The timing of puberty is highly variable and is associated with long-term health outcomes. To date, understanding of the genetic control of puberty timing is based largely on studies in women. Here, we report a multi-trait genome-wide association study for male puberty timing with an effective sample size of 205,354 men. We find moderately strong genomic correlation in puberty timing between sexes (rg = 0.68) and identify 76 independent signals for male puberty timing. Implicated mechanisms include an unexpected link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and Mendelian randomization analyses show a genetic association between male puberty timing and shorter lifespan. These findings highlight the relationships between puberty timing and health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes. Age at voice-breaking is used to determine puberty timing in men, recall of which is considered less accurate than age at first menarche in women. Here, the authors perform multi-trait GWAS for male puberty timing by including both age at voice breaking and age of first facial hair for improved phenotype definition and power. Understanding the biological mechanisms underlying such variation is an important step towards understanding why earlier puberty timing is consistently associated with higher risks for a range of later life diseases, including several cancers, cardiovascular disease and Type 2 diabetes4.The timing of puberty varies widely in populations and is determined by a broad range of environmental and genetic factors5. In contrast, genetic studies of puberty timing in men are much fewer and smaller in scale, due to lack of data in many studies on male pubertal milestones. We previously reported a GWAS for recalled age at voice breaking in men  from a single study, 23andMe, which identified 14 genetic independent genetic association signals, and many signals with similar effect sizes on voice breaking as for AAM in women6. This overlapping genetic architecture between puberty timing in males (in 23andMe) and females was also reported by others7, and supports the use of recalled age at voice breaking in men as an informative measure of puberty timing for further genetic studies.Most of our understanding of the genetic determinants of puberty timing is based on studies in women, as the age of first menstrual bleeding  is a well-recalled and widely measured marker of female sexual development. A recent large-scale genome-wide association study (GWAS) for AAM, in ∼370,000 women, identified 389 independent signals, accounting for approximately one quarter of the estimated heritability for the traitrg = 0.74) and many genetic variants show similar effects sizes in both sexes, there are a number of genetic signals that differ between sexes. Most notably, at the SIM1/MCHR2 locus, the allele that promotes earlier puberty in one sex delays it in the other6. Sex-specific expression patterns for several of such highlighted genes was reported in the hypothalamus and pituitary of pre-pubertal mice8. Further work to identify the pubertal mechanisms that are divergent between sexes may shed light on differences in later life disease risk associations between sexes.Although the overall shared genetic architecture for pubertal timing between sexes is high  study. For each of these measures, participants were asked if the event occurred relative to their peers: younger than average, about average or older than average. We previously reported that signals for AAM in women show concordant associations with dichotomised voice breaking traits in these UKBB menN = 13,226) vs. average onset ; and (2) relatively late onset  vs. average onset. These facial hair traits were in concordance with similarly dichotomised voice breaking traits defined in the same individuals, although more men reported early or late facial hair onset  than early or late voice breaking  (Supplementary Table −31) and 276  showed directionally concordant individual associations with relatively early and relatively late facial hair, respectively. Furthermore, substantially more AAM signals showed at least nominally significant associations (GWAS P < 0.05) with relatively early  or relatively late facial hair  compared to ~16 expected by chance for each outcome  relatively early onset ( relativerg) aligned to later timing of voice breaking ranged 0.57–0.91; Supplementary Table rg 0.61–0.81). These high correlations supported the rationale to combine the GWAS data across all five strata using MTAG10. This approach enables genetic data from correlated traits and overlapping samples to be combined in a single analysis, provided three key assumptions are met: (1) homogeneity of variance-covariance matrix for effect sizes for all SNPs across all traits; (2) sampling variation can be ignored and (3) sample overlap is adequately captured. Simulations involving large numbers of traits with extreme sample overlap have demonstrated that assumptions (2) and (3) may safely be made for most applications of MTAG. Violation of the assumption of homogeneity of effect sizes may be plausible in this setting if some SNPs have an effect on voice breaking and not facial hair (and vice-versa). We therefore calculated the upper bound for the false discovery rate (FDR) in our meta-analysis using the ‘maxFDR’ calculation developed by the MTAG authors. As the value for maxFDR was 7.9 × 10−4, this indicates that test statistics are unlikely to be biased due to any violation of the homogeneity assumption.We analysed four GWAS models in UKBB men : two models (early and late) for each of relative timing of voice breaking and facial hair. The shared genetic architecture between these four traits was high (genetic correlations (−8), comprising 76 independent signals  was located in/near LIN28B, consistent with previously reported studies in men and women. Of these 76 signals, 29 were not in linkage disequilibrium  with a previously reported signal for puberty timing (either AAM in women or voice breaking in men)  by using MTAG to combine data from the four dichotomous UKBB puberty timing traits all aligned to later timing of voice breaking; this approach yielded an effective GWAS meta-analysis sample size of 205,354 men for continuous pubertal timing. In this combined MTAG dataset, we identified 7,897 variants associated with male puberty timing at the genome-wide statistical significance threshold , and was associated with lower likelihood of attaining various milestones of pubertal maturation at specific time-points (Pmin (pubic hair development at age 14.7 years old): = 4.6 × 10−10, univariate model r2 ~ 1.5%)6, we observed a moderately strong genome-wide genetic correlation in pubertal timing between males and females , with similar effect estimates in both sexes for many individual variants  heterogeneity between sexes in their effects on puberty timing  . Only one signal showed significant directionally opposite effects ; rs6931884 at SIM1/PRDM13/MCHR2, as previously reported . Two variants located near to genes that are disrupted in rare disorders of puberty13 showed no effect or weaker effect in males than in females: rs184950120, 5′UTR to MKRN3 , and rs62342064, one of 3 AAM variants in/near TACR3 .Consistent with our previous studynts Fig.  and 15/3nts Fig.  showed sKDM4C (rs913588), encoding a lysine-specific demethylase, and a male-specific signal in ALX4 (rs3824915), encoding a homeobox gene involved in fibroblast growth factor (FGF) signalling that is mutated in rare disorders of cranium/central neural system (CNS) development with male-specific hypogonadism14. A further 10 lead variants were in strong LD (r2 > 0.8) with one or more non-synonymous variants, of which three have not previously been associated with puberty timing: FGF11, which encodes a FGF expressed in the developing CNS and promotes peripheral androgen receptor expression15, TFAP4, which encodes a transcription factor of the basic helix-loop-helix-zipper family16, and GCKR, which encodes a regulatory protein that inhibits glucokinase in liver and pancreatic islets and is associated with a range of cardiometabolic traits17 (Supplementary Table ). A further seven are reported signals for AAM, but were not previously reported for voice-breaking. These missense variants are in the following genes: SRD5A2, encoding for Steroid 5-alpha-reductase, which converts testosterone into the more potent androgen dihydrotestosterone; LEPR, encoding the receptor for appetite and reproduction hormone leptin; SMARCAD1, encoding a mediator of histone H3/H4 deacetylation; BDNF, FNDC9, FAM118A and ZNF446.Two of the 76 lead variants associated with male pubertal timing were non-synonymous: a previously reported AAM signal in DM4C rs91588, encoConsistent with genetic analyses of AAM in females, tissues in the CNS were the most strongly enriched for genes co-located near to male puberty timing associated variants , we assessed the broader relationship between these traits. It is known that men have darker natural hair colour than women in European ancestry populations18 and this sex difference appears following the progressive darkening of hair and skin colour during adolescence20. However, a link between inter-individual variation in natural hair colour and puberty timing has not previously been described. We assessed this phenotypic relationship in up to 179,594 white males of European ancestry in UKBB in a model including 40 genetic principal components (to adjust for even minor subpopulation ethnic variations). Men with red, dark brown and black natural hair colours showed progressively higher odds of early puberty timing, relative to men with blond hair. Similarly, women with darker natural hair colours had earlier puberty timing relative to women with blond hair . Such an approach can provide more robust and explicit evidence that there is shared biology between these two traits. The findings infer that susceptibility to darker natural hair colour also confers earlier puberty timing in men  with no evidence of heterogeneity across signals (Cochrane Q: P = 0.99) or directional pleiotropy (MR-Egger Intercept: P = 0.99) , and with modest heterogeneity across signals (Cochrane Q: P = 0.038)  approach, we modelled 119 recently reported GWAS hair colour signals as a single instrumental variableking in 2andMe menPIVW = 1.72 × 10−19) and lower risk for late voice breaking (PIVW = 5.90 × 10−6); but we found no association with AAM in women (PIVW = 0.23) . In general, early genetically predicted puberty timing in men was correlated with adverse health outcomes. We then performed MR analyses to test the genetic association with two exemplar traits which have previously been associated with puberty timing: lifespan22 and prostate cancer5.To assess the extent of shared heritability between male puberty timing and other complex traits, we calculated genome-wide genetic correlations across 751 complex traits/datasets using LD score regression23. Here, using a similar two-sample multi-variate MR approach, we did not find a significant association with prostate cancer  or advanced prostate cancer  . We used a similar MR approach to test the relevance of male puberty timing genetics to