Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history.With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’. In this context, being able to find genetic variants responsible for this diversity is an important step toward the elucidation of the genetic architecture of traits. Due to the emergence of cost-effective sequencing technologies, the generation of large-scale sequence datasets for a large number of individuals of the same species no longer represents a major bottleneck, in particular for model organisms. To this end, the last decade has seen the initiation of several large-scale resequencing projects, enabling the gathering of enough sequences to make high-throughput approaches possible. Large-scale polymorphism surveys have been reported in humans ,2, Arabithaliana , Saccharrevisiae  and Caen elegans , for exaThere are multiple motivations for carrying out large-scale population genomics projects. First, they are designed to enable a better understanding of the demographic and evolutionary histories of populations. Second, such datasets provide insight into the processes by which genetic diversity is generated and maintained. Third, they can help to distinguish effects acting on the entire genome  and those acting on individual loci . Finally, one of the major incentives of resequencing projects is to gain a better understanding of the relationship between genotypes and phenotypes, and more specifically to build genetic datasets that would make it possible to map allelic variants responsible for phenotypic diversity.Arabidopsis thaliana [Oryza sativa [Drosophila melanogaster [One way to access the genotype-phenotype relationship is to conduct genome-wide association studies (GWAS). This strategy has become an elegant approach to dissect natural variation by associating phenotypes with genotypes at the genome scale as it has the advantage of benefitting from the historical recombination in wild populations to detect non-random associations between phenotype of interest and genetic variants distributed throughout the genome. To date, many studies have already proven that GWAS is a very effective way to find genetic determinants associated with quantitative traits. These studies have been conducted primarily on model organisms, and investigated traits mostly focus on human genetic diseases ,7, agronthaliana –10, graia sativa , or longnogaster . The pownogaster . HoweverS. cerevisiae, are under-represented model organisms for GWAS. This is mainly due to the fact that a large number of natural isolates have only recently been sequenced [S. cerevisiae and try to appreciate its limits [S. cerevisiae, they all suffered from the lack of statistical power due to the limited size of the cohort used. The genomic sequences of 100 strains associated with 49 phenotypes were used to perform GWAS and made it possible to find associations representing proof of principle, with a high probability of detecting large-effect loci, but emphasizing that 100 genomic sequences still limit the power of analyses, with little or no significant association found for multiple phenotypes [Despite being a widely used model organism for genetics, yeasts, and more precisely equenced . They ars limits –16. Someenotypes ,18.S. cerevisiae genome sequences [S. cerevisiae. This survey highlighted differential genome evolution patterns across 26 subpopulations with their own and unique evolutionary history. In addition, the dataset was large enough to overcome aforementioned statistical power issues and was found to be suitable for performing GWAS, as genetic variants were significantly associated with 14 traits, most of them being complex (i.e. involving more than one locus) [S. cerevisiae, to see if the results follow our simulations.More recently, sequencing efforts have been carried out and allowed for the availability of more than 2000 equences ,17,19,20equences  aimed to. 2 (a)et al. [All the strains and sequencing data used for this study were obtained from Peter et al. . The ind (b)S. cerevisiae [ —the complete dataset, that gathers the 1011 isolates, —S. cerevisiae species and for which the strains were selected to avoid overrepresentation of some specific groups ,a dataset named ‘sampled diversity’ that is composed of 133 isolates representative of the genetic diversity of the  —et al. [four datasets selected according to the subpopulations defined in Peter et al.  and compFor this study, we worked with six different datasets, based on 1011 previously sequenced isolates of revisiae :— the c (c)The joint calling method of GATK  was run For each of these, we used vcftools  to keep We also used vcftools to filter missing genotypes as follows with an arbitrary threshold to exclude all variants present in less than 1000 individuals for the total matrix (--max-missing-count option has been set to 11), and we excluded all sites with missing calls for the subset matrices . Finally, we excluded from the matrices the sites that had a Minor Allele Frequency (MAF) < 5%, using PLINK 1.9 with the --maf option set to 0.05 .The size of the constructed matrices is recapitulated in et al. [Copy number variants (CNVs) detected in Peter et al.  were add (d)For each of the six datasets, we simulated 1000 Mendelian traits  and 1000 complex traits (governed by 10 SNPs). Causal SNPs were randomly chosen in the SNP matrix and a phenotype was generated accordingly using GCTA . The hergcta --bfile $snp --maf $maf --simu-qt --simu-causal-loci $snplist --simu-hsq 0.8 --simu-rep 1 -out $outputWith the simulation of a complex trait, we used GCTA's default effect size assignation method, which comprises generating them from a standard normal distribution among the 10 causal SNPs. (e)4 10 mM as well as on YPD 30°C as pinning and growth control. Each isolate was present in quadruplicates on the corresponding matrix (interplate replicates) and at two different positions (intraplate replicates). The plates were incubated at 30°C for 40 h and were scanned at a resolution of 600 dpi and 16-bit grayscale. Quantification of the colony size from plate images was performed using the software package Gitter [Quantitative high-throughput phenotyping was performed using endpoint colony growth on solid media . Strainse Gitter . Each va (f)The estimation of genome-wide heritability was completed by dividing the genetic variance of the null model by the total variance of the null model , computed using FaST-LMM. (g)We performed mixed-model association analysis using FaST-LMM . The comfastlmmc -bfile $snp -bfileSim $snp -pheno $pheno -out $assoc_file -verboseOutputλ for each run of simulation. This factor is defined as the ratio between the median of the empirically observed distribution of the test statistic on the expected median. For example, the λ for a standard allelic test for association is based on the median (0.456) of the 1 d.f. χ2 distribution. Under a null model of no association and unlinked variants, the expectation is for the λ to be 1. A λ superior to 1 indicates inflated p-values of association, possibly due to a confounding factor not accounted for.The mixed model adds a polygenic term to the standard linear regression designed to circumvent the effects of relatedness and population stratification. To quantify the extent of the bulk inflation and the excess false positive rate, we computed the genomic inflation factor  (h)p-value threshold for each condition by permuting phenotypic values between individuals 100 times. The significance threshold was the 5% quantile . With that method, variants passing this threshold have a 5% family-wise error rate. In order to evaluate and compare the power of our datasets to recover causal SNP(s), we built a table of confusion for each run of simulation, by measuring the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN), transposed into rates with the following formulas: —True positive rate (TPR) = TP/(TP + FN) —True negative rate (TNR) = TN/(TN + FP) —False positive rate (FPR) = FP/(FP + TN) —False negative rate (FNR) = FN/(FN + TP)We estimated a trait-specific . 3 (a)S. cerevisiae. Among the different clades identified though the analysis of phylogenetic relationships between the isolates [a). We have also included the complete dataset with 1011 individuals and the sampled diversity dataset composed of 133 individuals representing the genetic diversity of the S. cerevisiae species. These datasets vary in size, both in terms of number of individuals and polymorphic sites . However, the distribution of the MAF is more uniform for the mixed origins dataset, and the sake dataset has 13% of the SNPs with a MAF of around 0.49. These latter SNPs are distributed across the entire genome and this bias is linked to the fact that half of the sake strains are clustered on a thin branch of the tree, suggesting that they are very close and share a recent common ancestor (figure 1a).In order to avoid false associations, successive quality control filtering steps were applied to the SNP matrices generated for each dataset. We kept only biallelic SNPs and filtered out SNPs with missing genotypes for all matrices, except for the 1011 strains dataset, for which we kept SNPs with at least 1000 informed genotypes. The markers with a MAF lower than 5% were also removed. The retained matrices contain between 14 164 SNPs for the European wine group and 82 869 SNPs for the 1011 strains and cover the entire genome in each dataset . After tfigure 1p-value of each genome-wide test was recorded. And a genome-wide specific significance threshold of 5% family-wise error rate was estimated.In order to test the ability of each dataset to map the causal variants responsible for phenotypic variation, extensive simulations were performed. The causal genetic determinants of two types of traits, Mendelian and complex traits governed by one and a large number of genetic variants of varying effect sizes respectively, were selected at random. These simulated traits obviously do not summarize all the complexity of the genetic architecture of the traits but give an overview of the behaviour of the different populations on the GWAS performance according to the type of trait. Phenotypic data were generated based on the genotype of each strain. The association was run using FaST-LMM, which adds a polygenic term to the standard