNP effects were not identical at three locations, respectively Fig. , and theyc, ĝ)), using two different models (rrBLUP and Bayesian Power Lasso model) are shown in Figs. Genomic prediction was conducted based on the validated SNPs after quality control steps. Prediction accuracies of models were compared using block cross validation with initial crossing year as a block. Each block contained data from two consecutive years . For the tested traits in spring barley and winter wheat, the correlation of observed traits and GEBVs, , while Kristensen et al.11 assessed the Zeleny value of each sample based on the standard laboratory procedures . Our results showed that different phenotypic recording methods can lead to different SNP effect estimates.For winter wheat, no genome-wide significant markers were identified for protein content, while some markers approaching the genome-wide significance threshold were found located on chromosome 2D, 5B, 6A, and 7B Fig. . Previou27 study reported SNPs associated with starch content mapped on chromosomes 1B, 3B, and a positional unknown region. The markers identified in Cericola et al.27 was generally similar as we found in this study (except the marker on chromosome 3B). Furthermore, we also identified one genome-wide significant marker on chromosome 5B, and some markers with high P-value(-log10) on 6B  Fig. , Yield. 34. Our results showed that the adjusted GRM method Two different genomic relationship matries (ĝ)) were approximately 0.2 to 0.5 in spring barley, and 0.2 to 0.3 in winter wheat . Results indicated that the traits were governed by a complex genetic architecture explained by numerous QTLs with small genetic effects, without any major QTLs existing in the testing population.For the genomic prediction in winter wheat Fig. , the accOverall, this study applied GWAS to identify QTL, and used genomic prediction for several traits in commercial spring barley and winter wheat breeding programs. The GWAS analysis successfully identified several markers significantly associated with grain yield, quality traits and disease-resistance traits in spring barley and winter wheat. Furthermore, a clear QTL was found on chromosome 4H for powdery mildew in spring barley. The GWAS including genotype-location information in the model based on different experimental locations indicated that the significant markers associated with the QTLs were different in three experimental locations in the wheat breeding program. In addition, compared with GRM method (H. vulgare L.) and winter wheat (T. aestivum L.) were phenotyped from four breeding cycles  produced by seed breeding company . Every breeding cycle contained approximately 330 breeding lines for each crop (spring barley and winter wheat), and the F6 generation of every line were tested in two consecutive years at three experimental sites ) in Denmark. A new set of crosses were produced in every year. There were multiple trials nested within each experimental site, and the trial design was randomized complete block design35.In total, there were 1,317 advanced breeding lines in spring barley, and 1,325 advanced breeding lines in winter wheat involved in this study. The breeding lines of spring barley (http://www.dmi.dk/vejr/arkiver/vejrarkiv/). More information in terms of soil texture profiles and field practice can be found in Supplementary Information.For three experimental locations, the climate and soil type are slightly different. The soil types of Dyngby and Skive in Jutland region are sandy loam, and Holeby in Sealand region is loam soil which contains more clay. The yearly average temperature of Jutland region was approximately 0.5 celsius degree lower than Sealand region. The yearly average rainfall were similar, which were approximately 750 mm, in both Jutland and Sealand regions  after harvest. The moisture was to measure the water content (%) of grain after harvest evaluated by NIT as well. The instrument used operates in the near-visible range of 850–1,050 nm, using a monochromator. Last-squares regression was used to compute calibration and composition parameter . Zeleny value estimates the degree of sedimentation of flour suspended in a lactic acid solution during a standard time interval and is associated with baking quality. In this study, Zeleny value was measured by NIR (Near Infrared Reflectance)  (https://www.perten.com/).Protein content, Starch content, Moisture, and Zeleny sedimentation value: quality traits for protein content and starch content in winter wheat were measured by an instrument based on NIT (Near Infrared Transmission) technology )  the minor allele frequency < 0.01 and (ii) missing