lifespan (see Methods). The findings support a genetic association between later puberty timing in males and longer lifespan, corresponding to 9 months longer life per year later puberty (IVW P = 6.7 × 10−4) , and with methods robust to the effects of heterogeneous SNPs (weighted median P = 0.02) (Supplementary Table 7).We previously reported genetic associations between earlier AAM and higher risks for hormone sensitive cancers, when adjusting for the protective effects of higher BMIn = 55,871), although still smaller than similar studies in women . While there was overall moderately strong overlap between sexes in the genetic architecture of puberty timing (rg = 0.68), several findings point to mechanisms with particular relevance to men. The genes implicated in puberty timing include, ALX4 and SRD5A2, the disruption of which leads to male pseudohermaphroditism whereas higher 5-alpha-reductase activity has been reported in women with polycystic ovary syndrome24, and INHBB encoding the beta-B subunit of the hormone Inhibin B, which is secreted by testicular Sertoli cells in males. By contrast, the known female puberty timing locus at INHBA, which encodes the beta-A subunit of the hormone Inhibin A, showed little association with puberty timing in males.Here, we report the a genetic study of male puberty timing by performing a meta-analysis across closely related phenotypic traits in two large studies. The effective sample size of 205,354 men is substantially higher than the previous GWAS in men , is cleaved into several peptides with significant melanogenic activity  by the pro-hormone convertase enzymes, PC-1 and PC-2, both of which are implicated by previously reported loci for AAM in females5. The relationship in women is more complicated suggesting that while there is a relationship between pigmentation and puberty timing in both sexes, it may act in a sex-specific manner.Non-genetic observational studies have consistently reported darkening of hair and skin pigmentation in children of European ancestry during the peri-pubertal years26. In conclusion, our findings demonstrate the utility of multi-trait GWAS to combine data across studies with related measures to provide insights into the regulation and consequences of puberty timing.Finally, our findings substantiate links between puberty timing in men and later life health outcomes, including Type 2 diabetes and hypertension. Our observed genetic association between earlier puberty timing in males and reduced lifespan was consistent across three different MR analytical approaches, which reduces the likelihood of horizontal pleiotropy (where variants influence lifespan though mechanisms separate to puberty timing). Plausible interpretations are a causal effect of earlier male puberty timing on earlier mortality or widespread horizontal pleiotropy (affecting >50% of variants for puberty timing) whereby puberty variants affect biologic pathways, which on the one hand determine puberty timing and on the other hand influence the risk of mortality. We note that horizontal pleiotropy may have been underestimated in this analysis as the same UK Biobank population were included in both exposure and outcome samples27. SNPs were excluded prior to imputation based on the following criteria: Hardy-Weinberg equilibrium P < 10−20; call rate < 95%; or a large frequency discrepancy when compared with European 1000 Genomes reference data6. Imputation of genotypes was performed using the March 2012 ‘v3’ release of 1000 Genomes reference haplotype panel. Genetic associations with puberty timing were obtained by linear regression models using age and five genetically determined principal components to account for population structure as covariates, with additive allelic effects assumed. P values for SNP associations were computed using likelihood ratio tests. Participants provided informed consent to take part in this research under a protocol approved by Ethical and Independent Review Services, an institutional review board accredited by the Association for the Accreditation of Human Research Protection Programs.Genome-wide SNP data were available in up to 55,871 men aged 18 or older of European ancestry from the 23andMe study. Age at voice breaking was determined by response to the question ‘How old were you when your voice began to crack/deepen?’ in an online questionnaire. Participants chose from one of seven pre-defined age bins . These were then re-scaled to one year age bins post-analysis by a previously validated method9. For this analysis, we limited our sample to individuals of white European ancestry. Age at voice breaking for male participants from the UK Biobank cohort study was obtained by responses to the touchscreen question ‘When did your voice break?’ Participants were required to choose from one of five possible options . For age at first facial hair, respondents were asked to choose from one of the same five options in response to the touchscreen question ‘When did you start to grow facial hair?’ In total, data were available in up to 191,270 individuals for voice breaking and 198,731 individuals for facial hair. UK Biobank received ethical approval from the NHS National Research Ethics Service North West (11/NW/0382).Genotyping for UK Biobank participants has been described in detail previouslyRespondents who answered either ‘older than average’ or ‘younger than average’ were compared separately using the ‘about average’ group as a reference in a case-control design for both phenotypes. For both voice breaking and facial hair, relatively early and relatively late effect estimates were obtained using linear mixed models which were applied using BOLT-LMM software, which accounts for cryptic population structure and relatedness. Covariates included age, genotyping chip and 10 principle components.10. MTAG uses GWAS summary statistics from multiple correlated traits to effectively increase sample size and statistical power to detect genetic associations. Full details on the methodology have been described previously10. In brief, MTAG estimates a variance-covariance matrix to correlate the effect sizes of each trait using a moments-based method, with each trait and genotype standardised to have a mean of zero and variance of one. In addition, MTAG calculates a variance-covariance matrix for the GWAS estimation error using LD score regressions. The effect estimate for the association of each SNP on the trait of interest is then derived using a moments-based function, in a generalisation of standard inverse-variance weighted meta-analysis.GWAS summary results for each of the five strata  all aligned to later timing of voice breaking were meta-analysed using MTAGPrior to meta-analysis, we removed extremely rare variants (MAF < 0.01). In addition, for the four UK Biobank strata we calculated the effective sample size usingP < 5 × 10−8 to determine significant SNP associations. Independent signals were identified using distance-based clumping, with the SNP with the lowest P-value within a 1 MB window being considered the association signal at that locus.Effective sample sizes for early and late voice breaking were 15,711 and 21,217, respectively, and 17,391 and 23,011 for early and late facial hair, respectively. We used the genome-significant P-value threshold of r2 < 0.05). Heterogeneity between AAM and VB for each SNP was determined by the I2 statistic and P-value generated by the METAL software.For each independent signal identified in the voice breaking meta-analysis, we identified all previously reported age at menarche (AAM) and voice breaking (VB) loci within 1MB of that SNP. We assessed if each locus had previously been associated with puberty timing (AAM or VB) within 1MB, and if any such loci within 1MB were in LD . Full details of recruitment, follow-up and data collection have been reported previously11. Ethical approval for the study was obtained from the ALSPAC Ethics and Law committee and the Local Research Ethics Committees. A series of nine postal questionnaires regarding pubertal development was administered approximately annually from the time the participant was aged 8 until he was aged 17. The questionnaires, which were responded by either the parents or the participant, had schematic drawings and verbal descriptions of secondary sexual characteristics  based on the Tanner staging system, as well as information on armpit hair growth and voice change. Age at voice change was considered the age at which the adolescent reported his voice to be occasionally a lot lower or to have changed completely. Weight and height were measured annually up to age 13 years, then at ages 15 and 17 years by a trained research team. Age at peak height velocity (PHV) was estimated using Superimposition by Translation And Rotation (SITAR) mixed effects growth curve analysis28. The sample size available varied according to the phenotype, from 1126  to 2403 (for age at which armpit hair started to grow).The Avon Longitudinal Study of Parents and Children (ALSPAC) recruited pregnant women resident in the Avon area of the UK with an expected delivery date between 1 April 1991 and 31 December 1992. Since then, mothers, partners, and offspring have been followed up regularly through questionnaires and clinical assessmentsThe genetic risk score (GRS) was calculated based on 73 SNPs (genotypes at 3 SNPs were unavailable) weighted by the effect size reported for that SNP in the ReproGen Consortium. The GRS was standardised, and results are presented as increase in the phenotype per standard-deviation increase in the GRS. Linear (continuous phenotype) and logistic (binary phenotype) regression analyses were performed unadjusted and adjusted for age  and controlled for population stratification using the first 10 principal components.29. In brief, genes are mapped to an index SNP based on a 150 kb window, with a regression model applied to correct the P-value (gene score) for gene size, SNP density and LD-related properties. Gene scores are ranked, and the numbers of gene scores observed in a given pathway in the 75th and 95th percentiles are calculated. A P-value for gene-set enrichment analysis (GSEA) is calculated by comparing these values to one million randomly generated gene sets. Testing was completed on 3216 pathways from four databases . Significance was determined based on an FDR < 0.05 for genes in the 75th or 95th percentile.We used MAGENTA to investigate whether genetic associations in the meta-analysed dataset showed enrichment in any known biological pathways. MAGENTA has previously been described in detailP-value of 9.4 × 10−4 (=0.05/53) to determine significance.To determine tissue-specific expression of genes, we used information from the GTEx project. GTEx characterises transcription levels of RNA in a variety of tissue and cell types, using sample from over 1000 deceased individuals of European, African-American and Asian descent. We investigated transcription levels of significant genes identified in our meta-analysis of voice breaking in 53 different tissue types. We used a conservative Bonferroni-corrected Information on natural hair colour for UK Biobank participants was collected via touchscreen