These variants explained ~15.5% of the phenotypic variance, accounting for more than one quarter of the total ~50% GWAS-attributable heritability. There was no difference in genetic effect between sexes or among different age strata. Heritability was enriched in certain functional categories, such as conserved and coding regions, and in tissues related to the musculoskeletal system. Polygenic risk score prediction well distinguished participants with high and low ALM. The findings are important not only for lean mass but also for other complex diseases, such as type 2 diabetes, as ALM is shown to be a protective factor for type 2 diabetes.Appendicular lean mass (ALM) is a heritable trait associated with loss of lean muscle mass and strength, or sarcopenia, but its genetic determinants are largely unknown. Here we conducted a genome-wide association study (GWAS) with 450,243 UK Biobank participants to uncover its genetic architecture. A total of 1059 conditionally independent variants from 799 loci were identified at the genome-wide significance level ( Here, the authors report a genome-wide association study for appendicular lean mass using more than 450,000 participants in the UK Biobank Study. They identify 799 loci at genome-wide significance, explaining over 15% of phenotypic variance and >25% of the heritability, allowing the authors to uncover the genetic architecture of this sarcopenia-related trait. Lean body mass is associated with bone mineral density and hence may also influence the risk for osteoporosis3. Other lean body mass-related conditions include dysmotility syndrome4, sarcopenic obesity5 and cachexia6.Lean body mass is an important physiological index. Low lean body mass, together with low muscle strength and low physical performance, represents a key component for the definition of sarcopenia, which is a critical condition with functional impairment and physical disability and a major modifiable cause of frailty in the elderly8. However, findings of specific genes for human lean mass variation remain limited, even with the powerful genome-wide association study (GWAS) approach. A key reason for the limited findings, as in other human complex traits, is the modest sample size used in most GWASs performed for lean body mass13, resulting in few single nucleotide polymorphisms (SNPs) identified with genome-wide significance.Lean body mass has a significant genetic component, as evidenced by a high heritability of 50–80% observed in twin studies14. Of the 20 studied cohorts, 10  were characterized with the dual-energy X-ray absorptiometry (DEXA)-derived measure of lean mass, while the other 10  were characterized with the bioelectrical impedance analysis (BIA)-derived measure. Despite the large sample used, the percentage of phenotypic variance explained by the identified SNPs was still only 0.23% and 0.16% for whole body lean mass and ALM, respectively, suggesting that most of the heritability of lean body mass was still undetected. Therefore, even with such a large GWAS meta-analysis, it is still necessary to boost the sample size further to enhance the statistical power for detecting more causal SNPs underlying lean body mass.As a notable example, one previous large meta-analysis of GWASs amassed 20 cohorts of European ancestry with a total sample size of >38,000 for whole body lean mass and of >28,000 for appendicular lean mass (ALM)15. A total of 182 loci were identified, 78% of which were replicated in a population of 181,862 elderly (aged 60–74 years) individuals from the same UKB cohort.In a more recent study, a GWAS of ALM was conducted in a population of 85,750 middle-aged (aged 38–49 years) individuals from the UK Biobank (UKB)16 and as such a more suitable trait for sarcopenia-related genetic analyses.Unlike ALM, which is mainly affected by skeletal muscle, whole body lean mass is determined by skeletal muscle, smooth muscle and cardiac muscle. Therefore, ALM has a higher predictive power for sarcopenia-related health outcomes because sarcopenia is mainly due to a low skeletal muscle amount. ALM is also slightly more heritable than whole body lean massp < 5 × 10–9), we identified >1000 independent variants that were significant, all of which were significant in both sexes at p < 5 × 10–5. Our findings revealed a large number of genetic variants for lean body mass and contributed to the characterization of the genetic architecture of this complex trait. Through this GWAS, we demonstrated the power for mapping the genetic landscape of common human complex