Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs  account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants. Adult height, which is highly heritable and easily measured, has provided a larger number of common genetic associations than any other human phenotype. In addition, a large collection of genes has been implicated in disorders of skeletal growth, and these are enriched in loci mapped by GWASs of height in the normal range. These features make height an attractive model trait for assessing the role of common genetic variation in defining the genetic and biological architecture of polygenic human phenotypes.Since 2007, genome-wide association studies (GWASs) have identified thousands of associations between common SNPs and height, mainly using studies with participants of European ancestry. The largest GWAS published so far for adult height focused on common variation and reported up to 3,290 independent associations in 712 loci using a sample size of up to 700,000 individuals4.As available sample sizes continue to increase for GWASs of common variants, it becomes important to consider whether these larger samples can ‘saturate’ or nearly completely catalogue the information that can be derived from GWASs. This question of completeness can take several forms, including prediction accuracy compared with heritability attributable to common variation, the mapping of associated genomic regions that account for this heritability, and whether increasing sample sizes continue to provide additional information about the identity of prioritized genes and gene sets. Furthermore, because most GWASs continue to be performed largely in populations of European ancestry, it is necessary to address these questions of completeness in the context of multiple ancestries. Finally, some have proposed that, when sample sizes become sufficiently large, effectively every gene and genomic region will be implicated by GWASs, rather than certain subsets of genes and biological pathways being specifiedHere, using data from 5.4 million individuals, we set out to map common genetic associations with adult height, using variants catalogued in the HapMap 3 project (HM3), and to assess the saturation of this map with respect to variants, genomic regions and likely causal genes and gene sets. We identify significant variants, examine signal density across the genome, perform out-of-sample estimation and prediction analyses within studies of individuals of European ancestry and other ancestries and prioritize genes and gene sets as likely mediators of the effects on height. We show that this set of common variants reaches predicted limits for prediction accuracy within populations of European ancestry and largely saturates both the genomic regions associated with height and broad categories of gene sets that are likely to be relevant; future work will be required to extend prediction accuracy to populations of other ancestries, to account for rarer genetic variation and to more definitively connect associated regions with individual probable causal genes and variants.An overview of our study design and analysis strategy is provided in Extended Data Fig. 5. Altogether, our discovery sample includes 4,080,687 participants of predominantly European ancestries ; 472,730 participants with predominantly East Asian ancestries (8.8%); 455,180 participants of Hispanic ethnicity with typically admixed ancestries (8.5%); 293,593 participants of predominantly African ancestries—mostly African American individuals with admixed African and European ancestries (5.5%); and 77,890 participants of predominantly South Asian ancestries (1.4%). We refer to these five groups of participants or cohorts as EUR, EAS, HIS, AFR and SAS, respectively, while recognizing that these commonly used groupings oversimplify the actual genetic diversity among participants. Cohort-specific information is provided in Supplementary Tables 2, which contains more than 1,095,888 SNPs with a minor allele frequency (MAF) greater than 1% in each of the five ancestral groups included in our meta-analysis. Supplementary Fig. We performed genetic analysis of up to 5,380,080 individuals from 281 studies from the GIANT consortium and 23andMe. Supplementary Fig. P < 5 × 10−8) SNPs in the EUR, HIS, EAS, AFR and SAS groups, respectively  in EUR (Supplementary Tables ρb) at GWS SNPs for all pairs of ancestry groups. We estimated ρb using five subsets of GWS SNPs identified in each of the ancestry groups, which also reached marginal genome-wide significance in at least one group. After correction for winner’s curse12, we found that ρb ranged between 0.64 and 0.99 across all pairs of ancestry groups and all sets of GWS SNPs  with those found in the other (non-EUR) groups. We found that more than 85% of GWS SNPs detected in the non-EUR groups are in strong LD  with differing LD to rs2463169.To find signals that are specific to certain groups, we tested whether any individual SNPs detected in non-EUR GWASs are conditionally independent of signals detected in EUR GWASs. We fitted an approximate joint model that includes GWS SNPs identified in EUR and non-EUR, using LD reference panels specific to each ancestry group. After excluding SNPs in strong LD  is around 36, and around 18% of all HM3 SNPs are marginally GWS. Moreover, we found that allele frequencies in our METAFE were very similar to that of EUR (mean fixation index of genetic differentiation (FST) across SNPs between EUR and METAFE is around 0.001), as expected because our METAFE consists of more than 75% EUR participants and around 14% participants with admixed European and non-European ancestries that is, HIS and AFR). To further assess whether LD in our METAFE could be reasonably approximated by the LD from EUR, we performed an LD score regression16 analysis of our METAFE using LD scores estimated in EUR. In this analysis, we focused on the attenuation ratio statistic (RLDSC-EUR), for which large values can also indicate strong LD inconsistencies between a given reference and GWAS summary statistics. A threshold of RLDSC > 20% was recommended by the authors of the LDSC software as a rule-of-thumb to detect such inconsistencies. Using EUR LD scores in the GWAS of HIS, which is the non-EUR group that is genetically closest to EUR (FST ≈ 0.02), yields an estimated RLDSC-EUR of around 25% (standard error (s.e.) 1.8%), consistent with strong LD differences between HIS and EUR. By contrast, in our METAFE, we found an estimated RLDSC-EUR of around 4.5% (s.e. 0.8%), which is significantly lower than 20% and not statistically different from 3.8% (s.e. 0.8%) in our EUR meta-analysis. Furthermore, we show in Supplementary Note FE) does not improve signal detection over using an EUR LD reference. Altogether, these analyses suggest that LD in our METAFE can be reasonably approximated by LD from EUR.Given that our results show a strong genetic overlap of GWAS signals across ancestries, we performed a fixed-effect meta-analysis of all five ancestry groups to maximize statistical power for discovering associations due to shared causal variants. The mean Cochran’s heterogeneity FE, using genotypes from around 350,000 unrelated EUR participants in the UK Biobank (UKB) as an LD reference. We identified 12,111 quasi-independent GWS SNPs, including 9,920 (82%) primary signals with a GWS marginal effect and 2,191 secondary signals that only reached GWS in a joint regression model . We first re-assessed the consistency of allele frequencies between our METAFE and the EBB set. We found a correlation of allele frequencies of around 0.98 between the two datasets and a mean FST across SNPs of around 0.005, similar to estimates that were obtained between populations from the same continent. Of the 12,111 GWS SNPs identified through our COJO analysis, 11,847 were available in the EBB dataset, 97% of which  have a MAF greater than 1%  and a correlation of conditional SNP effects using the same LD reference panel of ρb = 0.80 . The proportion of sign-consistent SNP effects was greater than 75%  for variants with a MAF greater than 1%—also consistent with expectations  polymorphism at this locus (Supplementary Note P = 8.7 × 10−55) of phenotypic variance in EUR above what is explained by the VNTR alone . We chose this size window because it was predicted that causal variants are located within 35 kb of GWS SNPs with a probability greater than 80% (ref. 25). Accordingly, we grouped the 12,111 GWS SNPs identified in our METAFE into 7,209 non-overlapping loci —one for SNPs within these loci and one for SNPs outside these loci—and then used both matrices to estimate a stratified SNP-based heritability  and found no significant enrichment of SNP-based heritability for BMI within height-associated GWS loci  as a control trait, given its small genetic correlation with height (GWS) and on the basis of all HM3 SNPs (hereafter referred to as PGSHM3). PGSGWS were calculated using joint SNP effects from COJO, and PGSHM3 using joint effects calculated using the SBayesC method28  for height on the basis of GWS SNPs  was the second best of all PGSGWS across ancestry groups except in AFR. Indeed, PGSGWS-AFR (based on 453 SNPs) yielded an accuracy of 8.5% (s.e. 0.6%) in AFR individuals from UKB and PAGE; that is, significantly larger than the 5.9% (s.e. 0.6%) and 7.0% (s.e. 0.6%) achieved by PGSGWS-EUR in these two samples, respectively , whereas it slightly decreased the accuracy in AFR.We first present results from PGSGWS Fig. , partly ely Fig. . PGSGWS-AGE Fig. . Across HM3 consistently outperform their corresponding PGSGWS in most ancestry-groups. However, PGSHM3 was sometimes less transferable across ancestry groups than PGSGWS, in particular in AFR and HIS individuals from PAGE. In EUR, PGSHM3 reaches an accuracy of 44.7% (s.e. 0.6%), which is higher than previously published SNP-based predictors of height derived from individual-level data33 and from GWAS summary statistics35 across various experimental designs . Finally, the largest improvement of PGSHM3 over PGSGWS was observed in AFR individuals from the PAGE study  with PGSGWS-EUR and 55.2% (s.e. 3.2%) with PGSHM3-EUR. This observation reflects the fact that PGSs can explain within-family differences between siblings, whereas average parental height cannot. To show this empirically, we estimate that our PGSs based on GWS SNPs explain around 33% (s.e. 0.7%) of height variance between siblings  and 17,492 independent EUR sibling pairs from the UKB, who were excluded from our META30% Fig. . Moreovesiblings . Finallysiblings .38.In summary, the estimation of variance explained and prediction analyses in samples with European ancestry show that the set of 12,111 GWS SNPs accounts for nearly all of 20 and also down-sampled our meta-analysis into four subsets (including our EUR and METAFE GWASs). Altogether, we analysed seven GWASs with a sample size increasing from around 0.13 million up to around 5.3 million individuals , two gene-based metrics , we found that the same broad clusters of related gene sets (including most of the clusters enriched for OMIM genes) are prioritized at all GWAS sample sizes  closer to OMIM genes  plateaus at sample sizes of larger than 1.5 million, whereas the relative enrichment of GWS SNPs near OMIM genes first