Is the genetic liability to psychotic experiences shared with schizophrenia and/or other neuropsychiatric disorders and traits?ANK3 and CNR2.In this cohort study, genetic correlation, polygenic risk score, and copy number variation analyses indicated a shared genetic liability between psychotic experiences and major depressive disorder, schizophrenia, bipolar disorder, and neurodevelopmental disorders. Genome-wide association studies identified 4 genetic loci associated with psychotic experiences including loci in Findings suggest that the genetic liability of psychotic experiences is shared with several psychiatric disorders, which include, but is not specific to, schizophrenia. Psychotic experiences, such as hallucinations and delusions, are reported by approximately 5% to 10% of the general population, although only a small proportion develop psychotic disorders such as schizophrenia. Studying the genetic causes of psychotic experiences in the general population, and its association with the genetic causes of other disorders, may increase the understanding of their pathologic significance.To determine whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits and to identify genetic loci associated with psychotic experiences.Analyses of genetic correlation, polygenic risk scores, and copy number variation were performed using data from participants in the UK Biobank from April 1, 2018, to March 20, 2019, to assess whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits. Genome-wide association studies of psychotic experience phenotypes were conducted to identify novel genetic loci. Participants in the final analyses after exclusions included 6123 individuals reporting any psychotic experience, 2143 individuals reporting distressing psychotic experiences, and 3337 individuals reporting multiple occurrences of psychotic experiences. A total of 121 843 individuals who did not report a psychotic experience formed the comparator group. Individuals with a psychotic disorder were excluded from all analyses.Genetic associations with psychotic experience phenotypes.P = 2.49 × 10−4) and neurodevelopmental disorders more widely . Genome-wide association studies identified 4 significantly associated loci, including a locus in Ankyrin-3 (ANK3 [GenBank NM_020987])  with any psychotic experience, and a locus in cannabinoid receptor 2 gene (CNR2 [GenBank NM_001841])  with distressing psychotic experiences. The genome-wide association study of any psychotic experience had a low single-nucleotide polymorphism–based heritability estimate .The study included a total of 127 966 participants . Psychotic experiences were genetically correlated with major depressive disorder, schizophrenia, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Analyses of polygenic risk scores identified associations between psychotic experiences and genetic liability for major depressive disorder, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. Individuals reporting psychotic experiences had an increased burden of copy number variations previously associated with schizophrenia (odds ratio [OR], 2.04; 95% CI, 1.39-2.98; A large genetic association study of psychotic experiences from the population-based UK Biobank sample found support for a shared genetic liability between psychotic experiences and schizophrenia, major depressive disorder, bipolar disorder, and neurodevelopmental disorders. This cohort study uses data from the UK Biobank to examine whether genetic liability to psychotic experiences is shared with schizophrenia and/or other neuropsychiatric disorders and traits and identify genetic loci associated with psychotic experiences. It is currently unclear whether psychotic experiences in the general population are: (1) on a spectrum that, at the extreme, is associated specifically with schizophrenia, (2) largely unassociated with the psychotic symptoms experienced in schizophrenia and other major mental disorders, or (3) associated with liability to major mental disorders more generally.Psychotic experiences, such as hallucinations and delusions, are features of psychiatric disorders , but they are also reported by approximately 5% to 10% of the general population.5 and 3%-17% for single-nucleotide polymorphism [SNP]–heritability estimates7), indicating that common genetic variants play a role in their liability. There have been 3 GWASs of psychotic experiences to date in adolescent samples9 and no reported genome-wide significant findings. Although there was an initial assumption that psychotic experiences in adolescence would specifically increase the risk for schizophrenia in later life, epidemiologic evidence suggests a nonspecific increased risk for broader psychopathologic characteristics.10 However, to date, no study has found strong evidence for an association between genetic liabilities for schizophrenia or any other mental disorder and psychotic experiences.13Twin studies and genome-wide association studies (GWASs) have provided evidence that psychotic experiences are heritable  was sent to all participants who provided an email address from July 13, 2016, to July 27, 2017, and was completed by 157 387 individuals . For psychotic experiences, participants were asked about previous experience of visual hallucinations, auditory hallucinations, delusions of reference, delusions of persecution, as well as how often these experiences occurred and how distressing they found them (eAppendix in the 20462); and (3) multiple occurrences of psychotic experiences, defined as any psychotic experience that occurred on more than 1 occasion . As a comparator group, we included individuals who provided a negative response to all 4 psychotic experience symptom questions. In addition, we investigated each individual psychotic experience symptom for association with polygenic risk scores (PRSs).We selected 3 primary phenotypes for GWASs . One member from each related pair with a kinship coefficient greater than 0.15 was excluded from analyses, preferentially retaining individuals who had experienced a psychotic experience, and were otherwise removed at random.Genetic data for the study participants were provided by UK Biobank and the imputation and quality control procedures are fully described elsewhere.21000) and principal components supplied by UK Biobank16 (UK Biobank field ID: 22009) were used to make additional exclusions and control for population structure , and any additional principal components from the first 20 that were nominally associated (P < .05) with the GWAS phenotype in a logistic regression. To obtain relatively independent index SNPs, linkage disequilibrium (LD) clumping was performed in PLINK23  for each GWAS using a reference panel of 1000 randomly selected individuals with confirmed European ancestry in the UK Biobank. Functional annotation was conducted using FUMA24 and LDSC25 was used to calculate the LD score intercept and heritability on the observed scale using the summary statistics from each psychotic experience GWAS.To identify genetic risk variants for psychotic experiences, association analysis was performed in SNPTEST, version 2.5.427 which have been previously described.28 The psychotic experience PRS was generated for each ALSPAC participant using the widely used method29 and logistic regression was used to test for an association between PRS and psychotic experiences reported at 12 and 18 years of age  cohort,30 was used to calculate the genetic correlation between the GWAS of any psychotic experience and other psychiatric and personality traits. External GWAS data sets (that did not include data from the UK Biobank where possible) used to generate the correlations included schizophrenia,31 bipolar disorder,32 major depressive disorder,33 attention-deficit/hyperactivity disorder (ADHD),34 autism spectrum disorder,35 neuroticism,36 and intelligence.37 A Bonferroni correction was applied to control for multiple testing.LDSC29 and detailed in eMethods 4 in the P value threshold of P ≤ .05, but associations at 10 other P value thresholds were also tested. A logistic regression model was used to test the association of each PRS with various psychotic experience phenotypes, covarying for the first 5 principal components and genotyping array.PRSs were generated to investigate additional psychotic experience phenotypes that were not sufficiently powered for genetic correlation analyses. We selected the same summary statistics used for genetic correlations to create the risk scores for UK Biobank participants using the method described by the Psychiatric Genomics Consortium38 and is detailed in eMethods 5 in the 39 and neurodevelopmental disorders more widely40 with the 3 primary psychotic experience phenotypes used for GWASs. Association analyses were carried out using logistic regression and included age, sex, and genotyping array as covariates.Copy number variation (CNV) calling for the UK Biobank has been described in detail elsewhereA total of 7803  individuals from the UK Biobank who completed the MHQ reported at least 1 psychotic experience, 3012 individuals rated the psychotic experience as distressing, and a total of 4388 individuals reported multiple occurrences of at least 1 psychotic experience. A total of 147 461 individuals  who reported no psychotic experiences constituted the comparison group for our association analyses. The mean (SD) age of the first psychotic experience was 31.6 (17.6) years : rs10994278, an intronic variant within Ankyrin-3 (ANK3 [GenBank NM_020987]) , and intergenic variant rs549656827 .The GWAS of any psychotic experience in 6123 cases and 121 843 controls : rs75459873, intronic to cannabinoid receptor 2 (CNR2 [GenBank NM_001841]) , and intergenic variant rs3849810 .A second GWAS restricting the cases to 2143 individuals with distressing psychotic experiences identified 2 genome-wide significance variants . QQ plots for each GWAS are displayed in eFigure 5 in the The third GWAS restricting the cases to 3337 individuals who reported multiple occurrences of psychotic experiences did not identify any associated variants at genome-wide significance . The other GWAS analyses did not requirements (case n >5000 and z score >441) for heritability or genetic correlation analyses with LDSC. None of the associated regions from the GWAS for any psychotic experience and distressing psychotic experience showed evidence of colocalization42 with schizophrenia,31 bipolar disorder,32 or major depressive disorder.33Single-nucleotide polymorphism–based heritability estimates calculated by LDSCP value threshold of ≤.5 and definite psychotic experiences between 12 and 18 years of age in ALSPAC . This finding was consistent for thresholds above P < .05 and when using measures from age 18 years only .There was evidence of an association between the PRS calculated using the GWAS of any psychotic experience from the UK Biobank sample at the CNR2. Cannabis use itself (UK Biobank field ID: 20453) was significantly associated with distressing psychotic experiences , but rs75459873 was not associated with cannabis use . Furthermore, the association between rs75459873 and distressing psychotic experiences was unchanged in a model including cannabis use as a covariate  or as an interaction term .We investigated, but found no evidence for, a mediating or moderating association of cannabis use with the association between distressing psychotic experiences and rs75459873 at the cannabinoid receptor gene gr) were observed between any psychotic experience and major depressive disorder , autism spectrum disorder , ADHD , and schizophrenia . 