traits/diseases using extraordinarily large samples.Here, in this study, with a sample containing approximately half-million participants of European origin, we performed a GWAS of ALM in the full UKB cohort. At a stringent genome-wide significance level (p < 2.2 × 10–16). The Pearson correlation coefficient between ALM and AFM was 0.68 in males and 0.79 in females. Neither ALM nor AFM followed a normal distribution. Instead, both distributions were right skewed  > 0.1% and an imputation quality score >0.3 were available in 244,730 female and 205,513 male participants.Following QC of both ALMadj with BOLT-LMM17 . The LDSC mean Chi-square and intercept were 3.05 and 1.17 for females and 2.63 and 1.13 for males, corresponding to attenuation ratios of 0.083 and 0.077, respectively.In each sex group, the additive effect of each variant was tested on ALMM17 Fig. . The genLDSC estimated a genetic correlation coefficient as high as 0.93 (s.e. 0.01) between the two sexes, implying that most GWAS-attributable heritability was shared between sexes.N = 400,879, attenuation ratio = 0.078), suggesting a limited effect of population structure. However, some residual stratification may remain uncorrected because ancestry was based upon self-reporting.Given the shared heritability between sexes, between-sex meta-analysis was performed with an inverse-variance-weighted fixed-effects model to combine the sex-specific GWAS results. The meta-analysis signals had an attenuation ratio of 0.078 , which was equal to that estimated by analysing the subset of genetically determined “Caucasian” participants (–9) level in the combined meta-analysis and were significant at p < 5 × 10–5 in both sexes. Based on their physical coordinates, these variants were divided into 828 loci that were at least 500 kb apart. It was found that 57 lead variants were not in complete linkage equilibrium (LD r2 < 0.1) with each other due to the long-range LD pattern. After removing 33 lead variants, the remaining 795 lead variants were all in linkage equilibrium. Therefore, they were treated as independent loci.A total of 121,109 variants were significant at the genome-wide significance  with the 795 primary lead variants.Approximate conditional association analysis followed by between-sex meta-analysis was recursively performed, which further identified 4 additional loci and 264 conditionally significant variants at the GWS level that were also significant in both sexes at adj (Supplementary Data p < 5 × 10–9) in both sexes (categorized here as Tier 1 variants). Additionally, 208 variants achieved p values <5 × 10–9 in females and p values <5 × 10–5 in males; 94 variants achieved p values <5 × 10–9 in males and p values <5 × 10–5 in females (categorized here as Tier 2 variants). Finally, 404 variants achieved p values <5 × 10–5 in both sexes and p values <5 × 10–9 in the between-sex meta-analysis (categorized here as Tier 3 variants).In total, 1059 (i.e. 795 + 264) independent variants from 799 distinct loci were associated with ALM–5 , only one SNP, rs4338565, was significant (p = 2.30 × 10–5), which is not unexpected given the limited sample size. At the nominal level of 0.05, 124 SNPs were significant. Overall, the effect direction at 767 SNPs was consistent between the two analyses , and the correlation coefficient of the regression between them was 0.27 , partially demonstrating the replicability in the smaller South Asian sample.The associations of the 1059 lead SNPs in the 7,452 UKB South Asian  participants are listed in Supplementary Data 14 were successfully replicated at the nominal level of p < 0.05, and up to four of them were replicated at the 5.0 × 10–8 level. For another bivariate study in which eight loci were identified18, two were significant at the 5.0 × 10–8 level, five additional were significant at the nominal level, but the last SNP rs7672749 was not significant (p = 0.26). For the recent study of ALM in middle-aged and older UKB participants, all 182 lead SNPs were nominally significant in the present study, and up to 168 (92.3%) were significant at the GWS level. In sum, the present study well replicates the findings from previous smaller studies.Associations of previously reported SNPs in the present study are listed in Supplementary Data Of the 799 identified loci, 102 were reported in the abovementioned previous studies, while the remaining 697 were not.