questionnaire, in response to the question ‘What best describes your natural hair colour? ’. Participants chose from one of 6 possible colours: blond, red, light brown, dark brown, black or other. For our analyses, we restricted this to include only non-related individuals of white European ancestry, totalling 190,845 men and 238,179 women. Hair colours were assigned numerical values from lightest (blond) to darkest (black) in order to perform ordered logistic regression of hair colour for both relative age at voice breaking in men and AAM in women. In both cases, blond hair was used as the reference group and models were adjusted for the top 10 principle components to account for population structure. In men this produces an effect estimate as an odds ratio for early puberty , while in women the effect estimate is on a continuous scale for AAM (in years) relative to the mean AAM for those with blond hair.rg) were calculated between age of puberty in males and 751 health-related traits which were publically available from the LD Hub database using LD Score Regression30.Genetic correlations , UK Biobank Irish, and UK Biobank other white European descent. The resultant hazard ratios and their standard errors were then taken forward for two-sample MR using the 74 male puberty loci which were available in both datasets. We then performed a sensitivity analysis after removing outlier SNPs on the basis of heterogeneity using MR-PRESSO33.GWAS summary statistics for longevity were obtained from Timmers et al.34. The analyses were repeated using summary statistics from a comparison of the subset of 9,640 cases with advanced disease versus 45,704 controls, where advanced cases were defined as those with at least one of: Gleason score 8+, prostate cancer death, metastatic disease or PSA > 100. Two-sample MR analyses were conducted using weighted linear regressions of the SNP-prostate cancer log odds ratios (logOR) on the SNP-puberty beta coefficients, using the variance of the logORs as weights. This is equivalent to an inverse-variance weighted meta-analysis of the variant-specific causal estimates. Because of evidence of over-dispersion , the residual standard error was estimated, making this equivalent to a random-effects meta-analysis. Unbalanced horizontal pleiotropy was tested based on the significance of the intercept term in MR-Egger regression. The total effect of puberty timing on prostate cancer risk was separated into a direct effect  and an indirect effect (operating via BMI), as described in Burgess et al.19.Summary statistics for the association between the genetic variants and risk of prostate cancer were obtained from the PRACTICAL/ELLIPSE consortium, based on GWAS analyses of 65,044 prostate cancer cases and 48,344 controls  genotyped using the iCOGS or OncoArray chips19) using the most recently reported GWAS for hair colour18 and 23andMe data for SNP effect estimates on age at voice breaking in men, and published ReproGen consortium data on AAM in women. However, there is partial overlap  and the samples used for puberty timing . Therefore, we also performed a sensitivity analysis in non-overlapping samples; we used a more limited 5-SNP instrument for darker hair colour (identified in an earlier hair colour GWAS that did not include UK Biobank35), and assessed its effects on puberty timing in UK Biobank men and women in an individual-level MR analysis, controlling for geographical (assessment centre) and genetic ancestry factors .Genetic associations between hair colour and puberty timing were assessed in two ways. First, we performed a two-sample MR analysis based on summary statistics (as described in Burgess et al.Further information on research design is available in the Peer Review FileReporting SummarySupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Description of Additional Supplementary Files


Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods. In the present study, we proposed a causality interpretation of GBC based on path analysis. We applied this method to estimating GBC in two composite breeds of beef cattle, namely Brangus and Beefmaster. Three SNP panels were used to estimate GBC: a 10K SNP panel consisting of 10,226 common SNPs across three GeneSeek Genomic Profiler (GGP) bovine SNP arrays , and two subsets (1K and 5K) of uniformly distributed SNPs. The path analysis decomposed the relationships between the ancestors and the composite animals into direct and indirect path effects, and GBC was measured by the relative ratio of the coefficients of direct (D-GBC) and combined (C-GBC) effects from each ancestral breed to the progeny, respectively. Estimated GBC varied only slightly between different genotyping platforms and between the three SNP panels. In the Brangus cattle, because the two ancestral breeds had a very distant relationship, the estimated D-GBC and C-GBC were comparable to each other in the path analysis, and they corresponded roughly to the estimated GBC from the linear regression and the admixture model. In the Beefmaster, however, the strong relationship in allelic frequencies between Hereford and Shorthorn imposed a challenge for the linear regression and the admixture model to estimated GBC reliably. Instead, D-GBC by the path analysis included only direct ancestral effects, and it was robust to bias due to high genomic correlations between reference  breeds. Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. At the DNA level, every individual has two haplotypes, which are linages of genes and markers. One haplotype is inherited from the father and the other from the mother. In crossbreeding, a haplotype segment is usually present in many individuals as descendants of a common ancestor from which the segment originates. It is also possible that one progeny can carry two segments that are identical-by-status (IBS), meaning identical by chance, because they are not inherited from the same ancestor. The chance of IBS, however, is minimal if many markers are included in the segments, e.g., based on runs of homozygosity (ROH), which are long DNA segments containing consecutive homozygous loci . The infVarious statistical methods have been proposed to estimate GBC , but thePath analysis has been developed to model causal relationships between variables. In the path analysis, exogenous (independent) variables produce both direct and indirect path effects on one or more endogenous (dependent) variables. The indirect path effects due to the correlations between the exogenous variables are also referred to as the correlational effects . Path anIn the present study, we proposed the use of path analysis to decompose the causality relationships between composite (or crossbred) animals and their putative ancestors (or reference breeds) and to estimate GBC of individual animals in terms of the relative determination of respective ancestral (or reference) breeds. Two measures of GBC were used, one accounting only for the direct path effects of each reference breed, and the other including both direct and indirect path effects for each reference breed. The indirect path effects were attributable to the correlations between the reference breeds. Estimated GBC from the path analysis was compared with those obtained using the linear regression and the admixture model, and their similarities and dissimilarities were discussed as well.The genotypes of 150,676 animals sampled from two composite breeds and eight reference breeds of beef cattle were used in the present study . The comBos taurus indicus breed, and it had the most remote relationships with the seven Bos taurus taurus cattle breeds. The relationships between the eight reference breeds were depicted by a hierarchical clustering analysis  was estimated in the two composite breeds. Brangus was developed to combine the desirable traits of Angus and Brahman cattle . Angus cyi to be a M×1 vector of genotypes pertaining to animal i, where M is the number of SNPs involved, and denote xj to be an M×1 vector of allele A frequencies of the M SNPs genotype in reference population or breed j, for j = 1,…,K where K is the number of breeds. Then, the GBC is estimated based on the following linear model:These two models served as the benchmark for comparison in the present study. In the linear regression approach, the genotypes of a crossbred animal are coded to be the proportion (or frequency) of say allele A in the genotype for all involving SNPs across the genome. Then, the coded genotypes are regressed to the corresponding allele A frequencies of SNPs for a set of reference populations . Let AA bj is the regression coefficient pertaining to population or breed j, and ei is a vector of residuals. Because regression coefficients are not bounded between 0 and 1 by nature, some adjustments are necessary to restrict the sum of the regression coefficients for each animal to be 1  at each SNP locus, the probability of observing genotype yi at locus i is:For crossbred animals whose ancestors originated in different populations, their genetic composition exhibits multiple ancestries associated with multiple different genetic clusters or populations, which therefore can be described by the admixture model . The admg for an individual was defined as:For a given vector of admixture proportions, the log-likelihood of the observed genotypes Alternatively, the above likelihood can be written as:xij (1≤i≤Mand  1≤j≤ K) for k populations, our goal was to determine the vector W = ′ of admixture proportions that maximize L(W) subject to the constraints wj≥0 and wj≥0 and wj with where yi and each xj on zero , and after dividing both sides of equation (1) by the standard deviation of y, the linear regression model can be expressed as:Intuitively, path analysis can be viewed as an extension of linear regression in the form of standardized multiple regression, yet with a focus on inferring causality . By centwhich can be further re-arranged as:Now, Let Here, ∗” for the convenience of notation. If we replace the standardized regression coefficients with the path coefficient notation in (6), it gives:In (6), ienation . For theIn the path analysis, a path coefficient measures the fraction of standard deviation of standardized genotypes of a crossbred animal for which each ancestor or ancestral breed is directly responsible, in the sense of the fraction which would be found if the allele frequencies of one ancestral breed varies to the same extent as in the observed data while all other variables  are constant.xj is the sum of direct path coefficient plus a sum of terms each quantifying a correctional or an indirect path effect:The theory of path analysis states that the correlation between y and ry_i x_j reflects the genomic similarity between them. Then, the determination of an endogenous variable  on an exogenous variable  is measured by the coefficient of determination. For example, the coefficient of determination of xj on yi is given by the sum of the squared direct path coefficient and the terms representing the determination of all possible indirect paths. That is,Thus, a path coefficient represents the direct effect of an ancestor or ancestral breed to be a cause on the genome of a crossbred animal