41 for genetic correlation analysis.Significant genetic correlations , major depressive disorder , bipolar disorder , ADHD , and autism spectrum disorder . These associations were stronger for distressing psychotic experiences  and neurodevelopmental disorders . There was evidence of an increased burden of these CNVs in individuals reporting any psychotic experience, but not multiple occurrences of psychotic experiences , autism spectrum disorder (gr = 0.39), ADHD (gr = 0.24), and schizophrenia (gr = 0.21). Polygenic risk score analyses identified associations between psychotic experiences and genetic liability for schizophrenia, major depressive disorder, bipolar disorder, ADHD, and autism spectrum disorder, and we found particular enrichment of these PRS scores in distressing psychotic experiences and for delusions of persecution. However, given the possibility of sample overlap between the UK Biobank and the training sets, the PRS findings should be treated with caution. The mechanisms underlying the high genetic correlation between depression and psychotic experiences cannot be discerned from our analysis, but one possibility is that the psychotic experiences have arisen in the context of mood-related changes, consistent with the strong associations between psychotic experiences and depressive symptoms observed in population-based studies.43 Nonetheless, when individuals with a lifetime history of depression were excluded from PRS analyses, the association remained, indicating that the findings are not wholly attributable to depression.We conducted the largest genetic-association study of psychotic experiences, to our knowledge, using the population-based UK Biobank sample and found evidence of a shared genetic liability between psychotic experiences and several psychiatric disorders, which included, but was not specific to, schizophrenia. Genetic correlation analysis identified significant genetic correlations between psychotic experiences and major depressive disorder  was low and, given that the variance explained in our PRS analysis was also low, the findings suggest that understanding the genetics of psychotic experiences is unlikely to have an important effect on understanding the genetics of schizophrenia specifically.In the largest GWAS of psychotic experiences to date, we identified 4 genome-wide significant loci. However, consistent with other studies,ANK3 and CNR2. The GWAS of any psychotic experience identified 2 significant loci, the most significant of which was indexed by rs10994278, an intronic variant to ANK3 . The ANK3 gene encodes ankyrin-G, a protein that has been shown to regulate the assembly of voltage-gated sodium channels and is essential for normal synaptic function.48ANK3 is one of strongest and most replicated genes for bipolar disorder,32 and variants within ANK3 have also been associated in the Psychiatric Genomics Consortium cross-disorder GWAS,49 and in a rare variant analysis of autism spectrum disorder.50The primary GWAS findings are related to intronic variants in CNR2 . CNR2 encodes for CB2, 1 of 2 well-characterized cannabinoid receptors (CB1 being the other). Several lines of evidence have implicated the endocannabinoid system in psychiatric disorders, including schizophrenia52 and depression.53 The main psychoactive agent of cannabis, Δ9-tetrahydrocannabinol, can cause acute psychotic symptoms and cognitive impairment.54 Given that cannabis use is strongly associated with psychotic experiences, we tested, but found no evidence for, a mediating or moderating effect of cannabis use on the association of rs75459873 and distressing psychotic experiences. However, while no evidence was found in this study, a mediating effect of cannabis use cannot be ruled out given the relatively low power of such analyses and the potential measurement error in cannabis use assessed via lifetime self-report. Independent replication of genetic loci identified in this study will be required to further understand their role in subclinical psychotic experiences.The GWAS of distressing psychotic experiences also identified 2 significant loci, the most significant of which was indexed by rs75459873, an intronic variant to 55 We also found that the participants who completed the MHQ had significantly higher intelligence and lower schizophrenia, depression, and neuroticism PRS compared with UK Biobank participants who did not complete the MHQ (eTable 6 in the Strengths of this study include the large sample size (approximately 10 times that of previous studies), the use of an adult cohort, and the use of multiple psychotic experience phenotypes, which increase confidence in our findings. This study also has some limitations. Although we used all available information to identify and remove individuals with a psychotic disorder, it remains possible that some individuals were not identified and remained in the analysis. A further limitation of this study relates to the retrospective measurement of lifetime psychotic experiences by self-report from an online questionnaire, as this increases the likelihood of measurement error. A further limitation is the evidence of a “healthy volunteer” selection bias for the participants recruited to the UK Biobank and the sample cannot be therefore considered representative of the general population.In the largest GWAS of psychotic experiences from the population-based UK Biobank sample, we found support for a shared genetic liability between psychotic experiences and several psychiatric disorders including schizophrenia, major depressive disorder, bipolar disorder, and neurodevelopmental disorders, indicating that psychotic experiences are not specifically associated with schizophrenia, but rather with a general risk for mental health disorders.


Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies. Population structure can bias the results of genetic and epidemiological analysis. Here, Haworth et al. report that fine-scale structure is detectable in apparently homogeneous samples such as ALSPAC when measured very precisely, and remains detectable in UK Biobank despite conventional approaches to account for it. With the advent of data at genome-wide scale, it became possible to parameterise the impact of latent structure on genetic association results using principal components (PCs)2 derived from genotype data among other approaches3. These methods, in conjunction with stringent genome-wide significance thresholds4 and the requirement for replication in an independent population, led to substantial improvement in replicability of genetic association studies.Genotype data are used to answer an increasing number of research questions through an increasing number of methods. Latent structure within genetic data was originally identified as a problem in candidate gene association studies, where population stratification was “probably the most often cited reason for non-replication of genetic association results”6 because population structure and polygenic traits both impart genome-wide signatures which may be truly (rather than spuriously) related. For example, genetic height7 varies amongst historical populations from which modern populations represent different mixtures. Stratification may be part of the explanation for different signatures of selection for height in UK Biobank compared to the GIANT consortium9. Analysis of rare genetic variation is another challenge, as the latent structure within this subset of genetic data may not reflect the latent structure of common variants used to generate PCs10.In other contexts, detecting and accounting for latent structure remains problematic. Determining heritability of complex traits is challenging11. A good example of this is Mendelian randomisation, which aims to escape confounding in associations by using genetic variation to proxy risk factors of interest12. Recent literature has focussed on maximising the use of the current wave of genetic association evidence and accounting for undesirable pleiotropic effects of single variants13. This activity has largely assumed that structure is addressed during the discovery of associated genetic variants, an assumption which now warrants closer examination. If present, latent structure within datasets used to perform epidemiological analyses would violate the requirement that genetic instrumental variables are not related to potentially confounding features14 and may result in biased epidemiological inference.An entirely different context for using genetic data is in epidemiological analyses which have developed substantially with the availability of reliable genetic association results from published sources and very large collections of genetic and phenotypic data such as UK BiobankHere, we use geographical information in conjunction with genetic data to investigate latent structure (of unknown cause) in two population-based cohorts in the United Kingdom. We show that single genetic variants and polygenic scores incorporating multiple variants are associated with birth location in data from UK Biobank. Given regional differences in many health outcomes, this observed structure provides a source of covariance between genotypes and health outcomes which can bias epidemiological inference from genetic data. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies.16 as an exemplar. We studied 7739 mothers who were recruited during pregnancy in the Bristol area (South West UK) in the early 1990s. We undertook chromosome painting17 to describe fine-scale relatedness between each mother and each of the regions of the Peopling of the British Isles (PoBI) project18, acting as an external source of geographical information. We summarised each mother’s ancestral lineage as a mixture of the PoBI regions, allowing us to estimate the educational attainment that those regions would have were the ALSPAC mothers’ education levels explained by this variation. In doing this a pattern for lower educational attainment in lineages originating from the regions immediately surrounding Bristol 20. Conceptually, UK Biobank is analogous to a super-imposition of multiple ALSPACs, each of which recruited participants living near a study assessment centre. This design not only gives UK Biobank the capacity to represent a broad spectrum of UK ancestry and structure, but also means that the study is sensitive to important sampling phenomena including self-selection. The hurdles of location and attendance  are likely to influence the nature of the resultant participant collection and are related to behaviours with heritable contributions22. This may create collider biases24 which have the ability to induce association between otherwise independent variables.The structure in ALSPAC was detected here using a chromosome painting method, which is highly sensitive to ancestry. With greater power, it is entirely possible the same phenomena may become detectable in more routine analytical procedures for gene discovery or epidemiological analysis. We therefore turned to UK Biobank, an exceptional resource containing a catalogue of health, disease and genotype data of almost half a million participants25. The outcomes were North/South and East/West axes of birth location, both measured on a metre grid scale from an origin South West of the United Kingdom. Analysis of genetic data was performed within individuals of white British ancestry with non-missing data on birth location . GWAS for birth location identified that single variants are associated with geography within UK Biobank. An unadjusted model produced distorted and inflated plots with evidence for association at variants across the autosome. After adjustment for genotyping array, 40 PCs and a factor variable representing UK Biobank assessment centre single variants remained associated with birth location  for