SNP data per line > 0.02. For both crops, the reference genome assembly was used to anchor the markers to current existing linkage groups, accounting for 2,841 SNPs in spring barley and 9,290 SNPs in winter wheat were successfully mapped. There were 1,215 SNPs in spring barley and 1,864 SNPs in winter wheat where their positions were unknown4.Genomic DNA was extracted from leaves of three bulked, two weeks old seedlings for each breeding line using the CTAB approach25, G = ZZ’/2∑pj(1-pj), where the matrix Z was calculated as (M−P). M is a matrix of minor allele counts  with m columns (one for each marker) and n rows (one for each line). P is a matrix which contains the allele frequency, with column j defined as l is a vector of ones, and j. The default mean imputation approach was applied to impute the missing value in the genotype data, where there were approximately 1% missing genotyping data existing in both spring barley and winter wheat after quality control steps. The mean imputation approach was applied to impute the missing genotype. The principal component analysis (PCA) applied the genomic relationship matrix (GRM) to the analysis was performed to characterize the genetic relationship between breeding lines in both species.The genomic relationship matrix (GRM) was constructed based on the first method proposed by VanRadenpq(α)2, and divided by the total additive genetic variances, where the p and q are the major and minor allele frequency of the SNP, and α is the substitution effect of SNP based on the single marker regression analysis38.For rrBLUP and Bayesian Power Lasso models, the plot heritability was estimated by dividing the additive genetic variance , and to use remaining sets for model parameters estimation, and then predict the breeding value of the masked set. The whole procedure was finished repeatedly until all sets were predicted. The purpose of this strategy is that breeders can apply the same concept to predict the performance of the trait of interest in a future year based on current phenotypic collections. The line correlation (ρ(ĝ)) of the tests were estimated as the correlation between the mean of observed phenotypes corrected for the fixed effects based on the same breeding line (ĝ). The plot correlation ) was to estimate the observed phenotypes corrected for the fixed effects of each plot and GEBVs.A block cross validation strategy using crossing set as block was employed to evaluate the accuracy genomic prediction in spring barley and winter wheat breeding programs. The approach was to mask one set (as one block) from all available sets . However, for powdery mildew and Ramularia in spring barley, the random factors, location * line  in Denmark. In order to understand the effect between genotype and yield traits performance in different locations, the GWAS model including genotype-location information was used to estimate SNP effects in each location.g ~ N are the same as described in model (1); e is a vector of random residuals. Random residuals were assigned a normal prior distribution. The variances and rate parameter, 42:43. The Bayesian analyses were performed as the single chain, with a total length of 1,000,000 rounds of the Markov chain Gibbs sampling, and the first 200,000 cycles discarded as burn-in. Every 40th sample of the rest of the cycles were kept for posterior analysis . The Bayesian model analyses were performed using Bayz software44. Posterior means and convergence were computed using the scripts supplied with Bayz and R package CODA45.The genomic prediction was performed using the Bayesian Power Lasso model and ridge regression BLUP (rrBLUP) model. The Bayesian Power Lasso model is:(s) are the same as described in model (1); e is a vector of random residuals with N(0, IThe rrBLUP model used in this study is:Supplementary Information.


Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations  phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (gr = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (gr = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (pr = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (pr = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.In perennial ryegrass ( Perennial ryegrass is one of the most valued forage species in temperate regions of the world, characterized by relatively high nutritional value, and optimum seasonal and annual dry matter yield (DMY) from herbage, providing a cost effective source of nutrition, for ruminant livestock . The hisIn the context of a genomic selection breeding strategy, higher genetic gains will be achieved for a trait when the prediction model has high