linear regression designed to circumvent the effects of kinship and population stratification . These s (b)λ), which quantifies the extent of the bulk inflation and the excess of false positive cases. All datasets except the sake one have λ close to the expected value of 1 for most runs, indicating that they are well suited for detecting and mapping of Mendelian traits . The sake dataset shows much more scattered λ values, revealing a certain lack of power to efficiently detect associations.We first assessed the ability to detect an association between the different matrices and a Mendelian trait, governed by one genetic variant, on the basis of 1000 simulated Mendelian traits for each dataset. Overall, the unique causal genetic variant was identified as significant for all 1000 runs, with the exception of seven runs performed with the sake subpopulation, which clearly illustrates the high capacity of identifying the causal SNP for all datasets in case of a Mendelian trait. Equally important is the ability of the dataset to differentiate the causal variant from other markers. For the evaluation of this parameter, we computed the genomic inflation factor (−3), but can vary a lot among a run to another . The higher values are observed for the sake dataset, for which 145 runs show a FPR of more than 10%, making the results from this dataset unreliable. To a lesser extent, some runs of the European wine dataset reach a value of 5%, which is also much higher than expected for a high-confidence dataset. However, these high FPRs can be considered overestimated when there is linkage between some markers and the causal genetic variant .This observation leads us to determine the proportion of false positives among the variants detected as associated. The median value of the false positive rate (FPR) is very low for all datasets  performs poorly, which means that a careful selection of strains to avoid the overrepresentation of some closely related strains could be a good way to create an effective dataset for GWAS.Although the causal genetic variant governing a Mendelian trait is always found with the exception of a few runs, false positives rates can vary between the datasets that were tested. These results suggest that our datasets appear globally suitable for performing GWAS in the case of a low genetic complexity trait. Indeed, we do not notice any inflated  (c)a), suggesting that they share a very recent common ancestor that could be the source of a systematic bias in allele frequency such as the overrepresentation of variants with a MAF around 0.49 (b). Across the 1000 associations, we observed that these variants were overrepresented in both the false negative and false positive categories (b). Among the 135 simulated traits for which the causal SNP had a MAF around 0.49, our test failed to associate six and a higher number of FP was observed compared to the others .The population corresponding to the cluster of sake strains shows relatively poor performance, even for a Mendelian trait. Indeed, it corresponds to the only dataset for which the causal genetic variant has not been associated for some runs and, more importantly, it shows high FPR for many runs preventing any conclusion to be drawn from association studies. Several characteristics of this dataset can be attributed to this failure. First, this dataset only contains 47 strains, but sample size cannot be the only factor, as the performance of the dataset does not seem to be related to the size for the other datasets. We also observed that the global genetic diversity within this cluster in very low (π = 0.0008). Moreover, 23 out of the 47 strains are clustered on a thin branch of the tree a, suggesund 0.49 b. Acrosstegories b. Among In general, it seems that the genetic variants with a MAF around 0.49 will cause problems. If they are causal, it will be more difficult to detect them by association or will lead to an increase in the number of false positives. If they are not causal, they will still be wrongly associated with the trait. This case allowed us to measure the impact of relatedness (i.e. sharing a recent common ancestor) on the mapping of traits with GWAS, as the relationship will result in a MAF bias of the genetic variants. (d)To further assess the performance of our datasets, we tested their utility to map causal SNPs in the context of 1000 simulated complex traits governed by 10 SNPs for each dataset and estimated the fraction of heritability recovered. No dataset could detect all the causal SNPs that were used to simulate the phenotypes. The best results obtained are the detection of eight causal SNPs for 15 runs of the 1011 strains dataset. The median of the true positive rate (TPR) for this dataset is also the best, with five true positive SNPs out of 10 detected (TPR = 0.5). It seems that the TPR increases with the size of the sample, indicating that this parameter probably plays an important role in the detection power for a complex trait . To further test this, we downsampled the mixed, sampled diversity and wine datasets to match the number of individuals composing the sake population. The genetic diversity of these new populations matched the original populations, allowing us to assess only the effect of reduced population size. The GWAS simulations performed very poorly, with a median of the TPR of 0 for all three subpopulations and a maximum of 0.2 for 10 out of 1000 runs for the subsampled wine population . These results suggest that sample size correlates with performance of association studies, although it is not the only factor influencing the outcome .a). Rather, detection propensity increased with the effect size (b). This suggests that, as the genetic contribution to the phenotype is distributed over several SNPs, the smaller the effect size of a genetic variant, the more difficult it will be to detect. In addition, the p-values of the causal genetic variants are positively correlated with the effect sizes for all datasets, indicating that variants with high effect sizes are more likely to have high association scores . Taken together, these results illustrate how the missing heritability can be hidden behind a large number of variants with small effects and that datasets with a larger sample size are more likely to detect the causal variants.The MAF of causal SNPs does not influence their detection propensity a. Ratherect size b. This s (e)−4 for all datasets. Based on the FPR distribution, the sampled diversity and the 1011 strains datasets are the least likely to detect spurious associations. The sake dataset has the most outliers, with 17 of them being above 10%. This proportion is surprisingly better than for the mapping of a Mendelian trait, but still very high. The distribution of the genomic inflation factor λ of the 1000 runs per dataset  indicates that most datasets have λ values closely centred on 1. The sake dataset appears once again as an outlier with a much broadly distributed value of λ, confirming that a high proportion of false positives can be expected with this dataset.Interestingly, the median FPR in the case of complex traits is lower than when we simulated a Mendelian trait  and is less than 4 × 10We observed a tendency for FPR to increase as the TPR increases, illustrating the intuitive fact that causal variants can be mapped by chance if there are many SNPs that have been significantly identified. However, this tendency is less marked as the sample size increases , which is consistent with the previous result showing that a large sample size will reduce the false positive rate.These results show that the sample size clearly influences the GWAS result, in particular in the case of a complex trait but also that this parameter is not the only one to influence the GWAS result as the sampled diversity shows very good results with only 133 individuals. (f)CUP1-2 is the main copper-activated metallothionine in S. cerevisiae. The protein Cup1p binds and sequesters copper(I), Cu+, allowing the cell to control copper ion homeostasis. This ion, although essential for the survival of yeasts, is also an environmental heavy metal toxicant at high concentrations, which is used, for example, to kill downy mildew in vineyards, where S. cerevisiae is often found. Tandem duplications of the CUP1 gene are frequent in budding yeasts, and the tolerance to copper ion is correlated with the number of copies of this gene [CUP1-2 gene is known to be of major importance for this trait, it is expected that association with well-suited datasets should easily detect this genetic variant.To test associations with actual phenotypic values, we measured growth for all our isolates on 10 mM copper sulfate and normalized these sizes by growth on the standard YPD medium at 30°C. These measures are a proxy for the fitness of each isolate on stress induced by copper sulfate. This phenotype shows a high genome-wide heritability for the full dataset (83.78%), which is of the same order of magnitude as the heritability used to simulate the phenotypes in this study. The gene his gene . As the CUP1-2 was not detected in the sake, the mixed origins and the mosaic regions 3 datasets . The mosaic region 3 and the sake datasets also showed associations with other variants that are not known to be involved in the resistance to copper sulfate. While the involvement of these genes in this phenotype cannot be ruled out, one should also keep in mind that these datasets had high FPR for some simulations, especially the sake dataset. In the other datasets, the CUP1-2 gene was significantly associated with the phenotype, with no other significant association.The association was performed with the same method as that used for the simulations, with a dataset-specific threshold determined by 100 runs of permutations. The copy number variant of p-value (4.9 × 10−44), followed by the European wine dataset (6.03 × 10−18) and the sampled diversity dataset (1.04 × 10−14). These results are consistent with our simulations, as the 1011 strains and the sampled diversity datasets were the ones that showed the best results. The detection of the CUP1-2 gene for the European wine subpopulation is attributed to the evolutionary history of this population, and its link with human activity. Indeed, in that case, the acquisition of resistance to