A favourable genetic structure and diversity of behavioural features highlights the potential of dogs for studying the genetic architecture of behaviour traits. However, behaviours are complex traits, which have been shown to be influenced by numerous genetic and non-genetic factors, complicating their analysis. In this study, the genetic contribution to behaviour variation in German Shepherd dogs (GSDs) was analysed using genomic approaches. GSDs were phenotyped for behaviour traits using the established Canine Behavioural Assessment and Research Questionnaire (C-BARQ). Genome-wide association study (GWAS) and regional heritability mapping (RHM) approaches were employed to identify associations between behaviour traits and genetic variants, while accounting for relevant non-genetic factors. By combining these complementary methods we endeavoured to increase the power to detect loci with small effects. Several behavioural traits exhibited moderate heritabilities, with the highest identified for Human-directed playfulness, a trait characterised by positive interactions with humans. We identified several genomic regions associated with one or more of the analysed behaviour traits. Some candidate genes located in these regions were previously linked to behavioural disorders in humans, suggesting a new context for their influence on behaviour characteristics. Overall, the results support dogs as a valuable resource to dissect the genetic architecture of behaviour traits and also highlight the value of focusing on a single breed in order to control for background genetic effects and thus avoid limitations of between-breed analyses. Canis familiaris) is a useful animal model for identifying the genetic basis of various phenotypes  still occur that affect the welfare of dogs, owners and the public . The GSDs in this study represent unique samples of pet dogs from the United Kingdom (UK) and from a breeding programme of the Swedish Armed Forces (SAF) specifically selected for behaviour traits. By focusing on a single breed and controlling for background genetic structure that might be a consequence of analysing two populations, while also accounting for relevant environmental factors, the limitations of between-breed analyses and confounding with non-genetic effects were minimised. Moreover, different genetic approaches were applied to explore the complex nature of behaviour traits. In addition to employing a genome-wide association study (GWAS) approach based on single SNPs, a regional heritability mapping (RHM) approach was also conducted, which has been shown to perform better in the identification of multiple quantitative trait loci (QTL) with small effects  , its living situation  and its current and past management .Owners of registered UK GSDs that were at least 2 years-old were invited to participate in the study via email by the UK Kennel Club (KC). Participating GSDs from the UK cohort were primarily pet dogs. All GSDs from the Swedish cohort were bred within the breeding programme of the SAF. After a behaviour test at the age of 15–18 months, dogs started training for working with the SAF, Swedish Police, or other authorities or companies, and/or were selected as breeding animals, whereas others were kept as companions . To calculate the behaviour traits, a principal component analysis (PCA) was applied to the data to condense the 95 questions to a smaller number of components . Owing to allelic imbalance that can cause bias in association studies  or blood samples (Swedish cohort) using standard protocols. The genotyping was performed using the Illumina CanineHD Whole-Genome Genotyping BeadChip featuring 172,115 SNPs. When a filter for a sample call rate of > 90% was applied, 745 dogs passed the genotyping quality control. The data set was then checked using sex and relationship information estimated from the genotype data to identify potential sampling errors and four further samples were removed. The final data set included 741 dogs  with sex ratios of 0.8 and 0.7  for UK and Swedish dogs, respectively. SNPs were filtered in GenomeStudio software  for call rate > 98%, reproducibility (GTS) > 0.6 and signal intensity, characterised by AB R mean  > 0.3. Using PLINK version 1.9  was estimated using pedigree and genotype data . For the pedigree-based estimates, all GSDs with behaviour records (n = 1041) were used and the joint pedigree for the phenotyped dogs comprises 24,284 dogs. Heritability was estimated in ASReml . Dogs for which one or more fixed effects were missing were removed from the analysis, such that the number of GSDs included in the analysis varied across behaviour traits  (Table The heritability (ords n = 041 were h2 was tested using a log-likelihood ratio test (LRT) in ASReml  for P < 0.05. The significance of genotype-based estimates was defined by P-values < 0.05 from the LRT within the genome-based restricted maximum likelihood (GREML) analysis performed in GCTA  . The following linear model was fitted in GEMMA  with β as the vector of regression coefficients of the phenotype on the marker genotypes, Z is the incidence matrix for the random additive polygenic effect, a is a vector of random additive polygenic effects with G is the genomic relationship matrix, and ε is a vector of residual errors with I is an identity matrix. The fixed effects comprise the demographic and management factors obtained in the individual final models (S2 Table).A GWAS was performed on the filtered data set of 78,088 SNPs to identify associations between SNPs and behaviour traits based on an additive model. To account for population structure, models with different combinations of factors  were evaluated. Fitting only the cohort and the relationship matrix performed best, as assessed by the genomic inflation factor  between all pairs of SNPs within 10 Mb were calculated across the genome using PLINK version 1.9 , the GWAS results, −log10(P), were plotted within the regions identified by RHM to identify positional candidate genes. The pairwise r2 was calculated between all SNPs in the region and the SNP with highest −log(P) value to describe the pattern of linkage for the region, using PLINK version 1.9  genes harbouring or near identified SNPs (GWAS) and (II) genes located within identified regions (RHM). Regarding (I): to determine the size of the region around identified SNPs that should be scanned for candidate genes, the squared correlation (All genes within the regions described above (I and II) were submitted to Enrichr  shows population structure due to cohort by a clear separation of UK and Swedish dogs based on the first principal component. However, some GSDs overlapped between the cohorts, showing shared ancestry. In contrast to the cohort effect, there were no distinct patterns observable for eigenvectors PC1 and PC2 when considering the GSDs according to their function or coat colour.h2 were found for Human-directed playfulness and Non-social fear using pedigree and genomic approaches, while Stranger-directed interest was only significant for pedigree-based estimates and Chasing only for genomic estimates  and for Non-social fear using genotype data (0.16 ± 0.06). Non-significant heritabilities were estimated for Stranger-directed fear, Excitability, Attachment/ Attention seeking, Dog-directed fear and Touch-sensitivity using estimates from pedigree and genomic data.Heritability estimates for the 13 behaviour traits were calculated using pedigree and genomic data. Moderate and significant P < 6.4E-07) (Table Genome-wide association studies (GWAS) and a regional heritability mapping (RHM) were performed as complementary approaches to identify associations between genetic markers and the 13 behaviour traits  Table .Fig. 1JoThe identified SNPs were distributed over seven of the 38 canine autosomes, with the largest numbers on CFA33 (5) for Attachment/Attention seeking, CFA31 (3) for Dog-directed fear and CFA14 (3) for Stranger-directed interest. The genome-wide associations were found for Attachment/Attention seeking (two adjacent SNPs on CFA33). The greatest number of suggestive SNPs were found for Attachment/ Attention seeking (6), Stranger-directed interest (3) and Dog-directed fear (3).The RHM analysis was performed by testing for associations between 50-SNP sliding windows across the genome (with a 25-SNP overlap between consecutive windows) Fig. . ScanninMost of the SNPs identified by the GWAS overlapped with regions identified by the RHM Tables , only thARNT, PLCH1 and BRWD1) and 30 genes were located within 200 kb of suggestive or genome-wide significant SNPs (Table ENSCAFG00000009706). Gene ontology analysis of the 30 genes revealed that the top enriched biological processes were “polyphosphate metabolic process” , “negative regulation of axon regeneration”  and “regulation of hormone biosynthetic process” .According to the annotation of CanFam3.1, four of the SNPs identified by the GWAS were located within three genes (P) values obtained from the GWAS, gene annotations and local linkage disequilibrium patterns were plotted for these regions to pinpoint the most likely location of positional candidate genes . Overlapping regions, due to the sliding-window approach of the RHM analysis, were combined. There were 60 genes located in these regions  were also located near  or at (Table HIVEP2 and AIG1) were located in identified regions but the −log(P) values for nearby SNPs obtained in the GWAS did not exceed the suggestive threshold . The region on CFA33 with genome-wide significance for Attachment/Attention seeking comprised three unannotated protein-coding genes .To further investigate regions identified by the RHM analysis, −log, “histidine metabolic process”  and “imidazole-containing compound catabolic process” .According to the gene ontology analysis, the GO biological processes significantly enriched by genes located in identified regions Table  were “hiDogs express diverse behaviour phenotypes, some of which appear to be related to traits of other species (including humans), making them useful models for general insights into the genetic architecture of behaviour. However, behaviours are complex traits, which have been shown to be influenced by numerous non-genetic  factors and genetic variants of low to moderate effect Flint , which ch2 are within the range of most previously observed values in dogs .The heritabilities