predictive ability. Predictive ability is dependent upon trait heritability, training population size, relatedness between training and selection population, statistical model used, marker density, and the extent of linkage disequilibrium (LD) . While tAll of the prediction approaches assessed to date in perennial ryegrass can be categorized as single trait (ST) genomic prediction models, with the majority of them implementing a univariate linear mixed model for predicting GEBVs. In recent years, multi-trait (MT) genomic prediction approaches have been identified as a means of improving predictive ability for a primary trait. In MT genomic prediction, a secondary trait that is genetically correlated with the primary trait is incorporated in the prediction model, to predict the primary trait with higher accuracy . Multi-tTraining population size can be a limiting factor for implementing genomic prediction for complex traits . The optFor plant species, In perennial ryegrass, nutritive quality traits measurements using wet-lab assays are preferred, compared to NIR predictions. In this context the MT approach proposed by Perennial ryegrass training populations, derived from two different breeding programs (Barenbrug Agriseeds Ltd. and Grasslands Innovation Ltd.), were used in this study and designated as TP1 and TP2. Each TP consisted of half-sibling  families and maternal parents from five discrete breeding populations .TP1 was from the Barenbrug Agriseeds Ltd breeding program and was used in the study to investigate MT prediction for DMY. One hundred randomly sampled individuals from each of five F1 generation half-sib families provided by Barenbrug Agriseeds were polycrossed within-family, in separate isolation houses at AgResearch, Palmerston North during spring 2015. F2 generation half-sib seed was harvested from the maternal parents and cleaned prior to sowing field trials during April 2016 at two locations: Ruakura  and Darfield . A total of 463 half-sib families  were sown at two locations, in a row-column design with three replicates and measured over a period of 3 years. Populations were blocked within each trial, and two repeated checks (n = 103 each) were allocated evenly within and across populations and replicates. The remaining 37 half-sib families yielded insufficient seed for inclusion in the field trials. The unit for evaluation of half-sib families in the trials was a 1-m sown row (plot) of plants (0.3 g seed per plot). Plots were sown with 30 cm spacing between plots. When plants reached two to three leaf growth stages, both trials were defoliated by sheep grazing and if needed, subsequent to grazing, mechanical mowing was applied to residual height of 5 cm. Nitrogen was applied at each defoliation (15 kg N/ha) and superphosphate fertilizer (8.8 kg P/ha) was applied annually in late autumn.TP2 is the same training population described previously by TP1 was phenotyped seasonally for dry matter yield (DMY) and visual growth score (GS) over three years across multiple seasons. The schedule of measurements was as shown in th April 2014 at Lincoln and 29th April 2014 at Aorangi. To reduce the influence of diurnal variation on the measured components, harvests were undertaken at each site over three consecutive days, between 10:30 am and 3:00 pm each day. Plots were harvested 5 cm above the pseudostem to collect leaf lamina only and the tissue samples were snap frozen in liquid nitrogen. The collected samples were freeze-dried, milled and split into samples for analysis by AgResearch  for water-soluble carbohydrate (WSC) using the anthrone reagent assay, and for near-infrared spectroscopy (NIRS) and mineral analysis by Hill Laboratories .From the original set of 18 nutritive quality traits originally assessed in the trial  a subsetGenotyping-by-sequencing (GBS) was used to genotype the maternal parents of half-sib families in TP1 and TP2. GBS library preparation, DNA sequencing, data quality assessment, genotype calling and GRM (genetic relationship matrix) estimates largely followed the procedures previously described for TP2  as descrThe population structure of TP1 was assessed using multi-dimensional scaling based on the GRM, whilst the population structure used for TP2 was previously reported in Best linear unbiased predictors (BLUPs) were obtained for the traits measured by analyzing data from across the five breeding populations, for individual locations and across the two locations. In TP1, analysis was performed by fitting a linear mixed model in Genstat , consideFor each trait, BLUPs were predicted separately, pooling all five populations across the two locations. The linear