copper sulfate is certainly reflecting convergent evolution due to human selection for industrial processes [The score of association is an indicator of the ease with which a dataset is able to detect the causal variant. Here, we see that the 1011 strains dataset shows the lowest rocesses , thus em. 4S. cerevisiae isolates [CUP1-2 gene responsible for the variation in fitness. This genetic variant was successfully identified in three datasets without any other association while it was missed in the other three. The detection of the causal variant was consistent with our simulations and showed us that specific populations could represent good datasets to perform associations with given phenotypes.In this work, we performed extensive GWAS simulations by measuring the ability of linear mixed models to find a significant association between causal genetic variants and simulated phenotypes of each individual. These tests have been performed on five subpopulations as well as the complete dataset of the 1011 isolates . While vS. cerevisiae due to the highly stratified populations, which led to high type-1 error rates [GWAS studies have been problematic in or rates ,15. Heren = 323), would constitute a good set to perform GWAS because the population is not stratified. The number of false positives by run is very high for a Mendelian and complex trait and the results obtained with this dataset should therefore be taken with caution.The results of our associations vary considerably from one cohort to another, allowing us to evaluate the limits of GWAS and present what would constitute an ideal dataset for association studies. For mapping a Mendelian trait, the sampled diversity set is the best, with the fewest false positives, and is followed by the total dataset with 1011 strains. When the trait is complex, the entire population has the advantage of mapping more causal variants than any other. In addition, the false positive rate is close to that we observe for the sampled diversity dataset, which has, as for a Mendelian trait, the lowest value. It is now evident that the sake cluster is poorly suited to perform GWAS. First, the number of sequences composing this dataset is too low and therefore does not provide sufficient statistical power for trustworthy associations. Second, the strains composing this cluster are very close to each other and all share large parts of the genome due to the fact that the common ancestor is very recent compared to other datasets. We initially thought that the European wine subpopulation, being a clear lineage and composed of a high number of strains (To go further, it would be interesting to test whether larger sample sizes would improve the detection of causal loci. Indeed, since there is a skew toward low-frequency alleles in the yeast population, a dataset with a larger sample size might be able to identify rarer genetic variants, and therefore increase the proportion of phenotypic variation explained. Increasing the sample size will also allow more subsets to be created, while maintaining a high sample size, and could be of great help when association is tested with a specific phenotype. Another solution is to perform GWAS on an inbred population resulting from a diallel cross , i.e. al


Numerous studies demonstrated the lack of transferability of polygenic score (PGS) models across populations and the problem arising from unequal presentation of ancestries across genetic studies. However, even within European ancestry there are ethnic groups that are rarely presented in genetic studies. For instance, Russians, being one of the largest, diverse, and yet understudied group in Europe. In this study, we evaluated the reliability of genotype imputation for the Russian cohort by testing several commonly used imputation reference panels . HRC, in comparison with two other panels, showed the most accurate results based on both imputation accuracy and allele frequency concordance between masked and imputed genotypes. We built polygenic score models based on GWAS results from the UK biobank, measured the explained phenotypic variance in the Russian cohort attributed to polygenic scores for 11 phenotypes, collected in the clinic for each participant, and finally explored the role of allele frequency discordance between the UK biobank and the study cohort in the resulting PGS performance. Over the last decade, genome-wide association studies (GWAS) have discovered a substantial number of associated variants for many complex traits. Yet, the non-uniform representation of populations in genetic studies considerably limits the applicability of GWAS-based resources for individual risk prediction –4. For eHowever, even within European-centered GWAS data, there are significantly under-represented local populations. For example, Russians are one of the largest ethnic groups among the Europeans and, while there are numerous prior reports about genetic diversity in Russia –7, GWAS Given the absence of ancestry-matched supporting resources, other common publicly available databases  should Here, we present an analysis of the array-based genotyping data obtained from Russian-descent individuals. We selected an optimal genotype imputation panel based on the accuracy metrics calculated for masked genotypes and evaluated the concordance of polygenic score estimates based on UK-biobank GWAS results with clinically measured phenotypes.S1 Appendix.An extended description of the technical pipeline and parameters used for data processing is available in the S1 Table in S1 Appendix). All participants provided their written informed consent [239 DNA samples were collected from the elderly population at the Almazov National Medical Research Centre . All participants were Russians from Saint-Petersburg. Clinical information was obtained by physician specialists during the ambulatory patient visit ( consent .). The final dataset consisted of 230 samples and 501,100 directly genotyped variants.DNA samples were genotyped using the GSA Illumina v2.0 array at Broad Institute and subjected to the quality filtering pipeline (see ).Genotype imputation in the Russian population was carried out using three reference panels: the Haplotype Reference Consortium (HRC) , 1000 Ge). All imputations were performed with Beagle 5.2 [burnin = 6, iterations = 12, imp-segment = 6, ne = 1000000). The imputation quality for each variant was measured using R2 , as given in Beagle output [).Before the imputation, genotypes were pre-phased, strand-checked, and split into individual chromosomes  –18.Concordance rate (CR) was estimated as the sum of genotype probabilities for each matching genotypic class divided by the total number of genotypes . Thus, t2) was measured as the squared Pearson correlation between the directly measured genotypes and the imputed dosages. Directly measured genotypes were encoded according to the minor allele occurrence. Thus, major homozygote was encoded as 0, the minor homozygote as 2, and both heterozygotes (0|1 or 1|0) as 1. Allele dosages, taking values in the range from 0 to 2, were extracted from Beagle 5.2 output.The squared correlation coefficient (ro), but in this case adjusted for the chance agreement (Pc). The IQS calculation was performed using the following equation:o is the concordance rate and Pc is the chance agreement.As an alternative to the previous two methods, we used the imputation quality score (IQS). It measures the agreement between two genotype sets using the concordance rate (Pc) is the sum of the products of marginal frequencies that would occur if genotypes were called at random using the same marginal rates [Chance agreement , defined as follows:MAE is zero when imputed frequencies totally match the real ones and it is large when there is a major discordance between two sets.).The phenotypic variance explained by polygenic scores was evaluated according to the protocol described in Martin et al. . We usedFig 1). The genotype imputation is used to increase the number of DNA variants available for analysis by using a reference whole genome sequencing panel to predict the genotypes that were not a part of the genotyping array. In the absence of population-specific resources, other common reference panels, such as HRC (Haplotype Reference Consortium), 1000G (1000 Genomes project) or HGDP (Human Genome Diversity Project), are commonly used. However, their efficiency in predicting genotypes for individuals of Russian ancestry has never been evaluated. Furthermore, the comprehensive evaluation of the imputation results is greatly impeded due to the lack of sufficient population-specific whole genome sequencing data, except several projects included a limited number of Russian-descent individuals [The genotyping data analysis protocol for the estimation of polygenic scores involves several steps that require reference data  had non-zero MAF in the Russian population and were present in all used reference panels. Subsequently, we compared genotypes for these variants in genotyped and imputed data sets to calculate the accuracy scores of the imputation and the concordance of the produced allele frequencies.S1-S5 Figs in S1 Appendix). Our results provide further evidence that the concordance rate, in comparison with IQS and other scores studied, inflates accuracy estimates, particularly for low-frequency variants. The squared Pearson correlation was immeasurable for variants with uniform dosages, making it difficult to reliably compare accuracy for a fraction of rare variants. More specifically, some masked variants were imputed with uniform dosages, thus having zero MAFs. As a result, there was zero variation in the response variable, making the correlation coefficient between the masked and imputed dosage immeasurable. IQS was not exposed to the above biases and avoided overly permissive quality assessments for all of the frequency groups. Therefore, we used IQS as the main accuracy score for further comparison of the imputation reference panels. The observed results fit well with previous reports on other populations, suggesting that these accuracy measures perform the same regardless of the choice of the reference panel or the population studied [First, we tested three commonly used imputation accuracy measures—concordance rate (CR), squared Pearson correlation and