–8 level, while no flanking variants surrounding the remaining 581 lead SNPs showed associations at the 5.0 × 10–8 level, demonstrating their novelty and possible specificity to lean but not fat mass.We also evaluated the overlap of the identified loci with those identified for three obesity traits obtained through the GIANT consortium. Flanking variants surrounding 478 lead SNPs (defined as the lead SNP + 500 kb flanking region to either side) were associated with one or more obesity traits at the 5.0 × 10p < 5 × 10–5) in each sex group , most (175) of which were Tier 1 or 2 variants. A statistical test of sex differences in genetic effects revealed no difference in any SNP after accounting for multiple testing , suggesting that all of the identified variants had similar effect sizes between sexes.Of the 1059 identified variants, 181 (17.1%) exhibited high between-sex meta-analysis heterogeneity , no age-dependent effects were identified at the 1059 lead SNPs (Supplementary Data p = 2.01 × 10–3), where the per allele effect became larger as age increased. The second strongest hit was rs550793660 (p = 3.48 × 10–3). In contrast, the allele effect at this SNP tended to become zero as age increased.The 1059 lead variants were evaluated for their age-dependent effects. At the Bonferroni-corrected significance level (The 1059 identified variants included 938 common (MAF > 5%), 97 less common (5%≥MAF > 1%) and 24 rare (MAF ≤ 1%) variants. Variants with a smaller MAF generally had a larger per allele effect size  and 0.476 (s.e 3.63 × 10–3) in females and males, respectively. After removing all variants in the flanking 500 kb regions surrounding each of the 1059 lead variants from raw genotypes, the remaining heritability was estimated to be 0.207 (s.e 2.94 × 10–3) and 0.188 (s.e 3.34 × 10–3) in females and males, respectively, implying that the identified loci collectively explained heritabilities of 0.279 and 0.288 in females and males, respectively. Therefore, ~10% of the heritability at the identified loci remains undefined.The total phenotypic variance explained by the 1059 lead variants was 17.8% before correction for the winner’s curse effect and 15.5% after the correction. Using BOLT-REML20. Statistically significant enrichment was observed for 19 functional categories . Other categories with significant enrichment included coding regions , 3′ UTRs , transcription start sites , and H3K9ac histone marks . Neither the promoter nor the 5′-UTR region showed significant enrichment, although the 5′-UTR region had a high estimate of the enrichment ratio .Applying the stratified LDSC analysis, the GWAS-attributable heritability was partitioned into 24 functional categories21, three tissues were enriched at a Bonferroni-corrected significance level (P < 0.05/205 = 2.44 × 10–4), including cartilage (p = 1.01 × 10–7), chondrocytes (p = 9.48 × 10–5) and uterus (2.02 × 10–4).Using a new function of the stratified LDSC method2 > 0.8). A total of 27,988 CRVs were identified  at each locus were defined as variants with high LD with the lead variant r2 > 0.. A totalIGFBP3 (IGF-Binding Protein 3) gene at 7p12.3, for example, had four lines of supporting evidence. This gene was closest to the lead SNP chr7:46262082 at the locus. One CRV, rs11977526, was associated with its mRNA expression level in skeletal muscle (p = 3.21 × 10–5) and associated with its protein level (p = 3.80 × 10–5) in whole blood. Finally, it was prioritized by DEPICT (p = 5.66 × 10–4). Another example is CXXC5 (CXXC Finger Protein 5) at 5q31.2. The lead SNP rs3822742 is an intron variant in CXXC5. Multiple CRVs around rs3822742, such as rs356452, were associated with CXXC5 mRNA expression in whole blood samples. This gene was also prioritized by both DEPICT and SMR.A number of genes had multiple lines of supporting evidence . The imputation-based and sequencing-based p values were highly concordant  of these 51 missense mutations were predicted to be deleterious by one or more bioinformatics tools, including PolyPhen-2p = 6.77 × 10–6). Evidence of the enrichment was strengthened by comparing 24 missense mutations from 121 rare or less common variants with 27 missense mutations from 938 common variants , suggesting that low-frequency mutations are more likely to play a direct role in changing protein function.Missense mutations were enriched among rare variants. Eight of the 24 rare variants were missense mutations, in clear contrast to the 43 missense mutations among the remaining 1035 variants . The common lead SNP rs1260326  is located in the exon of the GCKR (glucokinase regulator) gene, resulting in an amino acid change from leucine to proline. This SNP was previously reported to