estimated for the 13 behaviour traits using pedigree and genomic approaches ranged from 0 to 0.23. These measures for h2 were Human-directed playfulness, Non-social fear, Stranger-directed interest and Chasing. The observation of the highest h2 across traits for Human-directed playfulness has been also made in a genetic study of 14 different dog breeds  with the aim of detecting novel candidate genes for behaviour and further evaluating genes previously linked to behaviour.ENSCAFG00000009706. According to the iDOG database . However, this gene has not yet been described in other canine association mapping studies.The only SNPs and region with genome-wide significance for the behaviour trait Attachment/Attention seeking point to a region on CFA33 that contains several unannotated protein-coding genes, including AQP4) gene identified by both GWAS and RHM for Attachment/Attention seeking is one of the most abundant molecules in the brain, with many physiological functions . Histidine is a precursor of the neurotransmitter histamine and it has been shown that the histaminergic system affects the central nervous system and thus also alters behaviours, e.g., by affecting fear memory , B3GALT5 (CFA31) and ARNT (CFA17). Two SNPs associated with Dog-directed fear are located within BRWD1. In human GWAS studies, this gene has been associated with cognitive function (Davies et al. B3GALT5, which has been linked to suicide attempts (Perlis et al. ARNT gene. Variation within ARNT has been linked to the severity of autism in humans (Fujisawa et al. Other genes were identified only by the GWAS, including The limited number of genome-wide significant associations found in this study indicates the challenges in the genetic dissection of complex traits like behaviour, which derive from the small effects of genetic variants on phenotypic variation, substantial environmental effects and difficulties in defining clear phenotypes. Although ours is one of the largest genomic studies of dog behaviour so far, it has been shown in human studies that much larger sample sizes are required for robust genetic dissection of complex traits, e.g., height (Visscher et al. Understanding the genetics of dog behaviour and the interaction with non-genetic factors can give general insights into animal and human behaviour and is relevant for animal welfare, e.g., to identify risk factors for problem behaviours. Our results support the hypothesis that behaviours are complex traits, influenced by multiple genetic and non-genetic factors, emphasising the need for large datasets incorporating both genetic and non-genetic information in future studies of dog behaviour. Furthermore, it is important to reach a consensus on the non-genetic factors with greatest effects on these traits in order to standardise analyses.If these requirements are met, dogs can provide a valuable resource for studying the genetics of behaviour characteristics, especially in terms of intra- and inter-species social interactions. In this study, genomic regions and SNPs associated with behaviour traits suggested a number of candidate genes that were previously described for psychological disorders in humans, indicating a potential new context for these genes in the general expression of behaviour variation. By analysing a single dog breed, we were able to highlight candidate genes for behaviour that are less likely to be confounded with morphological variation compared to between-breed analyses. However, further studies with larger sample sizes are required to identify and confirm the identified associations and candidate genes and, where associations are confirmed, subsequent functional analyses will be needed to progress in understanding how these genes influence expression of behaviour.Data for the UK dogs is available from the Dryad Digital Repository: 10.5061/dryad.493rk16.Supplementary material


The problem of missing heritability, that is to say the gap between heritability estimates from genotype data and heritability estimates from twin data, has been a source of debate for about a decade . It mighHeritability measures the overall importance of genetic inheritance in shaping differences between individuals and is defined as the fraction of trait variation in a population due to genetic inheritance . The advThe dawn of the genome-wide association study (GWAS) era, around the year 2007, brought with it the question: can we identify specific genetic variations that explain the heritability estimated from twin studies? The small sample sizes of early GWAS meant they had power to identify only common genetic variants with relatively strong effects, and the amount of trait variation that these variants explained was typically only a small fraction of the heritability estimated by twin studies. For height, by 2010 around 40 variants had been identified that collectively explained around 5% of the variation in height, compared to a twin heritability of around 80% [Many different explanations for the 'missing heritability' have been proposed . I will The deepest solution to the missing heritability problem would involve identifying all of the causal genetic variants and measuring how much trait variation they explain. An intermediate step towards this solution is to show how much variation we could hope to explain from all measured genetic variation, even if we do not have the statistical power to identify all of the specific causal variants.In 2010, a paper took a step towards this intermediate solution by showing that ~45% of the variation in height could be explained by the genetic variation captured on a particular genotyping array that measured ~250k common single nucleotide polymorphisms (SNPs) . This imThe authors employed a methodology that later became termed GREML (Genomic Relatedness Restricted Maximum Likelihood) ,10. The One approach to answering this question is to extend the GREML methodology to high quality whole genome sequence (WGS) data, an exteThe first challenge is one of precision. The information used to estimate heritability from rare variants by GREML-WGS comes from the variation in sharing of rare variants among distantly related pairs of individuals , 15. HowHowever, when the challenge of achieving sufficient precision of GREML-WGS estimates is overcome, questions about methodological assumptions remain. The methodology assumes that effect sizes are normally distributed within each bin, where the variants have been divided into bins based upon their frequency and the strength of their correlations with other variants (LD). Since GREML makes inferences about the distribution of effect sizes, GREML heritability estimates can become biased when assumptions about the distribution of effect sizes are violated . This coPopulation stratification presents another challenge for the GREML-WGS methodology. Population stratification occurs when two genetically distinct subpopulations have different mean trait values. This implies that any genetic variant that is differentiated between these subpopulations  will be correlated with the trait even though it has no causal effect on the trait. Recently, two papers were published showing that stratification has affected genome-wide association studies of height, leading to spurious inferences about selection on height in Europe –20. ThisIf mean trait values differ along the major principal components, adjusting for the major principal components can remove bias due to population stratification. However, it is hard to accurately infer all of the relevant axes of genetic variation that may be correlated with mean trait values in order to completely eliminate bias due to stratification . The sitThe linear mixed model methodology underlying GREML methods can also be used to adjust for stratification in GWAS –32. LineThe problem of population stratification is even trickier for the very rare variants used by GREML-WGS. The GREML-WGS methodology measures the contribution from rare variants in part by measuring the degree to which pairs of individuals who share rare variants tend to have more similar phenotypes than people who do not. However, if a pair of individuals share a very rare variant, then it is likely that they inherited this variant from a recent common ancestor, even if their genome-wide relatedness is low. Pairs of individuals who share a recent common ancestor are more likely to have similar environments than those who do not, implying that the GREML methodology could mistakenly infer contributions from rare genetic variants that are in fact environmental contributions. It is hard to see how this type of stratification could be corrected for by PCA because it is specific to particular pairs or clusters of individuals who share a recent common ancestor.It will be difficult to assess the impact of population stratification on GREML-WGS without using some form of family data, where the randomisation of genetic material during meiosis can be used to disentangle genetic from environmental influences –37. FamiWe can examine existing heritability estimates from RDR and Sib-Regression  to get aWhile my methodological concerns about GREML-WGS might be answered through further analyses for a trait like height, my own work has shown that the GREML approach leads to substantial overestimation of heritability for traits like educational attainment . This isFurther collection of family data may also contribute to solving a related puzzle about genetic prediction. By using the estimated effects of genome-wide SNPs, a model that predicts trait values from genotype data can be constructed, termed a polygenic score . While t


