Over the recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank (UKB) has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we used GWAS summary statistics derived from the UKB cohort to investigate functional mechanisms of pleiotropic effects across the human phenome. We find that highly pleiotropic variants often correspond to broadly expressed genes with ubiquitous functions, such as matrisome components and cell growth regulators; and tend to colocalize with tissue-shared eQTLs. At the same time, signaling pathway components are more prevalent among highly pleiotropic genes compared to regulatory proteins such as transcription factors. Our results suggest that protein-level pleiotropy mediated by ubiquitously expressed genes is the most prevalent mechanism of pleiotropic genetic effects across the human phenome. Most recent GWAS for common phenotypes such as type 2 diabetes include up to 900,000 individuals and allow identification of numerous novel risk loci2. Many methods have been developed to interpret the GWAS signal on both single-variant and genome scale, including a group of specialized gene set enrichment tests5 aimed at discovery of biological pathways and mechanisms related to complex traits. Integration of GWAS and expression quantitative trait loci (eQTL) data has also shed light onto the role of gene expression in complex traits, and allowed for identification and validation of causal genes6.Over the recent decades, many advances have been made in uncovering the genetic architecture of human complex traits. Genome-wide association studies (GWAS), emerging at the onset of the millennium, have facilitated the discovery of susceptibility loci for both complex disease and quantitative traits .Advances in biobanking, genetic data acquisition, and analysis have allowed first glimpse into the genetic architecture of multiple traits at once, with the UK Biobank (UKB) genetic dataset of 500,000 individuals being increasingly used for diverse studies in statistical genetics13. Historically, researchers have mostly used model organisms to study pleiotropic effects of loss-of-function mutations and gene deletions that are generally expected to have larger effect on related traits15. These studies have produced many estimates of how widespread pleiotropy is, which ranged from 5% to 30%. While some argued that gene deletions are expected to generate variants with very high effects, and thus represent an estimate of the upper limit of observed pleiotropy, others have highlighted the fact that pleiotropic variants with strong effects might undergo even more stringent purifying selection than non-pleiotropic ones16. On the other hand, pleiotropy of individual common variants with weaker effects only became accessible to the researchers with the advent of the GWAS. This, in turn, would mean that we should be able to detect higher levels of pleiotropy among more common variants with lower effect sizes, including numerous noncoding and regulatory variants, which are the most prevalent in GWAS.Genetic effects of the same locus on multiple traits, termed pleiotropic effects, are widespread in natural systems ranging from bacteria to humans and have attracted a lot of attention over the years13. Debates over how widespread pleiotropy is had started as early as 1930, and resulted in formulation of the hypothesis of universal pleiotropy16. The most stringent variant of this theory, strong hypothesis of universal pleiotropy (SHUP), implies that every variant influences every trait, which was quickly realized to be not supported by most experimental observations. Variations on the theme of weak hypothesis of universal pleiotropy (WHUP) predicted network-like structure of relationship between genetic architecture and complex phenotype space, which is now mostly accepted to be true. However, the extent to which pleiotropy influences complex traits remains a matter of debate. With the increasing number of studied traits the general definition of pleiotropy becomes useless. Indeed, as it was pointed out before, while the number of genes for an organism is finite, the number of traits can be increased indefinitely16. For this reason, it is important to separate the “true” biological pleiotropy from statistical and classification artifacts.The role of pleiotropy in genetic architecture has been discussed for over 100 years17. Other authors suggest more mechanisms of cross-phenotype associations, involving regulatory effects of genetic variants, multiple gene deletions and pleiotropy mediated by different protein isoforms18. Given the availability of rich tissue-level expression datasets6, a useful way to classify pleiotropy in complex human traits might involve different levels at which pleiotropic effects might arise  horizontal pleiotropy, with one variant independently affecting several phenotypes; (ii) vertical pleiotropy, when one of the traits is being mediated by the other; and (iii) spurious pleiotropy arising from one common marker variant being in close linkage disequilibrium (LD) with several independent causal variants20. Several attempts have been directed at studying pleiotropic effects of individual variants or genes across the whole phenome  , though no unified statistical framework to interpret such phenome-wide associations has been proposed. Mendelian randomization based approaches have also been proposed to detect pleiotropy in GWAS data22. The global picture of human pleiotropy, however, was obscured by differences in published studies, with many biases in phenotype and genotype data collection. UK Biobank dataset offers a unique opportunity to analyze shared components of the genetic architecture of uniformly processed human phenotypes on the genome-wide scale, with early global overviews suggesting widespread signals of pleiotropy in the UK Biobank data24.Pleiotropy of trait-associated variants in the human genome has attracted significant attention in the fieldIn this study, we analyzed all heritable traits with reported associations using GWAS summary statistics from UK Biobank dataset, and assessed the prevalence and functional implications of different levels of pleiotropy.2). A total of 469,013 (4.27%) SNPs had at least one phenotype associated at genome-wide significant level, with an average of 4.34 phenotypes associated with each variant. Interestingly, we observed numerous multiple associations across the dataset, with 230296 (49.21%) of SNPs having >1 associated phenotype, and more than 10 associated phenotypes for 57,856 (12.34%) SNPs.To conduct a genome-wide scan for pleiotropic loci, we first obtained sets of significantly associated SNPs for all phenotypes in the UK Biobank data using pre-calculated GWAS summary statistics provided by Benjamin Neale’s lab . The dataset included both standard GWAS loci as well as imputed variants, totaling 10,894,597 variants. We focused our analysis only on 543 complex traits that have significant non-zero partitioned heritability estimates . We applied hierarchical clustering combined with the analysis of silhouettes to determine the optimal number of independent clusters. We identified 308 clusters of complex traits including 1.76 phenotypes on average ). Among these, 48 clusters comprised 3 or more phenotypes see Fig. . The cluInterestingly, we observed a relatively small number of rare variants that have >5 associated clusters despite a much higher prevalence of rare variants in the dataset Fig. . In otheWe then focused on investigation of the genetic architecture of the major trait clusters. Many of the identified clusters comprised biologically similar complex traits or same phenotypes in different encoding . In order to investigate the molecular basis of traits and clusters, we developed a simple modification of gene set enrichment analysis dubbed Locus Set Enrichment Analysis (LSEA) which accounts for linkage disequilibrium (LD) between SNPs and can work with few significantly associated SNPs for each trait (see Methods for description of the method). Our approach was more sensitive in identifying molecular pathways using a limited set of significantly associated variants, for example, for blood clotting-related traits Figs.  and S4.26. Overall, we observed substantial overlap between gene sets enriched in trait clusters and individual phenotypes . Out of 11,035 gene sets analyzed, 10,897 (98.7%) gene sets showed significant enrichment for at least 2 phenotypes from distinct clusters, with 2411 gene sets enriched in GWAS for more than 50 individual traits. ts. Fig. . We alsoCIITA27. We found 8 trait clusters to be significantly associated with variation inside the MHC locus, supporting its pivotal role in complex traits. We next went on to assess the results of canonical pathway enrichment across the whole phenome . On aggregate, matrisome and matrisome-associated proteins had the highest number of associated phenotypes and trait clusters  molecular function terms across all traits . We identified that, while the strongest enrichment signal corresponded to signaling receptors, transcription factor genes were also commonly enriched in the association data. Interestingly, we observed that a slightly higher number of individual phenotypes showing enrichment for genes with DNA binding and transcription factor activity Fig. .Overall, our analysis suggest that all three modes of pleiotropic effects described in Fig. i.e. vertically pleiotropic (see Methods). We then used log-transformed pleiotropy score (PS) to filter out loci with multiple associations to closely correlated clusters, such as height and body mass clusters  , suggesting that variants with high pleiotropic effect tend to have a significant influence on gene expression. Interestingly, we observed that the degree of pleiotropy is positively correlated with the number of tissues with eQTL effect of the index variant . These results are in good concordance with other reports on genetic pleiotropy in the UKB dataset23, including a recent preprint by GTEx consortium28. Concordantly with these data, we found that genes at highly pleiotropic loci tend to be more broadly expressed across GTEx tissues  among the pleiotropic variants compared to variants associated with at least one trait  within 1 kbp windows around index variants marking pleiotropic loci.Analysis of the functional impact of the variants with different degrees of pleiotropy have generated numerous intriguing results. We identified a very strong enrichment for cis-eQTLs among index SNPs in all variant categories (data collected from the Genotype Tissue Expression (GTEx); 586/2,106 for all index SNPs in pleiotropic loci compared to 5,640/59,449 for all index SNPs, Fisher p-value = 1.6 × 10ant Fig.  and the ues Fig. , with thait Fig. . Further20. Such studies used GWAS Catalog data30 or electronic health records31. These studies highlighted widespread pleiotropy and pinpointed most notable pleiotropic variants, but could not offer functional and statistical characterization of pleiotropy in human genome due to limits of the datasets. Using UK Biobank data, Watanabe et al. have recently demonstrated some of the key features of pleiotropic variants23. However, the use of sparsely defined trait domains instead of statistically defined trait clusters led to very high estimates of pleiotropy, with 142,439 out of 236,638 (60%) significantly associated SNPs reported as pleiotropic. The loci that contained pleiotropic SNPs were estimated to cover as much as 1.6 Gb of human genome.Several attempts have been previously made to assess pleiotropic effects in human complex traits based on GWAS dataet al. have recently developed HOPS, a statistical “whitening” procedure that removed trait-based correlation and allowed robust identification of “biological” pleiotropy24. Additional modelling of polygenicity of each trait makes the proposed model especially attractive for functional dissection of pleiotropy. Authors