while the latter is assumed to be an effect, whereas the correlation xj), which includes correlational, indirect path effects. When the correlational effects are zero or ignored, the above reduces to the coefficient of direct determination of xj on yi,The above is referred to as the coefficient of combined determination for an exogenous variable . That is,Thus, in view of genomic determination, GBC can be measured by the relative ratio of the coefficients of either the direct or combined determination. Hereafter, the former is referred to as D-GBC and the latter C-GBC hereafter. That is,D-GBCThe sum of GBC for an individual animal is one when using either of the above two formulas. The difference between the above two measures of GBC is that correlational or indirect path effects are included in the estimated C-GBC with (14) but not in the estimated D-GBC with (13). From the viewpoint of genetic determination, the correlational or indirect path effects are attributable to genomic similarities. The proportion of the variance of the endogenous variable that is not accounted for by the set of exogenous variables in the system is then quantified to be:Note that 1-R can be used as a measure of reliability say for estimated C-GBC. Given two individuals having the same values of C-GBC but different R values, estimated C-GBC is more reliable for the one with a smaller value of 1-R.In the above, we have discussed the path analysis applied to estimate GBC as a form of standardized linear regression, in which standardized genotypes of each crossbred animal are regressed on standardized allele frequencies of reference breeds. The genotypes are coded as the portion of allele A in the genotypes , which can also be viewed as the frequency allele A at the individual level. Put in another way, the frequency of allele A at each SNP locus for a given population can be viewed as the average genotype for that population. Another approach is to obtain the path coefficients using the correlations between them, as suggested by the relationships shown in (9). If we extend each equation in (9) for each of the crossbred animals, it gives:ry_i x_1 is the correlation between the genotypes of the crossbred animals and the corresponding SNP allele frequencies in the first reference population. In matrix notation, the above becomes:where, for example, pyx = (pyix1… pyxT)′,ryx = (ryx1…ryxT)′, andwhere Therefore, the vector of path coefficients is obtained as:x1 and x2. The solutions of the path coefficients are obtained as the following:Now consider only two exogenous variables,py_i x_1 is also recognized as the semi-partial correlation of x1 on yi, and py_i x_2 is the semi-partial correlation of x2 on yi. Like a partial correlation, a semi-partial correlation compares variations of two variables after certain factors are controlled for. The difference between them is that, with a semi-partial correlation, one holds the third variable (x2) constant for either x1 or yi but not both, whereas with a partial correlation, one holds the third variable constant for both  and pCEC = 0  in this example. Let A numeric example is shown in In the above, AD is a direct path from Angus to an Ultrablack, and ACD is an indirect path from Angus to an Ultrablack via Brangus. Similarly, BCD is an indirect path from Brahman to an Ultrablack via Brangus. Note that both indirect paths, ACD and BCD, are two compound paths. The coefficient of a compound path is the product of all component segments. Hence, under the assumption of no correlation between the two ancestral breeds, computed GBC of an Ultrablack agreed with pedigree-expected ratios of genomic composition for an Ultrablack animal, which is 81.25% Angus and 18.75% Brahman. Therefore, assuming no correlation between the two ancestral breeds, the causality interpretation of GBC agreed with the pedigree-expected GBC for an Ultrablack animal.Bos taurus indicus cattle breed, and it had low correlations with the seven Bos taurus Taurus cattle breeds (0.035–0.239). Thus, a high correlation between the Brangus and a reference breed does not indicate the genomic causality but the genomic similarity between them.The Brangus was highly correlated in terms of allelic frequencies with Angus (0.671–0.714) and moderately correlated with Brahman (0.442–0.481) because the Brangus was descended from these two breeds . The BraThe path analysis estimated the genomic effects of these reference breeds on the Brangus. With the eight reference breeds, the estimated path coefficients for the two ancestral breeds were the largest among the eight reference breeds, which were 0.510–0.552 for Angus, and 0.396–0.407 for Brahman . The D-Ga priori information. Because Brangus cattle are descended from Angus and Brahman, we estimated D-GBC and C-GBC by including only the two ancestral breeds as the reference breeds. Then, with these two reference breeds only, the estimated D-GBC for the Brangus using the correlation data was 71.2–72.0% Angus and 28.0–28.8% Brahman, and the estimated C-GBC for the Brangus was 70.2–71.2% Angus and 28.7–29.8% Brahman  in Brangus. In the path analysis using the correlation data, for example, the average of estimate Angus GBC was 71.67% across the three genotyping platforms and the three SNP panels. There are possibly two reasons for the elevated Angus composition in these Brangus animals. Firstly, Brangus animals have been selected toward Angus type phenotypes for years, which in turn could have left up the Angus genomic composition in Brangus. Secondly, these Brangus animals included some UB individuals. The estimated GBC for these 7,696 Brangus animals was plotted in ascending order of their Angus composition . The mixThe Beefmaster was highly correlated with the three ancestral breeds: Brahman (0.544–0.570), Hereford (0.504–0.549), and Shorthorn (0.443–0.558) . There wrBH = 0.10,rBS = 0.05, and rHS = 0.40. The estimated C-GBC for each ancestral breed is computed to be a relative ratio of combined determination coefficients for each ancestral breed:With the correlation data and the eight reference breeds, the path analysis gave the largest estimates of direct path coefficients to the three ancestral breeds, which were 0.495–0.522 (Brahman), 0.342–0.380 (Hereford), and 0.216–0.245 (Shorthorn). Accordingly, the estimated D-GBC for the Beefmaster was 56.2–59.2% (Brahman), 27.5–31.3% (Hereford), and 11.1–12.4% (Shorthorn), whereas the estimated C-GBC for the Beefmaster was 42.6–51.3% (Brahman), 28.5–35.9% (Hereford), and 13.5–15.6% (Shorthorn). Like in the case of Brangus, with the eight reference breeds, estimated GBCs for the ancestral breeds were offset by the small GBC components for non-ancestral breeds . The estTherefore, with non-zero correlations between the three ancestral breeds as the reference, in particular when one or more of the correlations are large, estimated C-GBC would deviate considerably from the estimated D-GBC. Generally speaking, the larger the correlation between the ancestral breeds, the larger the deviation that it will generate.In Beefmaster, the estimated GBCs from the admixture model and the linear regression approach seemed to deviate substantially from pedigree-expected values . They did not correspond to those obtained from the path analysis neither. The estimated GBC of the Beefmaster obtained from the admixture model was 34.1–36.3% (Brahman), 34.8–38.0% (Hereford), and 26.8–29.5% (Shorthorn). The estimated GBC of the Beefmaster obtained from the linear regression was 34.7–37.4% Brahman, 35.0–38.8% Hereford, and 25.6–28.3% Shorthorn. Relatively speaking, the estimated GBC from the admixture model and the linear regression were closer to the estimated C-GBC than the estimated D-GBC, possibly because they all included correlational indirect path effects except the estimated D-GBC. The distributions of the estimated GBC for 7.605 Beefmaster animals in ascending order of the Brahman composition obtained using the three statistical models are shown in In the linear regression approach, high correlations between exogenous variables translate into strong multicollinearity, which imposes some challenges to the identification of the likelihood in the admixture model. The problem of model identification may not necessarily affect the prediction accuracy, but individual parameters can be unidentified and cannot be estimated uniquely or reliably. Similarly, high correlations between exogenous variables can bring challenges for the admixture model to precisely assess the weights for the underlying admixture components, which in the admixture model were the allele frequencies of ancestral breeds as random variables. Arguably, the linear regression approach and the admixture model are not appropriate for estimating GBC when the ancestral breeds are highly correlated. Instead, estimated D-GBC from the path analysis are robust to deviations due to correlational path effects.We proposed a causality interpretation of genomic breed composition implemented by the path analysis for composite animals in the present study. Two measures of GBC using path analysis were proposed in the present study. Of them, D-GBC considered only direct path effects of each reference breed, whereas C-GBC also included indirect path effects due to the correlation between reference breeds. In Brangus, because the two ancestors breeds are remotely related, or they have a close to zero correlation, the estimated D-GBC agreed with the estimated C-GBC in the path analysis, and they both agreed well with the estimated GBC by the admixture model and linear regression. However, when the ancestors are highly correlated, which was the case with Beefmaster, the estimated D-GBC showed relatively larger differences from the estimated C-GBC in the path analysis because the latter included correlational effects due to genomic similarity between ancestors. Relatively speaking, the estimated GBC from the admixture model and linear regression were closer to the estimated C-GBC by path analysis than the estimated D-GBC. A possible reason is that the estimated GBC from the admixture model and linear regression  included correlational effects. Thus, path analysis provides an alternative interpretation and an estiamation method of GBC, which arguably has advantages when reference  breeds are highly genetically correlated. Finally, estimated GBC varied only slightly between different genotyping platforms (30K/40K vs. 50K) and between the three SNP panel sizes  when subsets consisted of uniformly distributed SNPs.https://drive.google.com/drive/folders/1Vs3H-oKc5A9xmX1MU2juuI8gC925wLny?usp=sharing. Further inquiries can be directed to the corresponding author.All data were taken from the databases of the Neogen Global Laboratories. Example data and programs are available at: X-LW, RT, GR, and SB conceived and designed this study, with discussions participated by RF, JG, JP, and TS. X-LW, ZL, JH, and YW carried out the analyses. X-LW drafted this manuscript. All authors have reviewed and approved the final manuscript.X-LW, RF, JG, RT, JP, TS, and SB were employees of the Neogen GeneSeek, an agricultural genomics service company. The remaining 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.