MAP2 in handedness.Hand preference is a prominent behavioural trait linked to human brain asymmetry. A handful of genetic variants have been reported to associate with hand preference or quantitative measures related to it. Most of these reports were on the basis of limited sample sizes, by current standards for genetic analysis of complex traits. Here we performed a genome-wide association analysis of hand preference in the large, population-based UK Biobank cohort . We used gene-set enrichment analysis to investigate whether genes involved in visceral asymmetry are particularly relevant to hand preference, following one previous report. We found no evidence supporting any of the previously suggested variants or genes, nor that genes involved in visceral laterality have a role in hand preference. It remains possible that some of the previously reported genes or pathways are relevant to hand preference as assessed in other ways, or else are relevant within specific disorder populations. However, some or all of the earlier findings are likely to be false positives, and none of them appear relevant to hand preference as defined categorically in the general population. Our analysis did produce a small number of novel, significant associations, including one implicating the microtubule-associated gene  Hand preference is probably initiated during prenatal phases, and further established in early infancy5. Gene expression analysis has revealed left-right differences in the human central nervous system as early as four weeks post conception7, which indicate that laterality is an innate and pervasive property of the central nervous system. Across different regions of the world and throughout history, the behavioural variation has persisted, albeit that cultural suppression of left-handedness has sometimes occurred11.Hand preference is a conspicuous behavioural trait, with about 10% of people preferring to use their left hand for many tasks, about 1% having no preference, and the large majority preferring to use the right hand15. The existence of a genetic component for hand preference has been investigated in large twin studies, consisting of thousands of individuals. These studies found additive heritabilities of around 25% for left-hand preference19. In terms of individual genetic loci, monogenic models for hand preference were originally conceived, such as the dextral/chance model20. The model proposed that there are two alleles, D  and C (chance), with the homozygous DD genotype producing right-handers , the homozygous CC genotype producing random asymmetry (50% right-handers and 50% left-handers) due to a hypothesized loss of direction-setting in brain development, and the heterozygote, DC, being intermediate and producing 25% left-handers and 75% right-handers. Another related idea, conceived with respect to continuous measures of left-versus-right hand motor skill, was the ‘right-shift’ model, which proposed a major gene whose alleles cause either rightward mean asymmetry, or no directional bias21.There has been much interest in possible causes of hand preference, as well as its associations with cognitive variation and disorders24. Genome-wide association scan (GWAS) analysis has also not found any genomic locus with a large enough effect on hand preference to be compatible with monogenic models, unless there would be extensive genetic heterogeneity (i.e. many different Mendelian effects involved in the population)25. Meanwhile, as analyses based on twins have indicated 25% heritability, there is clearly ample room for environmental influences, although these remain mostly unknown. Early life variables such as birthweight and twinning can influence handedness, but not to an extent which is remotely predictive at the individual level27.However, genetic linkage analyses in families with elevated numbers of left-handed members have not found convincing evidence for single major genes28, to a broad region on chromosomes 229. A follow-up study suggested the signal came from paternal inheritance30. Further analysis suggested LRRTM1  as the most likely gene, as it showed evidence for paternal-specific association with relative hand skill of a particular haplotype, as well as imprinted expression in cell lines31.A sib-pair analysis (N = 89 sibships with reading disability) found suggestive linkage of left versus right relative hand skill, a continuous measure based on the pegboard taskAR (androgen receptor), located on chromosome X, has been studied in relation to hand preference, due to a longstanding theory that brain laterality is influenced by sex hormones32. One study found association effects which were opposite in males and females for the CAG-repeat33. A different study found that right-handedness was associated with shorter CAG repeats in both sexes34, while another, which only included males, found that mixed-handedness was associated with longer CAG repeats35.A CAG-repeat polymorphism in the gene COMT (catechol-O-methyltransferase) with relative hand skill, although this did not survive multiple testing correction36. COMT was studied because it was considered a candidate gene for bipolar disorder37.In a set of families affected by bipolar disorder, an association was found of a variant in SETDB1 (SET domain bifurcated 1) and SETDB2 (SET domain bifurcated 2) found that the single nucleotide polymorphism (SNP) rs4942830 in SETDB2 was significantly associated with hand preference as measured as a quantitative trait on the Edinburgh scale38 , although for the binary trait of left versus right-handedness, the p-value did not survive multiple testing correction39. This result was replicated, including the stronger effect for the quantitative trait 40. SETDB2 was chosen as a candidate for study as it has been shown to regulate structural left-right asymmetry in the zebrafish central nervous system41.A study of the genes PCSK6 (proprotein convertase subtilisin/kexin type 6)42. The same research group performed a second GWAS in a cohort of individuals from the general population (N = 2666), and found no genome-wide significant associations, and no indication of involvement of PCSK643. The GWAS results also yielded evidence that genes influencing visceral asymmetry might be involved in relative hand skill in both the dyslexic group and the general population group. For this analysis, the authors tested sets of genes for an enrichment of association signals in the GWAS data, where the sets were defined based on mutations in mice that influence the organization of the visceral organs on the left-right axis43.A GWAS study of relative hand skill, in a sample of people with reading problems , found a genome-wide significant association in the gene 25. The most significant associations were near the neighbouring genes SCN11A/TTC21A/WDR48 , and AK3/RCL1  with p < 1E-6, i.e. a suggestive level of association which does not survive statistical correction for multiple testing over the whole genome.In a GWAS study of 263 left vs 2092 right-handers, and 173 left vs 1412 right-handers , no genome-wide significant results for self-reported hand preference were found44.A GWAS of 4268 individuals from the general population, using a questionnaire-based definition of handedness, found no association p-values below 5E-06 in the genomeVarious molecular genetic studies have identified individual genes as potential contributing factors to handedness, with small effects, which would be consistent with a polygenic architecture underlying the heritable component to this trait. We review these genes briefly here:45. None of the studies listed above approached that level of sample size or statistical power. Low power reduces the likelihood that a statistically significant result reflects a true effect46. Most of the studies summarized above made secondary use of datasets which were collected for different purposes, so that left-handedness was present at only roughly 10% within the datasets. This meant that statistical power was even less than might have been achieved in such sample sizes, for example when selection schemes balanced for handedness had been used. Given the statistical issues affecting the earlier studies, it seems possible, from a contemporary perspective on statistical genetics, that most or all of the findings were false positives.It has become increasingly clear during the last decade that psychiatric, cognitive and behavioural traits are usually influenced by many genetic factors with very small effects of each individual locus, so that tens of thousands of subjects are needed to detect effects reliably42, and families with bipolar disorder36. Therefore, even if the genetic associations were real, they may not extrapolate to the general population. Finally, a variety of different measures related to handedness were used in these studies, including binary traits based on simple questions such as ‘which hand do you write with?’, to quantitative indices based on the peg board test.Some of the genetic associations summarized above were identified in groups with specific disorders, including children with reading disability47. This dataset comprises more than 300,000 subjects with handedness information, assessed by questionnaire (see below). SNP-based heritability analysis, based on variation over all of the autosomes  arrived at an explained variance (SNP-heritability) for left-handedness of only 1.8% in the UK Biobank (p = 6.4e-03), which is markedly lower than previous indications from twin studies (see above), while ambidexterity had an estimated SNP-heritability of 6.8% (p = 8.7e-04)48.An opportunity arose recently to perform a high-powered GWAS of hand preference in a very large, adult population sample from the UK, known as the UK Biobank datasetHere we performed GWAS for hand preference in this large and well powered dataset, to identify novel loci associated with this trait, and to investigate whether any of the previously reported loci, summarized above, show evidence for association with binary trait handedness in the general adult population. We also applied different methods of gene-set enrichment analysis to the GWAS results, in order to test the relevance for hand preference of gene-sets that are involved in laterality of the visceral organs.47 and provided to Clyde Francks as part of research application 16066. Signed informed consent was obtained from all participants, and the UK Biobank has ethical approval from the UK Biobank Research Ethics Committee under number 16/NW/0274. All methods were performed in accordance with the relevant guidelines and regulations. For descriptions of all traits in the UK Biobank cohort, see http://biobank.ctsu.ox.ac.uk/crystal/search.cgi.We used the data collected in the UK BiobankThe phenotype 1707 ‘Handedness” was based on a touchscreen question “Are you right or left handed?”, with four answering options: ‘right-handed’, ‘left-handed’, ‘use both right and left hands equally’, and ‘prefer not to say’. We merged ‘right-handed’ and ‘use both right and left hands equally’ into the category ‘non-left-handed’ to create a dichotomous