birth location in PLINKon n = 32,439. GWA27. As exemplars, we took genetic variants and weightings associated with educational attainment, height and body mass index (BMI) from published genome-wide meta-analyses30 excluding UK Biobank. Using an approach that is widespread in applied analyses, we used these externally derived variants and weightings in conjunction with the UK Biobank genetic data to create polygenic scores for the three traits. Aiming to understand the properties of these polygenic scores under a range of analytical contexts, we created both weighted and unweighted PS at a strict and more liberal threshold of association in the discovery sample (p < 5e−08 and p < 1e−05 respectively). We used general additive models31 in the ”mgcv” package (version 1.8)32 within R (version 3.3.1)33 to test for non-linear relationships between PS and geographical terms. All PS tested were associated with birth location in an unadjusted model and a model that adjusted only for genotyping array. These associations attenuated but were not extinguished in models incorporating adjustment for 40 PCs and study centre, especially for educational attainment and birth location on the North/South axis, where statistical adjustment had little impact on the fitted geographical distribution of the PS p < 2e−16 for non-linear relationship between observed traits and axes of birth location).Having found evidence for association between genotypic variation and geography, we used general additive models to test for non-linear relationships between four exemplar complex traits and geography. Reported household income, measured BMI, reported age at completion of full-time education and reported number of siblings showed strong evidence for geographical stratification . In this analysis, each PS was associated with at least one complex trait and these associations attenuated with adjustment for latent structure, collectively suggesting PS predict geographical location within the UK Biobank sample  with expected delivery dates between 1 April 1991 and 31 December 1992. Since then, participating mothers and their children have been followed up with serial clinical data, questionnaire data and biosample collection. A nested cohort following children of the index offspring has been formed and data on fathers of the index offspring are also available. Further information on the index offspring and mothers cohorts are availableEthical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees, and this research complied with all relevant ethical regulations. Participants gave informed consent. This publication is the work of the authors and N.T. and S.H. who will serve as guarantors for the contents of this paper.p value for Hardy–Weinberg equilibrium smaller than 1e−6. Participant-level quality control removed variants with uncertain X chromosome heterozygosity, extreme autosomal heterozygosity or more than 5% overall missingness. Next, multi-dimensional scaling of genome-wide data was performed including reference data from HapMap populations. Samples which clustered outside the CEU population were removed. Following these measures, data for 9048 participants and 526,688 SNPs were available. Related participants were identified by estimating inheritance by descent, where an estimate of greater than 0.125 was considered to represent cryptic relatedness. Data from these participants were included in phasing and imputation, but prior to analysis related individuals were removed from the dataset until no cryptically related pairs were present, yielding a final sample size of 8196 mothers.Genotype data for ALSPAC mothers were generated using the Illumina human660w genotyping array and genotypes were called with Illumina GenomeStudio, yielding 557,124 directly genotyped single-nucleotide polymorphisms (SNPs) in 10,015 participants. Participants and SNPs were carried through to imputation if they passed quality control measures implemented in PLINK (v1.07). SNP-level quality control removed variants with more than 5% missingness, or 17 coupled with high-resolution spatial data from the PoBI18. We first merged the ALSPAC mothers and PoBI data into a set of shared SNPs (using the ALSPAC imputed data). We then performed phasing using Shapeit46 and then Impute247 to impute any remaining missing SNPs. Finally, we used ChromoPainter17 to paint each individual against all individuals from the 35 labelled populations given in PoBI to obtain a genome-wide estimate of haplotype sharing.To infer within-UK ancestral origin of these mothers, we used Chromosome Painting48 for each of the ALSPAC participants, in terms of the 35 labels. In total, 7739 ALSPAC mothers and 2,039 PoBI participants were included in analyses.We then constructed a reference panel for the PoBI data by averaging the painting for all individuals in each of the labelled populations. We then used the Non-Negative Least Squares method for estimating ancestry49 for this. Let AB=C, where A is an N by K matrix of admixture estimates, B is the population phenotypes to be inferred, and C is the observed individual phenotype estimates, i.e., measured education. Then, we can solve B=(ATA)−1ATC.We did not observe many individuals with ancestry from only a single PoBI region. Therefore, we have to infer the underlying population averages that, given the mixture that we observed, would have given rise to the data. We use the method of Lawson et al.18) could result in the inference being unidentifiable. For this question regarding average phenotype, this lack of identifiability does not matter since populations are represented in the correct proportions, on average. This is evidenced by the clear structure visible in Fig. We note that this procedure is solely used to generate a visualisation of the relationship between genetic information and migration status and is not intended to reflect inference regarding individual-level ancestry. At the individual-level, using genetically non-distinct populations . In analyses adjusted for assessment centre, these codes were treated as factor variables.Participants who were born in the United Kingdom were asked to name their place of birth during a verbal interview at study assessment centres. These answers were used to derive approximate North and East co-ordinates . Values less than zero were coded as missing for both variables.Household income was obtained from baseline data in UK Biobank. Participants were asked to report the annual income for their household in pounds sterling using one of the following options  (field 738). To allow inclusion in a linear regression model these categories were recoded to values in pounds sterling in the midpoint of each category. For categories with only an upper or lower bound, the difference between the midpoint and boundary of the next adjacent category was used to estimate a midpoint as follows . Household income was coded as missing for participants who preferred not to answer or did not know their household income.Number of siblings was obtained from baseline data. Participants were asked to report how many full brothers they had  (field 1873). A matching question was asked for sisters (field 1883). The responses for these two questions were combined to create a number of full siblings. This variable was coded as missing for participants who preferred not to answer or did not know for either source question.2). This variable was coded as missing if either height or weight measures were missing.BMI was derived by UK Biobank from height and weight measures during the initial assessment centre visit (field 21001) as weight divided by height squared (units kg/mAge at completion of full-time education was taken from baseline data (field 845). Participants were asked to report their age (in years) when they completed full-time education. This variable was set to missing for participants who responded that they never went to school, did not know or preferred not to answer. This question was not asked for participants who had previously indicated they had a college or university degree and this is reflected in the smaller sample size compared to other complex traits.Participants were ranked by North/South axis of birth location within the United Kingdom, and divided into 100 bins, each with an equal number of participants. Within each bin, the mean and standard deviation of each complex trait  was summarised, then new values for that trait were drawn from a random distribution with the same mean and standard deviation. The procedure was repeated for East/West axis of birth location, yielding two new values for each simulated trait. These values were combined with equal weighting, producing a total of four simulated traits which aimed to preserve coarse geographical variation across the sampling frame of UK Biobank whilst greatly reducing or eliminating direct biological effects.20. Following imputation we removed variants that were not present within the haplotype reference consortium (HRC) imputation panel and applied a graded filtering on imputation quality. Rarer variants were required to have a higher imputation INFO score  > 3%; Info > 0.6 for MAF 1–3%; Info > 0.8 for MAF 0.5–1% and Info > 0.9 for MAF 0.1–0.5%). We removed 378 individuals with a mismatch between genetic sex and reported sex and 352 individuals with putative sex chromosome aneuploidy. We performed analysis within individuals who self-reported as “British” and had similar ancestral background from genetic PCs . We applied an exclusion list containing 79,448 individuals, whilst preferentially removing individuals related to the greatest number of other individuals so that no related pairs remained in the final sample used for analysis. A comprehensive description of quality control methods has been published online51.We used the UK Biobank 500k (July 2017) genotype release, for which pre-imputation quality control, phasing and imputation are described elsewhereWe used genetic PCs supplied by UK Biobank (field 22009). These were calculated using a set of 407,219 unrelated, high-quality samples and 147,604 high confidence markers after pruning for linkage disequilibrium. Participants with missing PCs were excluded from analysis.25. A full description of the analytical pipeline has been published online52. All models included adjustment for genotyping array and sex. Assessment centre was treated as a factor variable (where included) and PCs were treated as linear covariables.GWAS for birth location were performed using PLINK 28. Variants and weights for height and BMI were taken from the entire meta-analysis results from the GIANT consortium30. For each of these three traits, we obtained clumped instruments from the MRBase repository using the “extract_instruments” option in the TwoSampleMR R package53. Effect allele dosage was extracted for these variants from the filtered UK Biobank genotype data. Effect allele dosage was weighted by reported genetic effect (beta) and then summarised across all contributing variants to create per-individual PS. Unweighted PS were created in parallel, which included the same variants but only considered direction of effect, not reported effect size. PS were z-transformed after application of all exclusion criteria. For sensitivity analysis, variants and weights for BMI were taken from published results in Biobank Japan34, clumped using reference data from East Asian ancestry participants in 1000 genomes and then used to derive PS following the same workflow as the main analysis.We took variants and weights associated with educational attainment from the discovery phase of a recent genome-wide meta-analysis (excluding the replication phase in UK Biobank)32 in R (version 3.3.1)33. Traits were modelled against a spline function for either birth northings or birth eastings, in the form t ~ s(location). Approximate statistical significance for non-linear terms was taken from the model summary, which estimates a suitable number of degrees of freedom from cross validation.The relationship between complex traits and geographical parameters was modelled using the ‘mgcv’ package (version 1.8)ps ~ s(location)+array. Fully adjusted models included factor variables for study centre and up to 40 genetic PCs.The relationship between PS and geographical parameters was modelled in a similar way, but incorporated a variable for genotyping array as the minimum adjustment, in the form t ~ ps. Where relevant, non-linear covariables were included as spline terms in the form t ~ ps+s(birth_location)+other covariables. Simulated complex traits were modelled in exactly the same way as observed complex traits.The relationship between complex traits and PS was modelled as a linear relationship to obtain indicative effect sizes, and took the form Further information on experimental design is available in the Supplementary InformationReporting Summary