Oryza glaberrima, is an invaluable resource for rice cultivation and for the improvement of biotic and abiotic resistance properties. Since its domestication in the inner Niger delta ca. 2500 years BP, African rice has colonized a variety of ecologically and climatically diverse regions. However, little is known about the genetic basis of quantitative traits and adaptive variation of agricultural interest for this species.African rice, Rice yellow mottle virus. For this, we devised a pipeline using complementary statistical association methods. First, using flowering time as a target trait, we found several association peaks, one of which co-localised with a well described gene in the Asian rice flowering pathway, OsGi, and identified new genomic regions that would deserve more study. Then we applied our pipeline to panicle- and resistance-related traits, highlighting some interesting genomic regions and candidate genes. Lastly, using a high-resolution climate database, we performed an association analysis based on climatic variables, searching for genomic regions that might be involved in adaptation to climatic variations.Using a reference set of 163 fully re-sequenced accessions, we report the results of a Genome Wide Association Study carried out for African rice. We investigated a diverse panel of traits, including flowering date, panicle architecture and resistance to O. glaberrima genome, paving the way for in-depth studies of the genetic basis of traits of interest that might be useful to the rice breeding community.Our results collectively provide insights into the extent to which adaptive variation is governed by sequence diversity within the  Oryza glaberrima Steud., was domesticated independently of Asian rice Oryza sativa L.  is responsible for one of the most damaging diseases of rice in Africa , greenhouse experiments (RYMV resistance) or from available public databases for environmental data Table : Fig. S1O. glaberrima accessions was used in this study. The genome-wide linkage disequilibrium was high at short distance and slowly decayed with increasing genomic distance. It remained above 0.2 for at least 150 kb : NARROW LEAF8 (NAL8) which co-localized with the rOg-PAN-15 region  with the rOg-PAN-3 region , 73.56% and 17.90% of the variance was explained by the first PCA axis (TmaxPC1) and second PCA axis TmaxPC2) respectively. Ten associations were found considering TmaxPC1, while TmaxPC2 allowed us to detect 211 SNPs. Considering genomic regions detected by at least two methods, we found 16 regions for TmaxPC2 only  5% threshold frequently used in GWAS would have retrieved a far larger number of SNPs and regions. Most of the regions identified in this study would also have been identified with a FDR 5% threshold, confirming the stringency of our approach. Given the extent of the LD within our sample, we lack power to address fine dissection of quantitative traits. We chose a rather conservative approach by defining large genomic windows around the SNPs we detected as significantly associated with one of our traits of interest. The lists of genes we identified likely contain a large number of non-associated ones and therefore should be considered with caution. More studies, including functional ones, will be needed to precise which genes are good candidates for the phenotypic traits variation.The O. sativa, with several known key genes . This gene might be of interest as several genes from this family are involved in flowering control in long day or short day conditions in rice, as well as several homologs in A. thaliana, especially CONSTANS (CO) gene known to promote flowering in long day conditions  genes  from 163 high-depth re-sequenced SNPs with more than 5% missing data  were filtered out  under irrigated field conditions at the Institut de l’Environnement et de Recherches Agricoles (INERA) station in 2012 and 2014. Plants were sown at two different periods in the same year: the first at beginning of June (“early sowing”) and second in mid-July (“late sowing”). A total of 15 plants per plot of 0.5 mFlowering date (DFT) was scored when 50% of the plants for a given accession harbored heading panicles for both early and late sowings in 2012 and 2014 Table  see Tab packagesO. sativa ssp. indica) and Nipponbare (O. sativa ssp. japonica), and one as a high resistance control, Tog5681 (O. glaberrima). Three replicate experiments of all varieties were performed. In each experiment, plants were organized in two complete blocks with four plant replicates per accession.Resistance was evaluated based on ELISA performed on infected plants cultivated in the greenhouse, under controlled conditions. As high resistance to RYMV has been already well studied in African rice, we excluded highly resistant accessions, i.e. in which no virus can be detected with ELISA , the resistance level was estimated as the mean of the two sampling dates. Resulting variables were named RYMV1, RYMV2 and RYMV3 for the three different experiments  from the WORLDCLIM database at a 2.5 min resolution  = x + 1 − min(x) prior to apply the Box-Cox transformation. The histograms and quantile-quantile plots have been made again, as well as the normality tests for the resulting transformed variables . We then applied the transformation using the estimated λ with the BoxCox function. As some variables  had some negative values that could prevent the use of the transformation, we used a translation of the data whenever negative values occurred in the variable with the following formula: Apart from climate-related variables, each trait resulted from the combination of at least two repetitions. If one of the repetitions failed to reach the normality test, we used the transformed dataset for all repetitions. For climate variables, we used the transformation whenever the variable failed to pass the normality test.https://plant-breeding-genomics.extension.org/estimating-heritability-and-blups-for-traits-using-tomato-phenotypic-data/).Heritability was estimated for the following phenotypic trait: flowering time, panicle morphology and resistance to RYMV virus. We used a mixed model to estimate the inbred line variance, the block, the year and the residual variance. Raw (untransformed) data was used for this specific analysis. Heritability was calculated as the ratio of the line variance divided by the line variance and the residual variance  of our sample using the PopLDdecay software  approach implemented in the R package LEA. We assumed a number of ancestral groups (K) between one and 10 and we made five repetitions of the algorithm for each K. In order to evaluate which K best describe our data, we computed the cross-entropy criterion for each K and plotted it. We then selected the run for the considered K which exhibited the lowest cross-entropy and used it to plot the ancestries coefficient of each genotype. The estimated K was subsequently used as an input for some association genetics methods.We used the raw data to compute mean values of the quantitative traits under consideration in this study for the accessions having sampling coordinates in their passport data. We then plotted these data using the ggplot2 Wickham  package p-value < 0.01 threshold  estimation was realized using the R package qvalue  lower than 5% were filtered out. We first adjusted a simple linear model  to associate phenotype and genotype. This simple method did not take into account any putative confounding factor and allowed us to assess whether taking into account relatedness and/or population structure could reduce false positive rates. Two classes of methods accounting for confounding factors were used: 1) mixed models using kinship matrix and/or population structure , rachis length (RL), primary branch number (PBN), primary branch average length (PBL), secondary branch average length (SBL), primary branch internode average length (PBintL), secondary branch number (SBN), secondary branch internode average length (SBintL) and spikelet number (SpN) were evaluated in field conditions in 2012 and 2014. Resistance to RYMV was evaluated in greenhouse conditions during three experiments  shifted of about 1–2 months. Environmental data were extracted from the worldclim database at available sampling locations. A Principal Component Analysis was then performed on i) the whole set of variables and ii) only maximal temperature related ones. The two first axes of both these PCA were then used for association analyses and are reported in this table. A Box-Cox transformation was applied to the different data set in order to approximate the normality. The “T_” prefix was added to variables names to distinguish non-transformed and transformed variables. (XLS 161 kb)Additional file 2: Table S2. Statistics of the Shapiro-Wilkinson and Anderson-Darling normality tests, applied to the different non-transformed (a) and transformed variables (b).Additional file 3: Figure S1. Histograms of trait distribution and quantile-quantile plots for each non-transformed and transformed variables.Additional file 4: Table S3. Heritability estimates of the different traits. Heritability was estimated for each phenotypic trait: flowering time, panicule morphology and resistance to RYMV virus. The analysis was done for flowering time (FT), spikelet number (SpN), primary branch number (PBN), secondary branch number (SBN), rachis length (RL), primary branch average length (PBL), secondary branch average length (SBL), primary branch internode average length (PBintL), secondary branch internode average length (SBintL) and RYMV virus content based on the mean of estimation at 17 and 20 days after infection (RYMV). We used a mixed model to estimate the inbred line variance, the bloc, the year and the residual variance. Heritability was calculated as the ratio of the line variance divided by the line variance and the residual variance (https://plant-breeding-genomics.extension.org/estimating-heritability-and-blups-for-traits-using-tomato-phenotypic-data/). (ODS 10 kb)Additional file 5: Figure S2. Genome wide Linkage disequilibrium (LD) decay.Additional file 6: Figure S3. Structure of the population. (a) evolution of the cross-entropy criterion with increasing K, (b) bar plot of ancestries membership considering K = 4 ancestral population.Additional file 7: Figure S4. Geographic distribution of traits. We plotted the mean value of each trait for accessions having sampling location in their passport data.Additional file 8: Figure S5. Correlations between traits and structure. For each ancestry group (A1 to A4), we made a Spearman’s rank correlation test and plotted it as a correlogram. Colors sign the intensity of the correlation and white stars were added when the correlation was significant given the threshold retained, i.e. either a p-value cutoff of 0.01 (a) or a False Discovery Rate of 5% (b).Additional file 9: Figure S6. Linear scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different methods  taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.Additional file 10: Figure S7. Manhattan plots. Association analysis were performed independently for each trait and repetition and based on three different methods . The transformed data were used if at least one of the replicates failed to reach normality. P-values obtained for each replicate were then combined using a Fisher combined probability test method to obtain the final p-values represented in this Manhattan plots. The 10− 5 thresholds are indicated by red lines.Additional file 11: Figure S8. Log scale QQ-plots corresponding to association analysis performed independently on each trait and repetition. Three different models  taking into account relatedness and/or structure were used for association and ANOVA was used as a benchmark. For a given trait, the transformed data were used if at least one of the replicates failed to reach normality, otherwise non-transformed data were used.Additional file 12: Table S4. List of the SNPs associated with fifteen different phenotypic traits. Significant SNPs were identified based on three different methods , the Fisher combined probability test method to combine several repetitions of phenotypic data and a 10− 5p-value threshold. For each trait, the p-values and q-values obtained with the different methods are indicated when the p-values were significant. (XLS 349 kb)Additional file 13: Table S5. List of the genomic regions associated with four different categories of phenotypic traits. Regions were defined based on 50 kb windows around the significant SNPs detected with any of the three models. Overlapping regions were combined into a single one. Only regions detected with at least two methods were retained. The chromosome (Chr), the starting (Position 1) and ending (Position 2) positions, the size of the region  in base pairs, the number of significant SNPs included (Sign_SNPs_nb) and the lowest p-values obtained with the different methods are indicated. Sheets “RYMV” and “Tmax”concerned the regions identified for the resistance to RYMV and the maximum temperature related variables, respectively. The sheet “Flowering” concerned regions identified with the early sowing flowering time (Early) or the late sowing flowering time (Late) traits, as indicated in the columns “Trait 1” and “Trait 2”. The sheet “Panicle” concerned regions identified with rachis length (RL), primary branch average length (PBL), primary branch internode average length (PBintL), secondary branch number (SBN), secondary branch average length (SBL), and secondary branch internode average length (SBintL), as indicated in the columns “Trait 1” and “Trait 2”.Additional file 14: Table S6. List of genes located in each of the regions associated with four categories of phenotypic traits. Gene ID and annotations refer to MSU7  and three alternative ones .