phenotype. ‘Prefer not to say’ was treated as missing data.26. For the purposes of the present study, some of these variables were relevant to use as covariates when performing genetic association analysis: sex (trait 31), year of birth (trait 34), country of birth (trait 1647), and being part of a multiple birth (trait 1777). Descriptive statistics for these variables, their distributions, and their relations and effect sizes with respect to handedness, are available in that earlier reference26. The participants are older than the general population (birth years between 1934 and 1971), and females are slightly over-represented (54%)26. Although the traits ‘birthweight’ (trait 20022) and ‘being breastfed’ (trait 1677) also showed association with hand preference in our earlier study26, we did not include them in the present study as covariates, as they have significant SNP-heritabilities of their own in the UK Biobank dataset . In this case, some genetic loci may conceivably influence both handedness and one of these traits, while we wished to detect any genetic influences on hand preference in our GWAS without correcting for shared genetic effects.In a previous study of the UK Biobank dataset we modelled various early life factors with respect to left-handedness49, in order to control for population stratification. We restricted the analysis to participants with British ancestry, as reported before49. Individuals from this restricted dataset were selected for the present study when they had data for the handedness phenotype and covariates used. Within this subset, we excluded randomly one individual from each pair of participants whose genetic relatedness was inferred to be 3rd degree or closer, on the basis of genome-wide genotype data, as previously calculated49, individuals were removed when there was a mismatch of their reported and genetically inferred sex, putative aneuploidy of the sex chromosomes, excessively high genome-wide heterozygosity (>0.19) or genotype missingness (missing rate >0.05). The total number of participants remaining was N = 331,037: left-handed = 31,856, non-left-handed = 299,181, the latter consisting of right-handed = 293,857 and ambidextrous = 5,324.In addition, as covariates in genetic association analysis we used the first 20 principal components which describe genome-wide diversity within the dataset, as provided by the UK Biobank49 were available as dosage data for ~97 million SNPs per individual. QCtool  was used to assess SNP quality, and SNPs were retained if in the selected subsample the imputation ‘info’ score was >0.7, the minor allele frequency (MAF) was >0.001, and the P value for the Hardy-Weinberg equilibrium (HWE) test was >1E-07. This left ~15 million SNPs.Imputed genotype data49. We applied the widely-used threshold P value of 5E-08 to assign significance in the context of genome-wide multiple testing, which accounts for the number of SNPs tested in a modern GWAS study, and the correlation structure between SNPs in European ancestry populations51. The GWAS for left-handed versus non-left-handed was our primary analysis, the results of which we used for downstream analyses at the whole gene level and gene-set level (below). However, in addition, we ran GWAS for the trait right-handed vs non-right-handed and ambidextrous vs non-ambidextrous, for look-ups of previously reported individual SNPs (see Introduction).GWAS analysis was run using linear regression assuming an additive genetic model, with BGENIE v1.2When we ran GWAS for ‘ambidextrous’ versus ‘non-ambidextrous’, due to the much smaller size of the ambidextrous group than the other handedness groups, we raised the MAF threshold to 0.01.52 version 1.0.0 to the results of the GWAS of left-handed versus non-left-handed participants, to investigate the evidence for a polygenic component to the trait. This analysis was based on 1,171,358 SNPs which remained after pruning for LD following default settings, based on the reference panel eur_w_ld_chr, as provided with the software.In addition, we applied LDSChttp://www.ebi.ac.uk/gwas/search, 3 July 2018) for the terms ‘handedness’ and ‘hand’ to find SNPs and genes previously implicated in traits related to hand preference. In addition, we checked the literature through Pubmed (July 2018) for ((‘hand preference’ OR ‘hand skill’ OR ‘handedness’) AND genetic) to identify SNPs or genes found in non-GWAS studies (such as candidate gene studies). We also checked references in the papers we found. The results of this search are summarized in the Introduction.We checked the GWAS catalog (53 (November 2018). Each gene-based score summarizes the overall level of association at all SNPs spanning a given gene, into one single score and generates a corresponding P value for that entire gene.In the results of our GWAS for hand preference in the UK Biobank, we first checked the p-values for association, for each of the specific SNPs reported in the previous papers Table . We alsoet al.43 had selected a number of mouse phenotypes related to visceral asymmetry (see Introduction), such as ‘dextrocardia’ and ‘situs inversus’, from all those available in the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org/). In this database, mutations in specific genes have been linked to resulting phenotypes, such that each phenotype has a set of individual genes annotated to it. The exact criteria by which Brandler et al. selected their specific phenotypes for analysis were not described. In addition, new relevant mouse laterality phenotypes, and new associated genes, may have been added since 2013. We therefore made an up-to-date selection of phenotypes related to visceral asymmetry in the MGI, in the following way: In the hierarchical tree for mammalian phenotypes , we searched for ‘heterotaxy’, ‘situs inversus’, ‘isomerism’ and ‘dextrocardia’, and also included all child terms if they also related to asymmetry phenotypes. We only included terms with at least 15 genes. This approach resulted in a list of 11 mouse phenotypes (See Results). The mouse genes were then mapped to corresponding human genes, using homologene.data  from ftp://ftp.ncbi.nih.gov/pub/HomoloGene/current, by matching mouse genes with human genes that had the same HID (HomoloGene group identifier). The list of phenotypes and the associated human genes can be found in Supplementary Table To test for the involvement of genes associated with visceral asymmetry in relative hand skill, Brandler https://omim.org/, last updated on 26 Jun 2018), we also identified all genes in the human phenotypic series ‘Primary Ciliary Dyskinesia’ (MIM #244400), because half of the patients with this disorder present with situs inversus. These genes were used to form one additional gene-set, thus in this study we tested a total of twelve sets, eleven from the mouse database and one based directly on human genetics (See Results).In the Online Mendelian Inheritance in Man database OMIM , plus two newer algorithms called Magma  and Pascal . The three enrichment algorithms work in different ways, but essentially they first assign a single association score to each gene based on the GWAS data, and then test whether the genes in a given set show, on average, more evidence for association with the trait in question than the rest of the genes in the genome for which scores could be calculated 57, while accounting for non-independence of SNPs due to linkage disequilibrium. For Magma and Pascal, we used default settings, while for MAGENTA we chose the settings as used by Brandler et al.  had even a nominally significant effect on left-handedness in the UK Biobank dataset Table , i.e. noThe gene-set enrichment analyses did not convincingly support the proposition that sets related to visceral asymmetry are particularly associated with left-handedness Table . There wMAP2 (Microtubule associated protein 2); on chromosome 17q21 the SNP rs144216645 (p = 1.2E-09) within a large inversion haploblock spanning the genes STH, KANSL1, ARL17B, ARL17A, LRRC37A, LRRC37A2 and NSF; on chromosome 13q22 the SNP rs11454570 (p = 4.7E-08) within an intron of the non-coding RNA-gene LINC00381.In the GWAS for left-handedness vs non-left-handedness Fig. , we founMAP2 on chromosome 2 did not reach genome-wide significance. Instead, the major histocompatibility complex region on chromosome 6p21 showed significant associations. The most associated SNP was rs9366770 (p = 2.2E-09), located in an exon of the non-coding RNA gene HCG27. The inversion region on chromosome 17 was also associated with the right-handed versus non-right-handed phenotype, again most significantly with the SNP rs144216645 (p = 2.3E-08). An additional association on chromosome Xq25 was detected with the SNP rs767669906 (p = 1.0E-08), located 44 kilobases downstream of the gene DCAF12L1. However, with a minor allele frequency of 0.0035, this signal may not be reliable.The results for right-handed vs non-right-handed people were similar to those for left-handed versus non-left-handed . This analysis also indicated that 72% (95CI 0.59–0.84) of the inflation which is observable in the QQ plot for the left-handed versus non-left-handed GWAS .QQ plots from the GWAS analyses are shown in Figs WAS Fig.  was due In a very large dataset comprising hundreds of thousands of adult subjects from the general population, we found no evidence for an involvement in hand preference of any previously claimed genetic variants, genes, or gene-sets from the literature. We consider the most likely explanation to be that the earlier findings were false positives, as the study sample sizes were orders of magnitude smaller than the present analysis (see Introduction). However, as the earlier studies used various different trait measures related to handedness, and some made use of selected disorder populations, then it remains possible that some of the earlier findings were indeed real, but not detectable in the present analysis (see Introduction).et al.43 performed analysis of a quantitative handedness index based on peg moving, and included a sample with reading disability, when they originally reported such a relationship. Our present analysis was not an attempt to directly replicate the study of Brandler et al., but rather it was a related investigation, of genes involved in visceral asymmetry with respect to questionnaire-defined, binary-trait hand preference, in a large