report a much smaller number of pleiotropic SNPs, with the biggest estimate being 74,335 variants in 8,093 LD-independent loci. While this makes pleiotropy still very widespread, it does not quite reach the levels described in Watanabe et al. We have independently developed another statistical score that allowed us to filter the variants associated with highly correlated clusters, e.g. large clusters that included most of the anthropometrical traits. Our estimates  fall much closer to Jordan et al., especially taking into consideration that our method of pleiotropic SNP identification can only discover SNPs that are significantly associated with at least two traits in a univariate GWAS, while HOPS allows for discovery of novel associations24. We estimate approximately 180 Mbs of the human genome to be covered by pleiotropic loci; the exact coordinates and included SNPs can be found in Supplementary Table In a different approach, Jordan 17. Finally, lower degree of pleiotropy of rare variants might be caused by strong purifying natural selection acting against highly pleiotropic variants with larger effects, causing all pleiotropic variants to have lower effect sizes and higher frequency. Negative natural selection was shown to operate on trait-associated variation8, favoring the latter mechanism.We show that pleiotropic effects are much more widespread in common variants than in rare variants Fig. . This ob31. At the same time, an overwhelming prevalence of matrisome component genes among pleiotropic loci  and matrisome-associated proteins and regulators . Notably, we find that the core matrisome components are less pleiotropic compared to matrisome regulators/matrisome associated genes  that governs the structure of most tissues. ECM proteins have been shown to have a significant role in body composition and in various diseasesnes Fig. . This su23; moreover, a recent analysis of regulatory variation by the GTEx Consortium suggested that trait pleiotropy might also be connected to the “cis-eQTL pleiotropy”, i.e. regulatory effects of a single variant on multiple neighboring genes28.Such unusually high prevalence of matrisome components and cell growth regulators in GWAS signal across the phenome highlights the strongest influence of protein-level pleiotropy on human complex traits Fig. . This as34. For example, we find additional phenome-wide associations for previously reported pleiotropic genes, such as FTO, APOE, and SLC39A8. We also discovered several notable examples of novel pleiotropic loci. One such example is the MIR2113 gene encoding a microRNA of unknown function. This locus is associated with several phenotypes related to cognitive characteristics, including college or university degree qualifications, fluid intelligence score, as well as urine ion concentrations (sodium and potassium). Previously, MIR2113 was shown to play a role in memory processes35. However, the exact mechanism of its function has never been reported. Our data on its involvement in maintenance of ionic balance, together with a strong enrichment for potassium channel genes among its predicted targets according to miRBase . We focused our attention only on phenotypes with significant non-zero partitioned heritability estimates . After obtaining the list of heritable phenotypes we retained genome-wide significant variants for each phenotype . Association summary statistics for each variant for each trait were then merged into a single matrix for further analysis.We acquired data from the Benjamin Neale’s lab website  are omitted, as suggested previously3. The resulting intervals are then intersected with the pre-computed universe of all possible loci bearing GWAS signal.We implemented a novel Locus Set Enrichment Analysis (LSEA) method for gene set enrichment analysis of GWAS data. LSEA operates on the sets of pre-selected significant SNPs or summary statistics file for each trait. At first stage, LSEA groups associated SNPs into individual genomic loci. First, variants are clustered by LD using the clumping method in PLINKhttp://software.broadinstitute.org/gsea/msigdb/annotate.jsp) the following way: for each interval i in the intervals universe, we assigned i to a gene set if at least one gene in the gene set overlaps i. Such method efficiently corrects for functionally related genes that are genetically linked, as intervals spanning several genes belonging to each gene set are counted only once.To construct such universe, we merged all loci that were identified for the whole UKB dataset. We then transformed all of the curated gene sets obtained from the MsigDB database .HIST1 gene cluster were omitted during gene set analysis with LSEA, we analyzed the overrepresentation of variants inside these loci separately. To this end, we created sets of intervals for the HIST1 and HLA regions in a way analogous to the LSEA method. We then obtained the overrepresentation p-values using hypergeometric distribution, with the test statistic being equal to the number of intervals in the query that overlap the regions of interest.As the HLA locus and the In order to reduce the influence of trait correlation, we have clustered 543 traits with non-zero heritability into 308 clusters. However, some distinct pairs of clusters still contained substantially correlated traits, leading to incomplete separation of vertical pleiotropy. To address this, we developed a pleiotropy score that characterized the probability of a particular SNP being associated with two or more clusters of traits simply due to traits being correlated.v1,v3) can take only when c2 + c2 ≥ 138. The issue becomes more complicated with for 4 and more vectors involved. Using correlation between clusters of traits, we can eliminate the necessity to account for independence of traits. We defined the upper limit of correlation between two clusters as the highest absolute value of correlation between any pair of traits Ti, Tj, where Ti belongs to cluster Cn and Tj belongs to cluster Cm. After this, the probability a SNP being pleiotropic due to simple correlation of phenotypes included in clusters is calculated by multiplication of correlation of all possible pairs of clusters. That is, if a SNP is associated with N clusters (C1…CN), probability score is calculated asRelationship between correlation coefficients lacks transitivity; in fact, for correlation between three vectors, it can be shown that there are limitations which values c were overlapped with significant cis-eQTLs (p < 5 × 10−8). Overrepresentation of cis-eQTLs in the set of pleiotropic SNPs was assessed by comparison of the resulting proportion of significant eQTLs using the hypergeometric distribution.To analyze the overrepresentation of cis-eQTLs, we used the GTEx consortium dataset39 and separated the variants by their functional type (e.g., missense, intronic, etc.; the distributions are shown in Fig. We also annotated all SNPs from the UKB dataset with SnpEff29. For this analysis, sets of pleiotropic variants were converted to an interval list, and the resulting intervals were used as the input for ChIP-Atlas. To narrow down the search space, we limited the analysis down to a set of blood-derived genomic tracks. For each type of epigenetic mark, we retained the minimal enrichment p-value for further analysis.To analyze the enrichment of epigenetic marks and chromatin states in the set of pleiotropic loci, we used the previously described ChIP-Atlas serverSupplementary Figures S1-S6.Supplementary Table S1.


Feline hip dysplasia (FHD) is a debilitating condition affecting the hip joints of millions of domestic cats worldwide. Despite this, little is known about FHD except that it is relatively common in the large breed Maine Coon. We used 20 years of data from 5038 pedigree-registered Maine Coon cats in a radiographic health screening programme for FHD to determine, for the first time, its heritability, genetic correlation to body mass and response to selection. FHD prevalence was 37.4%, with no sex predilection; however, FHD severity increased with age and body mass. Heritability of the radiographic categories used to classify FHD severity was 0.36 (95%CI: 0.30–0.43). The severity of FHD symptoms was also genetically correlated with body mass (0.285), suggesting that selection for a large body type in this breed concurrently selects for FHD. Support for this was found by following generational responses to selective breeding against FHD. Not only did selective breeding successfully reduce the severity of FHD symptoms in descendants, but these cats were also smaller than their ancestors (−33g per generation). This study highlights the value of breeding programmes against FHD and cautions against breed standards that actively encourage large bodied cats. Because of its debilitating impacts on individual animals, considerable research effort has focussed on the disease’s genetic and environmental risk factors in order to mitigate these risks. Much of this work has been done in dogs because hip dysplasia is relatively common in larger dog breeds5; thus in dogs we know much about its breed predisposition, heritability, genetic regulation, environmental risk factors, symptoms and radiographic presentation, welfare implications and response to selection in health screening programmes9. Selective breeding against canine hip dysplasia through breed-specific programmes has become the mainstay of managing the prevalence of the disease for over 50 years10. However unlike dogs, hip dysplasia in cats has been largely overlooked and underreported13, with very little known about its demography (but see14), and almost nothing known about risk factors, heritability or potential response to selection14.Hip dysplasia is a degenerative condition affecting the coxofemoral joint, where abnormal anatomical development and weight-bearing forces combine to create ongoing degenerative joint disease17, and four studies reporting its breed-specific prevalence19. While there is evidence that FHD may be more common in some breeds , these differences are often difficult to quantify because of the small sample sizes reported (e.g. the 78 cats examined in19 are divided among 9 breeds). Despite this, there are clear indications that domestic shorthairs have a lower prevalence of FHD (10.4% from 899 cats19) compared to Maine Coons (24.9% from 2548 cats14). Because of these likely breed associations, FHD is expected to have a genetic basis similar to the polygenic condition in dogs21. Thus it is assumed that FHD can be similarly managed, with selective breeding and culling being used to limit its impact in specific populations. However, while these assumptions are reasonable, the heritability of FHD and its subsequent response to selection are still unknown; issues that have significant implications for our understanding and management of FHD21.Current knowledge about feline hip dysplasia (FHD) comes largely from individual case reports in the veterinary literature22. To determine heritability of a phenotypic trait, in this case FHD, we need many individuals of known genetic relationship to one another whose FHD symptoms can be compared. Currently in cats, such an analysis is only feasible in the Maine Coon. Two international registries collect data on FHD in the Maine Coon: the Orthopedic Foundation for Animals (OFA) whose demographics are described in Loder & Todhunter14 and the Swedish-based PawPeds programme (https://pawpeds.com). In both programmes, FHD is classified into ranked scores based on the severity of radiographic signs , but also an estimate of its heritability and response to selective breeding.Heritability is the proportion of phenotypic variation arising from additive genetic effects, and is critical for estimating the expected response to selection5, suggests some linkage between genes for body size and hip dysplasia risk. Thus, we also examined: (4) the genetic correlation between FHD scores and body mass