be associated with multiple metabolic traits26. GCKR encodes a regulatory protein that inhibits glucokinase in liver and pancreatic islet cells by binding noncovalently to form an inactive complex with the enzyme.2p23.3 (RIN3). The lead SNP rs117068593  results in a change from arginine to cysteine at the 204th amino acid of the protein encoded by the RIN3 (ras and rab interactor 3) gene. This change is predicted to be harmful by PROVEAN, Polyphen-2 and SIFT. This locus was previously reported to be bivariately associated with both bone mass and lean mass at the lead SNP rs75438818. rs754388 is in nearly perfect LD with rs117068593 (r2 = 0.95), implying the same association signal for both SNPs. The gene product of RIN3 is a member of the RIN family of Ras interaction-interference proteins. It functions as a guanine nucleotide exchange for RAB5B and RAB31.14q32.12 (STC2). The lead SNP rs148833559  is a rare mutation whose per allele effect is 10-fold larger than those of the above two common ones. The substitution of arginine to leucine at the 44th amino acid of the STC2  protein is predicted to be harmful by PROVEAN, Polyphen-2 and SIFT. This rare variant was previously reported to be associated with human height27. In a recent study of ALM in middle-aged and older participants of the UKB cohort, the same SNP was identified. The authors also verified that knockdown of STC2 had a significant effect on myotube length in C2C12 cells15.5q35.2 (ZFAT). Two missense SNPs in the ZFAT (zinc finger and AT-hook domain containing) gene are associated with ALMadj. The primary SNP is a common SNP rs12541381 , resulting in a change from proline to serine at the 102nd amino acid of the protein encoded by ZFAT, where the change is predicted to be benign by all four prediction tools. Conditional analysis identified a secondary rare SNP, rs112892337 , which results in a change from serine to cysteine at the 470th amino acid. The change is predicted to be harmful by three prediction tools. ZFAT encodes a protein that likely binds DNA and functions as a transcriptional regulator involved in apoptosis and cell survival.8q24.22 (ENPP2). The lead SNP rs10283100  results in a change from serine to proline at the 545th amino acid of the protein encoded by the ENPP2 (Ectonucleotide pyrophosphatase/phosphodiesterase 2) gene. This change is predicted to be benign by all prediction tools. ENPP2 encodes a protein functioning as both a phosphodiesterase and a phospholipase.8q24.12 , followed by ZBTB38 (p = 1.94 × 10–77), FNDC3B (p = 9.68 × 10–73), EDEM2 (p = 1.76 × 10–72) and CRADD (p = 2.36 × 10–71). Using gene level association statistics as input, a total of 123 gene sets were significant at the geneset significance level , GO:0002062 ‘chondrocyte differentiation’  and GO:0051216 ‘cartilage development’ .A total of 2885 genes were significant at the gene-based GWS level  were randomly selected as the training sample, with the remaining participants  as the validation sample.To assess the ability of the GWAS findings to predict ALM, a polygenic risk score (PRS) analysis was performed on the subset of unrelated white participants from the UKB cohort. Three-quarters of the participants , p < 2.2 × 10–16). Mean phenotype residuals in the top tail were significantly higher than those in the bottom tail of the PRS distribution , again demonstrating the capacity of the present findings to predict DEXA-measured lean mass.Using the same approach, we evaluated the capability of the GWAS finding to predict DEXA-derived ALM. The correlation coefficient between PRS and DEXA-derived ALM was 0.18  , implying that the two traits share a large number of developmental pathways. Furthermore, ALMadj was modestly correlated with BMI . However, the correlation with heel bone mineral density was low . ALMadj was most negatively correlated with BMI-adjusted 2-h glucose  and BMI-adjusted leptin . It was also negatively correlated with body fat . However, this correlation should be interpreted with caution given the following two confounding factors. The first is collider bias, as the trait that we analysed was fat mass-adjusted lean mass. The second is the phenotypic constraint between lean mass and fat mass, as the sum of the two measures defines body weight and body size.To test whether lean mass has a shared genetic aetiology with other diseases and relevant traits, a genetic correlation analysis was performed with LDSCadj is causally linked with other complex diseases and traits, a Mendelian randomization analysis was performed with