decreases with sample size, then plateaus when 44. However, given that newly identified variants tend to cluster near ones identified at smaller sample sizes, we also saw a saturation in the number of loci identified for n values greater than 2.5 million, where the upward trend starts to weaken . Of note, we confirm that increased genetic diversity in GWAS discovery samples significantly improves the prediction accuracy of PGSs in under-represented ancestries.Altogether, these analyses show that increasing the GWAS sample size not only increases the prediction accuracy, but also sheds more light on the genomic distribution of causal variants and, at all but the largest sample sizes, the genes proximal to these variants. By contrast, enrichment of higher-level, broadly defined biological categories such as gene sets and pathways and functional annotations can be identified using relatively small sample sizes  is not randomly distributed across the genome; rather, associated variants are more likely to be detected around genes that have been previously associated with Mendelian disorders of growth. Furthermore, we observed a strong genetic overlap of association across cohorts with various ancestries. Effect estimates of associated SNPs are moderately to highly correlated , suggesting even larger correlations of effect sizes of underlying causal variants13. Moreover, although there are significant differences in power to detect an association between cohorts with European and non-European ancestries, most genetic associations for height observed in populations with non-European ancestry lie in close proximity and in linkage disequilibrium to associations identified within populations of European ancestry.By conducting one of the largest GWASs so far in 5.4 million individuals, with a primary focus on common genetic variation, we have provided insights into the genetic architecture of height—including a saturated genomic map of 12,111 genetic associations for height. Consistent with previous studies45, which showed across 492 traits a strong colocalization between common and rare coding variants associated with the same trait. Nevertheless, our conclusions remain limited by the relatively low performances of imputation in this MAF regime47. Therefore, large samples with whole-genome sequences will be required to robustly address this question. Such datasets are increasingly becoming available50. Separately, previous studies have reported a significant enrichment of height heritability near genes as compared to inter-genic regions 51. Our findings are consistent with but not reducible to that observation, given that up to 31% of GWS SNPs identified in this study lie more than 50 kb away from any gene.By increasing our experimental sample size to more than seven times that of previous studies, we have explained up to 40% of the inter-individual variation in height in independent European-ancestry samples using GWS SNPs alone, and more than 90% of 38 that LD and MAF differences between European and African ancestries can explain up to around 84% (s.e. 1.5%) of the loss of prediction accuracy between these populations  heterogeneity of SNP effects between cohorts and background ancestries, which may have reduced the statistical power of our study as compared to a homogenous sample like UKB; (ii) inconsistent definitions of GWS SNPs (using COJO in this study versus standard clumping in ref. 52); and, most importantly, (iii) misspecification of the SNP-effects distribution assumed to make these predictions. Nevertheless, if these predictions reflect proportional levels of saturation between traits, then we could expect that two- to tenfold larger samples would be required for GWASs of inflammatory bowel disease , schizophrenia  or BMI  to reach a similar saturation of 80–90% of SNP-based heritability.Although large sample sizes are needed to pinpoint the variants responsible for the heritability of height , the prioritization of relevant genes and gene sets is feasible at smaller sample sizes than that required to account for the common variant heritability. Thus, the sample sizes required for saturation of GWAS are smaller for identifying enriched gene sets, with the identification of genes implicated as potentially causal and mapping of genomic regions containing associated variants requiring successively larger sample sizes. Furthermore, unlike prediction accuracy, prioritization of genes that are likely to be causal and even mapping of associated regions is consistent across ancestries, reflecting the expected similarity in the biological architecture of human height across populations. Recent studies using UKB data predicted that GWAS sample sizes of just over 3 million individuals are required to identify 6,000–7,000 GWS SNPs explaining more than 90% of the SNP-based heritability of heightn ≈ 350,000), which is sub-optimal given that around 24% of our discovery sample is of non-European ancestry. We emphasize that no analytical tool with an adequately large multi-ancestry reference panel is at present available to properly address how to identify conditionally independent associations in a multi-ancestry study. Fine-mapping of variants remains a particular challenge when attempted across ancestries in loci containing multiple signals (as is often the case for height).A third limitation of our study is our inability to perform well-powered replication analyses of genetic associations specific to populations with non-European ancestries, owing to the current limited availability of such data. Finally, as with all GWASs, definitive identification of effector genes and the mechanisms by which genes and variants influence phenotype remains a key bottleneck. Therefore, progress towards identifying causal genes from GWAS of height may be achieved by a combination of increasingly large whole-exome sequencing studies, allowing straightforward SNP-to-gene mapping45, the use of relevant complementary data  and the development of computational methods that can integrate these data.Our study has a number of limitations. First, we focused on SNPs from the HM3 panel, which only partially capture common genetic variation. However, although a significant fraction of height variance can be explained by common SNPs outside the HM3 SNPs panel, we showed that the extra information  remains concentrated within GWS loci identified in our HM3-SNP-based analyses  of genetic variance explained by common SNPs down to around 21% of the genome. Mapping the missing 5–10% of SNP-based heritability not accounted for in the four non-European ancestries studied here will require additional and directed efforts in the future.Height has been used as a model trait for the study of human polygenic traits, including common diseases, because of its high heritability and relative ease of measurement, which enable large sample sizes and increased power. Conclusions about the genetic architecture, sample size requirements for additional GWAS discovery and scope for polygenic prediction that were initially made for height have by-and-large agreed with those for common disease. If the results from this study can also be extrapolated to disease, this would suggest that substantially increased sample sizes could largely resolve the heritability attributed to common variation to a finite set of SNPs . These variants and regions would implicate a particular subset of genes, regulatory elements and pathways that would be most relevant to address questions of function, mechanism and therapeutic intervention.A summary of the methods, together with a full description of genome-wide association analyses and follow-up analyses is described below. Written informed consent was obtained from every participant in each study, and the study was approved by relevant ethics committees , which accounts for multi-allelic variants in the data. Study-specific GWASs are described in Supplementary Tables P value (PHWE) > 10−8 and a minor allele count (MAC) > 5 in each study. Next, we performed a fixed-effect inverse variance weighted meta-analysis of summary statistics from all five ancestry groups GWAS meta-analysis using a custom R script using the R package meta (see ‘URLs’ section).We first performed ancestry-group-specific GWAS meta-analyses of 173 studies of EUR, 56 studies of EAS, 29 studies of AFR, 11 studies of HIS and 12 studies of SAS. Meta-analyses within ancestry groups were performed as described beforeWe excluded 56,477 UK Biobank (UKB) participants from our discovery GWAS for following analyses including quantification of population stratification. More precisely, our hold-out EUR sample consists of 17,942 sibling pairs and 981 trios (two parents and one child) plus all UKB participants with an estimated genetic relationship larger than 0.05 with our set of sibling pairs and trios. We identified 14,587 individuals among these 56,477 UKB participants who were unrelated (unrelatedness was determined as when the genetic relationship coefficient estimated from HM3 SNPs  was  lower than 0.05) to each other and used their data to quantify the variance explained by SNPs within GWS loci (described below) and the prediction accuracy of PGSs.7. We used default parameters for all ancestry groups except in AFR and HIS, for which we found that default parameters could yield biased estimates of joint SNP effects because of long-range LD. This choice is discussed in Supplementary Note P < 5 × 10−8. In addition to GWAS summary statistics, COJO analyses also require genotypes from an ancestry-matched sample that is used as a LD reference. For all sets of genotypes used as LD reference panels, we selected HM3 SNPs with PHWE > 10−6. For EUR, we used genotypes at 1,318,293 HM3 SNPs (MAC > 5) from 348,501 unrelated EUR participants in the UKB as our LD reference. For EAS, we used genotypes at 1,034,263 quality-controlled  HM3 SNPs from a merged panel of n = 5,875 unrelated participants from the UKB  and Genetic Epidemiology Research on Aging . Data from the GERA study were obtained from the database of Genotypes and Phenotypes  under project 15096. For SAS, we used genotypes at 1,222,935 HM3 SNPs  from 9,448 unrelated individuals. For AFR, we used genotypes at 1,007,949 quality-controlled  HM3 SNPs from a merged panel of 15,847 participants from the Women’s Health Initiative , and the National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource . Both WHI and CARe datasets were obtained from dbGaP  and processed following the protocol provided by the dbGaP data submitters. After excluding samples with more than 10% missing values and retaining only unrelated individuals, our final LD reference included data from n = 10,636 unrelated AFR individuals. For HIS, we used genotypes at 1,246,763 sequenced HM3 SNPs (MAF > 1%) from n = 4,883 unrelated samples from the Hispanic Community Health Study/Study of Latinos  cohorts. Finally, we performed a COJO analysis of the combined meta-analysis of all ancestries (referred to as METAFE in the main text) using 348,501 unrelated EUR participants in the UKB as the reference panel. We performed COJO analyses of each of the five ancestry group-specific GWAS meta-analyses using the software GCTA (version v.1.93)To assess whether SNPs detected in non-EUR were independent of