to assess how selection for size in this large breed may concurrently select for FHD. By extension, we also examined whether selection against FHD in the health programme concurrently selected for a smaller body mass in subsequent generations [because of this genetic correlation]. These questions are not only invaluable for greatly expanding our currently limited knowledge of FHD, but also for validating the effectiveness of current and future breeding programmes. Information regarding genetic correlations between FHD and body mass will have significant implications for the breed standard and the sorts of traits breeders should be selecting for if they want to limit FHD in this (and other) cat breed(s).We analysed data collected by the PawPeds FHD Maine Coon health programme and linked these data to the Maine Coon pedigree registry. This allowed us to specifically examine: (1) demographics of hip dysplasia from 5038 Maine Coon cats and its relationship to probable risk factors , (2) response to selective breeding, by looking at the relationship between FHD scores and the number of generations within the selective breeding programme, and (3) the heritability of FHD, by using the linked pedigree to calculate the ratio of additive genetic variance to phenotypic variance. The Maine Coon is a large cat breed, and this in combination with evidence that hip dysplasia in dogs is associated with large dog breedsMore than one third of all Maine Coon cats surveyed showed some radiographic signs of hip dysplasia 37.4%), with the proportion of cats in each HD category being approximately the same for males and females  when breeders select against FHD Fig. .14) and provides additional justification for the screening and selective breeding programmes that have developed around this cat breed. However it is naive to think that FHD is a condition largely restricted to the Maine Coon. The Maine Coon was the focus of this study simply because it is the only breed with data routinely collected on FHD. Previous studies show that other cat breeds likely suffer from this condition ; however these are based on very small sample sizes (between 5 and 25 cats) because organisations associated with these breeds have not made specific recommendations to their members to screen for FHD. The only other type of cat with sufficient sampling to provide a good estimate of FHD prevalence is the domestic shorthair, where a combination of studies provides an estimate of 10.4% from a total of 899 cats19. This clearly suggests that FHD has a similar prevalence to canine HD 5. These findings are in stark contrast to Hayes et al.20 who estimated FHD prevalence to be extremely low , but neglected to consider that many of these patients would have had FHD but were not evaluated for it because cats do not show the same obvious clinical symptoms as dogs13. Thus, it is clear there has been a severe underestimation of FHD prevalence in the general cat population.Our study confirms the high prevalence of FHD in the Maine Coon . As has been previously noted, when bilateral HD occurs the severity of the condition is generally higher than when only one hip is involved  than those with a genetic predisposition for severe FHD  of FHD were within the range commonly reported for HD in dogs (0.1–0.7)5, suggesting a similar polygenic aetiology and an expected response to selective breeding. But it is important to consider exactly what is meant by h2 of FHD and why h2 estimates will likely vary between this study and future studies. Heritability is the proportion of phenotypic variance in our measure of FHD that is explained by additive genetic factors. Thus, one needs to consider the amount of variation in the population and the relative contribution of genetic versus non-genetic effects. This means that variation arising from different gene pools or differences in the general rearing conditions or nutrition of the animals being evaluated may significantly impact the h2 estimates, with these potentially differing because of spatio-temporal or sampling issues. But potentially more importantly is the way FHD is measured or categorised in the first place. Heritability is calculated from the phenotypic variance, with the phenotype in this case not being FHD itself, but how we measure FHD. PawPeds uses a 4-category ordinal ranking to define FHD severity and grades the cat based on the highest (worst) hip score, while the Orthopedic Foundation for Animals (OFA) registry uses a 7-category grading system and grades the cat based on an average of the two hip scores14. While these ways of categorising FHD are likely to be very closely aligned and thus should reveal similar h2 estimates, any differences will probably say more about the method used to categorise FHD rather than the underlying gene pool. Thus, where future work focuses on the relative advantages of different methods for diagnosing or categorising FHD 23, it should be kept in mind that these diagnostic methods and their phenotypic variance may influence h2 estimates, with this, in turn influencing the effectiveness of selective breeding programmes based on these FHD scores6. In addition, h2 estimates could be influenced through observational error depending on the grading system used in the selection programme. Here we used a single observer when categorising FHD, whereas other programmes use multiple observers14. Single observer programmes potentially limit between-observer variation24 and hence produce higher and more accurate h2 estimates; however, they run the risk of introducing systematic bias during the course of the programme if the observer subtly changes the magnitude of their assessments over time. The degree to which these observer errors impact on the effectiveness of FHD screening programmes is unknown, but could potentially be investigated using the PawPeds and Orthopedic Foundation for Animals data. Currently, our study shows that the method used by PawPeds provides a measure with a moderate level of heritability for selection to act on and with direct evidence of declining FHD scores in response to selection. Thus there is no reason to change the way FHD is classified in this programme, unless compelling evidence shows that other ways of scoring FHD are more effective at reducing its severity within a selective breeding context.Our estimates of heritability . This in itself is not a bad thing; however, there is likely to be temptation for some judges and breeders to favour the larger-than-average cats within the breed, to accentuate one of its defining features. This temptation is hinted at in the same Maine Coon breed standards when they specifically warn against this . Our study shows that these warnings need to be heeded, as selecting for a larger-than-average body type is likely to carry with it the unwanted genetic consequences of higher FHD risk. This raises ethical questions about exactly what traits should be promoted in the breed, and the possible trend that these cats are getting larger over time.In dogs, larger breeds are most commonly associated with the highest prevalence of HD. The genetic correlation analyses in our study clearly demonstrate that some of the genes responsible for the larger body type seen in the Maine Coon also increase FHD risk, either because of genetic pleiotropy or inheritance of a linked gene complex. Thus breeding for a large body type carries with it the increased risk of FHD. This finding was further supported when we looked at the average body size of cats within the selective breeding programme against FHD; here as the number of generations of selection against FHD increased, body size decreased. This has serious implications for how the breed standard should be interpreted by breeders and show judges. The Maine Coon breed standard describes the breed as large in type . This was initiated because of early reports that FHD had a higher prevalence in Maine Coons compared to other breeds25. The basis for the programme is a standardised score for each hip (ranked from 0–3) based on a ventrodorsal radiographic assessment . Evaluation of the radiographic FHD categories is based on joint conformation , fit  and signs of degenerative joint disease (e.g. bone remodelling and or new bone formation). Specifically, the hip score grades were determined using the following criteria: Hip Score = 0  - no signs of hip dysplasia and/or degenerative joint disease and the acetabulum covering at least 50% of the femoral head; Hip Score = 1 (mild hip dysplasia) - mild signs of hip dysplasia and/or the acetabulum covering less than 50% of the femoral head but no signs of deforming degenerative joint disease; Hip Score = 2 (moderate hip dysplasia) - moderate signs of hip dysplasia and/or signs of deforming degenerative joint disease; 3 (severe hip dysplasia) - severe signs of hip dysplasia and/or deforming degenerative joint disease  that allowed us to derive information on the genetic relationships between these individuals  from individuals with information on FHD scores, sex, age and parentage. Of these records, 2156 also had data on the individuals’ body weight  allowing body-weight-related analyses on this subset. We also had access to the Maine Coon pedigree database (14 (Table P = 0.01), we used the more general multinomial logistic regression using the ‘mlogit’ package26 in R27 for this analysis.Our initial approach was to examine the prevalence of different FHD scores based on sex to get a general demographic overview of the data for comparison toSymptoms of FHD are likely related to an interaction between the age and weight of an individual and its genetics. Thus we were interested in how age and weight influenced the phenotypic expression of FHD in male and female cats. Because body mass is age and sex dependent, we converted each individual’s body mass data to a sex- and age-adjusted residual body mass; thus, negative residuals were for individuals smaller than expected, and positive residuals for individuals larger (heavier) than expected for their age  of FHD into its additive genetic (VA) and the residual or additional variance components. To do this we fitted a generalised linear mixed-effects model known as an ‘animal model’ in quantitative genetics28. The animal model uses information about the genetic relationships between individuals from the pedigree  is then calculated using the formula h2 = VA/VP. Models were implemented using the R package ‘MCMCglmm’29 following pedigree checking using the ‘pedantics’ package30. These animal models were formulated as ‘threshold’ models for ordinal data, with the unit variance fixed to 1 and individual ID included to account for repeated measures on the same individual 31.To estimate heritability we needed to statistically partition the phenotypic variance . Genetic covariance between traits may occur because they share the same genes or linked gene complexes and is expressed as the genetic correlation (rG) using the formula: rG = COVA/We then estimated the 3, and (3) the genetic correlation between maximum hip score and the left or right hip scores was extremely high  because: (1) it seemed reasonable to classify a cat based on its worst HD score, since both hips were not always affected equally Table ; Fig. S1Supplementary information