HSL) and leptin receptor (LEPR) genes, and their association with growth traits in Barki lambs.Marker-assisted selection has many advantages over conventional selection in animal breeding. The candidate gene approach has been applied to identify genetic markers associated with economically important traits in livestock. This study was established to investigate variation in the hormone-sensitive lipase , pre-weaning average daily gain (ADG1), weaning weight (WW), post-weaning average daily gain (ADG2), and marketing weight (MW) were obtained from 247 Barki lambs. Polymerase chain reaction–single-stranded conformational polymorphism analyses were used to detect variation in exon 9 of HSL showed three variants , which contained two nucleotide-sequence differences (c.1865C>T and c.2038T>C). Two SSCP banding patterns (L1 and L2) were observed for LEPR and these contained two nucleotide-sequence differences (c.2800G>A and c.2978C>G). The HSL genotype showed no effect on the studied traits. The LEPR genotype was proven to have significant effects (p<0.05) on ADG2 and MW. The presence of the L1 variant was associated (p<0.01) with decreased ADG2 and MW.The SSCP banding patterns for LEPR gene variation and growth rate after weaning in Barki lambs warrants efforts to improve this trait.The finding of an association between  HSL) and leptin receptor (LEPR), which are responsible for transferring information related to controlling food intake, metabolism, lipolysis, and energy expenditure . The amplicons from the Two amplicons from lambs with homozygous SSCP patterns were purified using a PCR clean-up kit . The purified amplicons were delivered to the Macrogen sequencing company , to be sequenced in both directions using the BigDye terminator tool. DNA sequences, alignments, translations, analysis, and comparisons were achieved using DNAMAN and DNASTAR software.HSL/LEPR gene variation on the studied traits. In the first, genotype was modeled as a class variable. In the second, absence/presence of the detected variants was collapsed into two categories. Variation in the HSL/LEPR gene, year of lambing, parity of ewe, and gender of lamb were included as fixed effects. Sire was included as a random effect. Age at weaning was fitted as a covariate in the model testing the effect of variation in the HSL/LEPR gene on ADG1 and WW; additionally, age at marketing was fitted as a covariate in the model testing the effect of variation in the HSL/LEPR gene on ADG2 and MW. The significant differences were further explored using a Duncan test at p<0.05.Using the general linear mixed model in SAS (version 9.1), two sets of analyses were run to test the effect of The generalized statistical model was as follows:ijklmn = u +Ri+Gj+Pk+Bl+Tm+eijklmnY1ijklmno = u+Ri+Gj+Pk + Bl+Tm+bAWn+eijklmnoY2ijklmno = u+Ri+Gj+Pk+Bl+Tm+bAMn+eijklmnoY3where,Y1 = the evaluated BW;Y2 = the evaluated ADG1 or WW;Y3 = the evaluated ADG2 or MW;u = the overall mean;iR = the random effect of thi sire;jG= the fixed effect of thjof gender of lamb, j=1, 2;kP = the fixed effect of thk of parity of ewe, k =1, …5;lB = the fixed effect of thl of year of lambing, l=1, …3;mT = the fixed effect of th HSLm/LEPR genotype  or the fixed effect of the absence/presence of each HSL/LEPR variant ;nbAW = the partial regression coefficient of ADG1/WW on age at weaning as a covariate;nbAM = the partial regression coefficient of ADG2/MW on age at marketing as a covariate; andijklmn / eijklmnoe = Random error; assumed N.I.D. .HSL in the Barki lambs  (c.1865C>T and c.2038T>C) that were previously reported in the NBCI were detected by performing DNA sequencing of the three detected variants in exon 9 of the HSL gene. Notably, the c.1865C>T substitution results in the amino acid change threonine>methionine (p.Thr.622Met); however, the c.2038T>C substitution results in the amino acid change cysteine>arginine (p.Cys680Arg).Five genotypes were observed for ki lambs . These gki lambs , which wLEPR in the Barki lambs and three combinations of SSCP patterns corresponding to three different genotypes were detected. These genotypes comprised two variant sequences , which were previously reported in exon 19 of the LEPR gene. These are missense SNPs and do result in amino acid changes, namely, valine>methionine  and serine>cysteine (p.Ser993Cys), respectively.As shown in equences , which wThe sire exhibited a significant effect on WW (p<0.005) and MW (p<0.01).The gender of lamb showed a highly significant effect (p<0.001) on birth weight (BW), ADG1, WW, and MW. Male lambs were heavier than female lambs in BW, WW, and MW.Ewe parity significantly affected (p<0.001) BW, whereas the year of lambing did not affect any of the studied traits. Age at weaning showed a significant effect (p<0.01) on WW, whereas age at marketing had no significant effects on the studied traits.HSL exon 9 variants or genotypes with growth traits.The results presented in Tables-LEPR of 247 individuals were analyzed for correlations with growth traits . Individuals with genotype L2L2 had superior ADG2 and MW when compared with those with L1L1 and L1L2. As shown in L1 variant in the lamb genotype was associated (p<0.01) with decreases in ADG2 and MW.The SSCP patterns at exon 19 of h traits . StatistLEPR variants on the growth traits are shown in LEPR genotype on ADG2 (5.878 g/day±2.076) and MW (1.889 kg±0.688).The obtained results of the additive/dominance effects of HSL gene variation in Barki sheep. The SNPs detected in this study were not the same SNPs as were detected in Suffolk sheep  and tetracosanoic acid [C24:0]) and polyunsaturated fatty acid (docosahexaenoic acid (C22:6n3) [In this study, analysis of exon 19 in LEPR  revealedus study  sequenceone SNP A830G in eC22:6n3) .LEPR gene was confirmed to have associations with body weight and ADG at 6 and 12 months of age in Nanyang calves [LEPR gene with body weight and body gain. The LEPR Arg109Lys and Arg223Gln polymorphisms were shown to be positively associated with weight gain in Dutch adults [Moreover, in cattle, variation in the g calves . In humah adults . The weiLEPR genotype on growth traits after weaning might be due to the critical roles played by LEPR in the control of glucose metabolism and energy balance [The effect of the  balance . Energy  balance .LEPRs are present on β cells as well as on muscle and fat cells, enabling leptin to modulate the secretion and action of insulin [ insulin . Insulin insulin . Despite insulin .LEPR variation on growth traits after weaning might also be explained by other phenomena. Markedly, the L2 variant, which is associated with higher ADG2 and heavier MW, carries the nucleotide substitutions c.2800G>A and c.2978C>G, which would lead to putative amino acid substitutions of valine to methionine and serine to cystine, respectively. Furthermore, the L2 variant has a higher degree of additive effects because the L2L2 homozygote was found to have a higher value of additive effect than the heterozygote L1L2, which, in turn, had a higher value than the L1L1 homozygote. These findings might be due to the amino acid substitutions. The amino acid point mutations might change the structure of LEPR and thus its function, such as the affinity of binding to LEPR, the expression level of the protein, or the protein’s durability [LEPR as well as to determine the expression level of the LEPR.The effect of rability . These eLEPR gene with ADG2 and MW. The detected variant might be used in breeding programs to produce animals with superior growth traits after weaning in Barki sheep in Egypt and elsewhere.This study revealed associations of variation in the AHMI conceived and designed the research, conducted the sample collection, processed samples in the Molecular Laboratory, carried out the data analysis and writing of the manuscript, edited the manuscript and approved the submitted version of the manuscript.


Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred.Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve.Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6  exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66).Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools  were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies. The term “differences in sex development” (DSD) refers to congenital conditions in which chromosomal, gonadal, or anatomical sex development is atypical . They caMany methods have been developed for the computational prediction of the pathogenicity of SNVs, which are based on evolutionary conservation, protein structure/function, or assembly parameters, such as allelic diversity, pathogenicity, and association with genome-wide association studies . StudiesWe analyzed 40 disease-causing SNVs in four different genes associated with DSD: 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). All pathogenic allelic variants have been published with functional studies showing loss of function activity . To evalWe selected 11 widely used pathogenic prediction tools freely available on the Web: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), SIFT (Sorting Intolerant From Tolerant), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, and SNPs&GO .The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). In the equations below, tp, tn, fp, and fn refer to true positive, true negative, false positive, and false negative, respectively.The MCC  is an imWe randomized the results of prediction of SNV present in the DDS genes as pathogenic or benign, based on the classification given by each program. The classifications “probably benign”, “benign”, “low”, and “neutral”, provided by Fathmn, Mutation Assessor, Phanter, SIFT, Mutation Taster, Provean, and Polyphen-2, were considered as benign. The classifications “possibly damaging”, “probably damaging”, “high”, “medium”, “deleterious”, and “damaging”, were considered as pathogenic. For CAAD, which uses numeric scores, values ≤10 were classified as benign, and those >10 were classified as pathogenic. Align-GVGD also used numeric scores, which were classified as benign up to 25, and anything above was classified as pathogenic.Based on the results for each program, none of the tools were 100% precise in identifying pathogenic SNVs. The values for the parameters measured are listed in Mutation Assessor, MutPred, and SNPs&GO presented more consistent results regarding the nature of the SNVs (pathogenic or benign). MutPred had the highest accuracy, precision, and specificity , as seen in The comparative predictive performance of each tool was evaluated using the AUC scores from ROC plots and the true negative rate  as measurements. The analysis was separated into random groups, since the program analyzed a maximum of six samples at a time . The AUCIn the present study, we analyzed and compared the abilities of 11 widely available tools for predicting the pathogenicity of SNVs. Although some algorithms are based on the same data sets, they differ in the database for conservation analysis and structural attributes. They also differ in the information required to run the predictions, as some programs request the accession number of the gene, others the protein change, nucleotide change, or chromosomal position.Overall, we found that Mutation Assessor, MutPred, and SNPs&GO were the most reliable predictors for SNV classifications. They also exhibited the best AUC results. The accuracy of all tools evaluated ranged from poor to reasonably good (MMC=0.04-0.66). These results are consistent with what has been shown in previous studies ,9, whichIn conclusion, computational algorithms are important screening tools for prioritizing and identifying disease-causing SNVs, but their correlation with functional studies is not consistent. In the present analysis, the highest-performing tools were Mutation Assessor, MutPred, and SNPs&GO.Montenegro LR contributed to the acquisition, analysis, interpretation of data, and drafting of the article. Lerario AM contributed to the interpretation of data and revising the article. Nishi MY contributed to the interpretation of data, drafting and revising the article. Jorge AA contributed to the analysis and interpretation of data. Mendonca BB contributed to the conception and design of the study, drafting and revising the article.


Oryza sativa subsp. indica, and performed GWAS of the datasets. We compared the power of our method, the conventional single-SNP GWAS, the conventional haplotype-based GWAS, and the conventional SNP-set GWAS. Our proposed method was shown to be superior to these in three aspects: (1) controlling false positives; (2) in detecting causal variants without relying on the linkage disequilibrium if causal variants were genotyped in the dataset; and (3) it showed greater power than the other methods, i.e., it was able to detect causal variants that were not detected by the others, primarily when the causal variants were located very close to each other, and the directions of their effects were opposite. By using the SNP-set approach as in this study, we expect that detecting not only rare variants but also genes with complex mechanisms, such as genes with multiple causal variants, can be realized. RAINBOW was implemented as an R package named “RAINBOWR” and is available from CRAN (https://cran.r-project.org/web/packages/RAINBOWR/index.html) and GitHub (https://github.com/KosukeHamazaki/RAINBOWR).Difficulty in detecting rare variants is one of the problems in conventional genome-wide association studies (GWAS). The problem is closely related to the complex gene compositions comprising multiple alleles, such as haplotypes. Several single nucleotide polymorphism (SNP) set approaches have been proposed to solve this problem. These methods, however, have been rarely discussed in connection with haplotypes. In this study, we developed a novel SNP-set method named “RAINBOW” and applied the method to haplotype-based GWAS by regarding a haplotype block as a SNP-set. Combining haplotype block estimation and SNP-set GWAS, haplotype-based GWAS can be conducted without prior information of haplotypes. We prepared 100 datasets of simulated phenotypic data and real marker genotype data of  Detecting rare variants has been one of the most problematic problems in GWAS. Here, we proposed a novel SNP-set GWAS approach, which is superior in controlling false positives and detecting rare variants compared with conventional approaches, and implemented this method as an R package named “RAINBOWR” (Reliable Association INference By Optimizing Weights with R). In this article, we introduce the application of RAINBOW to haplotype-based GWAS by regarding a haplotype block as a SNP-set, which enables one to perform haplotype-based GWAS without prior haplotype information. We showed that the haplotype-based GWAS with the RAINBOW package succeeded in detecting causal variants with complex mechanisms that were not detected by any other conventional methods. RAINBOW also offers a fast single-SNP GWAS method. RAINBOW offers not only a SNP-set GWAS that can be applied to universal situations but also one that is faster with the restircted situations using linear kernel for constructing the Gram matrix of SNP-set of interest. We also used Rcpp (functions for using C++ in R) for the RAINBOW implementation to achieve faster computation. We believe that our package will lead to the detection of novel genes associated with biologically and agronomically essential traits. PLOS Computational Biology Software paper.This is a With the decreasing cost and increasing throughput of next-generation sequencing, the number of accessions that can be used for genome-wide association study (GWAS) is increasing –3. UsingDespite the enhancement of such public data, the conventional GWAS method still faces obstacles in the detection of unknown candidate genes. One common example is its difficulty in detecting rare alleles or rare variants. One problem caused by rare variants is that the non-causal markers that have a strong linkage disequilibrium (LD) with one causal rare variant indicate a higher detection power than the true causal rare variant, which may interfere with the detection of the true causal variant. This phenomenon is known as “synthetic association”, and often happens when the minor allele frequency (MAF) of the non-causal marker is higher than that of the true rare variant . This prSeveral methods have been proposed to solve this problem. The sequence kernel association test (SKAT) is one of the methods used to detect rare variants, and has been used mainly in human genomics . The SKAet al. implemented a computationally efficient SNP-set GWAS method using the LR test, and reported that the LR test showed greater power than the score test Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This is a very interesting research that attempts to improve rare variants detection of conventional GWAS-SNPs models via haplotypes. The proposed method performs as good as the conventional methods for controlling false positives; however, it shows to outperform the other models detecting causal induced variants that were not identified with the conventional models. Also, one of the advantages of this proposed model is that it does not rely on LD when causal variants are also genotyped.In general the materials and methods, Results and Discussion sections are well written; however, the abstract and introduction sections needs some improvements. Especially for describing better the scope and implications of the results of the proposed method.Here a few minor points.Page 2, lines 6-9. sequencing data is the "3000 rice genomes project" .such public data, the conventional GWASPage 2, line 29. in falseas in the world collection of rice germplasm used in thisdrawback: a weightingLine 39. which is a computationallymethod since it requiresPage 3, lines 47-49. Please rephrase.Derivations of the equations and model development is okAdding a diagram for explaining the proposed simulation scheme would help to understand better the results.Page 8, line 217. data an materialDiscussion was well conducted. Perhaps a conclusions section would be desirable if that is allow in the journal format.Reviewer #2: Please see attachment for review.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  - perhaps something that authors may like to think about in future developments of their software.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