There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases for which multiple correlated traits are often measured. Joint analysis of multiple traits could increase statistical power by aggregating multiple weak effects. Existing methods for multiple trait association tests usually study each of the multiple traits separately and then combine the univariate test statistics or combine p-values of the univariate tests for identifying disease associated genetic variants. However, ignoring correlation between phenotypes may cause power loss. Additionally, the genetic variants in one gene (including common and rare variants) are often viewed as a whole that affects the underlying disease since the basic functional unit of inheritance is a gene rather than a genetic variant. Thus, results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigation, whereas many existing methods for multiple trait association tests only focus on testing a single common variant rather than a gene. In this article, we propose a statistical method by Testing an Optimally Weighted Combination of Multiple traits (TOW-CM) to test the association between multiple traits and multiple variants in a genomic region (a gene or pathway). We investigate the performance of the proposed method through extensive simulation studies. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful tests. Additionally, we illustrate the usefulness of TOW-CM based on a COPDGene study. Complex diseases are often characterized by many correlated phenotypes which can better reflect their underlying mechanism. For example, hypertension can be characterized by systolic and diastolic blood pressure ; metabolEven though genome-wide association studies (GWASs) have been remarkably successful in identifying genetic variants associated with complex traits and diseases, the majority of the identified genetic variants only explain a small fraction of total heritability . FurthueIn GWAS, several methods have been developed for multivariate phenotypes association analysis  to test Recent studies show that complex diseases are caused by both common and rare variants –20. Genen unrelated individuals. Each individual has K  traits and has been genotyped at M variants in a considered region (a gene or a pathway). Denote yik as the kth trait value of the ith individual and xim as the genotype score in additive coding of the ith individual at the mth variant. Let Y =  denote the random vector of K traits and X =  denote the random variable of the genotype score at M variants for these n individuals where Yk = T and Xm = T. Consider a linear combination of Y denoted as w = T.We consider a sample with M genetic variants in the considered region using the linear modelβ0 is the intercept and β = T is the corresponding vector of coefficients. To test the association between the combination of the multiple traits and the M genetic variants is equivalent to test the null hypothesis H0: β = 0 under H0: β = 0 under U = (PX)′ YwP and w:P = P′ and PP′ = P. We propose to maximize S(w) to get the optimal weight and then define the statistic to evaluate the association between the optimally weighted combination of the target traits and test genetic variants.We model the relationship between the combination of multiple continuous traits with the D = Y′ YP is positive definite, maximizing S(w) is equivalent to maximizingL is the lower triangular matrix obtained from the Cholesky decomposition of D = LLT. However, the matrix of D is usually not full rank because of existing correlation between multiple traits. If the matrix D is semi-positive define matrix, we introduce a ridge parameter λ0, for which we suggest the choice n is the number of individuals in the testing data, and modify the adjustment to mitigate the effect of the non-positive matrix D in order to avoid the instability: D = Y′ YP + λ0I. Let C = L−1Y′ XP(X′ XP)−1X′ YLPT− and c be the eigenvector corresponding to the largest eigenvalue of the matrix C, then S(w) is maximized when L′(w) equals c. Hence ow = LT−c. In a special case, if all the traits we consider are independent and M = 1, we can get an analytical weight referred to ρ| = 0.2)Overall, we can see that the proposed TOW-CM is an attractive approach that provides good power in most of the scenarios.http://www.copdgene.org/sites/default/files/GWAS_QC_Methodology_20121115.pdf.Chronic obstructive pulmonary disease (COPD) is one of the most common lung diseases characterized by long term poor airflow and is a major public health problem . The COPBased on the literature studies of COPD , 29, we 7 permutations for TOW-CM and minP methods. For this verification study, we use 0.05 as the significance level for MANOVA, MSKAT and TOW-CM methods and use Bonferroni corrected significance level 0.05/7 = 7.14 × 10−3 for minP methods since this method perform association tests across each trait, respectively. The results are summarized in −3 for a multiple testing issue with 50 genes in total. Because the MAFs of most variants are not in the range of  which is a range favoring MSKAT, MSKAT performs worse than the other three comparable methods . TOW-CM and minP perform better than MANOVA, which is perhaps because only a proportion of phenotypes are associated with COPD. The method minP missed some genes in comparision to our method TOW-CM, it may because the method minP ignores the correlation between these seven phenotypes.To evaluate the performance of our proposed method on a real data set, we applied all of the 4 methods  to the COPD associated genes or genes containing significant single-nucleotide polymorphisms (SNPs) in NHW population with COPD-related phenotypes . In the GWAS have identified many variants with each variant affecting multiple phenotypes, which suggests that pleiotropic effects on human complex phenotypes may be widespread. Also, recent studies have shown that complex diseases are caused by both common and rare variants , 16, 19.https://github.com/Jianjun-CN/TOW-CM/blob/master/R%20Code Furthermore, TOW-CM method can not only be used for gene-based association studies, but also can be extended to transcriptome-wide association study (TWAS), which needs further investigations.One disadvantage of TOW-CM is that the test statistic does not have an asymptotic distribution and a permutation procedure is needed to estimate its P-value, which is time consuming compared to the methods whose test statistics have asymptotic distributions. To save time when applying TOW-CM to genetic association studies, we can use the “step-up” procedure  to deter


Urinary sodium and potassium excretion are associated with blood pressure (BP) and cardiovascular disease (CVD). The exact biological link between these traits is yet to be elucidated. Here, we identify 50 loci for sodium and 13 for potassium excretion in a large-scale genome-wide association study (GWAS) on urinary sodium and potassium excretion using data from 446,237 individuals of European descent from the UK Biobank study. We extensively interrogate the results using multiple analyses such as Mendelian randomization, functional assessment, co localization, genetic risk score, and pathway analyses. We identify a shared genetic component between urinary sodium and potassium expression and cardiovascular traits. Ingenuity pathway analysis shows that urinary sodium and potassium excretion loci are over-represented in behavioural response to stimuli. Our study highlights pathways that are shared between urinary sodium and potassium excretion and cardiovascular traits. et al. perform genome-wide association studies for urinary sodium and potassium secretion and identify 50 and 13 novel loci, respectively, some of which show a potential causal relationship with blood pressure based on MR analysis.Levels of sodium and potassium in urine are associated with cardiovascular traits. Here, Pazoki  Sodium consumption is a risk factor of CVD and is estimated to have caused 1.65 million CVD deaths in 20106. At the same time, epidemiological studies, animal models, and clinical trials support a strong link between sodium intake7, and BP;10 the leading modifiable cause of morbidity and mortality from CVD worldwide. Biological mechanisms that link these traits together are not exactly clear. To understand the genetic and physiological pathways underlying these electrolytes and their link to BP and cardiovascular events, we undertake a genome-wide association study (GWAS) on urinary sodium and potassium excretion among 446,237 European individuals in UK Biobank (UKB)13. Here, we identify 50 sodium and 13 potassium novel loci at stringent threshold of P < 1 × 10−8. Most of these loci had previously been found to be associated with lipid levels, anthropometric and lifestyle traits such as dietary intake, smoking-related behavior, and alcohol consumption at GWAS significance level (P < 5 × 10−8). In pathway analyses, sodium and potassium excretion loci are over-represented in biological functions involving behavioral response to stimuli, thermoregulation, and weight loss. A subset of loci involved in behavioral response to stimuli support a link to BP and coronary artery disease based on Mendelian randomization (MR) analysis.Cardiovascular disease (CVD) leads to 17.5 million annual deaths worldwide14. We, included ~8.8 M single-nucleotide polymorphisms (SNPs) imputed to the Haplotype Reference Consortium (HRC) panel at MAF < 0.5% from European ancestry participants in UKB (genotyping and imputation [GRCh37] data release 2017). Characteristics of the population are presented in Supplementary Table We performed GWAS of urinary sodium and potassium excretion (from spot urine) using linear mixed model (LMM) association testing implemented in BOLT-LMM (v2.3) software Fig. 14. We, iMLIP gene (rs838133) (P = 1.9 × 10−25) followed by CYP1A1 (rs2472297) (P = 6.7 × 10−23) and FTO (rs11642015) (P = 6.7 × 10−23) loci. MLIP is a muscular lamin A/C interacting protein with protein binding transcription factor activity. CYP1A1 encodes a cytochrome P450 superfamily enzyme involved in drug metabolism and lipid synthesis. It is also known for association with habitual coffee intake15. FTO is associated with body mass index (BMI) and other anthropometric traits16. We, additionally, identified SNPs within neuronal sodium channel and potassium channel loci SCN2A and KCD13, as well as ten microRNA and long intergenic noncoding RNA genes. The strongest urinary potassium signal was at ADRA2C  followed by CYP1A1 and AHR loci; the latter two genes have previously