mixed model used for generating BLUPs is expressed as:ijklmnoy is the vector of phenotypic values measured on half-sib family i in row j and column k of replicate l nested in location m of measure n within population o, and i = 1,…, gn, j = 1,…, rn, k = 1,…, cn, l = 1,…, bn, m = 1,…, sn, n = 1,…,mn, o = 1,…,pn, where g,r,c,b,s,m, and p are the half-sib families, rows, columns, replicates, locations, measures, and populations, respectively. In the equation, μ is the overall mean; ig is the random effect of half-sib family m; (gs)im is the random effect of interaction between half-sib family i and location i and measure o; onmlb is the random effect of replicate l within location m of measure n in population j within replicate l in location m of measure n in population k within replicate l in location m of measure n in population i in row r and column c of replicate b in location m of measure n in population gs)im and ms component in the model.2gh) was estimated using Eq. 1, fitting variance-covariance structure among genotypes as follows, G is the KGD genomic relationship matrix. The mixed model was fitted using ASReml-R  is the correlation between secondary and primary trait, gCov is the covariance between trait x and y; σ2(x) is the variance associated with trait x, and σ2(y) is the variance associated with trait y.where, Genomic estimated breeding values (GEBVs) were predicted using BLUPs from Eq. 1 as the dependent variable in the linear mixed model. The Reproducing Kernel Hilbert Space (RKHS) was used as a single trait (ST) genomic prediction model, which is equivalent to the Genomic BLUP (GBLUP) model when kernel is linear . The RKHFor both TP1 and TP2, a linear mixed model was fitted as follows:y is the BLUP values of the trait from the mixed model, β is the vector of grand mean, Z is the design matrix associated with random marker effects α, RKHS assumes that marker effects are distributed as K is an (n x n) additive genetic relationship matrix, and random residual errors as I is the identity matrix. The additive relationship matrix was calculated using KGD method (where D method . A singlD method .For multi-trait (MT) genomic prediction, a bivariate linear mixed model was fitted using two correlated traits as follows:y1 and y2 are the vector of BLUP values for trait 1 and trait 2, β1 and β2 are the vector grand means, Z1 and Z2 are the design matrices associated with random effects α1 and α2, in which K is the marker-based relationship matrix (KGD matrix) and G is the genetic variance-covariance matrix for all traits, ε1 and ε2 are the random residuals from bivariate models, with I is the identity matrix, and R is the residual variance-covariance matrix between two traits. The model was implemented assuming K as unstructured matrix and R as diagonal matrix  approach. For MT models, three different CV strategies were performed, CV1 CV2 and CV3 Figure 1The CV1 approach was applied to both ST and MT models . A random 70% of the TP genotypic and phenotypic data set (primary + secondary traits for MT-CV1) was used as a training set to train the genomic prediction model and the remaining 30% set aside as the test set. GEBVs were predicted for the test set individuals, and this process was iterated 100 times. The predictive ability of the model was the mean Pearson correlation coefficient between GEBVs and the observed phenotypes in the test set.The CV2 and CV3 cross-validation approaches were assessed for the MT model only . For MT-CV2, the methodology was consistent with previous studies Figure 1Training populations TP1 and TP2 were both composites of five breeding populations each. To test the feasibility of genomic prediction in a population that is genetically independent of the training set, genomic prediction models were trained using four breeding populations and used to predict GEBVs in the fifth. Predictive ability was assessed for the trait DMY All Cuts in TP1 and for the trait Total WSC in TP2, using the ST-CV1 and MT-CV2 approaches. A further evaluation was applied with the MT-CV2 approach, by testing using a single secondary trait with either low or high correlation to the primary trait. This procedure was repeated five times, with a different breeding population used as the test set each time.A random set of half-sib families, ranging from 10% to 90% of the full training population , were selected as a training set to evaluate the impact of training set size on predictive ability. In both TP1 and TP2, the predictive ability for a trait for each of the different-sized training sets was assessed in a test set made up of 20% of training population individuals, using both ST-CV1 