signals detected in EUR, we performed another COJO analysis of ancestry groups GWAS by fitting jointly SNPs detected in EUR with those detected in each of the non-EUR GWAS meta-analyses. For each non-EUR GWAS, we performed a single-step COJO analysis only including SNPs identified in that non-EUR GWAS and for which the LD squared correlation . Moreover, we performed a stratified LD score regression analysis to quantify the enrichment of height heritability in 97 genomic annotations curated and described previously40. as the baseline-LD model. Annotation-weighted LD scores used for those analyses were also calculated using data from 1KGP (see ‘URLs’ section).We used two statistics to evaluate whether an EUR LD reference could approximate well enough the LD structure in our trans-ancestry GWAS meta-analysis. The first statistic that we used is the Wright fixation index18 genes near GWS SNPs with a large signal density, by counting the number of OMIM genes within 100 kb of a GWS SNP, then comparing that number for SNPs with a density of 0 and those with a density of at least 1. The strength of the enrichment was measured using an odds ratio calculated from a 2×2 contingency table: 'presence/absence of an OMIM gene' versus 'density of 0 or larger than 0'. To assess the significance of the enrichment, we simulated the distribution of enrichment statistics for a random set of 462 length-matched genes. We used 22 length classes  × 10 kb, with i = 1,…,9; between i × 100 kb and (i + 1) × 100 kb, with i = 1,…,10; between 1 Mb and 1.5 Mb; between 1.5 Mb and 2 Mb; and >2 Mb) to match OMIM genes with random genes. OMIM genes within a given length class were matched with the same number of non-OMIM genes present in the class. We sampled 1,000 random sets of genes and calculated for each them an enrichment statistic. Enrichment P value was calculated as the number of times enrichment statistics of random genes exceeded that of OMIM genes. The list of OMIM genes is provided in Supplementary Table We defined the density of independent signals around each GWS SNP as the number of other independent associations identified with COJO within a 100-kb window on both sides. Therefore, a SNP with no other associations within 100 kb has a density of 0, whereas a SNP colocalizing with 20 other GWS associations within 100 kb will have a density of 20. We quantified the standard error of the mean signal density across the genome using a leave-one-chromosome-out jackknife procedure. We then quantified the enrichment of 462 curated OMIM39.We assessed the genomic colocalization between 2,747 GWS SNPs identified in non-EUR  are thoroughly described in a previous studyOur matching strategy consists of three steps. First, we calibrated a statistical model to predict the probability for a given HM3 SNP to be GWS in any of our non-EUR GWAS meta-analyses as a function of their annotation. For that, we used a logistic regression of the non-EUR GWS status  onto the 24 annotations as regressors. Second, we used that model to predict the probability to be GWS in non-EUR. Thirdly, we used the predicted probability to sample (with replacement) 1,000 random sets of 2,747 SNPs. Finally, we estimated the proportion of EUR GWS SNPs within 100 kb of SNPs in each sampled SNP set. We report in the main text the mean and s.d. over these 1,000 proportions.P value were greater than 0.6 and the proportion of P values < 5% was less than 1%, suggesting no significant differences in the distribution of these 24 annotations between non-EUR GWS SNPs and matched SNPs.To validate our matching strategy, we compared the mean value of each of these 24 annotations  between non-EUR GWS SNPs and each of the 1,000 random sets of SNPs, using a Fisher’s exact test. For each of the 24 annotations, both the mean and median ρb of estimated SNP effects between our discovery GWAS and our replication sample of 49,160 participants of the EBB was statistically different from 1. We used the estimator of ρb from a previous study60, which accounts for sampling errors in both discovery and replication samples. Standard errors were calculated using a leave-one-SNP-out jackknife procedure. We quantified the correlation of marginal and also that of joint SNP effects. Joint SNP effects in our replication sample were obtained by performing a single-step COJO analysis of GWAS summary statistics from our EBB sample, using the same LD reference as in the discovery GWAS. Correlation of SNP effects were calculated after correcting SNP effects for winner’s curse using a previously described method12. We provide the R scripts used to apply these corrections and estimate the correlation of SNP effects (see ‘URLs’ section). The expected proportion, E[P], of sign-consistent SNP effects between discovery and replication was calculated using the quadrant probability of a standard bivariate Gaussian distribution with correlation E[ρb], denoting the expected correlation between estimated SNP effects in the discovery and replication sample:−1 denotes the inverse of the sine function and E[ρb] the expectation of the ρb statistic under the assumption that the true SNP effects are the same across discovery and replications cohorts. E[ρb] was calculated asNd and Nr denote the sizes of the discovery and replication samples, respectively; hd and hr the average heterozygosity under Hardy–Weinberg equilibrium ) across GWS SNPs in the discovery and replication samples, respectively; and 60.) as the sample variance of estimated SNP effects in the discovery sample minus the median squared standard error.To assess the replicability of our results, we tested whether the correlation 7. This approach involves two main steps: (i) calculation of genetic relationships matrices (GRM); and (ii) estimation of variance components corresponding to each of these matrices using a REML algorithm. We partitioned the genome in two sets containing GWS loci on the one hand and all other HM3 SNPs on the other hand. GWS loci were defined as non-overlapping genomic segments containing at least one GWS SNP and such that GWS SNPs in adjacent loci are more than 2 × 35 kb away from each other . We then calculated a GRM based on each set of SNPs and estimated jointly a variance explained by GWS alone and that explained by the rest of the genome. We performed these analyses in multiple samples independent of our discovery GWAS, which include participants of diverse ancestry. Details about the samples used for these analyses are provided below. We extended our analyses to also quantify the variance explained by GWS loci using alternative definitions based on a window size of 0 kb and 10 kb around GWS SNPs  approach implemented in GCTAWe also repeated our analyses using a random set of 12,111 SNPs matched with GWS SNPs on MAF and LD. Loci for these 12,111 random SNPs were defined similarly as for GWS loci. To match random SNPs with GWS SNPs on MAF and LD, we first created 28 MAF-LD classes of HM3 SNPs (7 MAF classes × 4 LD score classes). MAF classes were defined as <1%; between 1% and 5%; between 5% and 10%; between 10% and 20%; between 20% and 30%; between 30% and 40%; and between 40% and 50%. LD score classes were defined using quartiles of the HM3 LD score distribution. We next matched GWS SNPs in each of the 28 MAF-LD classes, with the same number of SNPs randomly sampled from that MAF-LD class.Height was first mean-centred and scaled to variance 1 within each sex. We quantified the prediction accuracy of height predictors as the difference between the variance explained by a linear regression model of sex-standardized height regressed on the height predictor, age, 20 genotypic principal components and study-specific covariates (full model) minus that explained by a reduced linear regression not including the height predictor. Genetic principal components were calculated from LD pruned HM3 SNPs (P < 5 × 10−8 (GWS: reported in the main text) and P < 5 × 10−7, P < 5 × 10−6 and P < 5 × 10−5. For all COJO-based PGS, we used estimated joint effects to calculate the PGS. The second class of PGS uses weights for all HM3 SNPs obtained from applying the SBayesC method28 to ancestry-specific and cross-ancestry GWAS meta-analyses with ancestry-matched and EUR-specific LD matrices, respectively. The SBayesC method is a Bayesian PGS-method implemented in the GCTB software (v.2.0), which uses the same prior as the LDpred method62. In brief, SBayesC models the distribution of joint effects of all SNPs using a two-component mixture distribution. The first component is a point-mass Dirac distribution on zero and the other component a Gaussian distribution (for each SNP) with mean 0 and a variance parameter to estimate. Full LD matrices  were calculated using GCTB across around 250 overlapping (50% overlap) blocks of around 8,000 SNPs (average size is around 20 Mb). These LD matrices were calculated using the same sets of genotypes used for COJO analyses (described above). We ran SBayesC in each block separately with 100,000 Monte Carlo Markov Chain iterations. In each run, we initialized the proportion of causal SNPs in a block at 0.0001 and the heritability explained by SNPs in the block at 0.001. Posterior SNP effects of SNPs present in two blocks were meta-analysed using inverse-variance meta-analysis.We analysed two classes of PGS. The first class is based on SNPs ascertained using GCTA-COJO. We applied GCTA-COJO to ancestry-specific and cross-ancestry GWAS meta-analyses using an ancestry-matched and an EUR LD reference, respectively. We compared PGSs based on SNPs ascertained at different significance thresholds: Prediction accuracy was quantified in 61,095 unrelated individuals from three studies, including 33,001 participants of the UKB who were not included in our discovery GWAS  was assessed in 981 unrelated trios obtained as described above by crossing information from Bycroft et al.63  with that from Yengo et al.3 . We quantified the within-family variance explained by PGS as the squared correlation of height difference between siblings with PGS difference between siblings. We describe in Supplementary Note The prediction accuracy of sibling’s height was assessed in 17,942 unrelated sibling pairs from the UKB. Those pairs were determined by intersecting the list of UKB sibling pairs determined by Bycroft et al.n = 14,587) and the Lifelines study , two samples of AFR from the UKB  and the PAGE study , two samples of EAS  from the UKB and the China Kadoorie Biobank , one sample of SAS from the UKB  and one sample of HIS from the PAGE study . Analyses were adjusted for age, sex, 20 genotypic principal components and study-specific covariates . Genotypes of EUR UKB participants were imputed to the Haplotype Reference Consortium (HRC) and to a combined reference panel including haplotypes from the 1KG Project and the UK10K Project. To improve variant coverage in non-EUR participants of UKB, we re-imputed their genotypes to the 1KG reference panel, as described previously38. Lifelines samples were imputed to the HRC panel. PAGE and CKB were imputed to the 1KG reference panel. Standard quality control (PHWE > 10−6 and MAC > 5) were applied to imputed genotypes in each dataset.We quantified the accuracy of a PGS based on GWS SNPs as well as the variance explained by SNPs within GWS loci, in