shown association with coffee intake15.The strongest urinary sodium locus was in n = 262,531) excluding participants who suffered from renal diseases or participants who used medications which may affect sodium and potassium excretion showed that 17 sodium SNPs annotated to AHR, MLIP, CYP1A1, ADH1B, LINC01114, LINC02424, RARB, LOC105378330, DCDC1, PKHD1, NOVA1-AS1, MLXIPL, FTO, MIR642A, GCKR, HTR4, and SCN2A and 3 potassium SNPs annotated to ADRA2C, SLC4A7, CYP1A1 remained genome-wide significant despite the large reduction in sample size (Supplementary Datas P < 1 × 10−5) associated with urinary traits, the slightly larger P value is likely due to the decrease in the sample size as the effect estimates are quite small. Effect estimates of the lead SNPs were correlated (r = 0.98) before and after exclusion for urinary sodium. The correlation was 0.97 for urinary potassium SNPs. Genome-wide correlation of effect estimates before and after exclusion was 0.79 for urinary sodium and potassium SNPs.Our sensitivity analysis  urinary sodium loci and 10 (77%) urinary potassium loci were associated with UKB anthropometric traits and/or dietary habits including salt added to food, coffee, fruit, and water intake at P < 1 × 10−6 20. SNPs in loci annotated to MIR588, CYP1A1, DCDC1, DCTPP1, and MLIP were associated both with SBP and DBP. Of the potassium loci, only SLC4A7 and CYP1A1 loci were associated with SBP and DBP. SLC4A7 gene is a co-transporter of sodium/bicarbonate and is known to cause hypertension in model organisms21. We, additionally, observed that sodium excretion genetic risk score (GRS) comprising the collective effect of our lead urinary sodium SNPs among the European ancestry subset of the UKB was associated with average annual increase in SBP  and DBP .With regard to pleiotropy with BP, we observed that eight sodium loci were associated with systolic BP (SBP) and ten with diastolic BP (DBP) (Supplementary Datas 22 (Methods) showed that 12 sodium and potassium loci  and GCKR showed association with numerous metabolites. GCKR is known to be involved in diabetes risk and metabolism of glucose and lipids28. It is well established that insulin decreases excretion of sodium in the kidneys29. Insulin-stimulated sodium transporters are found all along the nephron, in renal proximal tubule, loop of Helen, distal convoluted tubule, and cortical collecting duct30.Our online metabolomics look-up within metabolomics database of Phenoscanner31, showed that GCKR was associated with mannose (an epimer of glucose), alanine, isoleucine, pyruvate , and various lipid metabolites. We also observed that FKBP8 potassium locus was associated with myo-inositol a metabolite of vitamin B complex group  associations. Urinary sodium variants showed eQTL effect on gene expression levels for 20 genes and potassium variants for four genes (Supplementary Table Using the Genotype-Tissue Expression (GTEx) databaseMLXIPL, MLLT10, BDNF, NRXN3, STK24, GCKR, and AHR) and three urinary potassium loci  that harbor genetic variants which demonstrate association with both urinary traits and gene expression ior Fig. , followeior Fig. . BehavioWe performed a second IPA analysis using loci from our sensitivity analysis excluding individuals under treatment with BP lowering medications, NSAIDS, corticosteroids, and self-reported or hospital records of renal diseases. This exclusive IPA analysis showed that our urinary sodium and potassium loci are involved in thermoregulation, body temperature, energy homeostasis, function of liver, benign lesions, and congenital anomalies of the kidney.n = 63) from Supplementary Table 36. The aim was to test effect of sodium and potassium excretion on BP and CVD. We performed MR analysis using effect estimates of sodium and potassium excretion lead SNPs in full UKB sample against effect estimates of these SNPs for BP from the International Consortium for BP (ICBP) results. There is no sample overlap between ICBP and UKB.The list of lead SNPs  and south  . According to our diagnostic plots, we infer that few outliers in the MR analysis of urinary traits on BP are likely to violate InSIDE assumptions. This leads to a large difference between effect estimate from MR-Egger and effect estimate from other methods. Upon removal of outliers, the remaining instruments are more likely to meet the InSIDE assumption and hence in our analyses, P value of MR-Egger increased to a non-significant level in most cases.We observed large differences between MR-Egger and other MR tests. MR-Egger estimates are less efficient than estimates from other methods and are highly sensitive to violations of the MR assumptions and the validity of the InSIDE assumption in particular  and on SBP, and DBP  identified in discovery stage together with all SNPs in 1 MB region surrounding the lead SNPs if they showed association with urinary traits at P < 1 × 10−8, were sought for replication among other ancestries in the UKB. SNPs within DCDC1 locus were replicated (LMM-based Preplication < 9.8 × 10−4) among the UKB African population (n = 7612) and SNPs in a region flanked by MIR-588 and CENPW genes replicated in the UKB South Asian population  (Supplementary Data DCDC1 is in close vicinity (<1 MB) of a potassium channel gene (KCNA4) and is not previously known to impact electrolyte levels. We also observed that urinary sodium GRS, which we calculated based on lead SNP effect estimates obtained from the main GWAS analysis, was associated with slight increase in urinary sodium excretion among south Asians .All lead SNPs . In such circumstances, including extensive pleiotropy, current MR methodologies are limited in their ability to provide causal interpretations.Our study supports a genetic link between urinary traits, adiposity, BP and CHD. Urinary traits reflect the complex interplay between dietary intake, homeostatic mechanisms that tightly control intra- and extra-cellular concentrations of sodium and potassium excretion via the kidney and other pathways13 study which includes approximately 500,000 individuals aged 40–69 years from European, African, and Asian ethnicity. North West Multi-Center Research Ethics Committee has approved the UKB study. Approval for this research was obtained from the UK Biobank Research Ethics Committee and Human Tissue Authority, and the participants gave informed consent. We removed from our analysis all participants from the UKB study who withdrew consent.We used data from the UKB13. We included individuals of European ancestry following quality measures and exclusions . Individuals with non-European ancestry were excluded from the main analysis. Allocating individuals to ethnicity groups was based on self-reported ethnicity matched with PCA ancestry clustering using the kmeans clustering method. We excluded participants who had withdrawn consent (n = 19), as well as those who were pregnant or unsure of their pregnancy status at baseline (n = 372). After removing participants of non-European ancestry  and individuals with missing values on urinary sodium  and urinary potassium , 446,237 individuals were left for GWAS of urinary sodium and 446,230 individuals for GWAS of urinary potassium. As part of the study, we performed MR analysis (see below) to investigate potentially causal effects of urinary traits on BP. According to MR methodology, variance of an instrumental variable estimate will be smaller if it is estimated from the same study52. When distinct samples are used, bias due to weak instrumental variable shifts results toward the null. In contrast, in a situation where the two samples perfectly overlap, the weak instrument bias shifts the results toward the observational association between the risk factor and outcome53. Since our study avoided sample overlap, potential weak-instrument bias is expected to be toward the null. To avoid sample overlap in our MR analysis, we divided the UKB sample into two subsamples of north  vs. south . This was according to the geographic locations where data were initially obtained. This division of samples was designed to ensure geographical independence of the two samples and to minimize sharing of similar environments, which may have not been achieved by random splitting of the sample. To ensure the results were not biased by geographical differences in the UKB sample, we compared our results with an additional MR analysis where we randomly split the full UKB sample into two equal subsamples.Study participants were ascertained through United Kingdom National Health Service registers across 22 centers in Great Britain between 2006 and 2010We performed a sensitivity analysis excluding participants who used antihypertensive medication, corticosteroids or NSAIDs as well as those suffering from self-reported kidney disease, or hospital records for diagnosis of renal disease. Diagnoses codes were based on International classification of diseases (ICD 10) codes N00.0–N39.9. The final sample for sensitivity analysis included 262,531 participants of European ancestry.55. Briefly, a custom Affymetrix UKB Axiom array (designed to optimize imputation performance) was used for genotyping of DNA samples obtained from the UKB study participants. Imputations in UKB were performed centrally using an algorithm implemented in the IMPUTE2 program. Genetic principal components to account for population stratification were computed centrally by UKB.Genotyping and imputation in the UKB have been described in detail elsewhere56. Sodium and potassium concentrations in stored urine samples were measured by the ion selective electrode method (potentiometric method) using Beckman Coulter AU5400, UK Ltd. Analytic range for sodium and potassium was 2–200 mmol/L, and 10–400 mmol/L, respectively.Details of quality control and sample preparation for urinary measurements have been published previously by UKB57. BP was measured at baseline, after a two-min rest using an appropriate cuff and an Omron HEM-7015IT digital BP monitor. We calculated mean SBP from all available automated or manual reading BP measurements. For individuals who reported taking BP-lowering medication , we added 15 and 10 mm Hg to SBP and DBP, respectively as has been done previously58.Details of calculation of BP values used for this analysis are described in detail elsewhere14 that corrects for confounding from population structure and cryptic relatedness. We assumed additive genetic model on continuous log transformed spot urinary sodium and potassium excretion adjusted for age and sex. Additional to the QC that was done centrally by the UKB, we applied some filters including MAF > 5%; HWE P > 1 × 10−6; missingness < 0.015 for the initial modeling step to estimate parameters of the LMM. We restricted the main association analysis to SNPs imputed from the HRC panel , of which ~8.8 million SNPs passed MAF ≥ 0.5% and INFO > 0.1 thresholds.For GWAS of urinary sodium and potassium, we performed LMM association testing implemented in BOLT-LMM (v2.3) software49 to be used as our pre-set significance threshold (1 × 10−8). We removed all SNPs in HLA region (chr6:25–34 MB). In order to shortlist the lead SNPs, association tests passing this pre-set permutation-driven threshold were ranked in order of significance with stronger associations locating at the top of the list. We then removed all SNPs in the region of ±500 kb surrounding the best ranking SNPs. We continued this approach until there were no SNPs that overlapped within ±500 kb region. We consequently LD pruned the final list of SNPs using UKB individual level data with LD threshold of r2 ≥ 0.1 using PLINK2 software60. To detect secondary signals, we used the urinary sodium and potassium GWAS summary-level data and performed