imputation quality score (IQS)—to compare performance of the proposed metrics . Also, the HRC reference panel showed the lowest number of discordant imputed variants . In contrast, 1000G had 1679 (~3%) and HGDP had 8494 (~17%) discordant variants . Mean absolute error (MAE) between imputed and observed allele frequencies yielded the same qualitative outcome—HRC had the lowest error (MAE = 0.007) in comparison with 1000G (MAE = 0.011) and HGDP (MAE = 0.021). Based on these results, we showed that the HRC provides the most confident estimates of allele frequencies than 1000G and HGDP.Further, we compared imputation reference panels based on the resulting IQS values and the concordance of imputed allele frequencies with the masked ones. HRC showed the highest IQS in comparison with other panels across different minor allele frequency (MAF) groups, especially for less common variants  and the nested model contained all covariates as full, excluding PGS. R2 attributed to PGS was estimated as the difference between R2 of the full and the nested models. Optimal p-value threshold for inclusion of variants in the PGS model was selected based on the highest incremental R2 .First, we determined variants that should be included in the PGS model using multiple 2 estimates for polygenic scores in the Russian population with the previously reported UK biobank GWAS-based R2 estimates for other populations [Fig 2B). PGSs calculated based on UK biobank GWAS summary statistics resulted in the largest phenotypic variance explained for UK biobank participants. The same weights used for Russian-descent individuals led to an intermediate place of the latter between the UKBB and Biobank Japan (BBJ) individuals. Remarkably, statistically significant differences between Russian and UK populations were not found for a number of phenotypes: DBP , SBP , BMI . This is expected given the reasonable genetic similarity between Russians and the rest of Europe and the significant difference between the Russian and Japanese populations ( in S1 Appendix).Further, we compared the Rulations  2) and they appeared negatively correlated . Thus, demonstrating that differences in allele frequencies between UK biobank and Russian cohort, contribute to systematic bias in polygenic score estimates.Finally, we calculated the mean absolute error (MAE) between the UKBB allele frequencies and the study cohort for each PGS model. We explored the relationship between MAE and phenotypic variance explained by the PGS . Before the concordance analysis we tested all possible sources of allele frequency discrepancies and applied different highly conservative QC filtration thresholds to check the validity of observed allele frequencies in study data and to keep only genotyped variants with confirmed frequencies (see ). Altogether, we kept 379,751 variants for further analysis. All of them were presented with nonzero MAFs in all three data sources . Variants with discordant allele frequencies were identified in the same way as it was done previously .We compared the UK biobank participants with two populations: Central Europeans (CEU) from 1000 Genomes (N = 99) and northwestern Russian individuals (RUS) from the study dataset (N = 230). The allele frequencies for UKBB  were extracted from the variant annotation file . In contrast, for Russian individuals, we discovered 2,436 (0.66%) discordant variants. It is noteworthy that 556 of them were significantly associated with 328 traits from the GWAS catalog (S5 Table in S1 Appendix). Interestingly, BMI, Hip/Waist and Blood protein level were the most frequent phenotypes with which these discordant variants were significantly associated, in addition to such diseases as Venous thromboembolism , Psoriasis , Inflammatory bowel disease  and Keratinocyte cancer .The CEU cohort showed high concordance with UKBB, demonstrating only 429 (0.11%) discordant variants, whose allele frequencies considerably deviated from the UKBB values  , Simons Usage of the UK biobank summary statistics demonstrated that PGS for Russian-descent individuals could be estimated with the quality slightly less than that of the UK Biobank participants. However, due to the reasonably close haplotype structure, the transferability of the UK biobank GWAS results to the northwestern Russian population is more appropriate than to more distant populations (e.g. East Asian) or yet to be studied more eastern Russian ethnicities .There are some potential indirect effects that could influence the PGS estimates for the study cohort. The older age of the Russian cohort to a limited extent might interfere with the estimate of phenotypic variance explained by PGS—the elderly population will generally have ’healthier’ PGS (lower compared to random population snapshot in Russia).Consistent with the certain lack of transferability of the GWAS results observed previously between populations, the phenotypic variance explained by PGS depends on the allele frequency concordance between UKBB and the study cohort for variants used in the PGS model , 32–34.After careful quality filtration, we found a set of variants with allele frequencies strongly differing between the northwestern Russian and British (UKBB) cohorts, demonstrating a potential source of a decrease in the quality of PGS or GWAS studies compared to the UKBB.In conclusion, it is important to note that we considered only individuals from the Saint-Petersburg area, yet there are more than 100 ethnic populations in Russia, some of which belong to non-European continental ancestries; therefore, an inclusive approach to polygenic trait studies is especially of great demand in Russia.S1 Appendix(PDF)Click here for additional data file.S1 File(GZ)Click here for additional data file.S2 File(GZ)Click here for additional data file.


Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank.Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White  and non-Hispanic Black  adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders.We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study. Adult height has been associated with several clinical traits, for example with increased risk of atrial fibrillation and with decreased risk of cardiovascular disease. Using data from the VA Million Veteran Program that includes genetic data linked to clinical records in >200,000 non-Hispanic White adults and >50,000 non-Hispanic Black adults, we examined associations of measured height and genetically-predicted height with clinical traits phenome-wide. By comparing associations of traits with measured and with genetically-predicted height, we aimed to discriminate between potentially causal associations  from associations that may be confounded by environmental exposures over the life course . Of approximately 350 traits associated with measured height, we found 127 associated with genetically-predicted height in non-Hispanic White individuals. While only 2 were also statistically significant in non-Hispanic Black individuals, we found evidence for consistent directions of effect for associations of traits with genetically-predicted height in non-Hispanic Black and White individuals. We conclude that height may be an unrecognized non-modifiable risk factor for several common conditions in adults. Height is not typically considered a disease risk factor but has nevertheless been associated with numerous diseases –5. Such Mendelian randomization (MR) is an instrumental variable approach that utilizes genetic instruments for exposures of interest under the assumption that genotype is less susceptible to confounding than measured exposures . Indeed,While the prior MR studies of height have tested hypotheses based on previously described epidemiologic associations, MR methods have also been combined with phenome-wide association studies (MR-PheWAS) to identify novel or hypothesis-generating associations . For exa2 (Table 1). AA and EA participants were similar with regard to sex, BMI, and height, but AA participants were younger on average (57.7 years versus 64.2 years). HA participants were younger (mean age 55.7 years) and shorter (172 cm) than AA and EA counterparts (Table 1). Of these, 280,451 individuals  were included in the PheWAS analysis based on availability of electronic health record phenotypes needed for PheWAS. We did not include HA in the PheWAS due to limited sample size relative to EA and AA but the HA sample is included in an ongoing multi-biobank PheWAS effort through the GIANT consortium with a larger sample size  in EA and of 0.78  in AA .The GRS based on 3,290 SNPs independently associated with height in a prior European-ancestry meta-analysis that did not include MVP explained 18% of height variation in EA and 4.8% of height variation in AA in MVP. Of the 3,290 variants examined, 89% had concordant direction of effect between the discovery study and MVP EA (p < 3.6×10−5) among EA individuals, 133 of which were phenome-wide significant (p < 5.0×10−5) in AA individuals as well . An additional 17 traits were phenome-wide significant in AA individuals but not in EA individuals . The standardized effect estimates (Z’) for height-trait associations that reached phenome-wide significance in at least one race/ethnicity group were well correlated  between EA and AA individuals. The regression line of best fit (EA as Y ~ AA as X) between the standardized effect estimates (Z’) for height-trait associations that reached phenome-wide significance in at least one race/ethnicity group had a slope of 0.94  . Out of 362 height-trait associations that were phenome-wide significant in at least one race/ethnicity group , 335 had concordant directions of effect between the two race/ethnicity groups (p < 2×10−16).A total of 345 traits were associated with measured height at phenome-wide significance (p < 0.05) in AA individuals . The standardized effect estimates (Z’) for phenome-wide significant associations of clinical traits with genetically-predicted height were reasonably correlated between EA and AA individuals  and improved when limited to traits that were at least nominally significant in AA individuals . The regression line of best fit comparing standardized effect estimates for phenome-wide significant associations of clinical traits with genetically-predicted height in EA and AA had a slope of 1.85  (with EA as Y ~ AA as X) and improved to 1.80  when limited to traits that were at least nominally significant in AA individuals . That the slope of the best fit line was >1 implies larger effect sizes in EA than in AA but such a quantitative interpretation is limited by a number of factors: differences in sample size, winner’s curse, and differences in the strength of the GRS as a genetic instrument in the two populations. An appropriately cautious interpretation would be that the regression slope and plots comparing effect sizes are consistent with concordant directions of effect for trait associations with genetically-predicted height between EA and AA individuals in MVP. Though only 48 out of 142 traits associated at phenome-wide significance with genetically-predicted height in EA were at least nominally significant in AA, 124 out of those 142 traits had the same direction of effect in EA and AA for association with genetically-predicted height (p = 4×10−16).A total of 142 traits were associated with genetically-predicted height at phenome-wide significance among EA individuals, 2 of which were phenome-wide significant in AA individuals as well, and another 46 of which were nominally significant . Thus, we found genetic evidence supporting associations with height for 37% (127/345) of the traits that were associated with measured height. In AA individuals, 2 traits (acquired foot deformities [phecode 735] and dermatophytosis of nail [phecode 110.11]) were associated with genetically-predicted height and measured height at phenome-wide significance . An additional 46 traits had nominally-significant associations with genetically-predicted height and phenome-wide significant associations with measured height in AA individuals. Of 11 traits with directionally consistent associations with measured height and genetically-predicted height previously reported in the UK Biobank We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Greg GibsonConsulting Editor - PLoS GeneticsPLOS GeneticsScott WilliamsSection Editor: Natural VariationPLOS GeneticsWe have received detailed responses from two expert reviewers, both of which provide thoughtful suggestions for improvement upon revision. These edits will both clarify some outstanding issues and add clarity with some new analyses. Overall, the appraisal of the manuscript is very positive.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this study, Raghavan et al. quantify the effect of a polygenic predictor of height on various clinical conditions in a large ancestrally diverse sample from the Million Veteran Program. Overall, they find hundreds of associations between height and various disease, including previously reported signals. This study is an important contribution to the literature, which demonstrates that height is indeed an underappreciated risk factor for many diseases. However, I found that the study did not fully utilise the potential of the dataset and raise below a points that could improve the quality of the study.Major points (in no particular order)1. I found the GRS analysis to be the most interesting part of the manuscript. The phenotypic analysis (height vs disease) could have been utilised as a way filter diseases to focus on but the fact that the authors present those analyses in parallel, reduces the interest of the phenotypic analysis alone.2. The authors report what seems to be a good concordance of the effect of GRS on disease risk across ancestries. Such an observation calls for a meta-analysis across ancestry groups, which may also increase power.3. The authors perform a GWAS in a European ancestry sample of the MVP. This analysis is interesting but they authors do not use those results to further improve power to address their main question: the association between height PRS and disease. By meta-analysing Yengo et al. with their MVP-EA GWAS, they could have increased power to detect association in African American.4. The authors rightly underline that an association between height GRS and disease may not necessarily reflect causality but possibly pleiotropy. Nevertheless, as I reader I’d like to know how many  independent associations are reported here. The grouping in terms of broad disease category partly addresses my question but it still possible that pleiotropic effect exist beyond phecodes. For example, is the effect of GRS on Atrial fibrillation independent of that on varicose veins?Minor pointsLine 153 (and elsewhere). Please specify what’s X and Y when reporting regression slopes. Do you account for estimation errors in both samples when estimating those slopes? If you then the authors could use the method developed by Qi et al. Nat Comm. 2018 (PMID 29891976)Line 161 – 164. Note that these comparisons are affected by winner’s curse. So I’d suggest correcting estimates for that before estimating those slopes.Line 186. “BMI as a coefficient” => “BMI as a covariate”Line 193 (and elsewhere). Specify the unit for the that OR. Is that risk increase per height SD or per GRS SD? I’d assume the former given that the authors use a two-stage least squares approach. Could you please clarify that?The diabetes analysis looks a bit artificial given that there is no statistically significant overlap between diabetes related complications and disease associated with height. The authors write (lines 179) “we noted that a number of conditions in Table 2 are associated with diabetes…”, which is no rigorous enough. So unless they can prove that there is such an enrichment, I’d suggest removing all diabetes-related sections from the main text to improve clarity.Line 308. It seems a bit odd that there could be a bi-directional causal effect between varicose veins (VV) and height given that VV is classically diagnosed in >18y. Can they authors revise that sentence ?Line 313. I think the authors should comment on why the 16 height-associated variants identified in their GWAS have not been detected before? Can they compare effect sizes and other GWAS? For example, that of Graff et al. AJHG 2021 (PMID 33713608)Sorry if I missed it but the authors should specify what software they have used for running GWAS. PLINK? BOLT-LMM? Also, can the authors report results from LD score regression univariate  and bivariate to compare with Yengo et al.?Loic YengoReviewer #2: This manuscript by Raghavan et al. describes an MR-PheWAS looking for associations between height and other EHR traits. The authors use the VA Million Veterans Program (MVP) for the study, which is one of the largest datasets available with EHR data from diverse ancestry groups. Overall, this is a very good study with some interesting results.There are a few areas of the manuscript that would benefit from additional information.Major revisions_____________- In line 103-105, the authors indicate that a similar study was performed in 50 traits from UK Biobank. Do any of the results from this paper replicate in UK Biobank? Were the 50 traits included in that published work included here? It does not look like the authors attempted to replicate the findings from this paper in the UK Biobank, but that seems like an obvious next step and may greatly improve the paper.- Since the VA MVP participants are 91.6% male, did the authors consider doing a sex-stratified analysis? I am a little concerned that if there is any trait that has an unusual case distribution by sex, then the results could be confounded especially due to the drastic skew in the dataset.- On lines 130-134, the authors describe how the sample sizes of the study participants included. Did you leave out HA because of sample size? I think it is important to state that. Also, why is the number of EA and AA lower than the first line of the paragraph? Why did people drop off?https://www.sciencedirect.com/science/article/pii/S0002929721000537#mmc2- On line 139, the variance explained for height in the AA population is pretty low. Did you also try a meta-analysis from AA? Perhaps from this study: - On line 155-156, the authors indicate that the phewas associations were directionally concordant. If they are concordant across ancestry then how do you have EA only and AA only results listed? This is not clear. Also on lines 164-166, how is it 142 traits concordant across ancestry when only 2 were significant in AA ? This is confusing.-On line 170, the authors indicate that 2 traits are significant in AA. The number of significant traits is substantially different between ancestry groups. Any idea what is driving this? Is it power and sample size? Is it differences in health care utilization with different volumes of EHR data and diagnoses? Is it because the AA group is much younger so less diagnoses? Or is it biology?- The relationship between diabetes and height is not totally clear at the end of the section ending on line 212. Perhaps this section needs a summary at the end.-On line 420, did case status apply to individuals with one instance of the ICD code or did you require a rule of 2 or 3?- On line 436, How did you decide on 10 PCs? How much variance was explained by these?Minor revisions_____________- Line 128, the abbreviation "HA" was not yet defined.- Line 137, the abbreviation "GRS" was not yet defined.- Throughout the paper, the authors use race/ethnicity to define the groups, however using HARE, they are also using ancestry. I wonder if the word "ancestry" should be used? Or perhaps race/ethnicity/ancestry? They mean different things of course. But since genetics was used to define, I think it is important to include ancestry in the paper. Similarly, should it be "trans-ancestry" or "cross-ancestry"?- On line 198, there is something odd with reference.- What software was used for the GWAS?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata 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?). 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Inform journal staff as soon as possible if you are preparing a press release for your article and need a publication date.Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Genetics!Yours sincerely,Greg GibsonConsulting Editor - PLoS GeneticsPLOS GeneticsScott WilliamsSection Editor: Human VariationPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the editors:Thank you for your thorough response. One of the initial reviewers was available to provide his/her second opinion, and the editors have also considered the response. 