eight different datasets independent of our discovery GWAS meta-analyses. These datasets include two samples of EUR from the UKB , we re-analysed five down-sampled GWASs as shown in Table 19, Wood et al.20 and Yengo et al.3. with ImpG-Summary (v.1.0.1)64 using haplotypes from 1KGP as a LD reference. GWAS summary statistics from Lango Allen et al. only contain P values (P), height-increasing alleles and per-SNP sample sizes (N). Therefore, we first calculated Z-scores (Z) from P values assuming that Z-scores are normally distributed, then derived SNP effects (β) and corresponding standard errors (s.e.) using linear regression theory as β/Z. Imputed GWAS summary statistics from these three studies are made publicly available on the GIANT consortium website (see ‘URLs’ section). We next performed a COJO analysis of all down-sampled GWAS using genotypes of 348,501 unrelated EUR participants in the UKB as a LD reference panel, as for our METAFE and EUR GWAS meta-analysis.In addition to our EUR GWAS meta-analysis and our trans-ancestry meta-analysis (META65 v.8 and the eQTLgen studies (see ‘URLs’ section). To ensure that our SMR results robustly reflect causality or pleiotropic effects of height-associated SNPs on gene expression, we only report here significant SMR results , which do not pass the heterogeneity in dependent instrument (HEIDI) test . The significance threshold for the HEIDI test was chosen on the basis of recommendations from another study66.We used SMR to identify genes whose expression could mediate the effects of SNPs on height. SMR analyses were performed using the SMR software v.1.03. We used publicly available gene eQTLs identified from two large eQTL studies; namely, the GTExhttps://www.omim.org/). From July 2019 to August 2020, we performed queries using search terms of “short stature”, “tall stature”, “overgrowth”, “skeletal dysplasia” and “brachydactyly.” We then used the free text descriptions in OMIM to manually curate the resulting combined list of genes, as well as genes in our earlier list from Wood et al.20 and all genes listed as causing skeletal disease in an online endocrine textbook . For short stature, we only included genes that underlie syndromes in which short stature was either consistent (less than −2 s.d. in the vast majority of patients with data recorded), or present in multiple families or sibships and accompanied by (a) more severe short stature (−3 s.d.), (b) presence of skeletal dysplasia ; or (c) presence of brachydactyly, shortened digits, disproportionate short stature or limb shortening (not simply absence of specific bones). We removed genes underlying syndromes in which short stature was likely to be attributable to failure to thrive, specific metabolic disturbances, intestinal failure or enteropathy and/or very severe disease . For tall stature or overgrowth, we only included genes underlying syndromes in which tall stature was consistent (more than +2 s.d. in the vast majority of patients with data recorded) or present in multiple families or sibships and accompanied by either (a) more severe tall stature (>+3 s.d.) or (b) arachnodactyly. For brachydactyly, we required more than only fifth finger involvement, and that brachydactyly be either consistent (present in the vast majority of patients) or accompanied by consistent short stature or other skeletal dysplasias. For skeletal dysplasias, we only considered genes that underlie syndromes in which the skeletal dysplasia involved long bones or the spine and was accompanied by short stature, brachydactyly or limb or digit shortening. We also included all genes in a list we generated in Lango Allen et al.19, which was curated using similar criteria. The resulting list contained 536 genes, of which 462 (Supplementary Table 11) are autosomal on the basis of annotation from PLINK (https://www.cog-genomics.org/static/bin/plink/glist-hg19).To generate a list of genes that are known to underlie syndromes of abnormal skeletal growth, we queried the Online Mendelian Inheritance in Man database (OMIM; https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files. Analysis script for within- and across-ancestry meta-analysis: https://github.com/loic-yengo/ScriptsForYengo2022_HeightGWAS/blob/main/run-meta-analyses-within-ancestries.R and https://github.com/loic-yengo/ScriptsForYengo2022_HeightGWAS/blob/main/run-meta-analyses-across-ancestries.R. Analysis script for correction of winner’s curse: https://github.com/loic-yengo/ScriptsForYengo2022_HeightGWAS/blob/main/WC_correction.R. Genotypes from 1KG: https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. eQTL data for SMR: GTEx v.8: https://yanglab.westlake.edu.cn/data/SMR/GTEx_V8_cis_eqtl_summary.html; eQTLgen: https://www.eqtlgen.org/cis-eqtls.html. Annotation-weighted LD scores for stratified LD score regression analyses: https://alkesgroup.broadinstitute.org/LDSCORE/LDSCORE/. LDSC software: https://github.com/bulik/ldsc.GIANT consortium data files: Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-05275-y.Supplementary InformationThis file contains Acknowledgements, Supplementary Figures 1–25, Supplementary Notes 1–6, and Supplementary References.Reporting SummarySupplementary TablesThis file contains Supplementary Tables 1–16.Peer Review File


Although SPET requires The potential for SPET application in fruit trees was evaluated by developing a 25K target SNPs assay to genotype a panel of apricot accessions and progenies. A total of 32,492 polymorphic sites were genotyped in 128 accessions  with extremely low levels of missing data and a significant correlation of allelic frequencies compared to whole-genome sequencing data used for array design. Assay performance was further validated by estimating genotyping errors in two biparental progenies, resulting in an overall 1.8% rate. SPET genotyping data were used to infer population structure and to dissect the architecture of fruit maturity date (MD), a quantitative reproductive phenological trait of great agronomical interest in apricot species. Depending on the year, GWAS revealed loci associated to MD on several chromosomes. The QTLs on chromosomes 1 and 4 (the latter explaining most of the phenotypic variability in the panel) were the most consistent over years and were further confirmed by linkage mapping in two segregating progenies.Besides the utility for marker assisted selection and for paving the way to in-depth studies to clarify the molecular bases of MD trait variation in apricot, the results provide an overview of the performance and reliability of SPET for fruit tree genetics.The online version contains supplementary material available at 10.1186/s12864-022-08901-1. The tremendous advance in DNA technologies and the advent of next generation sequencing (NGS) platforms are making possible a comprehensive cataloguing of genetic variation, providing an unprecedented resolution for analyzing ancestry, evolution and trait diversity . Over thPrunus armeniaca L.) is an economically relatively important Prunus species, with a worldwide production of 4.4 million tons in 2018 [Plum Pox Virus  resistance and fruit quality  have been the major emphasis of breeders [Ex situ apricot collections largely represent the source of parental material for crossing. Several studies have investigated genetic diversity and population structure in various germplasms backgrounds by simple-sequence repeats (SSRs) markers [Armeniaca species (P. sibirica and P. mandshurica) [Apricot  , 32 has shurica) –35 and fshurica) , 37, leashurica) , 39, or shurica) . HoweverIn this work, we described the first application of SPET technology in apricot. A collection of accessions and breeding selections was genotyped using 25K probes assessing SPET reliability for analyzing genetic variation and population structure. Furthermore, as a proof-of-concept, genome-wide association studies (GWAS) and Quantitative Trait Locus (QTL) mapping were performed to dissect the genetic architecture of maturity date in a panel of accessions and breeding selections, and two additional segregating progenies.A total of 25K SPET probes were designed, mostly located in gene-rich chromosome regions: SNP type and position based on the apricot genome reference assembly of ‘Chuanzhihong’ cultivar  is reporr = 0.82) was found between SPET and WGRS  reads were obtained (an average of 1.6 million reads per sample) with an average mapping rate of 97.9% and a mean depth per site of about 80×  and 62 seedlings from ‘Lito’ (L) × ‘BO81604311’ (B81) F1 cross (L × B). A total of 336 million PE reads were obtained, with an average of 2.2 million per sample  × ‘BO 93623033’ (B93) FPrunus reference map [R < 0.2, while the heterozygosity level ranged from 0.12 in the F3 self-crossed selection ‘BO06603111’ to 0.51 of ‘Hellin’, with the majority of SNPs showing an heterozygosity level in the range 0.4 – 0.5 . As shown in Fig. ence map ) with anR > 0.3, retaining a total of 1,593 SNPs , with nce Fig. . The clunce Fig. .−06 and 2.05E−05) were detected on chromosomes (chr) 1, 2, 3, 4, 5 and 7, depending on the year , expressed in Julian Days (JD) at harvest, was highly correlated among the three years of observation (r-squared higher than 0.9), although mean MD in 2019 was about one week later compared to 2017 and 2018 Fig. . MD rangAs estimated by REML analyses, cumulative heritability explained by all SNP loci in each year was 0.82, 0.66 and 0.72, respectively. The signal on chr 1 was consistent across the 3 years of observation, although the top SNP peaks were not identical: SNP16361  in 2017, SNP15833  in 2018 and 2019. Moreover, a main signal in the middle of chr 4 was stable across years, peaking at SNP25197  in 2017 and 2019, and at SNP25460  in 2018. However, others nearby SNPs beyond the significance threshold were observed at this locus , although distribution in both years was not normal Fig. .Fig. 6A p-value of 5.8E−06 and 7.03E−07, respectively)  and bottom (35 – 43 Mb). Interval mapping using the LG1 map highlighted a QTL in the region overlapping the SNP clusters detected with single marker analysis, locating qMD1.1 QTL within the 1.5-LOD interval comprised between 47.1 and 60 cM in 2016 and between 54.4 and 60 cM in 2019  was separately performed in F and B93 to test the association with MD variations. After excluding 15,448 monomorphic and 1,250 markers heterozygous in both parents (i.e. hk × hk according to JoinMap software code), a total of 5,157 (lm × ll) and 4,964 (nn × np) markers were respectively used for F and B93 parents. A SNP cluster on chr 1 was only detected in the F parent, peaking at 23.2 Mb in 2016 and 24.5 in 2019   of 5 years MD data showed an almost bimodal distribution Fig. . SMA wasRosaceae, the development of a SNP-array platform for apricot has been proposed by some research groups, but never realized due to the difficulties of assembling a large consortium in such a minor species. In this work, building upon the recently released high-quality reference genome sequences of some apricot cultivars, a SPET approach was, for the first time, developed and applied for apricot genotyping. While a direct comparison with other approaches is beyond the scope of this work, SPET has the ability of multiplexing samples in a single sequencing run, genotyping tens of thousands of