dataset from the general population. In the rare genetic disorder Primary Ciliary Dyskinesia (PCD)(MIM # 244400), recessive mutations in specific genes involved in ciliary biology result in a 50% chance of mirror-reversed left-right visceral asymmetry, a condition known as situs inversus totalis (SIT)58. When PCD and SIT co-occur, it appears that the rate of left-handedness is unchanged from the general population59. However, there may be an increase of left-handedness in people with SIT when they do not have PCD61, which would suggest the existence of some developmental mechanisms which link visceral asymmetry with brain asymmetry. Nonetheless, a recent genome-wide mutation screening study did not identify any individual genes as likely candidates to cause non-PCD SIT with left-handedness62, and it is possible that the aetiology is multifactorial or even non-genetic63. At present, the study by Brandler et al. remains the only one to report tentative evidence for a molecular genetic link between visceral laterality and handedness.There was no evidence for an involvement, in hand preference, of common variants in genes related to visceral left-right patterning. Note that Brandler http://big.stats.ox.ac.uk/variant/17-43659975-T-C). The variant on chromosome 13 resides in an intron of a non-coding RNA-gene of unknown function, LINC00381 . For the locus on chromosome 2q34, the most probable gene appears to be MAP2, which encodes microtubule-associated protein 2: the association signal extends from upstream into this gene’s coding region, and the single most associated SNP is associated with expression differences of MAP2 in oesophageal tissue, according to the GTeX database ), and not with any other gene’s expression level 68, and in mice seems to play a role in motor skills69. One report suggested that MAP2 is more highly expressed in right hippocampus than left during development in the rat70. How exactly this gene may act in affecting motor laterality in human development remains unknown. Regardless, as this genetic association is based on hundreds of thousands of study participants, MAP2 must be considered the single most reliably implicated protein-coding gene in left-handedness yet identified, although replication will be desirable.Our GWAS analysis in the UK Biobank dataset produced three novel loci associated with left-hand preference at a genome-wide significant level, on 2q34, 17q21, and 13q22. The most likely causative gene within the large chromosome 17q21 inversion cannot be determined on the basis of these data, as the region spans at least twelve genes showing similar levels of association with the trait  in the cerebellar hemisphere and frontal cortex, according to the GTeX portal. Other associated SNPs within the region, rs3130976 (p = 2.0E-08) and rs2854008 (p = 2.4E-08), are eQTLs for MICB (MHC class I polypeptide-related sequence B) and C4A (complement C4A) in the cerebellar hemisphere, but are also eQTLs for other genes in other tissues. We cannot conclude which may be the most likely causative gene or genes in the region, on the basis of this information.When pooling ambidextrous people together with left-handers, to create a ‘right-handed’ versus ‘non-right-handed’ phenotype, an additional locus within the MHC region of chromosome 6p was detected. Again this is a very broad region of linkage disequilibrium spanning many genes. We found that the most associated SNP is an eQTL for the non-coding RNA gene 26 suggested a reduction in left-hand preference of roughly 20% among the oldest participants (born around 1940), relative to younger participants (born around 1970), which suggests that some of the older left-handed people were forced to switch to right-handedness during childhood. Our GWAS analysis did adjust for year of birth as a covariate effect, but hand switching may have reduced power. In addition, the location of birth within the UK was found to influence the probability of being left-handed26, likely primarily for cultural rather than biological reasons. Country of birth was therefore included as a covariate in our GWAS. Furthermore, the ambidextrous group was previously found to give a high rate of inconsistent answers between different assessment visits26. However, ambidextrous people comprised only a relatively small subset.There may have been some particular sources of noise in the UK Biobank definitions of hand preference. The UK Biobank participants are older than the general population (birth years between 1934 and 1971), so that some effects in this cohort may be quantitatively or qualitatively different in younger cohorts. An earlier analysis of the UK biobank dataset71, while previous twin-based studies have indicated a heritability of roughly 25% for left-hand preference (see Introduction). Nonetheless, our analysis with LDSC52 did detect evidence for an overall polygenic component to this trait. It has been noted before that behavioural traits can show a particularly large difference between twin and SNP-based heritability estimates72. A commonly given explanation is that much of the heritability may be due to rare SNPs and mutations, which are not well captured by genotyping arrays and imputation protocols in GWAS studies. Rare mutations can be relatively recent in origin, and under negative selection. Whether these possibilities apply to hand preference will need to be investigated in future large-scale genome sequencing studies.It is striking that common genetic variants in the UK Biobank dataset have little overall effect on hand preference, as measured by a very low SNP-based heritability MAP2 providing the most robust lead so far.In conclusion, previously reported genetic associations with measures related to handedness, found in datasets other than the UK biobank, were not supported by analysis of this very large cohort. We also found no supportive evidence that common variants within genes involved in visceral left-right patterning contribute to hand preference. We consider the most likely explanation for the discrepancy to be that the previous findings were noise, which arose from limited sample sizes. Differences of trait measurement and population selection might also be involved. Regardless, what is known of the molecular genetic basis of hand preference remains tentative and extremely limited, with the gene Supplementary informationSupplementary Table 2


High yield and wide adaptation are principal targets of wheat breeding but are hindered by limited knowledge on genetic basis of agronomic traits and abiotic stress tolerances. In this study, 277 wheat accessions were phenotyped across 30 environments with non‐stress, drought‐stressed, heat‐stressed, and drought‐heat‐stressed treatments and were subjected to genome‐wide association study using 395 681 single nucleotide polymorphisms. We detected 295 associated loci including consistent loci for agronomic traits across different treatments and eurytopic loci for multiple abiotic stress tolerances. A total of 22 loci overlapped with quantitative trait loci identified by biparental quantitative trait loci mapping. Six loci were simultaneously associated with agronomic traits and abiotic stress tolerance, four of which fell within selective sweep regions. Selection in Chinese wheat has increased the frequency of superior marker alleles controlling yield‐related traits in the four loci during past decades, which conversely diminished favourable genetic variation controlling abiotic stress tolerance in the same loci; two promising candidate paralogous genes colocalized with such loci, thereby providing potential targets for studying the molecular mechanism of stress tolerance–productivity trade‐off. These results uncovering promising alleles controlling agronomic traits and/or multiple abiotic stress tolerances, providing insights into heritable covariation between yield and abiotic stress tolerance, will accelerate future efforts for wheat improvement. We uncovered consistent marker alleles for agronomic traits across different environments and eurytopic marker alleles controlling multiple abiotic stress tolerances in wheat. Selective sweep analysis showed that the gradual loss of advantageous variation for abiotic stress tolerances due to preferential selection for yield potential may be a common phenomenon in current wheat breeding. Two promising candidate genes identified in the present study provide potential targets for studying the molecular mechanism of stress tolerance‐productivity trade‐off. These results are timely in terms of developing efficient molecular markers and dissection of the functions of relevant genes for wheat improvement in times of unexpected climatic fluctuations. DHSdrought‐heat‐stressedDSdrought‐stressedDTFday to floweringESNPeffective spikes number per plantGNPgrain number per main spikeGWASgenome‐wide association studyGYPgrain yield per plantHSheat‐stressedNSnon‐stressPHplant heightSLmain spike lengthSMAsuperior marker allelesSNPPspikelet number per main spikeTKWthousand kernel weight1Triticum aestivum L.) is one of the three major crops providing about 19% of global dietary energy  is a powerful tool for dissecting genetic basis of agronomic traits and has been successfully applied for discovering relevant genes in rice and maize , drought‐stressed (DS), heat‐stressed (HS), and drought‐heat‐stressed (DHS) conditions and phenotyped for eight agronomic traits. Our study aimed to (a) identify stable genetic variations controlling agronomic traits under NS and abiotic (drought and/or heat) stress conditions; (b) identify genetic variations underlying responses for drought, heat and dual stresses; and (c) explore the heritable covariation between yield potential and abiotic stress tolerance, and the variations caused by artificial selection.22.1Four wheat collections were used in this study. Collection 1 consisted of 277 winter wheat accessions, most of whose flowering dates occurred within 1 week; the collection contained 12 landraces, 35 advanced lines, and 230 modern cultivars, mainly planted in the Yellow and Huai River Valleys Facultative Wheat Zone and Northern Winter Wheat Zone in China  with NS, DS, HS, and DHS treatments (Supporting Information Table 2.3anova) was performed to test the effect of genotype (G), treatment (T), and combined year and site (YS) with their interactions based on combined linear mixed