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  9 May 2022PGENETICS-D-21-01241R1 A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program Dear Dr Raghavan, We are pleased to inform you that your manuscript entitled "A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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Background: The aesthetic facial traits are closely related to life quality and strongly influenced by genetic factors, but the genetic predispositions in the Chinese population remain poorly understood.Methods: A genome-wide association studies (GWAS) and subsequent validations were performed in 26,806 Chinese on five facial traits: widow’s peak, unibrow, double eyelid, earlobe attachment, and freckles. Functional annotation was performed based on the expression quantitative trait loci (eQTL) variants, genome-wide polygenic scores (GPSs) were developed to represent the combined polygenic effects, and single nucleotide polymorphism (SNP) heritability was presented to evaluate the contributions of the variants.Results: In total, 21 genetic associations were identified, of which ten were novel: GMDS-AS1  and SPRED2  for widow’s peak, a previously unreported trait; FARSB  for unibrow; KIF26B , CASC2 , RPGRIP1L , and PAX1  for double eyelid; ZFHX3  and LINC01107  for earlobe attachment; and SPATA33  for freckles. Functionally, seven identified SNPs tag the missense variants and six may function as eQTLs. The combined polygenic effect of the associations was represented by GPSs and contributions of the variants were evaluated using SNP heritability.Conclusion: These identifications may facilitate a better understanding of the genetic basis of features in the Chinese population and hopefully inspire further genetic research on facial development. Facial features exhibit a higher degree of variability than other physical features, thus making human faces unique and recognizable. Appearance variations impact quality of life, in most cases, from the perspective of aesthetics. Although sometimes acquired over the lifespan due to external factors, the variation is closely connected with the inherited complexity of facial morphogenesis . The corPAX3 gene   . Moreoveucasians , while oucasians . In spitGMDS-AS1  and SPRED2  as genome-wide significant associations in the Chinese population. The other novel associations included FARSB  for unibrow; KIF26B , CASC2 , RPGRIP1L , and PAX1  for double eyelid; ZFHX3  and LINC01107  for earlobe attachment; and SPATA33  for freckles. This study was expected to facilitate a better understanding of the genetic basis of the facial features and inspire further research on the biological functions of the relevant genes.Therefore, we performed a large-scale GWAS on the Chinese population to gain insights into genetic variants contributing to the five aesthetic facial traits . Functional annotation of the genome-wide significant single nucleotide polymorphisms (SNPs) was performed based on the expression quantitative trait loci (eQTL) variants and genome-wide polygenic scores (GPSs) were subsequently developed to represent the combined polygenic effects in these five traits . In totaSubjects were voluntarily enrolled in the study and filled out the questionnaires designed by West China Hospital, Sichuan University. Questionnaires soliciting trait information including “widow’s peak,” “unibrow,” “double eyelid,” “earlobe attachment,” and “freckles,” were collected . SubsequEach participant donated 2 ml of their saliva into a sample tube which was later sent to the laboratory to extract DNA, and DNA quality was determined by examining the OD260/OD280 ratio and integrity in agarose gels. Due to the long time span of the project, samples were randomly genotyped with one of the three highly correlated versions of chip arrays – Mofang v1.0, Mofang v2.0, and Mofang v2.1, which were all Affymetrix Axiom Precision Medicine Research Array (PMRA)-based high-throughput SNP chip arrays .p-value < 1 × 10−6), 7) outliers ±3 SD from the samples’ heterozygosity rate, 8) individuals with cryptic relatedness, 9) outliers from multidimensional scaling (MDS) analysis  SNP with genotype call rate (CR) below 0.98, 2) individual CR below 0.98, 3) gender inconsistencies, 4) number of alleles >2, 5) minor allele frequency (MAF) below 0.01 , 6) devianalysis   (http://epigenomegateway.wustl.edu/browser/)  (The genome-wide SNPs were subjected to HaploReg database (reg.php) , WashU Erowser/)  and Genog/home/)  for func28 candidate GPSs based on a pruning and thresholding (P-T) method were derived for each trait using the GWAS discovery set and discovery GWAS summary statistics from the previous step. The best scores, defined by the maximal area under curve (AUC), were applied to the validation set with 20% samples to generate a polygenic score for each individual. The individuals were binned into 20 groups according to the GPS quantile and the prevalence of each trait  Khera e.To evaluate the contribution of the variants to heritability, SNP heritability of the five traits was estimated based on the GWAS summary statistics using linkage disequilibrium score regression analysis (LDSC) .N = 11,946), unibrow , double eyelid , earlobe attachment , and freckles . All volunteers had phenotype information of one or more traits. The discovery and validation sets for each trait were randomly drawn respectively  in the discovery cohort, including the strongest signals at 6p25.2 downstream GMDS-AS1 , followed by 2p14 downstream SPRED2  and 2q22.3 downstream ARHGAP15   . However × 10−8) .p = 3.11 × 10−3). Instead, we identified a locus 300–500 kb downstream of rs2218065 with the top signal at FARSB rs36015125 (p = 1.96 × 10−21) (r2<0.2).As for unibrow, the only previously reported significant association signal, rs2218065 at 2q36.1 , could n× 10−21)  and rs36, four of which have not been reported to our knowledge (EMX2 and RAB11FIP2 (p = 1.96 × 10−13), in high LD with the reported variant rs1415425 (r2 = 0.97)  exhibited an independent association , RPGRIP1L , and PAX1/LINC01432 , respectively  . A novelociation . The othectively .p = 9.8 × 10−14) and 2q37.3   . The oth× 10−10) .SPATA33, top SNP: rs35415928, p = 1.1 × 10−8) (HSPA12A (10q25.3), PPARGC1B (5q32), BNC2 (9p22), and EMX2/RAB11FIP2 (10q26.11)  . The oth0q26.11) .EDAR gene  with the coding variants based on LD calculations using 1,000 Genome Project data according to the HaploReg database (p < 1 × 10−4) in skin tissue and cultured fibroblasts that are of potential relevance to the five facial traits (RPGRIP1L (r2 = 0.26) and is a strong eQTL for RP11-36I17.2 expression in cultured fibroblasts  and fibroblasts, showing the strongest association with DBNDD1  and cultured fibroblasts , double eyelid , earlobe attachment , and freckles , respectively  using LDSC for the five traits was presented. The highest h2 was seen for double eyelid , and the h2 for widow’s peak, unibrow, earlobe attachment, and freckles were estimated to be 0.3046 (SE = 0.0591), 0.433 (SE = 0.0881), 0.2443 (SE = 0.0765), and 0.1431 (SE = 0.0733), respectively.SNP heritability  occurred at 2p14 near SPRED2. Of potential relevance, some other members of the SPRED family have been reported to activate MAPK cascade , associated loci for unibrow existed on several chromosomes, while significant variants for widow’s peak were located at 2q and 6p. Since detailed information from 23andMe Co.’s research is restrained, associations in our present GWAS provide novel insights for the public.To our knowledge, we are the first to provide public GWAS results on widow’s peak, presenting two associated loci. The strongest signal was at 6p25.2 (rs4959669), near RNA genes  cascade  which is cascade . Moreove cascade , Waarden cascade , but varPAX3 gene, which was not validated in the present study  in Japanese  is an intron point of the gene . ZFHX3 intiation , lack ofntiation . The othpression , the varsyndrome  whose masyndrome . For ins cohorts , and relted ears . Meanwhis3827760  also reaectoderm , and itsectoderm .SPATA33 rs35415928. SPATA33 has long been associated with facial pigmentation (MC1R. (BNC2 has been identified in Europeans (BNC2 (rs4455968) is in high LD with rs16935073 (r2 = 0.94) and rs10816035 (r2 = 0.83) that have been associated with pigmentary traits or tanning ability in Koreans (HSPA12A rs12245621 which is in LD with the reported variant rs12259842 (r2 = 0.76) (HSPA12A is affiliated to HSP70 family whose members (HSP70 and HSP47) are expressed in the dermis and epidermis following laser irradiation, which has been related to pigmentation  in the Korean and Latin American populations  . HSPA12Aentation . Interestransfer , and SNP samples  reached  (MFSD12  and is iulations .The study also has room for improvement. First, the individuals were recruited based on their self-reported traits instead of professional assessment. As the traits included are easily distinguished aesthetic traits and illustrations were added for each question, we consider the reports reliable. Second, functional annotation suggested that some of the variants may be associated with phenotype by impacting the expression level or coding sequence of the nearby genes, but the functions of these variants should be further determined and experimentally evaluated. Moreover, SNP heritability was presented for each trait, but since it can only include contributions from causal variants tagged by the measured SNPs, it is lower than total narrow-sense heritability, such as estimation from twin or family studies. For instance, an adult twin study on the relative contribution of genetic and environmental effects on the expression of nevi and freckles suggested that additive genetic effects explained 91% of the variance in freckle counts . Future This GWAS of five aesthetic facial traits in a large Chinese cohort of 26,806 uncovered ten novel genetic associations. Specifically, this is the first study, to our knowledge, to report genetic predispositions to widow’s peak. The identified variants indicated both important similarities and differences among different ethnic groups. Hopefully, the findings would facilitate an understanding of the genetic basis of facial traits and, more importantly, facial development.