conditional analysis using the Genome-wide Complex Traits Analysis (GCTA) software61. Our criteria for selection of secondary signals included MAF ≥ 0.5% and GCTA based P < 1 × 10−8. We used genome-wide conditional analysis with stepwise model selection as well as locus specific conditional analysis for urinary traits conditioned on the lead SNPs within each locus.We empirically assessed the correlation between nearby test statistics in order to calculate the number of independent statistical testsP < 1 × 10−8. We used BOLT-LMM regression among 10,095 individuals of south Asian and 7612 individuals of African ancestry within UKB. We considered statistical significance if the SNPs passed P value threshold of 9.8 × 10−4.To investigate the effect of lead SNPs on urinary traits among other ancestries, we performed association analysis on all the SNPs in 1 MB region surrounding the lead SNPs if they showed association with urinary traits at 2 and sex.We calculated weighted sodium and potassium excretion GRS based on the effect sizes of the final sentinel variants identified in the main UKB GWAS analysis. We then investigated association of this GRS with urinary sodium and potassium excretion among African and South Asian populations in UKB. We also investigated the association of GRS with average annual change in BP at first repeat assessment visit (2012–2013) in the European subset of the UKB study compared with baseline assessment. To obtain change in BP values, we subtracted values obtained for BP at baseline from values obtained at first repeat assessment divided by the time difference in years between baseline and first repeat assessments. For this analysis individuals with missing values for repeat BP measurement and individuals with first and second degree relationships were excluded. The final analysis on 19,686 individuals was adjusted for age, age18 (2018 version) performed on the 452,264 individuals and 778 phenotypes from the UKB. To claim significance, we used a Bonferroni corrected significance threshold of P < 1 × 10−6, Bonferroni corrected for the number of lead SNPs multiplied by 778 phenotypes.We looked-up phenome-wide association analysis (Phewas) results for urinary trait lead SNPs within GeneATLAS62 tool to perform LD score regression analysis using GWAS summary statistics data as well as calculation of SNP-based heritability. To investigate trait pleiotropy and evidence of association of urinary trait loci with other traits, we extracted from the GWAS Catalog17 all urinary sodium and potassium associated SNPs at P < 1 × 10−8 in 1 MB region surrounding the lead SNPs.To investigate shared heritable contribution between urinary traits with other phenotypes, we used the Broad institute LD hub22, a cohort of UK police forces. The Airwave study includes metabolomic data generated by Metabolon platform on 1967 plasma samples, including 1048 mostly identified metabolites and some unidentified species63. In addition, we analyzed serum metabolomics data from 2022 serum samples in which 105 lipoprotein subclasses were quantified by the Bruker IVDr LIpoprotein Subclass Analysis  (https://www.bruker.com). We computed significance thresholds using an estimated 5% family-wise error rate using a permutation approach (10.1021/pr1003449 and 10.1021/acs.jproteome.7b00344). We performed association tests with metabolomics data using Spearman partial correlation analysis adjusted for age and sex and genetic principal components.To study the metabolomics signature of our lead SNPs, we used data from the Airwave Health Monitoring Study (Airwave)31, a publicly available data set of SNP-metabolites associations to obtain more information regarding metabolomics signatures of urinary traits loci.We additionally used PhenoScanner64. VEP provides a report of SNP characteristics , and SIFT/PolyPhen based functional impact of amino acid substitution. We evaluated all genome-wide significant SNPs for evidence of eQTL using the GTEx database34. We additionally searched median gene expression levels in 53 tissues from the GTEX database and standardized gene expression values to map tissue-specific expression of genes near and/or in eQTL (eGenes) with urinary trait lead loci.We used multiple genomic tools to investigate functional impact of the lead loci. We annotated SNPs to the nearest gene within the distance of ±500 kB using University of California Santa Cruz (UCSC) genome-browser. We checked functional annotation of the lead SNPs using Variant Effect Predictor (VEP) toolcoloc, a package running under R. To obtain eQTL data for each SNP and gene combination per tissue, we used ensemble REST API65. Co-localization analysis was done for each tissue and gene region combination separately. The analysis provides posterior probabilities for H0 (no association with either trait), H1 , H2 , H3 , and H4 . We drew clustered heatmaps on the results of co-localization analysis to illustrate SNPs and genes that are similar with regard to tissue-based co-localization.To check if the loci we identified associated with urinary sodium and potassium are the same loci affecting gene expression, we performed a co-localization analysis using 35 software  to evaluate prior knowledge on sodium and potassium loci and to identify over-representation of sodium and potassium loci in various disease and functional pathways. We used the list of nearest genes and genes in eQTL with sodium and potassium loci within IPA and performed variant effects analysis. Out of the most significantly enriched diseases and functions in IPA’s knowledge base, we selected the most statistically significant functions that included at least three genes and collectively covered all urinary trait loci. We then used “displayed as network” function within IPA to illustrate networks connecting our urinary trait loci, using IPA’s organic design.We performed gene-based variant effect analysis considering direct and indirect relationships within IPAP value of overlap states the statistical significance of the enrichment of a biological attribute  in user's dataset.  It compares the proportion of input molecules  that are associated with a particular biological attribute to the proportion of molecules that we expect to see if the dataset was made up of randomly selected molecules. It is calculated using the right-tailed Fisher's exact test. The P value less than 0.05 or  is considered significant by IPA. The smaller the P value, the less likely that the association is random and the more significant the association66.In IPA, the 67. To avoid sample overlap for exposure and outcome, we divided the UKB sample into two samples according to geographic locations where data were initially obtained (north vs. south). We then performed GWAS investigations in each sample separately. We looked-up the lead SNPs for association with exposure (urinary traits) in northern UKB sample . We subsequently looked-up the lead SNPs for association with outcome (SBP and DBP) within the southern UKB sample . SNP-exposure effects were used as instruments against SNP-outcome effects to assess potential causal effect of urinary traits on BP. In separate MR analysis, we additionally used SNP-BP effect esimates from ICBP. To assess potential causal effect of urinary traits on CHD, we obtained SNP-exposure effects from the full UKB sample and SNP-outcome effects from CARDIOGRAM 1000G CHD GWAS results (CARDIOGRAMplusC4D) from Nikpay et al.37 as well as meta-analysis of UK Biobank SOFT CAD GWAS (interim release) with CARDIoGRAMplusC4D 1000 Genomes-based GWAS from Nelson et al.38.We tested the effect of sodium and potassium on BP and CHD using MR analysis implemented in R package68 that provides a robust estimate even when up to 50% of the SNPs are not valid for MR. An important assumption of MR is absence of horizontal pleiotropy that occurs when the effect of SNP on outcome is independent of exposure which can be tested using the MR-Egger method69. If MR-Egger indicated presence of pleiotropy we repeated the analysis after removing outliers. To detect outliers and correct for widespread horizontal pleiotropy, we used MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) test70. In MR analysis using IVW test, we used P < 0.008 as our statistical significance threshold taking into account multiple testing for two MR exposures and three different outcomes analyzed.For statistical analysis of MR, in the presence of heterogeneity, we used the inverse variant weighting (IVW) method assuming random effect. As sensitivity analysis, we used estimator from the weighted median methodCherry picking has been considered as a potential limitation of heterogeneity-based outlier removal methods. It is also noted that outlier removal improves statistical power by reducing noise in estimation whilst a drawback is that the method could increase type 1 error rate resulting in detecting causal effects that are not true. It is necessary to emphasize that any causal conclusion based on our MR results should be inferred with caution, though our MR analysis is supported by previous RCTs that have shown that short-term reduction in sodium intake decreases BP.www.gtexportal.org. For IPA, see www.qiagen.com/ingenuity. For PhenoScanner, see http://www.phenoscanner.medschl.cam.ac.uk . For GeneATLAS, see http://geneatlas.roslin.ed.ac.uk.For GTEx, see Further information on research design is available in the Supplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Supplementary Dataset 9Supplementary Dataset 10Supplementary Dataset 11Supplementary Dataset 12Supplementary Dataset 13Supplementary Dataset 14Supplementary Dataset 15Supplementary Dataset 16Supplementary Dataset 17Supplementary Dataset 18Supplementary Dataset 19Supplementary Dataset 20Supplementary Dataset 21Supplementary Dataset 22Supplementary Dataset 23Supplementary Dataset 24Supplementary Dataset 25Supplementary Dataset 26Supplementary Dataset 27Supplementary InformationReporting SummaryDescription of Additional Supplementary Files


Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding. However, GWAS is not as powerful as QTL linkage mapping  and pracNested association mapping (NAM) takes advantage of both linkage and association mapping to increase map resolution and statistical power  replicated across 22 environments in eight Midwestern USA states from 2011 to 2013. RILs from each family were sub-divided into four sets of 35, with each set augmented with the family’s two parents and three check cultivars chosen for adaptation to the field environments. If sufficient seed was not available for a RIL entry, the plot was planted with a check variety. The entries within each set were randomized and the 40-entry sets were randomized within field sites. All 5,600 RILs (160 sets) were evaluated at eight sites, while at the remaining sites only 25 to 100 sets, with an average of 50 sets, were evaluated due to capacity limitations of the cooperators . Plant maturity was rated as days from planting to stage R8 (95% of pods fully mature), plant height as the cm distance from the soil surface to the top node on the main stem, plant lodging score rated visually on a scale of 1 = all plants erect to 5 = all plants prostrate and the yield component of seed mass as g (100 seed)-1. Any plot data discarded by the cooperating scientist who judged that data to be of poor quality were treated as missing data. The number of plots evaluated for each trait ranged from 52,000 to over 66,000 , in any family were eliminated, resulting in 2,470 to 3,791 SNP loci segregating within individual families.NAM RILs were genotyped with SNP markers (The NAM common parent and founder lines (but not RILs) were evaluated with the SoySNP50K BeadChip (G. soja PI 479752 (WxP) population of 1083 RILs genotyped with 21,000 SoySNP50K SNPs , or they segregated for alleles that did not match the parent alleles. Most of the RILs from family N46 (PI507618B) fell into the latter category, indicating that a line other than the intended founder PI had been used in the mating with IA3023. Therefore, all lines from the N46 family were removed from the dataset.An initial analysis of the SNP-genotyped RILs was conducted to identify RILs that deviated from the expected marker segregation . Both covariates are treated as fixed effects in (2) and β are the slopes and intercepts for planting date and block effects. Z is an incidence matrix for entries (RILs and founder lines) indicating whether the measured trait value, y, for the entry was evaluated in a block, and υ is the vector of random effects for entries, I is the identity matrix, ε represents the residual value, not accounted for in the model and RIL agronomic phenotypes were evaluated in an unbalanced design across environments. Most RILs were evaluated for yield in seven environments, while subsets of RILs were evaluated for agronomic traits in the remaining environments . Yield dc traits . For yieEstimates of variance components for model (2) were obtained using the lmer package (https://CRAN.R-project.org/package=NAM). Genotypic values, υ, were BLUP values from (2). The polygenic term, φ, accounted for genetic structure among entries through the genomic relationship matrix, K. The model allowed each family, indicated by W, to have a unique estimated effect of allele substitution. The predicted values for allelic substitution, α, were interpreted as estimates of marker substitution effects from each of the founder lines. Because there were 39+1 founders, there were potentially 40 distinctive allelic substitution effects, conditioned by polygenic background (φ), at each of the marker loci. The polygene was estimated with the genomic relationship matrix K that captured the additive relationship among individuals, thereby accounting for population structure.We used the BLUP genotypic values from model (2) for the analyses of genome wide associations in which we identified marker trait associations (MTAs) as random effects dependent on family background:10 Implementation of (3) in the NAM package provided an option to exclude markers linked to the marker of interest from the polygenic background using a ‘linkage window’ . If thisTo identify candidate genes for the co-expression network analysis, plants of the NAM common and founder lines were grown in the Danforth Center Greenhouse  and seeds at the mid-maturation stage were harvested and used for purification of total RNA as previously described . The site also contains forms for requesting seed samples of the NAM RILs and founder parents, and also images of the field-grown parents. Supplemental material available at Figshare: https://doi.org/10.25387/g3.6970496.The quality-assured phenotypic and SNP genotypic data are available for download from SoyBase , N10 (LD00-3309) and N27 (LG05-4292) , Table 1Broad-sense heritability estimates for yield and agronomic traits on an entry mean (RIL) basis across families ranged from 0.82 to 0.93 . SlightlA 5-cM linkage window size was used to distinguish multiple linked MTAs from broader background polygenic effects , and to all founder families for five MTA regions , indicating that no founder allele was superior to the IA3023 allele at these loci  and for all but two in one other region . Conversely, a positive allelic effect was detected in all families in four significant MTA regions , implying that the yield potential of IA3023 might be improved by introgression of the corresponding estimated effect for the allele from founder parent alleles. The estimated allelic effects were split between the common allele and the founder alleles for the remaining nine MTAs, with negative allelic effects generally outnumbering positive allelic effects from the non-IA3023 founder, except for MTAs on chr 9 and 16. This implies that two different founder alleles might exist, with one being more, and the other less, favorable than the IA3023 allele. A sizeable contrast in the +/− allele effect magnitude was observed in some cases, such as the chr 9 MTA, for which N06 (CL0J173-6-8) contributed an allele associated with a +239 kg ha-1 effect, whereas N50 (PI 561370) contributed an allele associated with a -326 kg ha-1 effect , which was expected given that IA3023 is higher yielding than all but three founders. When averaged by founder class, the mean allelic effect was +3.4 kg ha-1 for EL founder families compared to -19.2 kg ha-1 and -52.1 kg ha-1 for BX and PI founder families, respectively  – three of which were EL-absent. BX founder N39 (LG04-6000) also was unique for contributing a rare but very large (+157 kg ha-1) effect allele on chr 8. These two founder lines were derived from recently introduced germplasm from China. It would be of interest to determine if an estimated yield effect of that size would be maintained were it to be introgressed into IA3023. Several EL founders also could be used for the improvement of IA3023 yield, notably N06 (CL0J173-6-8), N03 (4J105-3-4), and N04 (5M20-2-5-2), which contributed many more positive effect alleles (of often large magnitude) vs. few negative effect alleles .A key project goal was to identify positive effect alleles for yield in significant MTAs that were present in PI or BX founders, but non-existent, rare, or small in effect in EL founders. No such alleles were detected in any PI founder family , which was expected based on a founder parent E2 allele analysis , taller plants, more lodging and smaller seed mass compared to the e2 allele for early maturity (Table S1), an indication of potential multi-trait pleiotropism. Coupling phased positive yield - positive maturity also was evident in chr 9 bin 1 for all founders except one BX and three PI. In the other three bins, positive/negative allele effects for maturity and yield were inconsistently phase-paired, implying separate QTL for each trait. This inconsistency was also the case for five bins with yield and seed mass, four bins with yield and lodging, and three bins with yield and plant height . Fifteen different subnetworks were developed. Genes significant in at least two different subnetworks and displaying strong connectivity with one another were deemed candidate genes for the yield MTAs. Ten such genes were identified, connected in two distinct modules  and this yield effect was consistent with what Xavier et al. observed. This provides confirmatory evidence that the previously mapped major MTAs for canopy coverage may have an impact on agronomic traits across a broad range of environments and thus may be useful in yield improvement.This NAM population was previously used to study the development of canopy coverage (Seed yield is the most important trait for most field crops and this is the first study that we are aware of that uses a NAM population designed to map MTAs for this trait. We identified between 15 and 29 MTAs for the five measured traits, confirming the multi-genic inheritance of each. Both positive and negative effect alleles relative to the high yielding common parent IA3023 were discovered, and two BX founders with exotic ancestry contributed positive alleles that were rare or not existent in EL founders. This NAM population of inbred lines will be an important resource for future soybean research, including validating MTAs observed in this study and using these results to develop models to improve selection methods.


To reveal this a subset of 260 genotypes was characterized phenotypically for six quantitative traits  under five environments. Gene annotations mined ∼38 putative candidate genes which were confirmed using tissue and stage specific gene expression data from RNA Seq. We observed strong co-localized loci for four traits  on chromosome 1B (24.64 cM) annotated five putative candidate genes. This study led to the discovery of hitherto unreported loci for some less explored traits  besides the refined chromosomal regions of known loci associated with the traits. This study provides valuable information of the genetic loci and their potential genes underlying the traits such as awn characters which are being considered as important contributors toward yield enhancement.Wheat genetic improvement by integration of advanced genomic technologies is one way of improving productivity. To facilitate the breeding of economically important traits in wheat, SNP loci and underlying candidate genes associated with the 36 agro-morphological traits were studied in a diverse panel of 404 genotypes. By using Breeders’ 35K Axiom array in a comprehensive genome-wide association study covering 4364.79 cM of the wheat genome and applying a compressed mixed linear model, a total of 146 SNPs (-log Triticum aestivum L.) crop provides one-fifth of total food calories and a quarter of protein in the human diet on daily basis 1. To meet the increasing food demand of growing population, the breeders focused on the varieties having higher yield and yield stability, increased resistance/tolerance to biotic and abiotic stresses. Approximately 10,000 wheat varieties worldwide 2 including 448 wheat varieties in India  spanning across all the 5 continents4. This will allow the breeders to utilize the information in developing genotypes specific to different adaptation conditions.Some of the studied traits and the associated markers will be of immense importance in future toward developing input use efficient wheat varieties. Amongst them, genomic regions associated with days to heading (DTH) shall enable development of early maturing wheat genotypes to avoid terminal heat stress and allowing an intervening legume crop before rice in the ensuing season . For GWAThe present GWA study is the first attempt to undertake large scale evaluation of 35K Axiom array in a diverse panel of 404 Indian wheat genotypes. The 35K Breeders’ Array was selected for present work due to its proven efficacy on panel of 1807 accessions of hexaploid wheat  from 32 countries . The aimT. aestivum L.) genotypes comprising of indigenous collections (91), landraces (8), released varieties (134), genetic stocks (43), and improved genotypes (128) was used to constitute a diverse association panel. The diverse lines were selected on the basis of pedigree, to reduce associations of spurious markers as it provides a buffer against skewness in terms of the environmental effects. Recently, we analyzed trait based diversity analysis using Shannon Index with 16 traits out of 36 traits with a collection of 7,000 diverse germplasm lines (unpublished work). Out of these, 450 genotypes comprising of indigenous collections, landraces, released varieties, improved genotypes and genetic stocks for various traits were selected from 7,000 lines which was downsized to 404 genotypes after eliminating admixtures, duplicates, etc. Comparing the trait based diversity of these selected 404 genotypes using Shannon Index endorsed it as good representative of 7,000 germplasm lines, thereby proving the suitability of 404 genotypes for GWA study ; Seed Farm, Karnal , Haryana, India and Lokbharti-Sanosara Centre, Bhavnagar (21°46′ N 72°11′ E), Gujarat, India during year 2016–2017. Besides these three environments, an additional environment was taken by phenotyping the subset in crop season 2017–2018 at ICAR-IIWBR, Karnal. Experiment was conducted in two replications following alpha lattice design. To minimize the variations, every genotype was planted with a dibbling tool named IIWBR Dibbler (H2) thereby enhancing the probability of identifying genes