model using the software IciMapping 4.1. In addition, broad‐sense heritabilities for each trait under different treatments was calculated across environments (years and sites) as presented by Piepho and Moehring  using the Affymetrix GeneTitan System according to the Axiom 2.0 Assay Manual Workflow protocol. Wheat 660K SNP array consisted of 630 517 SNPs developed from the transcriptome and genome sequencing. Allele calling was carried out using the Affymetrix proprietary software according to Axiom Best Practices Genotyping Workflow. The physical locations of SNPs were identified based on the IWGSC wheat genome sequence (IWGSC RefSeq v1.0).Genomic DNA of all accessions in the four collections was extracted according to the protocol of Ogbonnaya et al. , DNA qua2.5r2 command in the software PLINK 1.9. LD decay graph was plotted according to LD of SNP pairs within 100 kb, and step size was set to 50 kb. Two GWAS methods, that is, single‐locus and multilocus analysis, were used to detect the marker–trait associations. We performed single‐locus analysis using TASSEL 5.0 based on general linear model (GLM) and mixed linear model (MLM) as follows:Y is the vector of phenotype, X is the vector of marker genotypes; Q is the principal components matrix ; K is the relative kinship matrix; α, β, and μ are the corresponding effects; and e is a matrix of residual effects. X and Q matrix were fitted as fixed effects, and K and e matrix were fitted as random effects. In addition, to correct the confounding effects of causal loci, a multilocus GWAS based on fixed and random model Circulating Probability Unification (FarmCPU) was also conducted in the present study, which allows a fixed effect model and a random effect model to perform iteratively to improve statistical power and computational efficiency  of the GWAS. Haplotype blocks were determined base on the interpretation of the block definition suggested by Gabriel et al. , 395 681 SNPs were used for GWAS in the present study. The population structure of the Collection 1 was analysed using software STRUCTURE 2.3.4. Principal component analysis was performed using GCTA software. Linkage disequilibrium (LD) was estimated as the squared allele frequency correlation using −l et al.  using Pl2.6By screening polymorphic markers with low missing rate (≤20%), a total of 114 545 SNPs were available for Collection 2; together with 224 SSR markers mapped previously by Hao et al. , a linka2.7F‐statistics (FST) is an index of population differentiation based on genetic polymorphism; the FST between accession groups of different eras or generations in Collections 1, 3, and 4 were evaluated using a 100‐kb sliding window with 50‐kb steps; and genomic regions with top 5% FST were defined as potential selective sweeps.Reported QTL were retrieved from PubMed database; the physical location of these QTL was obtained from URGI database. Putative genes near to significant SNPs in haplotype blocks were obtained according to IWGSC RefSeq v1.1 annotation, and the gene expression data of candidate genes were downloaded from the Wheat Expression Browser. 33.1anova was performed to divide the variation in genotype (G), treatment (T), and combined year and site (YS) components and their interactions (Supporting Information Table p < .01), treatments , and combined years and locations . Further, the agronomic traits except ESNP and DTF of each genotype responded differently to combined years and sites . GYP was moderately heritable with broad‐sense heritability (H2) of 0.58, 0.55, 0.54, and 0.48 under NS, DS, HS, and DHS, respectively, and other agronomic traits except ESNP under DHS (0.45) had higher heritabilities than GYP. The range of phenotypic variation differed among traits; the highest coefficient of variation (CV) was observed for PH‐NS (20.1%), followed by PH‐DS (19.6%); and the lowest coefficient of variation was observed for DTF‐NS (1.3%), followed by DTF‐DS (1.4%). More descriptive statistics of the investigated traits are summarized in Supporting Information Table 3.2p < .01) with GNP and TKW but was negatively correlated (p < .01) with ESNP and PH across treatments; non‐significant (p > .01) correlations of GYP were found with SNPP and SL. We also observed that GYP‐HS and GYP‐DHS were negatively correlated (p < .01) with DTF, suggesting that earlier initiation of grain‐filling is beneficial in reducing the losses of grain yield caused by heat stress at later growth periods. In addition, ESNP and PH was positively correlated (p < .01), but both of them were negatively correlated (p < .01) with GNP, TKW, and SNPP across treatments, which suggest the trade‐off between vegetative and reproductive growth.Significant correlations were observed among the same trait under different treatments Figure . GYP was3.3k) was calculated and showed a peak value when the given k (the number of subpopulations based on the model) is two  dropped to 0.5 at 450 kb for whole genome  in at least two models, were regarded as associated SNP in the present study, and the peak SNPs  for each loci were defined as candidate SNP (cSNP). In total, 189 cSNPs were identified for agronomic traits. To be specific, 109, 69, 22, and 26 cSNPs were identified under NS, DS, HS, and DHS, respectively (Supporting Information Table r2) between these two cSNPs was 0.892. For ESNP, 6, 8, 3, and 3 cSNPs were identified under NS, DS, HS, and DHS, respectively, but no cSNP was simultaneously identified under different environments. For TKW, 6, 7, 5, and 4 cSNPs were identified under NS, DS, HS, and DHS, respectively, and three cSNPs  on chromosome 2A, 5A, and 6B simultaneously associated with TKW under NS and DS, of which AX‐109330285 was also associated with TKW under HS and DHS, suggesting a consistent effect on TKW under multiple environments. For GNP, 38, 8, 7, and 11 cSNPs were identified under NS, DS, HS, and DHS, respectively. Five cSNPs  on chromosome 1A, 2B, 3D, 4A, and 4B associated with both GNP‐NS and GNP‐DS, of which AX‐94964288 was also associated with GNP‐DHS, remarkably, AX‐108935266 associated with GNP under four treatment conditions. Moreover, AX‐94964288 was simultaneously associated with GYP‐NS and GYP‐DS as mentioned above, suggesting that the causal gene indicated by this cSNP may control GNP, hence affect the GYP. For GNPP, PH, SL, and DTF, 7, 8, 6, and 20 cSNPs were identified under NS, and 7, 8, 5, and 23 cSNPs were identified under DS, and 3, 5, 3, and 11 cSNPs separately associated with GNPP, PH, SL, and DTF were identified under both NS and DS. More information of the consistent cSNPs is summarized in Figure The single‐locus GWAS based on GLM and MLM and multilocus GWAS based on FarmCPU were conducted to detect the marker–trait associations for agronomic traits under different treatments. Given the trade‐off between dismissing false positives and accepting false negatives ; and a GNP‐DHS‐R‐associated cSNP (AX‐109857541) significantly associated with GNP‐NS (1.26 × 10−7), located on the same haplotype block  with GNP‐NS‐associated cSNP (AX‐94433908). However, the allelic effect of these SNPs showed clear inversion of effects for abiotic stress tolerance and corresponding agronomic traits under NS , trait‐HS‐R (representing heat stress tolerance), and trait‐DHS‐R , respectively. To be specific, 20, 14, and 19 cSNPs were identified for GYP‐DS‐R, GYP‐HS‐R, and GYP‐DHS‐R, respectively. Among which, AX‐111496721 on chromosome 2B was simultaneously associated with GYP‐DS‐R and GYP‐DHS‐R, and a GYP‐HS‐R‐associated cSNP (AX‐109446428) located on the same haplotype block  with a GYP‐DHS‐R‐associated cSNP (AX‐109505217); the LD between these two cSNPs was 0.955. Moreover, AX‐94814333 (6A) and AX‐111021045 (6B) were associated with multiple stress tolerances . Eight, eight, and three cSNPs were associated with GNP‐DS‐R, GNP‐HS‐R, and GNP‐DHS‐R, respectively. A GNP‐DS‐R‐associated cSNP (AX‐110622619) and a GNP‐HS‐R‐associated cSNP (AX‐109329620) co‐located on the same haplotype block , whereas no significant associated SNPs were detected for GNP‐DHS‐R in this haplotype block, suggested that genetic dissection of combined heat and drought stress tolerance cannot be performed directly by studying plant response when heat and drought stresses were applied separately. Three, two, and six cSNPs were identified for TKW‐DS‐R, TKW‐HS‐R, and TKW‐DHS‐R, respectively. Among which, AX‐111160431 was simultaneously associated with TKW‐DS‐R, TKW‐HS‐R, and TKW‐DHS‐R. Furthermore, AX‐109259942, which associated with TKW‐DS‐R and TKW‐HS‐R, located on haplotype block , also harboured a TKW‐DHS‐R‐associated cSNP (AX‐109497315). In addition, 5, 26, and 1 cSNPs were identified for GNPP‐DS‐R, PH‐DS‐R, and SL‐DS‐R, respectively. More information of the cSNPs were summarized in Figure 3.6r2 = 0.07; Figures Hap4 were significantly higher (longer) than that of accessions with Hap2 and Hap3, and much higher (longer) than that of accessions with Hap1  controlling GNP , which harboured the PH‐associated cSNP (AX‐109665328), suggesting that predicting and annotating candidate genes on cSNP‐harbored haplotype blocks could be an effective method to identify causal genes. Therefore, the putative candidate genes in each cSNP‐harboured haplotype block were annotated and candidate genes were identified. The WRKY superfamily of transcription factors was reported to be involved in abiotic stress responses in wheat  were co‐located on haplotype blocks with cSNPs. Also noteworthy, TaWRKY8 located on the same haplotype block  with AX‐94471201, which was also associated with GYP‐DS‐R (3.41 × 10−5), GYP‐HS‐R (2.5 × 10−3), and GYP‐DHS‐R (3.48 × 10−6), suggesting that TaWRKY8 may confer multiple abiotic stress tolerances. In addition, we also found that two haplotype blocks on chromosome 6A and 6D harboured AX‐94814333 and AX‐111021045, respectively, which were simultaneously associated with abiotic stress tolerance and GYP‐NS. Interestingly, only two (TraesCS6A02G124100 and TraesCS6A02G124200) and one (TraesCS6D02G114400) genes were predicted in these two haplotype blocks, separately  exhibits pleiotropic phenotypes including enhanced tillering and yield .In total, 69.4% (206/295) of cSNPs identified in the present study fell within haplotype blocks  were defined as selective sweep regions. For agronomic traits, 58 cSNPs fell within selective sweep regions for at least one collection, accounted