Gasterosteus aculeatus) are an excellent model for such studies, as they have repeatedly adapted to divergent habitats across the Northern hemisphere. Here, we use genomic, ecological and morphological data from 16 independent pairs of stickleback populations adapted to divergent lake and stream habitats. We combine a population genomic approach to identify regions of the genome that are likely under selection in these divergent habitats with an association mapping approach to identify regions of the genome that underlie variation in ecological factors and morphological traits. Over 37% of genomic windows are repeatedly differentiated across lake–stream pairs. Similarly, many genomic windows are associated with variation in abiotic factors, diet items and morphological phenotypes. Both the highly differentiated windows and candidate trait windows are non-randomly distributed across the genome and show some overlap. However, the overlap is not significant on a genome-wide scale. Together, our data suggest that adaptation to divergent food resources and predation regimes are drivers of differentiation in lake–stream stickleback, but that additional ecological factors are also important.The repeated evolution of similar phenotypes in independent populations  provides an opportunity to identify genetic and ecological factors that influence the process of adaptation. Threespine stickleback fish (This article is part of the theme issue ‘Convergent evolution in the genomics era: new insights and directions’. Independent, replicate pairs of populations adapting to similar ecological conditions provide a useful opportunity to address this question. Recent genetic studies of replicated phenotypic evolution have provided tantalizing clues that evolutionary trajectories may be more repeatable than previously thought –3. In paGasterosteus aculeatus) is a good system in which to quantify the repeatability of genomic differentiation and to identify the ecological conditions and phenotypic traits that are associated with regions of repeated genomic differentiation. These small fish have frequently colonized diverse freshwater habitats in the Northern Hemisphere since the retreat of the glaciers 12 000 years ago [The threespine stickleback  For a given genomic region, what proportion of the 16 lake–stream pairs show similarly high genetic differentiation, and what proportion of the genome overall exhibits shared genetic differentiation?; (2) Which genomic locations are associated with variation in environmental factors (biotic and abiotic) and variation in morphological traits across lake and stream populations?; (3) Do the genomic regions underlying these traits co-localize to genomic locations that are repeatedly differentiated? If so, is there enrichment of particular categories of traits within the repeatedly differentiated regions?2.(a)et al. [FST) [FST values were calculated by dividing the sum of the numerators of all SNP-wise FST estimates within a given window by the sum of their denominators. For downstream analysis, we required that each window contained at least three variable sites.Illumina sequence data for pairs of lake and stream stickleback from 16 independent watersheds on Vancouver Island, Canada  was previously generated using the double-digest restriction-site-associated DNA-sequencing (double-digest RAD) method , with 24l. [FST)  was usedFST. We classified outlier windows as those with mean FST values falling within the top 5% of the genome-wide FST distribution within a given lake–stream comparison. Outlier classification was performed using custom R scripts. Read coverage did not differ significantly between windows classified as outliers and those classified as non-outliers for any of the 16 population pairs (data not shown).Genomic windows were classified as ‘outliers’ or ‘non-outliers’ based on their mean k is the number of population pairs with an outlier for a given window and n is the number of population pairs with data for that window.To identify the genomic regions (windows) that had repeatedly differentiated between independently derived lake–stream pairs, the outlier windows in each single lake–stream comparison were compared across all 16 population pairs. Repeatability was estimated window-by-window as the proportion of population pairs that had a given outlier, using the following equation:p-values were corrected for multiple testing using the p.adjust function with the BH  method in R [To test whether the level of repeatability was greater than that expected by chance, we ran a permutation with 10 000 iterations. For each iteration, the outlier status of a given window was randomly shuffled among the 16 population pairs and the magnitude of repeatability was re-estimated. Missing data were held in place during resampling so that the total number of windows with data for a given population pair remained the same for all iterations. These 10 000 iterations yielded a null distribution of repeatability for each window, and empirical estimates were compared against these nulls to determine statistical significance. The resulting hod in R .(b)n = 5), diet items (n = 45) or morphological phenotypes (n = 34) across the 32 freshwater populations from 16 watersheds . Data for individual traits in each of these three categories were all previously reported, with full details on the method of measurement provided in Stuart et al. [The same SNP data outlined above were used to identify genomic loci associated with variation in abiotic factors  were identified using SNPrelate [et al. the among-population standard deviation, as suggested by Coop et al. [et al. [ρ correlation coefficients were estimated for all SNPs and traits; both statistics were averaged across the five independent Bayenv runs before downstream analysis. The log Bayes factors for each trait were individually plotted against the corresponding Spearman's ρ values (i.e. in volcano plots); this allowed us to visually ensure that loci with large Bayes factors did not tend to have small correlation coefficients, as this would be an indicator of false positives.Bayen v2.0  was usedNPrelate  and remoNPrelate . This cop et al. ; this wa [et al. . Each inρ fell in or above the 99.9th quantile of their respective distributions . If a given 50 kbp window contained one or more of these candidate trait SNPs, it was defined as a ‘candidate trait window’.The SNPs from the Bayenv analysis were classified as significant candidates for explaining variance in a trait if they met the following criteria: both the log Bayes factor value and Spearman's (c)For all permutation tests described below, we ran 10 000 iterations to generate the null distribution, against which empirical estimates were compared to determine statistical significance.First, we tested whether the number of candidate trait windows on each chromosome was greater than expected by chance. In each iteration, the candidate status of a window (i.e. candidate containing or not) was randomly shuffled among the 21 chromosomes and the total number of candidate trait windows per chromosome was re-estimated. Three permutation tests were run, one for each category of the trait . The same method was used to test for enrichment of repeatedly differentiated windows, with repeatability status of a window randomly shuffled.p-values were corrected for multiple testing using the p.adjust function and BH  method in R [Second, we tested whether more traits within each category type  mapped to a given window than expected by chance. For each iteration, the presence or absence of a candidate for each trait within a category was randomly shuffled among the genomic windows and the total number of mapped traits per window was re-estimated. In this permutation, the resulting hod in R .and classified as candidate trait windows than expected by chance. To accomplish this test, we randomized the candidate trait windows and separately randomized the windows that were repeatedly differentiated. Here, any window shared by at least two pairs of populations was coded as repeatedly differentiated, although the results were the same if only the significantly repeatedly differentiated windows  were coded in this way. For each iteration, we re-quantified the total number of windows that were repeatedly differentiated and contained a candidate trait locus.Third, we tested whether there were more windows that were repeatedly differentiated different categories (e.g. candidates for diet and morphology) than expected by chance. In these tests, the candidate trait windows in each category were individually randomized and the total number of windows containing candidates for both trait categories was re-estimated.Fourth, this same permutation structure was used to test whether there was more overlap between the candidate trait windows of the 3.(a)FST outliers) in at least one lake–stream comparison. Across the genome, 377 windows  were outliers in two or more population pairs, indicating that there is some evolutionary repeatability at the genomic level (p < 0.05) even after correction for false discovery rate. These significant windows were all outliers in multiple watersheds  or gene density .Of the 2513 windows (50 kbp) across the stickleback genome, 1013 windows were highly differentiated  of repeatedly differentiated windows on chromosomes VIII, XI and XXI. The location of this highly repeatable window on chromosome XXI is biologically reasonable given previous work has indicated the clustering of quantitative trait loci (QTL) on this chromosome [The outlier window shared by 10 populations was located on chromosome I. There was significant enrichment (romosome . However(b)Using a population-level association mapping approach (i.e. Bayenv), we detected windows containing at least one SNP associated with environmental variables or fish traits. Specifically, Bayenv calculates the correlation between population allele frequency and mean trait value across the 32 populations (16 lake and 16 stream), while controlling for their genetic structure. We applied this analysis to three categories of data . Electronic supplementary material, figure S2 summarizes the locations of all windows.All five abiotic factors tested had candidate windows that significantly explained population-level variation (correlation coefficients 0.32–0.60). Salinity and dissolved oxygen had the fewest candidate windows (four each), while the other factors had 17–20 significant candidate windows. All 34 morphological phenotypes considered had multiple candidate windows significantly associated with population-level variation (correlation coefficients 0.28–0.67). Pectoral fin area had the fewest candidates (six) while other traits had 30 or more significant candidate windows. Of the 45 diet items tested, 43 had one or more candidate window(s) and some factors had up to 20–30 significant candidate windows (correlation coefficients 0.27–0.69). See electronic supplementary material, table S2 for locations and summaries of mapped windows for each trait.