Oryza rufipogon species complex (ORSC), wild progenitor of Asian rice (Oryza sativa L.). The ORSC comprises perennial, annual and intermediate forms which were historically designated as O. rufipogon, O. nivara, and O. sativa f. spontanea , respectively, based on non-standardized morphological, geographical, and/or ecologically-based species definitions and boundaries. Here, a collection of 240 diverse ORSC accessions, characterized by genotyping-by-sequencing , was phenotyped for 44 traits associated with plant, panicle, and seed morphology in the screenhouse at the International Rice Research Institute, Philippines. These traits included heritable phenotypes often recorded as characterization data by genebanks. Over 100 of these ORSC accessions were also phenotyped in the greenhouse for 18 traits in Stuttgart, Arkansas, and 16 traits in Ithaca, New York, United States. We implemented a Bayesian Gaussian mixture model to infer accession groups from a subset of these phenotypic data and ascertained three phenotype-based group assignments. We used concordance between the genotypic subpopulations and these phenotype-based groups to identify a suite of phenotypic traits that could reliably differentiate the ORSC populations, whether measured in tropical or temperate regions. The traits provide insight into plant morphology, life history  and mating habit (self- versus cross-pollinated), and are largely consistent with genebank species designations. One phenotypic group contains predominantly O. rufipogon accessions characterized as perennial and largely out-crossing and one contains predominantly O. nivara accessions characterized as annual and largely inbreeding. From these groups, 42 “core” O. rufipogon and 25 “core” O. nivara accessions were identified for domestication studies. The third group, comprising 20% of our collection, has the most accessions identified as Oryza spp. (51.2%) and levels of O. sativa admixture accounting for more than 50% of the genome. This third group is potentially useful as a “pre-breeding” pool for breeders attempting to incorporate novel variation into elite breeding lines.Crop wild relatives represent valuable reservoirs of variation for breeding, but their populations are threatened in natural habitats, are sparsely represented in genebanks, and most are poorly characterized. The focus of this study is the  The Oryza rufipogon species complex (ORSC), previously referred to as O. rufipogon Griff. or O. perennis Moench, is the wild progenitor of Asian rice (Oryza sativa L.) and is comprised of perennial, annual, and intermediate ecotypes 1 the list of available ORSC accessions includes 846 O. rufipogon, 1,455 O. nivara, and 1,134 accessions listed as either ‘Oryza spp.’ or ‘Oryza hybr.’. Many of the Oryza spp. or Oryza hybr. were previously designated as O. spontanea (O. sativa f. spontanea) or referred to as hybrids between O. sativa and O. rufipogon and/or O. nivara. The online wild strain database of the Japanese National Institute of Genetics, Oryzabase ,’ or ‘O. sativa f. spontanea,’), and life habit designations, such as annual or perennial2.To this day, wide differences in the nomenclature and trait-based definitions assigned to the wild ancestor of ryzabase , acknowllleagues . There aORSC, studies using molecular markers  present conflicting and often contradictory results that collectively fail to support the existence of two, well-differentiated species. Global studies of genomic diversity in the ORSC document three to eight genetically distinct groups  ascertained six ORSC subpopulations: four unique ORSC subpopulations that were correlated with geographic distribution and two that clustered with O. sativa subpopulations. Of these, Or-A accessions had the broadest range with the highest proportion from Oceania, Or-B accessions were almost exclusively found in China, Or-C had the highest proportion from West India and Sri Lanka, and Or-D was mainly from the SE Asia, Bangladesh, and East India, Or-E accessions clustered with O. sativa-aus accessions, and Or-F clustered with O. sativa-indica  and evaluated at 25 polymorphic sites in the chloroplast genome  previously genotyped by ORSC accessions from Southeast Asia, 79 accessions from South Asia, 55 accessions from East Asia and four accessions from Australasia. Of these, 222 accessions were evaluated in a screenhouse at the IRRI, Los Baños, Philippines and 130 were evaluated under greenhouse conditions in the United States, with 112 accessions phenotyped in more than one location. At IRRI two plants were grown of each accession with one plant per pot and each pot was considered a single replication. Seed of each accession was planted between June 20 and July 4, 2008, and the plants were phenotyped for the 44 morphological traits related to the vegetative, reproductive and harvest growth stages ; August 1 to September 12, 2008 (124 accessions) and August 12 to 13, 2009 (41 accessions). In the United States, 104 accessions were phenotyped at both Cornell University (CU) in Ithaca, New York and the Dale Bumpers National Rice Research Center (DB) near Stuttgart, Arkansas. Three plants were grown of each accession with one plant per pot and each pot was considered a single replication. At CU, 104 accessions were planted between August 31 and September 8, 2008, and each plant was phenotyped for 16 morphological traits. At DB, 129 Using the replicated trait measurements taken at each location and a relationship matrix constructed from the GBS genotypes , we fit yi. are row-vectors of the corresponding parameter matrices that have data points as rows and traits as columns. The overall intercept μwas modeled with a high-variance (I6) Gaussian prior. The errors were modeled using a multivariate Student-t distribution with three degrees of freedom to dampen the effects of outliers  mixed effect model .where rather than values from the marker covariance matrix tability .Using estimates of phenotypic values for each accession, we fit a Gaussian mixture model  using a μm⋅ is a row vector of group means. Each group has a separate mean and covariance matrix. The prior population size α is set to a small value, shrinking groups with few members to zero. The number of groups must be set a priori.where the notation is the same as for the hierarchical model above and 4. We use two methods to assess how many groups are statistically supported: the DIC and the number of groups with at least two accessions. We include the pipeline (implemented in R) that runs the mixture model, processes the results, and generates all the plots and summary tables presented in this manuscript in the We perform inference by running the model multiple times from different starting points and picking the best fit, as assessed using the deviance information criterion DIC, . Small DORSC and O. sativa accessions were imputed using Beagle 5.0  , aus, (AUS) , temperate japonica (TEJ) , tropical japonica (TRJ) , and aromatic (ARO) . Of the 60 ORSC individuals in the training panel, 49 were phenotyped in the current study. Prior to RFMix analysis, the genotypic data were imputed and phased using Beagle 5.0  for each accession in the IRRI data set (n = 222), we fit a Gaussian mixture model  using a odel fit  using thodel fit .O. rufipogon, O. nivara, or, if the taxonomic assignment is uncertain, as Oryza spp. or Oryza hybr. We combined the latter two categories into one, calling it Oryza spp. We tracked each accession, starting from the species designations, across phenotypic groups inferred given different a priori group numbers (NG) using a Sankey plot  classified as O. nivara. About half of the P2/P3 group are designated as Oryza spp. in the IRGC, with the remaining accessions about equally divided between O. nivara and O. rufipogon  are classified as perennial ufipogon .NG = 3 a priori is sufficient to separate O. rufipogon and O. nivara from each other and from a group that largely corresponds to the Oryza spp. designation from IRRI-GRIN-Global. Taking these observations together with the above statistical robustness analyses, we see that a core set of accessions can be reproducibly assigned to a phenotypic group, while inference for others is less certain.To further test the reproducibility of phenotypic group inference, we used data collected at DB and CU. These data sets include different, but overlapping, phenotype sets that were measured on fewer than half the accessions represented in the IRRI data , 3. DespORSC accessions, we next wanted to define a minimal combination of phenotypic traits that is sufficient to reproduce these divisions. Our mixture model allows both phenotype means and covariance to vary among groups. We started by ranking traits according to how well their values correlate with our groups  are over twice the value of the next seven highest trait-group associations for which T2 ranges from 0.17 to 0.28  to assess statistical evidence for across-group variation . The mosT2 > 0.17. Hotelling values showed a marked drop below this cut-off. These two sets have four traits in common . We also used the union of the two sets, containing 16 traits, or half the number in the full data set. Although covariance matrices include both correlations and variances, all traits with appreciable among-group variance changes are already accounted for by using correlation and mean differences  to re-fit our mixture model, setting the ns to P1 . The 11 Our approach identified a minimally sufficient set of traits that captures most of the phenotypic group structure in the IRRI data. This does not mean that other sets of traits could not achieve similar results but an exhaustive