for 30.7% (58/189) of the total cSNPs, suggesting that some of the favourable genetic variations in cSNPs were artificially selected in wheat breeding. However, only 23 cSNPs simultaneously fell within selective sweep region for two or three collections, among which 12 cSNPs were environment‐specific cSNPs, accounting for 7.6% (12/157) of the total environment‐specific cSNPs. Eleven cSNPs were consistent cSNPs (cSNPs associated with agronomic traits under different treatments), accounting for 34.3% (11/32) of the total consistent cSNPs suggesting that the favourable variations of consistent cSNPs are more likely to be selected in different breeding process when compared with that of environment specific cSNPs. For abiotic stress tolerances, only 10 cSNPs fell within selective sweep regions in at least one population; moreover, four of the cSNPs, that is, AX‐111021045, AX‐94814333, AX‐109329620, and AX‐110631132, simultaneously related to agronomic traits with adverse allelic effect. To be specific, AX‐111021045 and AX‐94814333 are the common cSNPs for GYP‐NS, GYP‐DS‐R, GYP‐HS‐R, and GYP‐DHS‐R, which were mentioned above that co‐located with putative MIP1‐like genes on 6A and 6D, and AX‐109329620 is the common cSNP for GNP‐NS and GNP‐HS‐R. We observed that the frequency of superior marker alleles (SMAs) for GYP‐NS or GNP‐NS increased sharply along with the decade or derived generation  were identified due to the close linkage among the three SNPs. Hap1 presented high YP‐DHS‐R but low GYP‐NS, whereas Hap2 exhibited low YP‐DHS‐R but high GYP‐NS , Xiaobaimai‐derived population (Collection 3), and Mazhamai‐derived population (Collection 4) were used to perform selective sweep analysis. For Collection 1, d Figure .4TraesCS6A02G124100 and TraesCS6D02G114400 controlling GYP and multiple stress tolerance also open up an interesting and novel field of investigation on the mechanisms underlying the stress tolerance–productivity trade‐off in wheat. These key findings from our study are further discussed below.Wheat plays a crucial role in sustaining food security but greatly threatened by drought and heat stresses worldwide. To gain insights about genetic architecture and candidate genes underlying high yield and wide adaptation of wheat, we investigated the genotypic variability for drought and heat‐responsive agronomic traits under multiple environments and conducted GWAS and linkage mapping using high‐density wheat 660K SNP array. To the best of our knowledge, several findings of this study are novel. Firstly, 295 associated loci including consistent loci for agronomic traits across different environments and eurytopic loci for drought and heat stress tolerances were identified and anchored on newly released wheat reference genome with exact physical locations, and cSNP‐harboured QTL for each of the agronomic traits were identified by comparing the results of linkage mapping and GWAS; these information further clarified the genomic regions controlling yield‐related agronomic traits and combined drought, and heat stress tolerance in wheat will be of great value for scientists and breeders to discover functional genes and realize molecular breeding in times of unexpected climatic fluctuations. Secondly, selective sweep analysis showed that the gradual loss of advantageous variations for abiotic stress tolerances due to preferential selection for yield potential may be a common phenomenon in current wheat breeding. More importantly, two promising candidate paralogous genes 4.1It is often difficult to perform GWAS for drought and heat stress tolerances at later growth periods owing to two major reasons. The first is that diversity panels of crop species often have diverse phenology, whose flowering time in the field likely varies by a long duration  and TraesCS6D02G114400 (6D), near cSNPs within the haplotype blocks for GYP‐NS and abiotic stress tolerances , and the overexpression of MIP1 resulted in enhanced tillering and grain yield  and heat stress (b)Click here for additional data file.Figure S2. The air temperature and soil water content under different environments.Click here for additional data file.Figure S3. The physical position of haplotype blocks which harbored candidate genes (TraesCS6A02G124100 and TraesCS6D02G114400) and cSNPs for tolerance‐production trade‐off.Click here for additional data file.Figure S4. The chronological variation of agronomic traits and stress tolerancesClick here for additional data file.Table S1. Information of 277 wheat accessionsTable S2. Cultivars derived from Xiaobaimai and MazhamaiTable S3. Information of investigated traits and precipitation in each year and siteTable S4. Statistical information of linkage map for DH populationTable S5. Analysis of variance (ANOVA) of agronomic traits components, and other related traitsTable S6. Summary statistics of BLUPs of agronomic traitsTable S7. Number of SNPs, haplotypes and haplotype blocks on each chromosomeTable S8. Information of candidate SNPs (cSNPs) detected by GWASTable S9. Information of QTL detected by linkage mappingTable S10. Summary of cSNP‐harbored QTLClick here for additional data file.


To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex disease. The polygenicity of complex diseases is a widely accepted phenomenon through which a vast number of risk variants, each with a modest individual effect, collectively contribute to the heritability of complex diseases. This imposes a major challenge on fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect individual risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy.Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide-scale analysis. To demonstrate the advantages of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and applied LPG to analyze two pairs of disorders . The results indicate that by levelaging pleiotropy, LPG can improve the power of prioritization of risk variants and the accuracy of risk prediction.In this study, we propose a statistical framework for https://github.com/Shufeyangyi2015310117/LPG.Our methodology provides a novel and efficient tool to detect pleiotropy among GWAS data for multiple traits/diseases collected from different studies. The software is available at The online version of this article (10.1186/s12864-018-4851-2) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) have reported more than 51,000 single-nucleotide polymorphisms (SNPs) to be significantly associated with complex human phenotypes, including quantitative traits and complex diseases , where individual-level data for two traits are obtained from different studies. LPG provides a statistical framework for the evaluation of the local false discovery rate and prediction accuracy and a formal test of the pleiotropic effects between two traits. LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient for genome-wide analysis. We conducted extensive simulation studies to evaluate the performance of LPG. We then applied LPG to conduct a joint analysis of two pairs of disorders  using data from the Welcome Trust Case Control Consortium (WTCCC) . The simThe remainder of this article is organized as follows. First, we introduce the statistical model and describe the VBEM algorithm used to estimate the parameters in the model. Second, we describe the statistical inference procedure used to evaluate the local false discovery rate and the prediction accuracy of the identified genetic variants. We also describe a formal hypothesis test for pleiotropy. Third, we evaluate the performance of LPG using simulations and real data analysis of WTCCC data. Finally, we conclude with a discussion.y,X} with n independent samples, where n individuals and p SNPs. Without loss of generality, we assume that both X and y have been centered. We assume the following standard linear model, Suppose that we have a GWAS data set {β=⊤ is a vector of effect sizes, and γ=⊤ indicate the association status of all p SNPs, where γj=1 indicates that the j-th SNP is associated with trait y, and γj=0 otherwise. In this paper, we consider a spike-slab prior ⊤ is a vector of effect sizes for study k, and k. Compared with traditional linear regression, the latent vector of binary variables γk=⊤ indicates the association statuses in study k, and α=⊤ is the vector of parameters in a multinomial distribution, and Mul∈L(α) is the multinomial distribution with parameter α for each possible value of L={00,01,10,11}, i.e., α00=Pr, α10=Pr, α01=Pr, and α11=Pr.where γj1 and γj2 are no longer independent. We demonstrate that all the parameters in our model can be adaptively estimated from the data without ad hoc tuning. Let When comparing model  with theβ1,β2,γ1,γ2), the probabilistic model of observed data becomes By marginalizing over the latent variables  and the true posterior probability (p) are equal. Similar to the expectation-maximization (EM) algorithm, we can maximize the lower bound q, which is equivalent to minimizing the KL divergence =l, f0(βkj) is the posterior distribution of βkj when γkj=0, and fkj(βkj) is the posterior distribution of βkj under γkj=1. Following algebraic manipulation, we show that f0(βkj) and fkj(βkj) are the density functions of Gaussian distributions where The details of the derivation of the updating equations and the corresponding VBEM algorithm (Algorithm 1) can be found in the supplementary document. The VBEM algorithm performs similarly to the coordinate descent algorithm, which comes from the factorization of the variational distribution . Hence, y1,X1,Z1} and {y2,X2,Z2} with n1 and n2 samples, respectively. We may apply the definitions introduced earlier with p0 covariates in study k. Note that the first column of Zk is a vector of ones corresponding to the intercept. Then, conditional on the observed genotype Xk, hidden status γ, and effects βk, we have Suppose that we have two GWAS case-control datasets {ηki, i is the index for individuals, and k such that β and γ are the effect sizes and indicator variables as defined earlier. Let where Z1 and Z2) as fixed effects, which are included in the parameter space θ. By marginalizing over latent variables , we can obtain the marginal likelihood, similar to expression . Using Jensen’s inequality and the lower bound of the complete-data likelihood , j in the k-th trait, where k=1 or 2, L1={10,11} and L2={01,11}. Namely, the smaller the lfdr is, the more confident