probes with a good coverage, and the advantage of combining targeted SNP analysis and complexity reduction, respectively typical of SNPs array and RRS approaches [In the last decade, the considerable expansion of apricot cultivation has renewed the breeding interest for improving agronomical and pomological traits, as well as the adaptability to different environmental conditions. As in other fruit tree species, the increase of knowledge on the genetic bases of traits variation and the development of molecular markers to assist selection could represent a useful strategy to complement and boost conventional breeding , 42. Alsproaches .The choice of 25K probes for SPET assay was based on the search for a compromise between cost-effectiveness , prior knowledge on the extent of LD decay in apricot , as welAscertainment bias  is the main limiting factor of genome complexity reduction approaches, caused by the non-random selection of SNPs or individuals set . HoweverP. armeniaca reference genome. Collectively, they provide a sufficiently robust framework for downstream QTL analyses.SPET technology was also used for SNP linkage map construction in two segregating progenies. The obtained parental maps showed metrics comparable with those recently obtained through GBS approaches , 48 in tMaturity date (MD) was dissected as a proof-of-concept of the utility of SPET approach for QTL mapping studies. MD is a relevant trait for fruit marketability, as early ripening cultivars usually command the highest prices while late-maturing cultivars allow the extension of the harvesting season . UnderstPrunus species, such as peach and hybrids [A major effect locus for MD was found in middle of chr 4 (qMD4.1), with associated SNPs distributed in a region of 1.7 Mb from SNP25197  to SNP25581 at 11,574,290 bp). However, the LD pattern does not allow to exclude the presence of multiple alleles and/or QTLs at this locus. Consistent with GWAS results, a major QTL in this same region segregated in the L×B progeny, where it explained most of the observed PEV for MD about 62%). A major QTL on linkage group (LG) 4 associated to ripening date was previously reported close to the UDAp439 marker in the cross ‘Goldrich’ × ‘Moniqui’ and also in the same L×B progeny . This ma hybrids , 57–59, 4,290 bp. hybrids  and cher hybrids , 62, sug%. A majo hybrids . At the  hybrids , 65. FutApart from chr 4, other QTLs have been reported for maturity date in apricot, on LG1, 2, 3, 4, 5, 6 and 7, although affected by the season of observation –56. HoweIn this work, a SPET targeted genotyping approach was for the first time developed and applied in apricot. The robustness and efficiency of this technique was assessed in a germplasm collection and segregating progenies. Results demonstrate that the SPET approach is a valid alternative to other RRS methods and arrays, highlighting the potential of this technology to provide dense, easy-to-handle genotypic information at the selected polymorphisms and additional untargeted sites. Linkage and association mapping revealed several genomic loci associated to fruit maturity date which may be a starting point for a fine characterization of the genetic basis of trait variation, and ultimately, a useful tool to assist breeding selection and germplasm characterization.1 apricot progenies from the crosses ‘Fiamma’ × ‘BO93623033’ (F×B) and ‘Lito’ × ‘BO 81604311’ (L×B)  and trained as slender spindle . Maturity date (MD) trait was recorded when 2—3% of the fruits on the tree has reached the full physiological maturity, assessed by sensory and visual inspection, also recording IAD  and fruit firmness . MD was expressed as the number of days from 1 January . MD was evaluated in the accession panel for 3 years  and 2 years (2016 and 2019) in 85 F×B seedlings. In L×B, best linear unbiased prediction (BLUP) was used to pool historical phenotypic data of 58 seedlings from multiple-years records .The assayed plant material included 128 apricot accessions and two Fextended ) with 90Apricot whole-genome sequencing (WGS) libraries were retrieved from available NCBI resources, specifically from the previous work of Mariette et al.  PRJNA29. Data prbedtools v2.26.0 [DNA was extracted from young leaves using the modified CTAB method . Librari v2.26.0 .Genetic diversity measures on VCF dataset were performed by the Geno Summary tool implemented in Tassel v5.2.15 , includivs expected p-values distribution under null hypothesis were compared through quantile–quantile (QQ) plot inspection to evaluate GWAS performance. Thresholds for SNP significance were calculated based on a conservative Bonferroni’s correction for a type I error rate and less stringent permutation test. Manhattan plots were designed using MVP R package [The fixed and random model Circulating Probability Unification (FarmCPU) algorithm implemented in GAPIT R package, was used for association analyses . Fixed e package . SNP-basp ≤ 0.05). Linkage groups were defined using a minimum logarithm of odds (LOD) value of 10.0 using the regression algorithm (Kosambi mapping function), with a recombination frequency threshold of 0.4, LOD value of 1.0 and a goodness-of-fit jump of 5.0. The positions of markers in the F×B and L×B linkage maps were aligned with their position on the apricot reference genome [p = 0.05. The interval mapping (IM) function was employed for QTLs detection with 95% significance and estimation of the percentage of phenotypic variation. MapChart v2.1 software [Linkage maps were constructed following the two-way pseudo-testcross strategy for outcrossing species (CP) using JoinMap v4.1 . The inpe genome . Single-e genome  was usedsoftware  was usedAdditional file 1.


Background: The purpose of this study was to identify genetic variations associated with the metabolism of dabigatran in healthy Chinese subjects, with particular focus given to pharmacokinetics (PK) and pharmacodynamics (PD).Methods: Healthy Chinese adults aged 18–65 years with unknown genotypes from a bioequivalence trial were included according to the protocol registered at ClinicalTrial.org (NCT03161496). All subjects received a single dose (150 mg) of dabigatran etexilate. PK  and PD  parameters were evaluated. Whole-exome sequencing and genome-wide association analyses were performed. Additionally, candidate gene association analyses related to dabigatran were conducted.Results: A total of 118 healthy Chinese subjects were enrolled in this study. According to the p-value suggestive threshold (1.0 × 10−4), the following three SNPs were found to be associated with the AUC0–t of total dabigatran: SLC4A4 SNP rs138389345 (p = 5.99 × 10−5), FRAS1 SNP rs6835769 (p = 6.88 × 10−5), and SULT1A1 SNP rs9282862 (p = 7.44 × 10−5). Furthermore, these SNPs were also found to have significant influences on the AUC0–t of free dabigatran, maximum plasma concentration, and anti-FIIa activity (p < 0.05). Moreover, we identified 30 new potential SNPs of 13 reported candidate genes  that were associated with drug metabolism.Conclusion: Genetic variations were indeed found to impact dabigatran metabolism in a population of healthy Chinese subjects. Further research is needed to explore the more detailed functions of these SNPs. Additionally, our results should be verified in studies that use larger sample sizes and investigate other ethnicities. Oral direct thrombin inhibitor (DTI), a new type of direct oral anticoagulants (DOACs), has been increasingly used in recent years for the prevention of stroke and systemic embolism in cases of non-valvular atrial fibrillation (NVAF) and are currently recommended as the first line of medication-based treatment for this condition, according to American CHEST Guidelines . DabigatAlthough the above characteristics of dabigatran are either equivalent or superior to Vitamin k Antagonists , more atABCB1 gene and generally not metabolized by cytochrome oxidase P450 (CYP450), except at supra-therapeutic concentrations  and CES1 . Only two studies , which has been verified in a recent Asian study , in healthy subjects under fasting and fed conditions  with 240 ml of warm water after either being fed a standard high-fat meal  trial conducted in China, which was mainly performed to assess the BE of domestic generic dabigatran etexilate capsules, with reference to the original product  and the Third Hospital of Changsha . The Serial blood samples used for PK parameter analysis were collected from the BE trial. Venous blood samples (4 ml) were collected before dosing and then at 17–18 time points after drug administration . We also2 tubes and centrifuged for 10 min at 4°C and 1700 g within 1 hour of sampling. Plasma samples were transferred to cryovials and stored at −80°C until PK analysis. For PD analysis, blood samples were collected in sodium citrate (3.2% v/v) tubes and centrifuged for 15 min at room temperature and 2500 g within 1 hour of sampling. Plasma samples were transferred to cryovials that were stored at −80°C, and were subjected to PD analysis within 6 months of sampling. For genetic polymorphism testing, blood samples were collected in EDTA-K2 tubes, transferred to cryovials, and stored at below −60°C until genotype testing.For PK analysis, blood samples were collected in EDTA-Kmax) and the time to Cmax (Tmax) were recorded from the observed data. Values for the terminal half-life (t1/2) and area under the curve (AUC) for plasma concentration at time zero versus the last measurable time point (AUC0-t) were calculated. AUC0-t, Cmax, Tmax, and t1/2 reflected the PK parameters of total dabigatran, while AUC0-t(free), Cmax(free), Tmax(free), and t1/2(free) represented that of free dabigatran. No significant matrix effect was observed. All samples were analyzed within their established storage stability periods.Plasma concentrations of total and free (nonconjugated) dabigatran from BE trial center samples were determined by a home-validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, which has been previously published . PK para® CS-2100i fully automated multiparameter hemostasis analyzer  at Peking University First Hospital. PT and APTT were measured by validated Coagulation Method Assay Kits . During the 2018 International Council for Standardization in Hematology (ICSH) (p < 0.001) was demonstrated in our previous study of Chinese patients . Specific calibrators and controls were used for dabigatran . The results of the anti-FIIa assay were reported in dabigatran units (with limits of detection for dabigatran of 0–550 ng/ml). The two QC sample concentrations used were 24 and 399 ng/ml. The inter- and intra-day precision (RSD%) were measured as < 6.76% and 7.53%, whereas inter- and intra-day accuracy were determined to be 97.71% and 86.69%. Relative changes in PT and APTT were also used for PD assessments, which involved taking the ratio after administration to before administration .The prothrombin time (PT), activated partial thromboplastin time (APTT), and levels of anti-FIIa activity were assessed using the Sysmexy (ICSH) , anti-FIpatients . Anti-FI® Spectrophotometer ; 3) DNA concentration was measured using a Qubit® DNA Assay Kit in a Qubit® 3.0 Flurometer .For DNA preparation, the quality of isolated genomic DNA was assessed by using the following three methods in combination: 1) DNA