(c)p < 0.05 in a permutation test; see electronic supplementary material, table S3 for individual chromosomal p-values) of candidate windows associated with all trait categories: abiotic factors were enriched on chromosome VII, diet items were enriched on chromosomes VII and XXI, and morphological traits were enriched on chromosomes IV, VII and XX.There was significant enrichment ((d)p > 0.05 for all windows). By contrast, 45 windows  exhibited significant co-mapping of two or more of the 34 morphological phenotypes . Genomic windows containing candidate loci for multiple morphological traits were located primarily on chromosomes IV, VII, XII and XX. For diet, there were 14 windows (0.6%) that had significant co-mapping of candidate loci for the 45 different diet components ; these genomic regions were located primarily on chromosomes IV, VII and XV.Across the genome, there was no significant co-mapping (clustering of candidate regions) of the five different abiotic factors to a given window (a). Three hundred and forty-three of these windows were associated with at least one morphological phenotype, 347 were associated with at least one diet item and 62 were associated with at least one abiotic factor (b). There was considerable overlap of these candidate windows across the three trait categories (b). Correspondingly, the degree of genome-wide overlap between candidate trait windows for different trait types  was significantly more than expected by chance for all three trait combinations . Seventeen windows contained candidate loci for all three categories of traits (b); these windows were found on chromosomes I, IV, VII, VIII, IX and XII.Overall, there were 586 windows (23%) that contained candidate loci for at least one category of trait a. Three c factor b. There tegories b. Corresf traits b; these p < 0.0001). These windows appear to be key in determining feeding capabilities and the corresponding prevalence of prey items in the diet; they were primarily located on chromosomes IV, VII, XIII and XX, with 8–14 candidate windows on each of these chromosomes.When we only considered the overlap between candidate windows for diet components and the 18 morphological phenotypes that have a known role in feeding, we found that 85 of the possible 223 regions (38%) were shared ((e)and contained a candidate locus for at least one trait (a). Six of these windows contained candidate loci for adaptation to abiotic factors, 49 windows contained candidate loci for morphological phenotypes and 49 windows contained candidate loci for diet items (c). There was an overlap of 18 of these windows among the different trait categories; two windows on chromosome VIII contained candidate loci for all three trait categories (c). However, the degree of genome-wide overlap between windows with repeated differentiation and windows containing any type of candidate loci was not greater than expected by chance (p > 0.05 in permutation tests for each of the three trait categories). The 40 windows displaying significantly repeatable differentiation (three or more pairs sharing an outlier window) contained candidates for between 1 and 13 individual traits . Interestingly, the four windows differentiated in the most population pairs  that also contained candidate loci were associated with diet components, swimming, feeding and armour traits (see highlighted windows in Eighty-four genomic windows (3.3%) were repeatedly differentiated (shared by two or more pairs) ne trait a. Six ofet items c. There tegories c. Howeve4.FST) in multiple independently derived populations implies that there may be some interesting genes or genetic features located within the region. A limitation of the reduced representation sequencing methods (double-digest RAD) employed by this study is that we are unable to distinguish between patterns of differentiation generated from direct versus linked selection [In this study, we aimed to detect genomic regions with a signature of repeated differentiation across multiple independent pairs of lake–stream stickleback. We then asked whether those regions contained SNPs associated with environmental conditions and morphological divergence. Before discussing our results, we clarify that here we are not looking at parallel evolution in the strict sense, that is, whether the same mutation or allele is used repeatedly Ectodysplasin (Eda) gene, which has been directly shown to underlie the reduction of lateral plating in numerous freshwater populations relative to their marine ancestors [ATP1a1) gene, which mediates salinity tolerance [Across biological systems, the degree to which differentiation is repeatable appears to be highly variable and differs depending on the biological level (gene versus phenotype versus genomic region) being considered (reviewed by ). Previoncestors . A patteolerance ,36.Engraulis encrasicolus) [Littorina saxatilis) where 3–13% of outliers are shared between at least two of the three surveyed islands [Recent work characterizing genome-wide patterns of repeatability in sticklebacks has generally found that a small to moderate fraction of highly differentiated loci/regions evolve in a repeatable fashion ,37–39. A islands . In gene islands . Both ge islands . Howeveret al. [et al. [It is possible that the repeatability we observe for some genomic regions (upwards of five population pairs with shared outliers) is a signature of adaptation from standing genetic variation, as is often the case when marine sticklebacks colonize freshwater habitats ,39–41. Ret al.  also sho [et al.  have pre(b)Many of the candidate trait windows identified in this study correspond to genomic regions previously identified as QTL for morphological, behavioural and reproductive traits (reviewed by ). In parIt is possible that the non-random distribution of candidate trait windows is a signature of either pleiotropy or tight physical linkage. In many systems, the tight clustering of multiple loci affecting different traits  involved in adaptive divergence has been observed . Cluster(c)r = 0.15, F1,82 = 2.88, p = 0.09). This suggests that loci are to some degree pleiotropic (i.e. influencing the variance of more than one trait) and may be more frequently used during adaptation to a common agent (or agents) of selection. However, such a pattern could also be generated if different agents of selection are acting in different population pairs on independent traits (and loci) that map to the same windows. Future fine-scale mapping and selection studies will be required to disentangle these alternative mechanisms.For the candidate trait windows that were repeatedly differentiated, there was a positive, but marginally non-significant, correlation between the magnitude of repeated differentiation (number of population pairs sharing an outlier window) and the number of environmental or phenotypic traits that mapped to that window  mapping to repeatedly differentiated genomic regions (14%) than candidate regions for abiotic factors (9%). This pattern may suggest biotic factors play a relatively greater role than abiotic factors in shaping patterns of repeated differentiation in this system. However, this pattern may also be due in part to greater variance in abiotic factors between the watersheds than between the lake and stream habitats within a watershed .Despite the constraints of our methods discussed above, the repeated genomic differentiation found in this study cannot be explained solely by the variety of ecological factors and morphological traits that we considered here. This is not surprising as we know that selection in threespine stickleback is generally multifarious, involving a multitude of biotic factors such as competition , predati


During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays , the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis.We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K.Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level. High-density single nucleotide polymorphism (SNP) genotyping has driven rapid improvements in rates of genetic progress in livestock populations –3. To inLow- to medium-density common variant SNP assays are widely used for genetic evaluation in both beef and dairy cattle. Since the development of the BovineSNP50 (SNP50) BeadChip   in 2008 To use these large datasets to their full potential, the accuracy of imputation must be maximized. The most accurate imputation software packages for cattle , 25 wereInitially, SNP assays for cattle were designed with common, evenly spaced markers that would presumably be in LD with causal variants . WhereasThis study focuses on maximizing imputation accuracy from several commercially available low-density common variant SNP genotyping assays to a set of high-density variants (850 K), many of which are rare and potentially functional. We test the effectiveness of a large, multi-breed composite reference panel for imputation in several beef and dairy cattle populations that are genotyped with several commercially available common variant SNP genotyping assays. We use both well-established and novel measures of imputation accuracy to categorize precisely the causes of imputation errors. These metrics provide insights for interpretation of imputation performance and define situations in which researchers should be cautious when using imputed variants. In addition, we explore how the starting chip density impacts the accuracy of imputation to 850 K variants. Finally, we introduce and describe the design of the GGP-F250 functional genotyping assay. The GGP-F250 is a tool not only for genotyping numerous functional variants but also for increasing the imputation accuracy of rare variants.To identify the best practices for achieving imputation accuracies that approach the error rates of modern SNP genotyping arrays, we compared the impact of altering reference panels and marker numbers in the starting assay when imputing genotypes to the level of the combined Illumina BovineHD  and GeneSeek Genomic Profiler F250  referred to herein as the HD and F250 assays, respectively. The HD assay contains 777,962 evenly spaced variants that have relatively high MAF across many breeds of cattle common to North America. The F250 assay contains 227,234 markers, of which 31,392 are present on the HD assay and included in the assay design for use in imputation, and another 195,842 potentially functional markers, many of which are rare (MAF < 0.1). Due to these rare alleles, the MAF distribution for the F250 assay is more similar to the site frequency spectrum of the bovine genome  panel.AA, AB, and BB genotypes, respectively), both Pearson’s correlation coefficient (r) and count-based metrics could be used to evaluate the imputation accuracy for each variant and individual. Pearson’s correlation coefficients for individuals were calculated in two ways. First using unscaled, raw genotype values and then using genotype values centered by the variant’s MAF in the entire set of research assays . To center genotypes, twice the variant’s MAF was subtracted from the raw genotype value. For both methods, r values were calculated and compared.Imputation accuracy was measured for both individuals and variants within each imputation scenario. By coding alternate allele counts as 0, 1, and 2  measures of accuracy are valuable, they overestimate the quality of imputation at low MAF and are ambiguous as to the nature of the error that created an incorrectly imputed genotype. Rather than concordance rate, an imputation quality score (IQS)  was calcAA or BB imputed as AB), false homozygotes (genotyped AB imputed as AA or BB) or completely discordant (BB imputed as AA or vice versa). These more detailed error descriptions, in conjunction with MAF, genome position, and assay-of-origin information, allow for a detailed analysis of how these factors influence imputation accuracy to 850 K in each scenario.In addition to the IQS, the exact nature of each error was catalogued and tallied for each individual and variant. This allowed the errors to be categorized as either false heterozygotes  as described in  using thThe MAF spectrum of the SNPs on the HD and F250 assays for the individuals that composed our reference panel is shown in Fig. r)] with the imputation quality score (IQS), a metric that has been used in several human studies, but not in livestock ) Fig. . Gelbvier = 0.874, range = ), although their genetic similarity values were comparable to those for the Hereford and Jersey samples. The low imputation accuracy for the Romagnola breed likely stems from the low representation of its haplotypes within the CR panel (15 HD and 8 F250 genotypes). We observed the opposite for Nelore; although the Nelore individuals were distantly related to the members of the CR panel as a whole, the larger number of samples contained in the reference panel (858 HD and 7 F250 genotypes) resulted in accurate imputation . This was also observed for Gir, which is as diverged from taurines as the Nelore breed, but its reduced imputation accuracy was due to the presence of only 13 HD and nine F250 individuals in the CR panel. The average genetic relationship with individuals of the same breed in the CR panel had varying magnitudes of correlation with individual imputation accuracies, depending on the breed , negatively correlated in Gelbvieh (r = − 0.138), and moderately correlated in Angus and Holstein .We used a GRM that was created with observed genotypes from all reference and test individuals