and MT-CV2 approaches. As mentioned above, the MT-CV2 approach was further investigated by using a single secondary trait in the prediction model, either low or high correlation to the primary trait, for both TP1 and TP2.gr = 0.88 with GS All  and the lowest correlation was with GS Spring (gr = 0.56). Among seasonal DMY traits, DMY Autumn was the most highly correlated with all GS traits (mean gr = 0.70), followed by DMY summer (mean gr = 0.66), DMY Spring (mean gr = 0.63) and DMY Late Spring (mean gr = 0.62)  Table 1.Genomic heritability and variance components for nutritive traits phenotyped in TP2 were previously reported by Population structure in the TP1 training set was estimated using multi-dimensional scaling (MDS) based on the GRM. MDS analysis revealed clustering of individuals into five groups, corresponding to the five breeding populations making up the training set Figure 2pr = 0.51) and low predictive ability was observed for GS winter (pr = 0.26). Based on BLUPs estimated only from Ruakura, the predictive ability for DMY and GS traits ranged from 0.25 to 0.62 (pr = 0.20 to 0.40)  and the lowest predictive ability was for ADF (pr = 0.20). When the model was based on BLUPs estimated from Aorangi, the predictive ability was higher for all seven nutritive traits in comparison to Lincoln , the predictive ability for DMY All Cuts in TP1 and for WSC traits in TP2 was similar to that of the ST genomic prediction models (ST-CV1) 6. Howevgr = 0.88) the predictive ability increased by 59%. The lowest correlation was observed between GS Spring and DMY All cuts (gr = 0.56) and the improvement in predictive ability in this model was the smallest (25%). For GS Summer and GS Autumn, the improvement in predictive ability was similar (51%), although the correlation with DMY All Cuts was slightly higher for GS Autumn (gr = 0.86), compared to GS Summer (gr = 0.81). In the MT-CV3 model, using GS All as a secondary trait, improved predictive ability for DMY All Cuts by 34% compared to ST-CV1  Figure 6pr = 0.1 and 0.3, respectively) whereas the predictive ability in Pop B1, B2, and B3 was negative (pr = −0.06 to −0.19) (gr = 0.56) in the model, the predictive ability for DMY All Cuts was positive when predicting in all five populations, with values ranging from 0.31 to 0.69 (gr = 0.88) was highest, ranging from 0.65 to 0.89  to train a model that was subsequently used to predict individuals in the fifth population. In TP1, using the ST-CV1 approach the predictive ability for DMY All Cuts was only positive when predicting individuals in Pop B3 and Pop B5 (o −0.19) Figure 7pr = 0.0) and Pop V (pr = −0.09) and the highest predictive ability was seen in Pop I (pr = 0.27). Using a low correlated secondary trait  in the MT-CV2 model, predictive ability was positive for all five populations but still relatively low (pr = 0.05 to 0.36). Predictive ability was highest for all five populations when using a highly correlated secondary trait  in MT-CV2, with values ranging from 0.46 to 0.65  for prediction of Total WSC, the point at which predictive ability began to decline was when the training set size was reduced by 22% (n = 287) from its original size (gr = −0.29), this reduction in predictive ability occurred when the training set had declined by 11% (n = 328) from its original size  compared to ST-CV1 (pr = 0.74) that was only marginally lower than this  as well as for prediction of each of the seasonal DMY traits. For instance, GS Summer was evaluated as a secondary trait to predict DMY All Cuts, DMY Summer, and DMY Autumn 6. This The potential of genomic selection for improving genetic gain in forage plant species has been intensively assessed in recent years  but, as pr= 0.31–0.50) were comparable to other studies of perennial ryegrass  genomic prediction models  or even higher for some traits (DMY All Cuts 2gh = 0.65 and GS All 2gh = 0.35)  . Furthergr = 0.80 to 0.88). In TP2, nutritive traits DOMD and NDF were highly correlated with LMW, HMW, and Total WSC traits (gr = 0.36 to 0.57 for DOMD and gr = −0.44 to −0.63 for NDF). Inclusion of these highly correlated traits in the MT-CV2 model resulted in the highest improvement in predictive ability. Among nutritive traits, CP was poorly correlated with LMW (gr = −0.19), HMW (gr = 0.11) and Total WSC traits (gr = −0.08). Inclusion of CP as secondary trait in the MT-CV2 model resulted in low or negative improvement in predictive ability. Genomic heritability estimates for the secondary traits were lower than for the primary traits, in both TP1 and TP2. This implies that, in our study, it was the level of correlation between primary and secondary traits, rather