degradation and contamination were monitored on 0.8% agarose gels; 2) DNA purity was checked using the NanoPhotometerXT Human All Exon V6 Kit  according to the manufacturer’s standard protocol. Briefly, qualified genomic DNA was fragmented to an average size of 200bp. The ends of the DNA fragments were subjected to end repair processes, followed by A-tailing and adapter ligation. DNA fragments with adapters ligated on both ends were selectively enriched by polymerase chain reaction (PCR). After PCR, library hybridization and exome capture were performed using biotin labeled probes and magnetic bead selection. Captured libraries were enriched and tagged by PCR to prepare for sequencing. The final libraries were quantified using a KAPA Library Quantification Kit  and an Agilent 2100 Bioanalyzer. Paired-end sequencing (2 × 150 bp) was performed on an Illumina NovaSeq 6000 sequencer .For library preparation and sequencing, the whole exome library was prepared using the Agilent SureSelectp-value was >10–6. Based on the high-quality data obtained after quality control, PCA conducted in PLINK 1.09 was used for stratified population assessment to remove outlier samples. Finally, 75,348 SNPs were tested for association with PK and PD parameters of dabigatran.Variant filtering and prediction were performed on 347,010 single-nucleotide polymorphisms (SNPs) detected in exons, with 200 bp extensions on either end. SNPs were retained for association analysis when the missing rate was <10%, minor allele frequency was >5%, and Hardy-Weinberg equilibrium p-value significance threshold was set at the Bonferroni limit of 6.6 × 10–7 in the discovery phase–SNPs reaching this threshold were taken forward for replication. The significance level for successful replication was set at p = 0.05/75,348 of SNPs for which replication was attempted. Following the genome-wide analyses of the merged and imputed discovery and replication phase data, follow-up analyses were performed by adding each top hit SNP as an additional covariate in GWA. This procedure was performed in a sequential manner until no significant genome-wide signals remained. Regional plots were generated with LocusZoom  to correct for residual population stratification  and the Mann-Whitney or nonparametric Kruskal–Wallis H test to analyze the data. Age, sex, food, and HDL were adjusted for by multivariate linear regression. Two-sided p-values < 0.05 were considered statistically significant.To extensively investigate the influences of previously reported candidate genes on PK and PD parameters of dabigatran in our study population, we also identified related genes and made detailed analyses of these genes. According to previous pharmacogenomic studies , respectively. Basic participant characteristics are presented in 0–t: 1247.43 ± 442.08 ng h/mL, Cmax: 139.99 ± 55.61 ng/ml, Tmax: 3.61 ± 2.09 h, and t1/2: 8.78 ± 1.33 h; for free dabigatran, AUC0–t: 1110.42 ± 404.57 ng h/mL, Cmax: 125.22 ± 48.26 ng/ml, Tmax: 3.65 ± 2.10 h, and t1/2: 8.57 ± 1.57 h.A total of 118 healthy Chinese subjects were enrolled, of which mean age and BMI were 24 years (range: 18–41 years) and 22.2 kg/mp = 6.6 × 10–7), no SNP was identified as significant. Considering the sample size of this exploratory experiment was small (n = 118), the p-value was adjusted to 1.0 × 10−4 for screening suggestive genetic variations on the main metabolic parameter of AUC0–t. Three SNPs, SLC4A4 SNP rs138389345 (p = 5.99 × 10−5), FRAS1 SNP rs6835769 (p = 6.88 × 10−5), and SULT1A1 SNP rs9282862 (p = 7.44 × 10−5), exceeded our suggestive threshold for association with AUC0–t , while the minor allele of FRAS1 SNP rs6835769 was associated with a higher AUC0–t. The trends of influence on AUC0–t(free) for these suggestive SNPs could be also shown significantly (p < 1.0 × 10−3), with regional association plots presented in max, Cmax(free), and IIa2h were also similar (p < 0.05). However, these SNPs had no significant effect on the Tmax or t1/2 of total or free dabigatran. As for the other PD parameters, the minor allele of SLC4A4 SNP rs138389345 corresponded to a significantly lower APTT2h, and the minor allele of SULT1A1 SNP rs9282862 had a significantly lower ∆PT2h (p < 0.05). However, FRAS1 SNP rs6835769 was not associated with any index of APTT or PT.According to the genome-wide significance threshold  were associated with Cmax, six  with Tmax, and two (CES1 rs56278207 and CYP1A2 rs4646427) with t1/2. For the PD parameters, only two SNPs (CYP2B6 rs2279342 and rs34433978) might influence IIa2h, where the latter has also been associated with APTT2h, PT2h, and ∆PT2h. SLCO1B1 SNP rs11045748 might influence APTT2h, ∆APTT2h, and ∆PT2h. CYP2C19 SNPs rs12769205, rs3758580, and rs4244285 might affect both APTT2h and PT2h. The remaining two (ABCG2 rs2231138 and SLC22A1 rs622591) and one (CYP1A2 rs4646427) SNPs were associated with APTT2h and ∆APTT2h, respectively. At last, CYP3A5 SNPs rs15524 and rs4646453 had effects on ∆PT2h.For correlation analyses of candidate genes, the following 18 previously reported genes were finally included in this study: SLC4A4 rs138389345, FRAS1 rs6835769, and SULT1A1 rs9282862) that were associated with the metabolism of dabigatran. AUC0–t, AUC0–t(free), Cmax, Cmax(free), and IIa2h values in the minor alleles of SLC4A4 rs138389345 and SULT1A1 rs9282862 were significantly lower compared to in their wild type counterparts. Conversely, these values were significantly higher in FRAS1 SNP rs6835769. Additionally, SLC4A4 rs138389345 and SULT1A1 rs9282862 were found to have significant influences on APTT and PT, respectively. Moreover, through correlation analyses of reported candidate genes, we found inconsistencies in the results for three previously reported SNPs  regarding their associations with AUC0–t, Cmax, and PD indexes. Furthermore, we identified 30 new potential SNPs of 13 reported candidate genes in our study. ABCB1, ABCC2, ABCG2, CYP2B6, CYP1A2, CES1, SLCO1B1, UGT1A1, UGT1A9, and UGT2B7 might influence PK parameters, while ABCG2, CYP1A2, CYP2B6, CYP2C19, CYP3A5, SLCO1B1, and SLC22A1 might influence PD characteristics. Since our study was based on an original BE trial in healthy subjects, the accuracy of the PK/PD results was high and no known diseases or other factors affected drug metabolism. These same advantages of the previous study, which followed a similar experimental design , variable peak concentration, steady state concentration attainment within two to 3 days, bi-exponential distribution phase, volume of distribution (50–70 L), 35% bind rate to plasma proteins, hepatic conjugation with glucuronide, and ability for renal elimination (80%) . As a BCSLC4A4, located in chromosome 4q13.3, encodes an anion exchange protein named electrogenic sodium bicarbonate cotransporter 1 (NBCe1) (SLC4A4 SNP rs138389345 is a variant of SLC4A4 in which a single G base is deleted (G > -). From the HPA RNA-seq normal tissue data, the SLC4A4 gene is most expressed in the kidney. NBCe1 plays a major role in renal bicarbonate absorption via the proximal tubules and maintaining normal blood pH  . Additionally, this SNP resulted in increased values for AUC0-t(free), Cmax, Cmax(free), IIa2h, and APTT2h.Drug transporters, including members of the ATP-binding cassette (ABC) and solute carrier (SLC) superfamilies, are recognized as important determinants for governing the therapeutic response of many drugs . SLC4A4, (NBCe1) . NBCe1 i (NBCe1) . SLC4A4 blood pH . AbnormaH values , it is rFRAS1 gene, also known as KIAA1500, is located in chromosome 4q21.21, encodes the extracellular matrix (ECM) proteins, and regulates epidermal-basement membrane adhesion and organogenesis during development (FRAS1 SNP rs6835769 involves the mutation of a C base to a T base (C > T) within the gene, which can lead to the A817V mutation at the protein level. FRAS1 mutations have been confirmed to cause an extremely rare autosomal recessive genetic disorder named Fraser Syndrome (FRASRS), and seem to be responsible for non-syndromic unilateral renal agenesis  and 667A > G . Previous reports have confirmed that these alleles have significantly different frequencies depending on race , as seen in data from the Gambian Genome Variation Project (GGVP). SLC4A4 SNP rs138389345 showed a significantly decreased AUC0-t , with similar trends observed in AUC0-t(free), Cmax, Cmax(free), IIa2h, PT2h, and ∆PT2h. Although this linkage disequilibrium needs to be verified in Asian populations, and UGTs were the main phase II enzymes involved, SULT1A1 variation may have the potential to be a new biomarker for antithrombotic therapy–it would certainly be worthy to conduct more detailed studies to investigate this possibility.Drugs usually undergo two phases of metabolism in the body. The main purpose of phase II metabolic reactions is to combine reactions, increase drug polarity, and facilitate drug exclusion from the body. The most common phase II biotransformation enzymes include sulfotransferases (SULTs), N-acetyltransferases (NATs), and methyltransferases (MTs). SULTs utilizes 3′-phospho-5′-adenylyl sulfate (PAPS) as a sulfonate donor to catalyze the sulfate conjugation of a wide variety of acceptor molecules that bear a hydroxyl or an amine group . Sulfonalatelets . The two on race . SULT1A1efficacy , and havefficacy . Althougefficacy  has inveabolites . For theabolites . Severalactivity . SNP rs9ABCB1, ABCC2, ABCG2, CYP2B6, CYP1A2, CYP2C19, CYP3A5, CES1, SLCO1B1, SLC22A1, UGT1A1, UGT1A9, and UGT2B7) were found to have significant effects on drug metabolism. The most commonly reported SNPs, including ABCB1 rs1045642 and CES1 rs2244613, had no influence on any PK or PD parameters in our study, which was a finding consistent with Ji Q’s research , CYP, and SLC gene families and dabigatran metabolism in humans  had positive change. ABCG2 and CYP3A5 variants on bleeding and thromboembolic events in Finnish patients who were given dabigatran, where the results were all negative. In our study, the positive SNPs which had the most effects on PK or PD parameters included SLCO1B1 rs11045748 , SLCO1B1 rs2291075 & rs4149056 (AUC0-t and Cmax), UGT1A9 rs7586110 , UGT1A1 rs4148323 & rs4148327, ABCC2 rs2273697 & rs4148395 (AUC0-t and Cmax), CYP2B6 rs56156262 (AUC0-t and Cmax), and CYP2B6 rs34433978 . The reported positive SNPs of CYP2D6, SLC22A1, CYP3A5, and ABCG2 were not consistent with previous studies  should be performed to document their detailed pathways in drug metabolism.There were a few key limitations to our study worth discussing. First, as the sample size was limited, no SNP was found to meet the genome-wide significance threshold. Therefore, future multi-center studies with larger sample sizes should be conducted. Second, not all potential SNPs could be detected by our genotyping method, such as the reported SNPs of  UGT2B15 . Supplem UGT2B15 . Our finSLC4A4, FRAS1, and SULT1A1 may be novel biomarkers and potential targets for anticoagulation therapy involving dabigatran. Furthermore, newly identified SNPs of 13 reported candidate genes  may also affect dabigatran drug metabolism. Further studies are required to explore the more detailed functions of these SNPs. Additionally, the results of our study should be verified in a larger study among patients and to investigate their relationships with the clinical outcomes of antithrombotic therapy.Our study found that genetic variations do impact the metabolism of dabigatran in healthy Chinese populations. The suggestive genes of