to determine if an individual’s genetic similarity to individuals in the CR panel was related to its imputation accuracy Fig. . There wTo test the impact of the starting assay number of markers on 850 K imputation accuracy, we used the TAUR dataset masked to represent the contents in markers of five common commercial assays. Each successive increase in assay marker number led to increases in imputation accuracy both overall and for low-MAF variants Table . The lar−5) assays, but not between the GGP-LD and SNP50 (p = 0.1486) assays  between observed and imputed genotypes. Although both statistics make sense at the level of individuals, their ability to identify markers for which genotypes are poorly imputed is not optimal, particularly for markers with a low MAF. Because our dataset contains a large proportion of rare variants (24.30% markers with MAF < 0.1), a statistic that more robustly represents the quality of imputation is essential. Using the IQS statistic, we show that r and especially concordance rate, overestimate the accuracy of imputation for low-MAF variants [r, which requires that markers be variable in both the true and imputed datasets, IQS can be calculated for variants that are not variable in either the observed or imputed datasets. This provides a more complete view of the imputation accuracy at each locus, particularly for those with an extremely low MAF. This information is lost when using r, and imputation accuracies are grossly inflated if measured using concordance rate. That said, r and IQS are highly correlated (r = 0.9892) and provide equally useful diagnostics for imputation quality at most sites. We note that while we treated the HD and F250 genotype calls as being correct, a ~ 0.2% error rate is associated with these genotyping platforms [Most studies on imputation accuracy in livestock populations have used two methods to assess the adequacy of imputation: concordance rate, i.e. the proportion of correctly imputed genotypes, and the Pearson correlation  requires that allele frequencies at two markers be similar. In the absence of selection, rare variants are assumed to have been recently derived, and thus are likely in LD with other recently derived rare alleles [The F250 assay was designed to query genotypes at a large number of rare, potentially functional variants and is very gene-centric (i.e. they are not evenly spaced). Common variants were also included in the F250 assay design to allow for imputation and genomic prediction applications. The rare variants present on the F250 assay are important in the context of this work for two reasons. First, imputing an additional ~ 170,000 variants at the population level will increase researchers’ ability to refine GWAS signals and identify putative QTN due to increased marker density within QTL regions. Second, because the variants are very gene-centric, it is anticipated that the accuracy of imputation to the whole-genome sequence level will be improved within genic regions. The inclusion of rare variants will likely increase the imputation accuracy of other rare variants that are not directly assayed, as strong LD  that were consistent with the error rates of the genotyping assays  genotypes, with the largest gains coming from modest increases in the numbers of individuals from the less well-represented breeds. Imputation accuracies for the breeds that are adequately represented in the multi-breed composite-reference panel when the starting assay comprises at least 50 K SNPs should approach accuracies of 1.0 minus the genotyping assay error rate. We anticipate that the CR panel presented here will serve as a foundation reference panel, on which the global cattle community can build to further increase the accuracy of genotype imputation.Additional file 1. Additional information describing the design of the GGP-F250 assay [Table S1. Number of variants present on the GGP-F250 assay for each of the design waves. Description: The wave refers to the order in which candidate variants were selected to be added to the manifest in order to use 250 K beads. Designed: number of variants present in the manifest submitted for synthesis, Final Manifest: number of variants in the final delivered manifest, Failed Synthesis: number of variants that failed oligo synthesis, Filtered: number of variants that passed filtering based on automated and/or manual clustering using 18,684 individuals, Final List: number of variants in the final marker list that were used to produce genotypes, Variable: number of variants with at least one alternate allele observed in 18,684 individuals. Table S2. Description of abbreviations used in Table S1 as filtering criteria. Table S3. Animals by breed used to develop the Illumina cluster file for genotyping. Removed denotes samples that were excluded from clustering due to low call rate. Table S4. Genotype reproducibility for samples genotyped twice. Variation in the total number of compared genotypes is due to the individual sample call rates on each assay.50 assay , 46–50. Additional file 2: Figure S1. Schematic representation of genotype masking for imputation testing.Additional file 3: Table S5. Shared variants between analyzed assays. Counts of shared, unfiltered markers between assays used in this analysis. Table S6. Outlier samples identified as Angus and their CRUMBLER-estimated breed composition. Bold values represent the largest values that sum to at least 75% of an individual’s total breed composition. The percentage of individuals from each breed with HD genotypes in the CR panel is indicated.Additional file 4: Figure S2. Schematic representation of “one-round” vs. “two-round” imputation. Description: Dotted lines represent imputation. In “one-round” imputation (a), HD and F250 reference samples are cross-imputed to create a partially imputed composite reference panel (1). This is followed by a single round of imputation of low-density genotypes using the CR panel (2). For “two-round” imputation (b), two rounds of imputation occur: first from low-density to HD (1) and then from HD to 850 K (2).Additional file 5: Figure S3. Imputation quality metrics when using breed-specific (green) and composite (purple) reference panels for 850 K imputation in the GEL dataset across the entire MAF spectrum (a), and at low MAF (b). Points are individual variants.Additional file 6: Figure S4. Impact of genetic similarity to the reference on imputation accuracy. Description: Genetic similarity is the mean genomic relationship between testing individual and reference individuals with > 50% ancestry of the same breed. Gelbvieh testing individuals (a) are colored by the change in r when using CR versus the BR. (b–d) show r vs. genetic similarity for Angus, Holstein, and Brahman/Nelore respectively.


The limited studies addressing the estimation of genetic parameters for ram semen traits in different breeds show a wide variation, highlighting the importance of studying these traits for individual breeds. Therefore, this work aimed to estimate genetic parameters for traits related to semen production and quality in five dairy sheep breeds. For that, ejaculates of rams from Assaf, Churra, Latxa Cara Negra, Latxa Cara Rubia, and Manchega breeds were analyzed. Estimates of the genetic covariance structure were obtained with multiple-trait animal models using the average information REML (restricted maximum likelihood) method in the BLUPF90 family of programs. Repeatability estimates for all the traits were also calculated. Heritability estimates were of low to moderate magnitude, although the estimates differed among the breeds. The estimated genetic correlations among the three semen traits showed adequate precision only in the Manchega (MAN) breed. The heritability estimates reported here suggest that improvement of these traits may be achieved by genetic selection.This work aimed to estimate genetic parameters for traits related to semen production and quality in Spanish dairy sheep breeds. For that, ejaculates of rams from Assaf, Churra, Latxa Cara Negra, Latxa Cara Rubia, and Manchega breeds were analyzed to measure volume, semen concentration, and motility. Estimates of variance components were obtained with multiple-trait animal models using the average information REML method in the BLUPF90 family of programs. Repeatability estimates for all the traits were also calculated, with values ranging from 0.077 to 0.304 for the motility and the semen concentration traits, respectively. Heritability estimates were of low to moderate magnitude, ranging from 0.014 (motility in Latxa Cara Rubia) to 0.198 (volume in Churra), although the estimates differed among the breeds. The estimated genetic correlations among the three semen traits showed adequate precision only in the MAN breed. The heritability estimates for the semen traits reported in the present paper suggest an adequate response to selection. The practical extension of these results to the other breeds studied here will be secondary to the estimation of more reliable genetic correlations in these breeds.  Of the countries in the European Union, Spain has the second-highest number of dairy sheep, with a population of up to 16 million in 2018  because For all the above-mentioned breeds, the genetic improvement for milk production is performed by the corresponding breeder association through official breeding programs, in which artificial insemination (AI) becomes a fundamental tool of progeny testing. AI plays a significant role because it is directly related to fertility and is the primary reproductive tool used for genetic improvement by introducing genes from superior sires into flocks . Hence, In relation to AI, it is important to study the characteristics of semen production and semen quality. The number of doses produced per ram ejaculate depends on the volume, sperm concentration, and motility of the sperm, which are mainly affected by environmental and genetic factors . To increase the efficiency of the dairy sheep AI centers, studies focused on the assessment of the environmental factors affecting semen production and the estimation of genetic parameters for seminal production and quality traits are required. In cattle, different authors have reported moderate heritabilities for some semen traits ,7, suggeIn sheep, the limited studies addressing the estimation of genetic parameters for ram semen traits in different breeds show a wide variation ,9,10, hiGiven these considerations, the present study aimed to estimate genetic parameters for traits related to semen production and quality in Spanish dairy sheep breeds. For this purpose, four local breeds  and one foreign highly productive breed (Assaf) were analyzed. Although the analyses here presented have been performed individually for each breed, this study provides an opportunity to assess the genetic parameters for semen traits across different breeds.Three AI centers provided records for semen production and quality traits measured in five Spanish dairy sheep breeds: Ovigen  for the Churra (CHU) and Assaf (ASS) breeds; Ardiekin, S.L. for the Latxa Cara Negra (LCN) and Latxa Cara Rubia (LCR) breeds; and Cersyra  for the Manchega (MAN) breed. According to the breeding program of the different breeds under study, a certain number of young males  are handled in the AI center every year. After a training process for semen collection, young males that do not show mounting problems and are free from certain diseases are used as semen donors.6 spermatozoa per mL), and mass motility (MOT), which was assessed subjectively by examining the undiluted unstained semen sample under a microscope according to a wave motion continuous scale, from 0 (no motion) to 5 (numerous rapid and vigorous waves). The semen samples analyzed in this study were collected from the five dairy sheep breeds between 2003 and 2019 and belonged to a total of 3289 rams distributed among the five considered breeds as follows: 