Vigna unguiculata (L.) Walp] in sub-Sahara Africa is curtailed by a lack of farmer-preferred and improved cultivars and modern production technologies. The objectives of the study were to determine the extent of genetic diversity present among a collection of cowpea accessions from Zambia and Malawi using phenotypic traits and single nucleotide polymorphism (SNP) markers and, to select distinct and complementary parental lines for cultivar development. One hundred cowpea genotypes were evaluated for agronomic traits in two selected sites in Zambia, using a 10 × 10 alpha lattice design with two replications. Ninety-four of the test genotypes were profiled with 14,116 SNP markers.Productivity of cowpea [− 1 (NPP), pod length (PDL), and number of seeds pod− 1 (NSP), were significantly (p < 0.05) affected by genotype × environment interaction effects. Genotypes such as CP411, CP421, CP645, CP732, Chimponongo, and MS1–8–1-4 exhibited higher grain yield of > 1200 kg/ha with excellent performance in yield components such as NSP, PDL, HSW and GYD. Grain yield had significant (p < 0.05) associations with NPP (r = 0.50), NSP (r = 0.46) and PDL (r = 0.42) useful for simultaneous selection for yield improvement in cowpea. The SNP markers revealed gene diversity and polymorphic information content of 0.22 and 0.17, respectively, showing that the tested cowpea accessions were genetically diverse. Test genotypes were classified into four genetic groups irrespective of source of collection allowing selection and subsequent crosses to develop breeding populations for cultivar development.Number of pods plantGenotypes Bubebe, CP411, CP421, CP645, Chimponogo and MS1–8–1-4 were identified to be the most genetically divergent and high yielding making them ideal parental lines for breeding. This study provided a baseline information and identified promising cowpea genetic resources for effective breeding and systematic conservation. Vigna unguiculata (L.) Walp., 2n = 2x = 22] is a relatively low cost source of plant-derived protein, amino acids and essential nutrients globally. It is the main food staple supporting millions of people in sub-Sahara Africa (SSA) . repon gains , 31.In the present study qualitative traits such as seed coat, pod and leaf colour were more important traits for selection. These traits affect the market value of cowpea in Africa given that farmer and consumer preference are based on these attributes. Seed coat colour is often associated with processing quality (e.g. cooking time) and farmers deliberately select varieties that have shorter cooking time . The inhIn this study, the heritability estimate for grain yield was high (64%), suggesting that the grain yield achieved by the accessions was highly repeatable ensuring genetic improvement through selection. The high heritability value for grain yield corroborates with the findings of  but loweThe relationships among yield and yield components are critical in devising a selection strategy. Selection of one trait may amplify or negatively affect performance in the other traits. The high contribution and strong association of PDL, NSP, GYD, and NPP to PC1 as well as DTF and DTM with the PC2 indicated that these traits were highly discriminatory explaining the variation among the genotypes , 39. fouGenetic analysis using SNP markers delineated the test populations in to four genetic groups. This demarcation was irrespective of source of collection, suggesting that geographical sources of collection are not the sole factor for classification of cowpea genotypes. The germplasm exhibited high heterozygosity of 0.30, which showed that alternative alleles were represented in this population. This could be attributed to genetic exchange between and among cowpea breeding programs in sub-Sahara Africa, particularly in southern Africa. Heterozygosity estimates were highest for mutant lines, followed by elite lines and landraces. This suggests that mutant lines exhibited more variability possibly due to continuous gene segregation and other chromosomal aberrations. Landrace varieties are not subjected to artificial selection by breeders. Hence, landraces of self-pollinating crops including cowpea consists of heterogeneous populations due to genetic variation until complete homozygosity is reached through selection . Mutant The PIC and GD values were essential to assess genetic diversity within the whole population, and sub-populations for identification selection of divergent parental lines useful for breeding. The moderate PIC and GD values exhibited by the whole population could be attributed to the inherent nature of self-pollination in cowpea, which limits genetic diversity. Self-pollinating species often lack genetic diversity due to higher levels of homozygosity at multiple loci. The within-population diversity in self-pollinating species is relatively low but varies among populations , 42. In Phaseolus spp) [Dioscorea spp) [Brassica spp. [The phenotypic and genotypic distance matrices exhibited low and non-significant correlation, which confirm that phenotypic and genotypic matrices were independent but complementary. The discordance between phenotypic and genotypic matrices is partly attributed to the significant environmental effect on the variable expression of the assessed phenotypic traits. Phenotypic performance is the result of the genotype, environment and genotype-by-environment interaction effects . In addilus spp) ), yam (Drea spp)  and Brasica spp. . Due to ica spp. , 48. Theica spp. .Phenotypic analysis using qualitative and quantitative traits and genotyping using high density SNP markers revealed the presence of significant variation among 100 cowpea germplasm collections of southern Africa. Trait association analysis revealed significant correlation between NPP, NSP, PDL and GYD that could allow direct selection to improve grain yield. The SNP markers used in the study were able to deduce genetic variation among the tested cowpea populations. The largest proportion of variation was attributable to individual genotype differences that is essential for improving grain yield by crossing lines from different divergent populations. Test genotypes were classified in to four genetic groups irrespective of source of collection allowing selection for subsequent cross combinations to develop breeding populations for cultivar development. Genotypes Bubebe, CP411, CP421, CP645, Chimponogo and MS1–8–1-4 were identified being the most genetically divergent and high yielding making them ideal parental lines for breeding. This study provided a baseline genetic profile and identified promising cowpea genetic resources for effective breeding and systematic conservation.The study evaluated 90 cowpea genotypes acquired from different sources (Supplementary Table 1). The germplasm included 61 elites lines from Malawi, International Institute for Tropical Agriculture (IITA), Nigeria, The National Gene Bank of Zambia and the University of Zambia, 15 landraces collected from smallholder farmers in Zambia and 14 mutant lines, which were derived from three parental lines; Lutembwe, Bubebe and Msandile. The accessions from IITA and the released cultivars were used as standard checks.o 32′S and a longitude of 28o11’E with an altitude of 1206 m above sea level. The total mean annual rainfall at the SSCI site is 1092 mm, while the mean daily minimum and maximum temperatures were 12 °C and 26 °C, respectively. The GART site is situated at a latitude of 14o 96′S and a longitude of 28o10’E and an altitude of 1103 m above sea level. The GART site receives a total mean annual rainfall of 884 mm with mean daily minimum and maximum temperatures of 10 °C and 30 °C, respectively. The soils at both sites are classified as Haplustalf clays with pH of 5.8 and 5.2 at SCCI and GART, respectively [The 100 genotypes were field evaluated during the 2017/2018 main crop season at the following two sites: the Seed Control and Certification Institute (SCCI) in Chilanga and Golden Valley Agricultural Research Trust (GART) in Chisamba/Zambia. The SCCI site is situated at a latitude of 15ectively .2. The inter-row and intra-row spacings were 75 and 45 cm, respectively. Two seeds were sown per station at a depth of 2 cm and later thinned to one plant two weeks after emergence. Basal fertiliser (N: P: K), containing 20% nitrogen, 10% phosphorus and 20% potassium, was applied at a rate of 200 kg ha− 1 prior to planting. All other agronomic practices for cowpea production were followed as recommended for Zambia [The experiments were laid out in a 10 × 10 alpha lattice design with two replications. Each genotype was sown in a plot with two rows of 5 m long. The plot area was 3.75mr Zambia . The cro− 1 based on the following formula:Data was collected from six qualitative and eight quantitative traits following the descriptors of the , 51. The legumes  and 10,0βijk = μ + Gi + Ej + Gi ∗ Ej + Ei (rk)(b) + εijk, where; βijk = observed response; μ = grand mean Gi = the effect of ith genotype; Ej = the effect of jth location, Gi ∗ Ej = the genotype x location interaction effect; Ej(rk)(b) = error associated with kth replication in blocks in the jth location and εijk = experimental error. The blocks within replications were considered as random factor, while genotypes and locations were fixed factors. Trait means of test genotypes were separated using the Fischers Unprotected LSD at p ≤ 0.05 significance level. Genotypic, genotype by location interaction and phenotypic variances were computed from the excepted mean squares of the analysis of variance as follows; σ2g = genotypic variance, σ2gl = genotype by location interaction variance, σ2 : p = phenotypic variance, msg = mean square of genotype, mse = mean square of error, l = number of location and r = number of replication. Variances below zero were adjusted to zero according to [2) was computed according to [σ2g is genotypic variance and σ2p is phenotypic variance. Heritability was categorized as low (0–0.30), moderate (0.30–0.60) and high (> 0.60) following [σ2g = genotypic variance, σ2p = phenotypic variance, k) (σp) (h2), where, GA = Genetic advance; k = selection differential at 5% selection intensity; σp = phenotypic standard deviation; h2 = broad sense heritability; Genetic advance as a per cent of mean (GAM) was computed following [The frequency of test genotypes displaying the assessed qualitative traits were summarised and statistical significant tests conducted using the cross tabulation procedure with the Statistical Package for the Social Sciences (SPSS) version 24 . The quarding to . Heritabrding to ; \documeollowing . A covarollowing  as folloollowing  as folloollowing : \documee 0.30–0. and highr) using the SPSS version 24 [The magnitude of traits relationship was determined using Pearson’s correlation coefficients  using the Lysis Buffer 1 (based on the CTAB method) according to the manufacturer’s procedures. The DNA concentration of each sample was measured using a NanoDrop 1000 . For verifying DNA integrity, 2 μL of DNA were subjected to gel electrophoresis on 1.0% (w/v) agarose gel, stained with ethidium bromide. Subsequently, 40 μL of a 50 ng/μL DNA of each sample were genotyped with Illumina Cowpea iSelect Consortium Array using Diversity Arrays Technology (DArT) markers. In total, 94 cowpea genotypes were genotyped by the genotyping by sequencing (GBS) technology as described by  with 20,For quality control, DArTseq SNP derived markers were filtered to remove bad SNPs and genotypes using the software’s PLINK 1.9 in MS window and R statistical package. Markers and genotypes with > 20% missing data were eliminated. Rare SNPs with < 5% minor allele frequencies were also pruned from the data. Only 14,116 informative SNP markers and 90 genotypes were used for analysis after removal of 4240 SNPs and four genotypes. The four genotypes, CP1, CP2, CP479, and CP2223 were removed due to extreme heterozygosity (< 90%), duplication or high levels of missing data (> 20%). The chromosomal coverage of the 14,116 SNPs was presented in Supplementary Fig. 3.The Bayesian clustering method was used for infering the population structure of the germplasm using the STRUCTURE version 2.3 software . The STRA joint analysis based on a combination of phenotypic and genotypic dissimilarity matrices was conducted. A Gower’s distance matrix was generated from the phenotypic data while the genotype dissimilarity matrix was computed based on Jaccard’s coefficient. The correlations among the phenotypic, genotypic and joint dissimilarity matrices were compared using the Mantel’s test with 999 permutations  using thAdditional file 1. Statistical tests and distribution of 100 cowpea germplasm collections based on qualitative traits.Additional file 2. Correlations among phenotypic, genotypic and joint matrices based on Mantel test with 999 permutations showing the correlations  and p-values .Additional file 3. List, source, and description of 90 cowpea genotypes used in the study.Additional file 4. The Mantel test between phenotypic and genotypic matrices.Additional file 5. The Mantel test between phenotypic and joint matrices.Additional file 6. The Mantel test between genotypic and joint matrices.Additional file 7. Chromosomal distribution of the 14,166 SNPs used to evaluate 90 cowpea genotypes.