Validation of the EphA2 ELISA showed good recovery (87%) and linearity (103%) and no cross-reactivity with other Eph receptors. Patients with kidney function decline had lower diastolic blood pressure, and higher UPCR and EphA2 than those without kidney function decline. The association of age and eGFR with EphA2 was maintained in the stepwise multiple regression analysis. In a multivariate logistic model, EphA2 was associated with a lower eGFR (<60 mL/min/1.73 m2) after adjustment for age, sex, and UPCR. High circulating EphA2 levels have potential application as a clinical biomarker for the presence of CKD in patients with hypertension.Identifying novel biomarkers of kidney function in patients with chronic kidney disease (CKD) has strong clinical value as current measures have limitations. This study aims to develop and validate a sensitive and specific ephrin type-A receptor 2 (EphA2) enzyme-linked immunosorbent assay (ELISA) for human serum, and determine whether its results correlate with traditional renal measures in patients with hypertension. The novel ELISA of the current study was validated and used to measure circulating EphA2 levels in 80 hypertensive patients with and without kidney function decline (eGFR less than 60 mL/min/1.73 m Elevated blood pressure is a leading cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD). CKD has been identified as one of the world’s most critical health issues with increasing global morbidity and mortality . People We have previously demonstrated that high levels of TNF-related biomarkers such as TNF, progranulin (PGRN) , and TNFEph receptors constitute the largest known family of receptor tyrosine kinases, comprising at least 16 distinct receptors . Park etThis study aims to develop and validate a human EphA2 ELISA using a sensitive monoclonal antibody and investigate the relationship between proteinuria, eGFR, and EphA2 in hypertensive patients.Hybridomas producing mAbs 230-1 and 57-1 against human EphA2 were generated by immunizing BALB/c mice  with human EphA2 proteins purified from supernatants of HEK239-F  transfected with pcDNA3.1 encoding human EphA2. The immunized splenocytes were then fused with P3X63Ag8U mouse myeloma cells . The supernatants were harvested 14 days postfusion, and then screened in reactivity against EphA2 transfectants, but not against ErbB2 transfectants with a cell-based ELISA system. After cloning by limiting dilutions twice, hybridoma cell colonies termed 230-1 and 57-1  were selected. To confirm this, the reactivity of mAbs 230-1 and 57-1 to EphA2 transfectants was examined with flow cytometry .A novel ELISA was developed for detecting serum EphA2 levels. A 96-well ELISA plate  was coated with 5.0 µg/mL of captured (human anti-EphA2) antibody (230-1). The plate was maintained overnight at 4 °C and then blocked with SuperBlock Blocking Buffer  for 24 h at 4 °C. A 100-mL serum sample or standard  was diluted with Can Get Signal Solution 1  to a final dilution of 1:5 and was then added in duplicate to separate wells. The wells were washed thrice with washing buffer (PBS-Tween 20 (0.05%); Merk, Darmstadt, Germany) after the plate was incubated at room temperature for 2 h. Thereafter, 100 μL of diluted  biotinylated detection antibody 57-1 in Can Get Signal Solution 2  was added to each well, and the plate was incubated at room temperature for 1 h. Wells were then washed four times. Then, 100 μL/well of diluted  streptavidin poly horse-radish peroxidase  in ELISA Assay Diluent  was added and maintained for 45 min at room temperature. Wells were then washed five times. TMB 1-component microwell peroxidase substrate, SureBlue  at 100 μL/well was added and maintained for 15 min at room temperature. Sulfuric acid was added at 100 μL/well to stop the reaction. The absorbance values of each well were measured using an automated microplate reader  with compatible software  with measurement and reference wavelengths of 450 nm and 570 nm, respectively. The optical density (OD) of the blank well was subtracted from each sample OD. The initial segment of the standard curve was observed to be linear with a second-degree polynomial.The novel in-house EphA2 ELISA system of the current study was verified by applying four different concentrations  of recombinant EphA2  to determine whether the assay worked properly. The specificity of the novel EphA2 ELISA assay was examined next by demonstrating the absence of an increased signal after the addition of the same concentrations  of recombinant human EphA1 , EphA3 , or EphA4 .n = 6) with a wide range of EphA2 levels  were spiked with 10 μL of known amounts of recombinant EphA2  were used to evaluate the linearity of dilutions. The serum samples were serially  diluted with Can Get Signal Solution 2 to determine the optimal working dilutions of the serum EphA2 analysis. Percent recovery was calculated using the following formula: / × 100.The lower limit of detection and the lower quantitation limit was calculated using the formula: 3.3 × [standard deviation (SD) of blank OD/the gradient of a standard curve around the lower limit of the detection] and 10 × (SD of blank OD/the gradient of a standard curve around the lower limit of the detection), respectively.Precision was expressed as the percent coefficient of variation (CV) for both intra- and inter-assay variability. Intra-assay variation was calculated by running 16 samples (in duplicate) on a single plate. Moreover, the inter-assay variation was determined by running 16 samples across five different days.Serum EphA2 levels was also measured using a commercially available ELISA kit  to compare the novel in-house EphA2 ELISA system of the current study with the standard system.2) and 64 patients with renal dysfunction (eGFR < 60 mL/min/1.73 m2) were enrolled in the present study. Individuals with diabetes and glomerulonephritis at baseline were excluded.Sixteen hypertensive patients with normal renal function . Informed consent was obtained from all participants included in the study.t-test. Skewed continuous variables were handled as continuous variables after common logarithmic transformation and then presented as the median (25th and 75th percentile). A Mann–Whitney U test was performed for comparisons of continuous variables. Correlations between two continuous variables were assessed using Spearman’s rank-order correlation test. Stepwise multiple linear regression was performed to determine the factor that affects the EphA2 level. Logistic regression analysis was performed to determine the factors that predict a lower eGFR (<60 mL/min/1.73 m2). A two-sided p value < 0.05 was considered statistically significant. All statistical analyses were performed with the SAS Enterprise Guide .Normally, distributed continuous variables are expressed as the mean ± SD and were compared using Student’s Sufficient concentrations of recombinant EphA1, EphA2, EphA3, and EphA4 proteins were added to separate wells of a plate to exclude the possibility of artifacts due to cross-reactivity among EphA receptor antibodies. The in-house EphA2 ELISA system of the current study showed remarkable specificity for EphA2, and no increased signals for EphA1, EphA3, and EphA4 were noted .The lower detection and lower quantitation limits were found to be 9.7 and 29.5 pg/mL, respectively. The hook effect was not observed, at least at 4000 pg/mL. Therefore, the ELISA system of the current study could reliably detect human EphA2 with a working range from 29.5 to 4000 pg/mL .Recovery was evaluated by the ability to recover a known amount of recombinant EphA2 protein . The perDilutional linearity was evaluated by serial dilution (two- to 16-fold) of a wide range of serum concentrations ]. The moderate (r = 0.41) correlation between the assay of the current study and the Thermo Fisher Scientific assay may indicate that the two ELISA methods detect different alterations in EphA2 serum levels.In the current study, a novel in-house ELISA for EphA2 was developed and validated to assess serum EphA2 concentrations of hypertensive patients. To achieve this aim, the determination of lower detection and quantitation limits, specificity, dilution linearity, spike recovery, and precision experiments were included. Eph receptors are divided into EphA and EphB subfamilies based on their ligand specificity. EphA2 is localized on the cell membrane  and bindRecent investigations revealed that EphA2 is a key modulator for a wide variety of cellular functions. EphA2 is also a cardiovascular molecule involved in the regulation of cell–cell interactions and angiogenesis . EphA2 eA novel assay that reliably detects EphA2 in human sera was developed. The assay has several strengths including high sensitivity and specificity. Circulating EphA2 levels were significantly higher in patients with renal function decline compared with patients with normal kidney function, suggesting the potential diagnostic efficiency of circulating EphA2 levels in evaluating kidney function in patients with hypertension. Further studies are needed to find out if EphA2 levels are associated with renal measures in patients with other types of kidney disease and whether they can predict future renal function decline. Additionally, it is necessary to verify whether EphA2 is useful in predicting renal function decline compared to previously reported candidate CKD biomarkers ,4,5. Rev


SNP) array. A genome-wide association study (GWAS) and principal component analyses (PCA) were performed to identify genomic regions associated with body size related-traits such as wing length, shank length, shank thickness, keel length, and body weight. Hierarchical clustering of genotype data showed a clear genetic difference between the investigated Asian Game and Asian Bantam chicken types. GWAS identified 16 genomic regions associated with wing length , shank thickness , keel length , and body weight . PCA showed that the first principal component (PC1) separated the 2 chicken types and significantly correlated with the measured body size related-traits (P ≤ 2.24e-40). SNPs contributing significantly to PC1 were subjected to a more detailed investigation. This analysis identified 11 regions potentially associated with differences in body size related-traits. A region on chromosome 4 (GGA4) (17.3–21.3 Mb) was detected in both analyses GWAS and PCA. This region harbors 60 genes. Among them are myotubularin 1 (MTM1) and secreted frizzled-related protein 2 (SFPR2) which can be considered as potential candidate genes for body size related-traits. Our results clearly show that the investigated Asian Game type chicken breeds are genetically different from the Asian Bantam breeds. A region on GGA4 between 17.3 and 21.3 Mb was identified which contributes to the phenotypic difference, though further validation of candidate genes is necessary.Body size is one of the main selection indices in chicken breeding. Although often investigated, knowledge of the underlying genetic mechanisms is incomplete. The aim of the current study was to identify genomic regions associated with body size differences