than trait heritability, that made a more substantial contribution to improvement in predictive ability for DMY in TP1 and WSC in TP2 when using the MT-CV2 approach.In contrast to MT-CV1, the inclusion of a correlated secondary trait in both the training and test set MT-CV2, Figure 1We also assessed the predictive ability of a model that includes secondary trait information only in the test set MT-CV3, Figure 1Application of the MT-CV2 genomic selection approach may enable further efficiencies through the reduction of training population size without compromising predictive ability. Using the MT-CV2 approach, The acquisition of GS measurements across multiple seasons and years is expensive and determining a single, optimum seasonal timepoint to score secondary traits would reduce resource requirements and increase the viability of adopting the MT-CV2 genomic prediction approach for DMY genomic selection. The current dataset indicates that the optimum season for phenotyping visual GS as a secondary trait was during summer and autumn, enabling a predictive ability for DMY close to that of using GS measurements across multiple seasons and years. In TP1 improvement in predictive ability for DMY All Cuts using GS All Cuts as the secondary trait in the MT-CV2 model was 0.78. However, using visual GS measured across multiple years in either summer (GS Summer) or autumn (GS Autumn) alone as the secondary trait, gave a predictive ability of 0.74 Figure 5The multi-population nature of the training set used in this study enabled investigation of the efficacy of predicting GEBVs in a population unrelated to the training set. When a ST genomic prediction model was trained using data from four populations and GEBVs were predicted for the fifth population, predictive ability was low or negative for most of the populations in both TP1 and TP2 8. The rFor traits such as DMY in forage breeding, which require a high level of effort for multi-environment phenotyping , data acFor nutritive quality traits, wet laboratory procedures are generally expensive and time-consuming. Obtaining NIRS data is regarded as a simpler, less expensive alternative but is still sample-destructive and relatively laborious . Recent developments in non-destructive NIR technology could be an option to explore, but to obtain reliable data from non-destructive NIR, multiple scans are needed which adds extra time and cost . RecentlIn this study, we have shown that applying MT genomic prediction can improve predictive ability for DMY or WSC when compared to a ST model. Inclusion of a correlated secondary trait, either in both training and test set (MT-CV2) or in the test set alone (MT-CV3), was highly effectual for enhancing predictive ability. The improvement in predictive ability was in line with the degree of correlation between primary and secondary trait. In contrast, when the secondary trait was included only in the training set (MT-CV1), there was no improvement in predictive ability for either DMY or WSC. The MT-CV2 approach opens up the possibility to assemble larger training sets needed to implement genomic selection for resource-intensive and complex traits in forage species, whereby the full training set is phenotyped for an inexpensive secondary trait and a smaller subset for the primary trait. Use of MT genomic prediction models also appears to be a promising approach for successfully implementing genomic prediction in populations unrelated to the training set, if the independent population has phenotypic data for the secondary trait. MT genomic prediction approaches, coupled with HTP systems, has great potential to accelerate the rate of genetic gain in forage species for key economic traits such as DMY and nutritive quality.https://doi.org/10.25387/g3.10074323.MF and BB conceived, designed, and coordinated the study. SA, MC, and MF performed the analysis. MT and CI coordinated the data collection for training population TP2. SA and MF drafted the initial manuscript. SA, MF, MC, BB, MT, CE, CI and AS contributed to interpretation of results and preparation of the final manuscript. All authors contributed to the article and approved the submitted version.This work was supported by Pastoral Genomics (PSTG1501), a research consortium funded by the Ministry of Business, Innovation and Employment (MBIE), DairyNZ, Beef + Lamb New Zealand, Dairy Australia, AgResearch Ltd, Barenbrug Agriseeds Ltd and Grasslands Innovation Ltd.Authors SA, MF, BB, MC, and MT are employed by AgResearch, a New Zealand Crown Research Institute. Authors CE and CI are employed by Barenbrug Agriseeds Ltd, and AS is employed by PGG Wrightson Seeds Ltd.