of minor effects related to complex traits , days to maturity (DTM), spike length (SL), plant height (PH), awn length (Awn_L), and leaf length (Leaf_L)] at three locations  Dibbler  having fx traits .5. Procedure of recording the data for each trait is summarized in Supplementary File Thirty six agro-morphological characters including coleoptiles anthocyanin coloration (C_Col), plant growth habit (PGH), foliage color (Fol_Col), flag leaf anthocyanin coloration of auricle (Aur_Col), flag leaf hairs on auricle (pubescence) (Aur_Pub), flag leaf attitude (Leaf_Att), ear emergence/days to heading (DTH), flag leaf waxiness of sheath (Wax_LS), flag leaf waxiness of blade (Wax_LB), ear waxiness (Wax_Ear), waxiness of peduncle (Wax_Ped), flag leaf length (Leaf_L), flag leaf breath (width) (Leaf_Br), PH, ear shape (Ear_S), ear density (Ear_D), ear (spike) length (SL), awn presence (Awn_P), awns length (Awn_L), awn color (Awn_Col), awn attitude (Awn_Att), outer glume pubescence (Glu_Pub), ear color (Ear_Col), lower glume shoulder width (Sh_Wid), lower glume: shoulder shape (Shl_Sh), beak length (Beak_L), beak shape (Beak_Sh), spike (peduncle) attitude (Ped_Att), grain coloration with phenol (Grn_Ph), grain color (Grn_Col), grain shape (Grn_Sh) grain germ width (Germ_Wid), brush hair length (Brush_L), seed (grain) size (Grn_Size), grain hardness (texture) (Grn_Tex) and DTM were recorded as per guidelines laid out by Protection of Plant Varieties and Farmers’ Right Authority 6). Pearson pairwise correlation was calculated for all the traits. Histograms were created in R (H2 = VG/(VG + GE) where VG and VE represent estimates of genetic and environmental variance, respectively.Phenotypic data was analyzed using SAS v.9.3 . Quality preprocessing of 35,143 markers obtained from 35K chip was done by using PLINK software . Three independent runs were generated for each K. The results of the analysis were used as input to the Structure Harvester tool  and polymorphism information content (PIC) was evaluated by using PowerMarker v3.2.5 . The modter tool  to predio method . Principo method  and DARwo method , respectr2 (squared correlation coefficient) values among markers of all pairs of loci were calculated using PLINK 1.9 tool8 , on the basis of centiMorgans (cM) distance, using ggplot2 package of R Bioconductor (r2 > 0.02) was also compared in the sub-genomes. Pairwise LD estimates in the region of interest for significantly associated markers were investigated using Haploview 4.2  was used to state significant marker trait associations. Associations with false discovery rate (FDR) adjusted at 10% was used to determine the P-values thresholds.Association analysis was performed using compressed mixed linear model (CMLM) implemented by Genomic Association and Prediction Integrated Tool (GAPIT) in R  which toPC model  where kiPC model . The bes9. Each MTA was searched for IWGSC sequence information in Ensembl plant for T. aestivum10. The flanking sequence available for the SNP marker with maximum bases  was considered for BLASTx analysis. We also looked at the number of high confidence genes adjacent to the significant MTAs using the RefSeq v1.0.Gene Ontology (GO) annotation of the potential candidate genes was carried out using Blast2GO pro tool v.3.1.3  at 3 different time points (growth stages) each and environmental treatments , the effect of environment and their interaction (G × E) indicating the environmental effect on these traits  and B genome (0.372 and 0.291) were higher than the D genome (0.345 and 0.276). The number of markers, map length, GD and PIC for each chromosome are shown in K method of model-based Bayesian clustering using 14,160 SNP markers. Population structure analysis clearly indicated the existence of three distinct major subpopulations in the bread wheat panel, which was found consistent with the results of the PCA and neighbor-joining (NJ) tree analysis  as a common progenitor in their background  as compared to ∼2 cM in A and B genomes  showed the highest frequency of physically linked locus pairs followed by B (58.0%) and D (53.6%) genomes.Linkage disequilibrium (LD) decay distance in the selected panel was found highest in the D genome which decayed at about 5 cM  hitherto not reported , 3A  were found significantly associated with phenol color indicating the importance of this region. These SNPs explained 15.1–18.6% phenotypic variation. For grain texture (phenotype scored as hard or soft), a total of two regions were detected; one on chromosome 7A containing three markers (100.09 cM) and other on chromosome 6B comprising four markers (62.83 cM). Both these MTAs for grain texture contributed negatively to the trait. For germ width, significant association was detected on chromosome 4A. Only one MTA (AX-94670534) for ear shape was detected on chromosome 7B explaining 8.3% of phenotypic variance having negative effect. We did not find any significant MTA for ear density.For the six quantitative traits , a total of 47 significant SNPs were identified in five environments which explained 5.2–47.3% of phenotypic variation . We succFor PH, a total of six MTAs were identified, one each on chromosome 1B (24.64 cM), 2A (83.23 cM), 2B (104.59 cM), 7A (29.9 cM), and two on 5D (1.58 cM) considering all the environments . MTAs siIn the present study, we observed same SNPs with multiple traits which could be due to pleiotropy or different causal genes in LD for instance SNP AX-94656878 at 83.23 cM (chromosome 2A) explained variation for two traits (PH and DTM) . Similarb-ZIP transcription factor, for the phenotypic variations of the traits , DIMINUTO, replication protein A, carboxypeptidase D, and basic region/leucine zipper].We identified several putative candidate genes such as storage protein activator (spa), beta-amylase 2 (bmy2), cytochrome P450, shikimate kinase, e traits . These pTo determine the relative expression profile of the identified transcripts in broad range of tissues from different developmental stages, the published RNA-seq data and the Wheat-Exp web tool of the wheat cultivar, Chinese Spring was explored . The expR2 of 6.4%. Results of the study showed that favorable alleles exhibited significant positive effects on the phenotypic traits as compared to the unfavorable alleles. This would help in cultivar adaptation and finally to grain yield.Early maturing, high yielding wheat genotypes are of immense importance toward increasing the cropping intensity as well as ensuring high input use efficiency particularly for inputs like water, which are going to be scarce. Therefore the present study dissecting important agronomic traits such as DTH, PH, and SL enables utilization of available diversity by exploiting associated markers. SNP alleles which led to decrease in DTH, PH and increase in SL were considered as “favorable alleles” and vice-versa was defined as “unfavorable alleles.” vis-à-vis greater recombination frequency with other genomic regions. Notably, this locus harbored three candidate genes for SL. SNP AX-94981940 (-log10P = 4.21), was annotated as a replication protein A subunit and its transcript expression was almost solely abundant in the young spike at Zadoks 32 stage , associated for Glu_Pub at this locus (24.64 cM) was annotated as metal tolerance protein (MTP) which is known for its potential involvement in providing a sink for trace element storage in wheat grains  and 26.22 cM with Awn_L. The genomic co-location of loci (24.64 cM) with four traits implies either a strong physical linkage between genes underlying these important traits, or a pleiotropic effect. Therefore, to dissect the genetic cause of the observed association, LD patterns and candidate genes underlying the region and transcript profile of the targeted region were investigated . The SL 32 stage . Replica32 stage . Similar32 stage . The seqstresses . It is istresses . SNP AX-t grains . Earliern weight  and F-bon weight . The expn weight  are consThe diversity panel selected in this study has high GD (0.363) and PIC (0.29) indicating higher polymorphism than listed in the previous reports . Further10P ≥ 4) for 23 traits. For majority of the heritable traits, at high significant level single locus has been identified indicating that they are controlled by small number of loci, for instance PGH (1B), LS_wax , Ped_Wax (3A), Sh_Wid (2D), Ear_Col (1B), Ear_Sh (7B), Awn_Col (1B), Awn_Att (5A), Brush_L (3A), Germ_Wid (4A), and Grn_Ph (2A). For coleoptile color, the genes that regulate anthocyanin biosynthesis pathway, have been cloned and mapped on homoeologous groups 3 and 6 , homoeologous groups 1 and 2 , homoeologous group 5  (Rc-1 homeologous loci responsible for anthocyanin pigmentation in coleoptile was correlated with F3H (flavanone 3-hydroxylase) gene on chromosome 2A  on chromosome 1B responsible for the brown ear character of bread wheat. For Aur_Col, contrasting to the region reported on chromosome 4A and 5B  . The preosome 2A . Howeverny study  while suA and 5B , we deteEpicuticular wax is associated with increased drought tolerance in wheat , rice H, maize , barley Hg) responsible for Glu_Pub on chromosomes 1A. In addition to this, we detected genomic regions associated with Glu_Pub on chromosomes 1B (24.64 cM) and 2B  which might be considered as novel region controlling the trait. MTA AX-95023665 linked to Glu_Pub encoded fatty acid biosynthetic process. Solanum habrochaites f. glabratum and are very effective in protecting the plant from pests. Pubescent plants also produced a higher number of grains per spikelet compared to non-pubescent plants  on chromosomes 5A (Vrn-A1and Vrn-A2) and 7BS (Vrn-A3), photoperiod response gene on chromosome 2, and earliness genes on chromosomes 1A and 3A (Vrn-A1 (90 cM) indicating its role for determining DTH (Oryza sativa) two shikimate kinase isoforms OsSK1 and OsSK3 accumulate to high levels during the heading stage of panicle development and involved in floral organ development  (Rht4 (Xwmc317-106 cM)   suggesting either these genes were eliminated during filtering or may not reach significant threshold level. Similarly, For PH, as many as six MTAs were identified on chromosome 1B, 2A, 2B, 5D (2), and 7A. The genomic region of MTA (AX-94941145) identified at 29.9 cM on chromosome 7A falls in the region of the reduced height gene 20.1 cM) . Similar-106 cM)  and Rht211.1 cM) . We also and DTM , a signi defense . Similar defense . The MTA defense  but coulQ, compactum (C), sphaerococcum (S1) related to spike morphology have been detected on chromosomes 5A, 2D, and 3D, respectively  on chromosome 7B (viz., 2B, 3A, 4A, 4B, and 5A were detected for flag Leaf_L (Awns were reported superior to the flag leaves on a cellular and physiological level during the grain filling period contributing 40–80% of the photosynthetic assimilates accumulated in the wheat grain . Most si, and 7B . Howeverosome 7B  have beeosome 7B . The geng Leaf_L . Positivg Leaf_L .Mining of superior/favorable alleles is essential for improving the complicated earliness trait in wheat using marker assisted selection. In recent years, association mapping has been widely used in exploring the elite alleles of many agronomic traits such as yield related traits , headingThis study identified 146 MTAs for 23 agro-morphological traits , and putRT and DiK conceived the theme of the study. SS, DeK, NR, RuS, SusP, SJ, MI, AJ, NK, UA, and SurP did the computational analysis. SS, SJ, MI, RaS, PS, RT, and DiK drafted the manuscript. DeK, NR, RuS, CS, and AG did the phenotyping. NR and DeK contributed in wet lab work. SJ, MI, DiK, AR, GS, and RT edited the manuscript. All authors read and approved the final 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.