between Asian Game and Asian Bantam type chickens. In this study, 94 and 107 chickens from 4 Asian Game and 5 Asian Bantam type breeds, respectively, were genotyped using the chicken 580K single nucleotide polymorphism ( QTL) have been mapped on autosomes for body size related-traits such as shank length, keel length, and body weight (GHR) gene results in sex-linked dwarfism , and selection pressure can be grouped together in what is called a chicken type . CompariGWAS) followed by a principal component analysis (PCA) using body size related-traits and high-density single nucleotide polymorphism (SNP) data.Game and Bantam are 2 chicken types with Asian origin which differ considerably in body size . Since AASrb, n = 19), Malay black red , Orloff red spangled , Indian Game dark ] and 5 breeds as Asian Bantam type chickens  ] . In totan = 18)] . All phen = 18)] .Table 1NSNP) genotyping was performed using the Affymetrix Axiom Genome-Wide Chicken Genotyping Arrays encompassing over 580K SNPs . SNPs were excluded using the following criteria across all chickens: 1) SNPs with call rates < 95%; 2) SNPs monomorphic across all breeds; 3) SNPs with a minor allele frequency (MAF) < 0.05. After quality control, 373,514 SNPs remained for hierarchical cluster analysis.Blood collection and DNA extraction were performed according to the SYNBREED protocol . Single 80K SNPs . The posFor genome-wide analysis, the genotypes of a genotype class of a specific marker were set to missing data, if the genotype class contained less than 10 individuals. This was done to prevent spurious associations in the GWAS. Afterward, markers were rechecked using the same procedure as outlined above. After filtering, 352,863 SNPs remained for GWAS. Since PCA requires full genotype data, markers containing missing data were removed leading to 172,737 SNPs suitable for PCA.Hierarchical clustering of genotypes was performed to measure genetic distances between the 9 chicken breeds and to identify outliers. Outliers are defined as individuals which clustered within the wrong breed. Hierarchical clustering was carried out by calculating distances between individuals using Euclidean distance based on their genotypes. Afterward, the “hclust” function in R (3.2.3)  was usedt test was used to compare mean values of each trait and sex between the 2 chicken types. The significance threshold after standard Bonferroni correction for multiple testing was set to a P-value of 0.005 (0.05/(NTraits(5) * NSexes(2))).The two-tailed Student's Before performing the GWAS each phenotype was corrected for sex differences within each breed using the following linear model:P-values were corrected for multiple testing by estimating the false discovery rate (FDR) using the Benjamini-Hochberg procedure . Association analysis between the corrected phenotype and SNP genotype data was performed using R (3.2.3) with the following linear model:rocedure . SNPs wirocedure . Additio freedom .β) of a SNP effect represents the additive effect of the minor allele of this SNP (LD) with neighboring SNPs was calculated as r2 values using the “genetics” R package (2 > 0.5)  . Further2 > 0.5) .PCs) which capture the majority of the genetic variation observed among genotypes (PC1) were further investigated to identify those SNPs that allowed the PC1 to separate the 9 chicken breeds. The method for calculating the contribution of each SNP to a certain PC has been described previously (2) was calculated as the squared variable correlation. Afterward, all the cos2 values for PC1 were summed up. Contribution of each SNP was expressed as the percentage of total variation.In another approach, the genetic relationship between Asian Game and Bantam chicken types was analyzed by PCA using the “prcomp” function in R . PCA canenotypes . SNPs coeviously . In briepcaDObs). Afterward, the pcaDObs measurements were converted to P-values to identify SNPs significantly contributing to PC1 using the R “pnorm” function with the following equation:P-value). The mean and standard deviation (SD) of the observed distribution were given to allow conversion of the observed values. The “lower.tail = FALSE” parameter meant that probabilities were defined as: P[X > x]. This approach allowed the identification of those SNPs capturing a significant differentiation among breeds which are separated by a given PC. In our analysis, only PC1 was investigated since this PC separated Asian Game from Asian Bantam type chickens. The P-values were adjusted for multiple testing using Bonferroni correction (PBonf). SNPs with PBonf < 0.05 were considered as significant contributors to PC1. Compared with the previous approach which selected SNPs with more than 10 times the expected contribution to a given PC  .GO) terms and biological pathways were investigated using the “Functional Annotation Table” tool in the DAVID Bioinformatics Resources 6.8 (Genes within the identified candidate region(s) were extracted and investigated for potential functional association with body size. Gene function and its associated gene ontology , in males as well as in females (GGA) 1, 2, 4, 6, 10, 23, 27, and Z  which showed an additive effect of −0.918 cm per C allele. In the same region, the top SNP for body weight was SNP rs314732179  which had an additive effect of 0.227 kg per A allele. Out of the 16 identified regions, the 3 identified regions on GGA6, 10, and 27 for keel length, shank thickness, and body weight, respectively, overlapped with QTLs that had been identified for similar traits in other populations (GWAS identified 622 SNPs significant for wing length (30), shank length (2), shank thickness (507), keel length (42), and body weight (41)  correlated with all the measured morphological traits  and secreted frizzled-related protein 2  having an effect on muscle and bone development, respectively . SFRP2 affects bone morphogenesis (GO:0060349) and is a positive regulator of osteoblast differentiation (GO:0045669).Since the region on GGA4 from 17,284,783 to 21,270,248 bp  was not only associated with keel length and body weight in the GWAS but also contained SNPs that significantly contributed to PC1 separating Asian Game and Asian Bantam type chickens, we focused on genes in this region. The functional annotation of the 60 genes  located in this region yielded the 2 genes encoding myotubularin 1 .2 intercross population of 2 meat-type chicken lines divergently selected for high and low digestive efficiency  to a QTL for tibia dry matter weight that was found in an Fficiency . The genelopment . As a me ligands . In huma ligands . Therefomyopathy .SFRP2 and MTM1) are expressed in the chicken brain and in the skeletal muscle (experiment E-MTAB-3724 in the “Expression Atlas” database). Bone expression data for chickens are not publicly available, nevertheless, in mice; SFRP2 is highly expressed in the osteoblast (probeset of 1448201_at in “BioGPS” database). In chicken, SFRP2 is involved in embryogenesis; development of the neural system (brain tissue) and muscles (myogenesis) (Both genes (genesis) . HoweverOur results suggest that at least 2 independent growth QTLs are present on GGA4; one between 17.3 Mb and 21.3 Mb, which has been detected in this study  and another one that previously has been identified in a region between 74.5 and 78.0 Mb . The region on GGA10 from 4,534,620 to 7,754,637 bp for shank thickness was associated with body weight and blood glucose level in a Broiler × Fayoumi advanced intercross lines (QTL_ID in QTLdb: 95422 and 71191). The region on GGA27 from 3,375,301 to 3,375,436 bp for body weight was associated with femur bone mineral content and femur weight in an F2 cross between a Chinese indigenous breed and White Leghorn (QTL_ID in QTLdb: 130479 and 130480). With respect to PCA, which analyses the genetic diversity, seven regions on GGA1, 2, 7, 14, and Z overlapped with previously reported QTLs for body weight, feed conversion ratio, feed intake, egg laying performance, feather pecking, and earlobe color. PC1 likely also captures genetic variation that is associated with other traits than growth differences between Asian Game and Bantam type chickens. This is supported by regions that were detected in PCA and that are overlapping with QTLs for traits such as egg laying performance, feather pecking, comb shape, and wattles weight. Therefore, the regions identified by PC1 might contribute to the identification of genetic factors underlying other morphology traits besides the body weight trait investigated here.Besides the region on GGA4, additional 15 and 10 regions were identified in the GWAS and PCA, respectively. The overlap between these regions and published QTLs  providedIn the GWAS, breed was considered as a fixed effect to correct for the population structure. However, the genomic inflation factor lambda (1.12 ≤ λ ≤ 1.35) was indicative for either an additional hidden subpopulation structure within breeds and/or the occurrence of several SNPs significantly associated with the trait under inspection. Considering the breed as a fixed effect in the model, some loci might be missed, namely SNPs that are correlated with the population structure itself, for example, regions harboring genetic variants that occur in a single population/type only or regions harboring variants with different genetic effects in different breeds. Therefore, an association analysis within each breed or in crosses between those breeds could help to identify additional genomic loci associated with body size variation within a specific breed. However, this breed-specific analysis would always require a larger sample size, as well as segregation of the specific locus either in the particular breed or, if the locus is fixed in this breed, in a cross-bred population with another breed to detect significant associations.MTM1 and SFRP2 as interesting candidate genes for the differences between Asian Game and Asian Bantam chickens with regards to muscle and bone development. However, effects of other genes in the GGA4 region on body size cannot be excluded.In conclusion, our study using genome-wide SNP data showed that significant genetic differences exist between Asian Game type  and Asian Bantam type chickens . GWAS identified 16 genomic regions, associated with chicken wing length (2), shank thickness (6), keel length (5), and body weight (3). Only 3 of the 16 regions overlapped with previously identified growth QTLs. This shows the value of investigating highly diverse breeds for identifying novel genomic regions involved in chicken size, including growth bone and muscle development. Based on PCA we identified SNPs in 11 genomic regions that significantly contribute to the difference in body size between Asian Game and Asian Bantam type chickens. Nevertheless, our study cannot exclude that some of these regions affect other traits which are consistently different between the 2 chicken types. The region on GGA4 between 17.3 and 21.3 Mb  was identified in GWAS and PCA. This shows that the PCA method is able to identify similar regions as GWAS, but is also able to detect regions which do not show a direct association with any of the morphological traits measured. Further investigation of positional candidate genes within the GWAS and PCA overlapping GGA4 region suggested