424 from the ASS rams, 497 CHU rams, 685 LCN rams, 655 LCR rams, and 1228 MAN rams.In each AI center, measurements of the traits of interest were obtained during the routine semen collection by artificial vagina and preparation of doses for AI. All the ejaculates were obtained by the same team of collectors at each AI center and a pool of 1  to 2 successive ejaculates obtained over a 2–5 min period  was evaluated immediately after collection. For each pool, the total volume (VOL) was measured using a graduated collection tube (mL). After that, a sample of each pool was used to measure two other traits: semen concentration (SC), using a standard spectrophotometer  is the minimum quality threshold that AI centers usually use to consider an ejaculate suitable for insemination. After data filtering, the number of ejaculates available for each of the studied breeds, and used for subsequent analyses, were 19,938, 13,789, 16,342, 15,086, and 31,637 for ASS, CHU, LCN, LCR and MAN respectively. These records corresponded to the following number of rams per breed: 424 ASS, 497 CHU, 685 LCN, 653 LCR, and 1228 MAN for the first mount . LikewisFinally, the pedigree information was collected from different breed associations. All the known generations were used, generating a total coancestry matrix of 1969, 3902, 3866, 3089, and 3202 individuals in the ASS, CHU, LCN, LCR, and MAN breeds, respectively.p < 0.001) for all the traits and breeds   were analyzed individually for each breed. First, an initial general linear model (GLM) analysis was performed with R software version 3.6.1  to evalu breeds) , all of ijklmnY are the individual trait values for VOL, SC, and MOT; Fi is the fixed effect of the flock of birth ; jSY is the fixed combination effect of the season  and was classified as follows: class 1 for rams from 10 to 24 months, class 2 for rams from 25 to 36 months, class 3 for rams from 37 to 48, class 4 for rams from 49 to 60, and class 5 for rams from 61 months onwards; lJ is the fixed effect of the number of consecutive mounts with 2 levels in the ASS, CHU, LCN, and LCR breeds. For the MAN breed, as the rams usually mounted only once a day, the lJ effect included in the model referred to the mounting regime or interval between consecutive mounts expressed in days (from 1 to 8); mRPE is the random permanent environmental effect ; na is the random additive genetic effects of the animal ; and ijklmnr is the random residual effect.The estimates of the (co)variance components, heritabilities, and genetic correlations were obtained with multiple-trait animal models using the average information REML (restricted maximum likelihood) method by the BLUPF90 family of programs . The datl et al.  and yeara2σ and RPE2σ are the variance estimates for the additive genetic and random permanent environmental effects, respectively, and p2σ is the phenotypic variance described as the sum of the additive genetic, random permanent environmental and residual variance effects.The variability in the measurements made on the same subject can be ascribed only to errors due to the measurement process itself. Hence, considering the different components of the phenotypic variance previously estimated through the multiple-trait model implemented for each breed, repeatability estimates were calculated for the different traits as follows:The descriptive statistics of the semen traits for the five breeds are shown in The estimates of the genetic, permanent environmental, and residual variances and repeatability are presented in Regarding the genetic parameters presented in Among the genetic correlations estimated in this work , only thIn the ASS and CHU breeds, moderate and positive phenotypic correlations were found for the SC-VOL and SC-MOT trait pairs (with a range from 0.16 to 0.23), with close to zero negative estimates between MOT and VOL; in the LCN and LCR breeds, the MOT-SC phenotypic correlation was positive , whereas all the phenotypic correlations in MAN were low .Dairy sheep are traditionally reared in Mediterranean areas on a wide range of production systems. In the last 30 years, dairy sheep systems have kept their dual purpose and have maintained an income characterized by a 65–75% contribution from milk and 25–35% from meat. The efficiency of selection for milk traits is now whetting interest in new breeding goals, which are relevant to satisfying consumer demand with other products, such as quality-labeled cheeses, and “healthy” food products [Milk yield is one of the most important economic traits in dairy sheep, but in practice (for genetic improvement), fertility is also a critical trait for the economic efficiency of dairy sheep flocks . AccordiThe dataset compiled through this study has provided the opportunity to analyze different seminal traits rarely studied in the ovine species and to compare phenotypic and genetic parameters among five of the most important Spanish dairy sheep breeds. Traits such as volume, sperm concentration, and sperm motility are relevant traits to analyze in the ejaculate of rams.In general, the mean values reported here for these traits were very similar to those obtained in other sheep breeds ,18. The Seminal traits are affected by environmental, management, and genetic effects and physiological status . In our The effects of flock and the interaction of collection season–year were significant for all the traits and breeds. Our results are in agreement with those obtained by Anel et al.  in the CThe highest additive genetic variances were found for the VOL and SC traits in the ASS breed and the MOT trait in the CHU breed. The large additive genetic variances observed in ASS can be explained by the fact that this breed has been recently created by the absorption of several Spanish breeds, the large current census of the Assaf population and its recent breeding program (initiated in 2005), compared with that of the other studied breeds.The coefficients of repeatability describing the degree of similarity between consecutive measurements affected by random environmental factors were also estimated. The SC trait showed the highest repeatability value in all the breeds studied. The repeatability values found in this work were lower than those estimated in other sheep breeds  and boviThe heritability estimates obtained in this study are similar to those described in the literature. For example, David et al.  reportedUnderstanding the relationship between the different seminal traits is very informative for the efficient development of selection strategies aimed at the genetic improvement of the considered target traits. Generally, similar values of phenotypic correlations to those estimated here for VOL, SC, and MOT were found in the literature . The lowThis influence of non-controllable environmental factors might explain the high standard error of the genetic correlations reported for most of the breeds in this study. Only for the MAN breed did genetic correlation estimates among the semen traits show adequate accuracy (low SE). This may be due to the higher number of animals analyzed in the MAN breed (1200 vs. 500 in the other breeds) and to a more suitable family structure for the determination of genetic relationships. These are key factors influencing the precision of the genetic correlation estimates . Hence, In the present study, genetic parameters for three semen traits were estimated in Spanish dairy sheep breeds. The heritability estimates reported were, in general, low to moderate, suggesting that improvement of these traits may be achieved by genetic selection. The genetic correlations among the three semen traits showed adequate precision in the MAN breed. Considering the difficulties of fully understanding the genetic relationships between semen quality traits based on existing datasets (prospective studies), future efforts should be focused on experiments specifically designed to maximize control of the environmental factors influencing these traits.


Based on sequence alignment of the markers to the reference genome of bread wheat, we identified 14 putative candidate genes involved in enzymes, hormone-response, and transcription factors. The GWAS in durum wheat and a previous quantitative trait locus (QTL) analysis in bread wheat identified a consensus QTL locus.4B.1 conferring drought tolerance, which was further scanned for the presence of potential candidate genes. A haplotype analysis of this region revealed that two minor haplotypes were associated with both drought tolerance and reduced plant stature, thought to be the effect of linkage with the semi-dwarfing gene Rht-B1. Haplotype variants in the key chromosome 4B region were informative regarding evolutionary divergence among durum, emmer and bread wheat. Over all, the data are relevant in the context of durum wheat improvement and the isolation of genes underlying variation in some important quantitative traits.Uncovering the genetic basis of key agronomic traits, and particularly of drought tolerance, addresses an important priority for durum wheat improvement. Here, a genome-wide association study (GWAS) in 493 durum wheat accessions representing a worldwide collection was employed to address the genetic basis of 17 agronomically important traits and a drought wilting score. Using a linear mixed model with 4 inferred subpopulations and a kinship matrix, we identified 90 marker-trait-associations (MTAs) defined by 78 markers. These markers could be merged into 44 genomic loci by linkage disequilibrium ( Triticum aestivum ssp. aestivum) or tetraploid durum wheat (Triticum turgidum ssp. durum). The latter shares two of the former’s three sub-genomes, both of which evolved from wild emmer wheat (T. turgidum ssp. dicoccoides) by way of cultivated emmer wheat . Drought stress can adversely affect grain yield of the wheat crop at various growth stages from germination to grain filling -based genotypic data set to conduct a GWAS, focusing on DT. The analysis has provided valuable information to understand the major genetic components of important agricultural traits and a more detailed haplotype and candidate gene of DT in durum.2. This panel has previously been exploited to identify the genetic basis of resistance against certain foliar pathogens  were selected from the durum diversity panel and, for each population, 120 F2 plants coupled with their F3 progenies were sampled for linkage analysis.The line information and genotype data of the 493 durum wheat entries were reserved in the T3/Wheat databaseathogens . In addiathogens  and a brathogens  diversitathogens . Two F2 The durum wheat panel was evaluated for the following 18 morphological traits: plant height (PH), heading date (HD), plant waxiness (WX), glume pubescence (GP), glume color (GC), flag leaf length (FLL), flag leaf width (FLW), flag leaf length/width ratio (FLR), flag leaf angle (FLA), panicle length (PL), spikelet number per spike (SN), panicle compactness (PC), single kernel weight (KW), grain length (GL), grain width (GW), grain length/width ratio (GR), grain projection area (GA) and factor form density (FFD).3). The SDU field experiment (planting date: March 02) applied a randomized block design with four replicates  with no irrigation during the crop growing season. The annual average rainfall in Jinan was 521.20 mm. For the two F2 populations, their F3 families were planted at 2015 and 2016 growth season at Jinan using a randomized block design with three replicates.Of the 18 traits, 13  were measured in 2012 and 2013 at North Dakota State University  research site at Prosper , and in 2014 at Shandong University research site . Fielded trails at the NDSU research site in 2012 (planting date: April 12) and 2013 (planting date: May 08) applied an unduplicated augmented design with four checks in each block  and no irrigation during the crop growing season. Average annual rainfall and snowfall in Fargo are documented as 573.53 and 