search for all possible combinations that could achieve the same result was not undertaken considering time and the computational expense. Instead, we pursued two approaches using the Cornell and Dale Bumpers data sets to find alternative sets of traits that could be used to re-infer the three major phenotypic groups identified using the full data set. First, we used the process outlined above to identify traits associated with the phenotypic groups that were inferred from IRRI data by value or that change correlations with other traits. ORSC subpopulations (W1-W6). Based on RFMix . We partitioned accessions with more than half of their genome introgressed from O. sativa into a separate “admixture with O. sativa” group (ADM/OSAT). By means of a Sankey plot, we related the species designation, phenotypic group and genetic subpopulation for the ORSC accessions with complete information  and are identified in While our phenotypic groups largely correspond to IRRI-GRIN-Global species designations, we wanted to know how the groups related to previously characterized genotypic subpopulations. The accessions used in this study were genotyped by on RFMix  analysisormation . Across s as per  clusterepulation . Eight aO. sativa introgressions  with that generated by n = 435), a merged SNP dataset was constructed and analyzed using fastSTRUCTURE  to Or-B, and W2 (predominantly O. nivara) to Or-C. W4 (aus-like wild ancestor as described by Or-D (wild) and Or-E , and interestingly, the accessions clustered as Or-E by K value (K = 8) was used in the analysis.To compare the population structure of the collection of TRUCTURE . The comTRUCTURE . The six by Or-A , W6 , which represents the southeastern extreme of the geographic distribution of the the IRGC . Accessi. sativa , but theex situ germplasm collections. Of the traits measured here, four were significantly associated with phenotypic groups and with life history; these included days to heading, plant height, percent filled spikelets per panicle (seeds/panicle), and spikelet/seed size (related to grain weight). These traits provided information that was valuable for differentiating annual and perennial life habits and were similar to traits evaluated by O. rufipogon/O. nivara mapping populations. We also measured several traits that were potentially informative about mating habit, including anther length, style length and stigma length, but only anther length discriminated the groups based on both Hotelling T2 value and correlation estimates   measured at Dale Bumpers, which served as a proxy for spikelet width (SPKWD) measured at IRRI, and seed volume,  calculated from seed width and length. Spikelet width and seed width were highly correlated (r = 0.62) and both width and volume were informative for distinguishing groups. Seed traits were not measured at Cornell and therefore could not be used for further confirmation.The single most informative trait for distinguishing ORSC accessions, O. nivara and O. rufipogon accessions. The other important traits included anther length (ANTLT), days to heading (DTHD) and culm length (CULT). Of note, spikelet fertility was not included in the PCA because it was highly correlated with anther length and spikelet width, leading to the conclusion that anther length and spikelet width serve as a proxy for fertility when plants are grown under controlled screenhouse conditions. Our data on DTHD collected at IRRI differentiated P1 from the other groups. The trend is the same in the Cornell and the Dale Bumpers data, but with more overlap between groups. This may be a consequence of evaluating DTHD during the short days of fall at higher latitudes. Across our three environments, unfilled grain number (UNFILGRNB) in the IRRI and Dale Bumpers data and seed weight (Seed_S2) collected from bagged and unbagged panicles at Cornell provided useful proxies for spikelet fertility. Lastly, culm length (CULT) which was measured from the soil surface to the panicle base at IRRI and plant height (PTHT) measured from the soil surface to the tip (end) of the panicle at Dale Bumpers and Cornell were prominent differentiators across all three studies. This comparison confirms that many traits distinguishing the phenotypic groups can be identified across different environments and panels of ORSC accessions. These observations are confirmed by earlier studies summarized in O. nivara was characterized by having wider seeds, higher seed production (fertility), shorter culm length (plant height), and earlier flowering (due to photoperiod insensitivity) compared to O. rufipogon which was characterized by relatively narrow seeds, low seed production, longer culm length  and later flowering (due to photoperiod sensitivity)  of phenotypic data collected at IRRI on a different set of 116 itivity) .O. rufipogon and O. nivara species, we recommend including seed width and length in the suite of traits collected on ORSC accessions in genebanks around the world. With the availability of scanners and appropriate software, this would be an inexpensive investment that would help improve the classification of ORSC accessions, making collections of these crop wild relatives more valuable to users.Considering the high information content of SPKWD (spikelet/seed width) as a distinguishing feature of the phenotypic groups identified in this study and its utility for differentiating the ORSC accessions phenotyped in this study, approximately 41%  had less than 5% introgression from O. sativa and can be considered non-admixed and wild. Most of these (n = 91) clustered in either P1 (n = 48) where most were classified as O. rufipogon or in P4 (n = 43) where most were classified as O. nivara, while a few (n = 7) clustered in P2/P3.Of the O. rufipogon and 25 “core” accessions consistently classified as annual O. nivara. These “core” accessions all have <20% admixture from O. sativa and intersect with the group carrying <5% O. sativa introgression. This collection of 67 accessions provides a useful subset of ORSC materials for studying the genetic basis of the phenotypic divergence that distinguishes the two ecotypes of wild rice in Asia.Based on analyses of both phenotypic and genotypic data using different model parameters, we identified 42 “core” accessions that are consistently classified as perennial ORSC accessions that are highly admixed with O. sativa are also of interest. In this study, 25% of accessions were classified as admixed because they had >40% of the genome introgressed from O. sativa. In the previous study by ORSC accessions and a different threshold for determining admixture, 42% of wild rice samples were reported to be substantially admixed with >20% of the genome showing introgression from O. sativa. In both studies, indica and aus introgressions accounted for the largest proportion, and japonica introgressions were rare. Admixed accessions found in P1 and P4 in this study had an average of 19.8% and 19.0% of their genomes comprised of introgressions from O. sativa, respectively, while levels of admixture in P2/P3 averaged 52.6%; indeed, two P2/P3 accessions carried ∼85% O. sativa DNA , and 62.5% of accessions classified as P4 were identified as annual or intermediate (compared to 61.7% classified as O. nivara). In P2/P3, 47.4% of accessions were classified as perennial and 52.6% as annual or intermediate using the SINE code . Thus, the SINE-based classifications approximated the species designations in these groups, though they were not entirely concordant.In rice, SINE codes have been associated with life history  and found useful for distinguishing ufipogon . Kim . Taken together, these data suggest that the population structure of the ORSC is the result of complex evolutionary pathways that intersect and loop back upon each other due to highly permeable ‘species’ boundaries and, as documented in this study, highly plastic phenotypic variation. Information about SINE-code and chloroplast haplotype variation (as determined by a priori taxonomic species descriptions. This group is comprised of 29 (67%) admixed ORSC accessions with > 40% of the genome derived from O. sativa but it also contains accessions classified as annual O. nivara or perennial O. rufipogon. The fact that the P2/P3 accessions were collected in diverse ecological and geographic regions, especially from coastal regions of China, Southeast and South Asia  are associated with a shift toward the annual, early-flowering, seed-bearing habit or conversely, serve to maintain the perennial, stoloniferous, late-flowering and vegetatively vigorous habit. During times of climate change, a strategy of balancing selection to maintain components of annual, intermediate and perennial life forms may be favored by evolution to allow for the emergence of new resilience mechanisms. We have a lot to learn from the existence of these dynamic ORSC populations. They have survived many waves of climate change in the past and are likely to hold secrets about survival strategies for the future.The P2/P3 group is characterized by 43 accessions with trait combinations that form a coherent group, despite the fact that they do not match either of the uth Asia , and yet and CO2 . The ORSThe original contributions presented in this study are included in the article/KM, MEN, MB-H, GCE, HJK, JJ, SH, JK, and LH phenotyped the plants. YS managed the data. AG, JE, GCE, HJK, JJ, and SRM analyzed the data. GCE, AG, JE, and SRM wrote the manuscript. SRM conceptualized the project. All authors contributed to the article and approved the submitted version.AG is employed by Bayesic Research LLC. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.