GSMR29. Ten diseases and eight continuous traits from a variety of categories were chosen for evaluation. The scatter plot for all 18 traits is displayed in Supplemental Fig. –3 (0.05/18), ALMadj was causally associated with five diseases  .To investigate whether ALMadj was also causally associated with 6 metabolic traits, including four serum lipid traits and two blood pressure traits.At the same significance level, ALMp < 5 × 10–9). In particular, more than half of these variants achieved genome-wide significance (p < 5 × 10–9) in one sex and were replicated in the other sex (p < 5 × 10–5). Overall, these >1000 variants accounted for ~15% of ALM variation, again, the largest explainable fraction of variation in lean mass reported so far in a GWAS. Our finding of >1000 variants is expected for a complex trait with high heritability, particularly considering another trait with comparable heritability, height, for which ~700 variants were detected30. Interestingly, the majority of the loci detected in a previous smaller GWAS12 and meta-analysis14 of lean mass were also significant in the present study, providing solid evidence of replication.This study of lean mass with approximately half a million participants, the largest sample used for a GWAS of lean mass so far, was successful. More than 1000 variants were identified at the genome-wide significance scale  and the second stage involved replicating top hits in the other group . As a maximal number of 1000 independent loci was assumed, we could have selected the top 1000 hits from the first stage for replication at the second stage. As a result, a significance level of 5 × 10–5 (0.05/1000) was sufficiently conservative to declare successful replication. In our actual analysis, the numbers of independent loci with p < 5 × 10–5 were 1988 and 1713 in the female and male groups, respectively, which were almost twice the presumed number (n = 1000) of independent loci. This may have inflated the type I error rate for the variants whose p values fell within the range from 5 × 10–5 to 5 × 10–9 (i.e. the Tier 3 variants).To declare an association as significant, we required that the signal not only be significant at the GWS level in the combined analysis but also be significant at the 5 × 10The present study had the following strengths. First, the large sample size of over 400,000 participants is the largest used for a lean mass GWAS to date, offering a unique opportunity to discover loci that were undetected by previous smaller GWASs. Second, instead of analysing the sample as a whole, the two sexes were analysed separately, and then meta-analysis was performed. This may have reduced the statistical power for identifying new loci but allowed us to replicate significant findings between the sexes. Third, via a series of comprehensive downstream analyses annotating the identified SNPs, a deep understanding was achieved for the genetic mechanism underlying ALM and its interplay with other complex traits and diseases.35. Therefore, the present study only discovered the genetic mechanism of sarcopenia from the lean mass perspective, far more than enough to begin understanding the genetic basis of sarcopenia as a whole. Third, physical activity is known to influence lean mass36, the confounding effect of which was not controlled for in the present study.Certain limitations existed in the present study. First, lean mass was measured by the BIA approach, which is not as reliable as the gold standards for quantifying lean mass, such as magnetic resonance imaging and computed tomography, because these latter methods are direct measures. Instead of measuring lean mass directly, BIA derives an estimate of lean mass based on electrical conductivity. Therefore, it can be influenced by the hydration status of the subject. Moreover, the derivation equation of BIA relies on a calibrated reference population, which may not be well validated across populations. Second, the genetic findings for lean mass alone are inadequate for characterizing the full genetic basis of sarcopenia. This is because there is a consensus that sarcopenia is defined not only by low lean mass but also, more importantly, by low muscle strength and poor physical performanceIn summary, we performed a GWAS using approximately a half-million participants for lean mass. The variation (~15%) in lean mass explained by the identified variants represents a significant leap in explaining the hidden heritability of this complex trait using the GWAS approach. The translational value of these findings