914.40 mm, respectively . Water was withheld once the plants had reached the three-leaf stage. The plants were re-watered until approximately 2/3 entries were wilted. Two days after re-watering, a drought wilting score was assigned using a 1–6 scale, where score 1 represented a completely wilted plant, 2 a plant in which the first three leaves were wilted, 3 a plant in which only the first two leaves were fully wilted and the third leaf was partially wilted, 4 a plant in which the first two leaves were fully wilted and the third leaf was not wilted, 5 a plant in which the first leaf was fully wilted, the second partially wilted and the third not wilted, and 6 a plant in which only the first leaf was wilted.5. The broad sense heritability was calculated as the ratio of total genetic variance to total phenotypic variance, and the best linear estimators were used for GWAS. The consensus linkage map of tetraploid wheat (6) were applied to assign a genomic location to each SNP marker. Pairwise linkage disequilibrium  was calculated using the package “genetics” in R (see text footnote 5). The program “Structure7” was used to assign ancestry proportion for each durum wheat entry based on a set of 366 unlinked markers . The admixture model was fitted by varying the K parameter from 2 to 10. Marker-trait associations (MTAs) between each of the 18 morphological traits and DT and the set of informative SNPs were detected by fitting the mixed linear model (MLM) implemented in TASSEL software  = 3.53 equal to a false discovery rate (FDR) = 0.05. At the same time, a two-step approaches combining single- and multiple-locus LMM model was used to claim significant MTA  which should be considered as covariates in GWAS analysis. The population structure of the durum wheat panel based on the full set of informative SNPs has been previously described . Due to  of <0.7 .r2 = 0.73 with leaf water content, 0.36 with chlorophyll content, 0.46 with rolling index, and 0.64 with survival rate), and moderately correlated with some of the yield-related DT index . Therefore, this value could be a reflection to durum DT especially for early developmental stage.For assessment of drought tolerance, we first compared the drought wilting score with other seedling drought syndromes and DT index of yield components . The droThe durum wheat panel displayed a broad range of phenotype with respect to each of the traits . The broWith respect to seven of the agronomic traits, the elite breeding lines and/or cultivars differed as a group from the landraces , reflectr2 > 0.2), 10 of which were relevant to more than one trait. The number of loci detected and the total PVE highly varied, where more loci were associated with SN (12), HD (6), DT (5), FLL (5), GL (5) and total PVE from 16 to 39%; and other traits were less associated with less than five loci and total PVE from 3 to 14%.The GWAS results are summarized in Rht-B1  in the locus.4B.1 region (IWA4854 exceeded the chosen significance threshold (P < 0.001).A set of markers used to define nine regions accounted for variation in more than one of the various grain morphology traits, while the co-location of MTAs associated with two or more unrelated traits also occurred . For exa1 region . The allRht-1B for locus.4B.1 (PH) and Ppd-A1 for locus.2A.5 (HD). In addition, three MTAs, include IWA2023 on 3A for SN, GW, and GR, IWA2816 on 4A for SN, and IWA4363 on 7A for SN, GL, and GA, were validated through linkage mapping in two F2:3 families with the parents selected from the association panel , three tandemly arranged genes TraesCS4B01G072100, TraesCS4B01G072200, and TraesCS4B01G072300 (WD40 family protein), TraesCS4B01G076400 (Adenine/guanine permease), and TraesCS4B01G077900 (gibberellin-regulated protein 1). Among the six genes, the 4D homeolog of the three WD40 protein coding genes was shown by WD40 genes might be candidates for DT of locus.4B.1. Sequence analysis of the three WD40-coding genes indicated a C-T SNP present in the coding sequence of TraesCS4B01G072200, which lead to premature stop of translation. In addition, a mixed linear model was applied on this variation by taking the top markers from other DT-related loci, population structure and kinship in consideration, the result showed that the premature stop variation was significantly associated with the drought sensitive phenotype in the durum panel  in durum wheat, with hap9 carried by 84% of entries in the durum wheat panel , S7. WitA similar analysis was carried out in the emmer wheat and the bread wheat panel, respectively. In the emmer wheat panel, the most common haplotypes were hap9, hap7 and hap5, with hap8 present in just two entries. The bread wheat panel was dominated by hap1 (72%) and hap6 (15%), and added a further six minor haplotypes (hap10 through hap15) , S7. A hIn this study, we identified 44 chromosome loci associated with 17 agronomic traits and a drought wilting score using GWAS on a durum wheat panel. For a given trait, the total PVEs varied from 16 to 39%, illustrating that most of the traits were under polygenic control. Similarly, previous GWAS and QTL analysis has shown that most of the traits investigated here, including PH, HD, WX, flag leaf-, spike- and grain-associated traits are controlled by multiple genes, each contributing only modestly to the overall genotypic variance . SurprisRht-B1  and photoperiod (Ppd genes), in addition to a number of genes promoting earliness per se (Eps genes). A GWAS study has identified the dependence of bread wheat’s flowering time on the identity of the Ppd-D1 allele present and on the gene copy number at Ppd-B1, with a number of minor effect loci also contributing , 5B (WX), 4A (FLA), 5B (PC), 3A and 6A (grain morphology traits), and 3A and 4B (DT). The discovery of a large number of non-coincident MTAs may simply reflect the extent of the divergence between durum wheat and bread wheat, since the bulk of the genetic analyses reported in the literature relate to bread, rather than to durum wheat.A number of the genomic sites associated with a trait coincided with the location of previously discovered QTL and/or major genes . The sitRht-B1  and Rht9B1  gene, was a primary candidate for the PH-related locus.2A.13. Its close homologs in maize (ZmGA3ox2/qPH3.1) and rice (OsGA3ox2/d18) have been verified to function for PH (TraesCS2A01G269700 (flowering promoting factor-like 1). Previous reports in Arabidopsis and rice have highlighted the critical role of cytokinin oxidase (CKXs) to promote flowering or seed number  are related to the DT phenotype in durum wheat . Both DTage drag . HoweverWD40 genes under locus.4B.1 are suggested as primary candidate genes for DT according to three clues. First, each of the genes was significantly up-regulated by exposure to drought; second, their 4D homeolog is known to confer tolerance to osmotic stress (WD40 family have been identified as functioning in the plant stress response (WD40 (TraesCS4B01G072200) gene was found to be significantly associated with reduced DT (TaWD40s was much depressed when combined with heat stress (WD40 genes are associated with DT QTL in wheat and are worth further analysis.Three tandemly repeated c stress ; and thiresponse . In thisduced DT . Furthert stress , revealiIn this manuscript, we presented an insight into the worldwide genotypic and phenotypic diversity in durum wheat, as well as the genetic control for major agronomic traits and drought resistance. The highly phenotypic variation and correlation were shown to be controlled by a large number of genomic loci with multi-polygenic and pleiotropic effect. A few candidate genes were selected by comparable genomic analysis plus their expression pattern and genomic variation. In addition, the haplotype investigation in a DT-related loci locus.4B.1 showed potential application in breeding DT durum cultivars, as well as a complex evolutionary history and gene candidature. All these data are relevant in the context of durum wheat improvement and the isolation of genes underlying variation in some important quantitative traits.All datasets for this study are included in the manuscript and the This study was designed by GX, SL, and SW. The evaluation of traits was conducted by SW, SL, and SX. The emmer wheat panel was collected by SX. The genotypic evaluation of the durum wheat and emmer wheat panels was carried out by SC and QS. Data were analyzed by SW. The manuscript was drafted by SW and SL, and revised by GX. All authors have reviewed and approved the final version of 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.


Brassica napus vegetative tissues is described. This article includes the phenotypic data of leaf and root glucosinolate (GSL) profiles across a diversity panel of 288 B. napus genotypes, as well as information on population structure and levels of GSLs grouped by crop types. Moreover, data on genetic associations of single nucleotide polymorphism (SNP) markers and gene expression markers (GEMs) for the major GSL types are presented in detail, while Manhattan plots and QQ plots for the associations of individual GSLs are also included. Root genetic association are supported by differential expression analysis generated from root RNA-seq. For further interpretation and details, please see the related research article entitled ‘Genetic architecture of glucosinolate variation in Brassica napus’ .The transcriptome-based GWAS approach, Associative Transcriptomics (AT), which was employed to uncover the genetic basis controlling quantitative variation of glucosinolates in  The relenotypes A. The seenotypes B with thenotypes C. Full denotypes  with disenotypes  and analenotypes .Fig. 1Po10P value, passing both false discovery rate (FDR) threshold at 5% and threshold for Bonferroni significance of 0.05 suggested that the surrounding genomic region has a strong association with the trait. To assess how well the model accounts for population structure and familial relatedness, quantile-quantile (QQ) plots from SNP association analyses have been generated (These phenotypic data were used to generate association data identifying single nucleotide polymorphism (SNP) markers and gene expression markers (GEMs) in transcriptome-based genome wide association studies, Associative Transcriptomics (AT) enerated . AppendiB. napus leaves. SNP-based associations of leaf aliphatic GSL revealed strong associations with markers in the defined regions of chromosome A2, A9, C2, C7 and C9 (B. napus (HAG1 (AT5G61420), a transcription factor that positively regulated aliphatic GSL biosynthesis, have been discovered within all of these SNP-based associated loci  was performed between accessions which differs in root aromatic GSLs but are low in aliphatic GSLs (In osome A3 . As descs region . To supps region . Within d-change  with higtic GSLs . This antic GSLs .GTR1 (AT3G47960) and GTR2 (AT5G62680), was conducted to investigate the role of transporters on GSL accumulation pattern  diversity population A subset of 288 2.2As per the recommended quantification method previously tested 2O and 30% (v/v) acetronitile, as detailed in Desulfoglucosinolates were separated by HPLC  on a reverse phase C18 column at 30 °C , 150 mm × 4.6 mm) with mobile phase solutions consisting of 100% diH2.3Statistical analyses were carried out with R statistical software 2.4Plant growth conditions, sampling of material, RNA extraction and Illumina transcriptome sequencing was carried out and described previously in 2.5An overview of Associative Transcriptomics (AT) analysis is shown in 2), constant, F-value and significance P-values were produced. When genomic inflation factor (λ) was >1, genomic control with P-value adjustment AT was performed using R 2.6Differential gene expression was analyzed using root transcriptome sequences from four biological replicates (i.e. using root RNA-seq from 4 separate plants of each plant type). The methods in Bioconductor package EdgeR