we are in prioritizing a SNP. Then, we use the direct posterior probability approach ⊤, the predicted phenotypic value is ck0 and ck1,…,ckp are the mean of the phenotype and each SNP before centering for the k-th study, respectively. We measure the Pearson’s correlation between the observed phenotypic values and the predicted phenotypic values in the testing set for quantitative traits. For case-control studies, the predicted linear predictor is In addition to the identification of risk variants, we can also use the LPG approach to conduct risk prediction. In the LPG model, the effect size of SNP ve (AUC) .It is of great interest to quantify the significance of pleiotropy between two traits. The presence of pleiotropy means that the null and non-null groups in two traits are not distributed independently. Formally, we can set up a likelihood ratio test (LRT) as follows: α1∗=α10+α11 and α∗1=α01+α11 are marginal probabilities. The LRT statistic is where λ approximately follows a χ2 distribution with df=1.where We applied the LPG approach to both simulation data and real data. First, we evaluated the performance of the LPG approach using simulation studies. We examined its performance in risk variant identification as measured by AUC, statistical power and FDR and its performance in risk prediction as measured by the Pearson’s correlation and AUC for quantitative traits and binary traits, respectively. We compared the LPG performance with two other single-trait analysis methods that do not leverage pleiotropy, namely, the two-group model (BVSR ) and LasXk  from a normal distribution, where an autoregressive correlation (AR) j and j′ with ρ = 0.2, 0.5 and 0.7. Next, the entries of both Xk  were discretized to obtain genotypes {0,1,2} according to the Hardy-Weinberg equilibrium-based minor allele frequencies, which were drawn from a uniform distribution of . In all scenarios, unless otherwise specified, the sample size used was nk=3000  and the number of variants was set to p=20,000. To evaluate the prediction performance, we generated an additional ntest=500 samples for each study under the same model. For all scenarios, except for those specifically evaluating Type 1 error rates for the test of pleiotropy, we assumed α01=α10. Denote the proportions of the null and non-null SNPs of both GWAS as α0=α00+α10=α00+α01 and α1=α11+α10=α11+α01, respectively. Then, the hidden association status in the first study (γ1) can be sampled randomly with the number of nonzero entries – pα1. α1 is set to 0.005 for the quantitative traits and 0.0025 for the binary traits. To account for pleiotropy between two GWAS, we controlled the number of SNPs with pleiotropic effects for the two traits as p(α11+α10)(α11+α01+g(α10+α00)) and p(α11+α01)((α11+α10+g(α01+α00)), where g=0 and g=1 correspond to the two extreme cases of no pleiotropy and full pleiotropy, respectively. We considered g=0 to 1 in intervals of 0.2, where larger values of g represent larger pleiotropy. Next, the effect sizes β were simulated from βk was replaced by ζk. We set the population prevalence to 0.1 and case-control ratio to 1 while controlling heritability at 0.3, 0.4 and 0.5 using a liability model [g=0), we considered different values of α=⊤. The values of α=⊤ are given in Additional file β were generated from either a truncated normal distribution or a t-distribution for a quantitative trait (ρ=0.5 and h2 = 0.5). Finally, to more accurately mimic the LD and minor allele frequency patterns present in real data, we excerpted a subset of variants from real dataset  and conducted a simulation study in which we sampled the genotypes from these data. We considered a binary outcome generated using a logistic regression model with case-control sampling (instead of using a liability model as above), with n1=n2 = 7000. For each simulation, we randomly selected 10 causal SNPs, where half of the causal SNPs had odds ratios of =e0.25=1.28 and half had odds ratios of =e−0.25=0.78. The causal SNPs were randomly selected such that they had at most a moderate correlation with each other (correlation < 0.8). However, the tested SNPs could be highly correlated (correlation > 0.8) with each other. Additionally, when calculating the true positive rate (for the AUC and power), each of the 10 causal SNPs was considered to have been correctly discovered if either the causal SNP or an SNP in high LD with it (correlation > 0.8) was discovered . When calculating the true negative rate, the collection of true null-SNPs excluded the 10 causal SNPs and any SNP in high LD (correlation > 0.8) with any of the 10 causal SNPs. This mimics the situation in GWAS where the identified SNPs may or may not be causal but are capturing the “signal” from the true causal SNPs.The simulation datasets were generated by simulating genotype matrices ty model . To evalXk k=,2 from aFor both the quantitative and binary traits, we analyzed the simulated data using the proposed LPG jointly on two traits in comparison with other alternative methods, including BVSR and Lasso, on each separate trait. For the probabilistic approaches, i.e., LPG and BVSR, we evaluated their risk variant identification performance using the area under the receiver operating characteristic (ROC) curve (AUC), statistical power, and false discovery rate (FDR). Note that for all settings, we evaluated the statistical power to identify risk variants with the global FDR controlled at 0.2. As Lasso is a deterministic approach and its FDR is not controllable, we did not evaluate its statistical power. The tuning parameter in Lasso was chosen via 5-fold cross-validation . We evalρ=0.5 and h2=0.5. It demonstrates that LPG, which incorporats the pleiotropy between two traits, improves the risk SNP identification compared with the single-trait analysis (BVSR). In particular, when there is no pleiotropy (g=0), the performance of LPG is the same as that of the single-trait analysis (BVSR), suggesting that LPG can exploit available pleiotropic information. Another observation is that the risk SNP identification performance  of LPG improved with increasing proportion of shared risk SNPs. Additionally, the probabilistic approaches (LPG and BVSR) outperformed Lasso in terms of risk SNP identification, regardless of the presence of pleiotropy, as Lasso does not leverage pleiotropy between two traits and its performance depends on the extent of sparsity and strong signals. The FDR rates of both probabilistic models (LPG and BVSR) were well-controlled at the target 0.2 level. In terms of prediction performance, as pleiotropy became stronger, the Pearson’s correlation coefficients between the observed and predicted phenotypic values in LPG increased slightly over those of BVSR. For the binary traits, we observed similar results . First, the improved AUC and statistical power of LPG increased as the strength of pleiotropy increased, and the global FDR rates of both LPG and BVSR were well-controlled. The prediction performance of LPG showed a slight improvement over that of BVSR when pleiotropy was strong. In our simulation studies, the performance of Lasso was worse than that of its probabilistic counterpart, BVSR. A similar observation was previously reported [ρ (strength of the correlation between genetic variants) and h2 (heritability) . The results demonstrate that LPG performs well even when the underlying generating distribution for the effect sizes β differ from our assumed prior distribution for β. The simulation results when the genotypes were sampled from real data are given in Additional file g) for both quantitative and binary traits  for various configurations of α=⊤ were close to the nominal 0.05 level , rheumatoid arthritis (RA) and type 1 diabetes (T1D) are autoimmune diseases, and previous work suggests they share common genetic risk variants . We appl by GCTA .p-values for RA but the separate trait analysis using BVSR gave a p-value of 1  and UK Blood Services (UKBS)), we used one control cohort for RA and the other for T1D. After quality control filtering, 240,101 SNPs in 1,812 cases from RA, 1,932 cases from T1D, and 2,897 controls  from the two data sets were retained for the following analysis. First, we conducted the analysis for the 58C controls with RA and the UKBS controls with T1D using LPG and BVSR. The prioritization results are shown in Fig. IL23R gene was identified to be strongly associated with CD by both methods, consistent with an earlier report by the WTCCC [p-value for the pleiotropy test was 2.96×10−2, suggesting the existence of pleiotropy between CD and T1D  from the two datasets were used for the analysis. After excluding the MHC region SNPs, leaving a total of 239,931 SNPs, we performed the same four comparisons. Here, we discuss the comparison with 58C controls for CD and UKBS controls for T1D after excluding the MHC region. Manhattan plots are shown in Additional file he WTCCC . Additiohe WTCCC . Anotherhe WTCCC , 33. TheIn this article, we proposed a novel statistical framework for leveraging pleiotropy in GWAS data. Compared with a single-trait-based analysis that does not leverage pleiotropy, LPG offers improved statistical power and prediction accuracy in the identification of risk variants. We developed an efficient algorithm based on VBEM, which not only enabled us to evaluate the posterior quantities of interest but also made the evaluation computationally scalable. These advantages make LPG a powerful tool to analyze GWAS data exhibiting pleiotropic effects. In this article, we analyzed two pairs of traits from WTCCC, namely, RA vs T1D and CD vs T1D. The findings reported here are consistent with the published literature.Despite these advantages, a current limitation of LPG is that it is not applicable to more than two traits. Modeling pleiotropic effects in a combinatorial fashion for more than two traits is challenging as the number of hidden association statuses increases exponentially with the number of traits. LPG was designed to leverage pleiotropy when GWAS data for multiple traits are collected from different study individuals, and LPG therefore complements the earlier methods proposed for incorporating pleiotropy when GWAS data are collected from the same study individuals , 9. HoweAdditional file 1The supplementary document contains additional simulation and data analysis results as well as derivation details. (PDF 11571 kb)