lies in the importance of lean mass for other complex diseases, such as type 2 diabetes, as our Mendelian randomization analysis showed that ALM is a protective factor for the latter. Overall, our study provides another example in which a GWAS with a very large sample size ultimately and thoroughly delineates the genetic architecture of a complex human trait. This epitomizes the value of big data in human genetic research.The study sample came from the UKB cohort, which is a large prospective cohort of ~500,000 participants from across the United Kingdom, aged between 48 and 73 at recruitment. Ethics approval for the UKB study was obtained from the North West Centre for Research Ethics Committee (11/NW/0382), and informed consent was provided by all participants. This study (project number 41542) was covered by general ethical approval for the UKB study.As careful data quality control is critical to avoid false positives, we only analysed self-reported white individuals (data field 21000). Participants who had a self-reported sex inconsistent with the genetic sex, whose sex chromosome was aneuploid, who had unusually high heterozygosity and/or missing rates or who withdrew their consent were removed. Overall, 487,378 participants were identified to have both phenotypic and genotypic data, 37,135 of whom were excluded. The final sample consisted of 450,243 participants, including 244,730 females and 205,513 males.Body composition was measured by the BIA approach. ALM was quantified by the sum of fat-free mass at the arms (data fields 23121 and 23125) and legs (data fields 23113 and 23117). Appendicular fat mass (AFM) was quantified by the sum of fat mass at the arms (data fields 23120 and 23124) and legs (data fields 23112 and 23116). In each sex, covariates, including AFM, age, age squared, the top 10 principal components, assessment centre (23 levels) and genotyping array (2 levels), were used to adjust raw ALM values. To avoid collider bias due to height, height was not included as a covariate. The adjusted residuals were normalized into inverse quantiles of a standard normal distribution, which were used for subsequent association analysis.A small subset of 4294 participants also received a DEXA body composition scan, and hence, their DEXA-derived ALM was also available. Therefore, raw ALM values derived from DEXA and from BIA were compared in these participants to evaluate phenotypic consistency from these two measurements.2 < 0.3. As a result, ~19.4 million well-imputed variants were retained for subsequent analysis.Genome-wide genotypes were available for all participants at 784,256 genotyped autosome markers and were imputed into UK10K haplotype, 1000 Genomes project phase 3 and Haplotype Reference Consortium reference panels. Genotype imputation was performed by the UKB. A total of ~92 million variants were generated by imputation. We excluded variants with a MAF < 0.1% and with an imputation r17. Upon the completion of sex-specific association analyses, we meta-analysed the summary statistics of the two sexes by an inverse-variance-weighted fixed-effects model with METAL37. The GWS level was set at α = 5 × 10–9 to account for both common and rare variants38. The variants that passed this threshold in the between-sex meta-analysis were then checked for significance in both sexes. Previous studies on human height and body mass index (BMI), two of the most polygenic traits, identified 712 and 536 loci in up to ~700,000 European participants39. By referring to these previous findings, we selected an arbitrary but reasonable number, i.e. 1000, for the maximal number of independent loci for ALMadj and set the suggestive significance level to be 5 × 10–5 (0.05/1000) to account for multiple testing. An association was declared only if the signal was significant at the GWS level (p < 5 × 10–9) in the meta-analysis and was significant at the suggestive level (p < 5 × 10–5) in both sexes.In each sex group, we used BOLT-LMM to perform linear mixed model analysisz-score test with the following equation:βfemale and βmale are regression coefficients for females and males and var(·) are their variances, respectively.Differences in genetic effects between females and males were examined by a two-sided 40, which invokes dbNSFP41 to annotate nonsynonymous SNPs.The identified variants were annotated by Variant Effect Predictor (VEP)42. From the UKB sample, a reference sample of 100,000 unrelated participants was generated for estimating LD patterns. Specifically, KING software43 (with a kinship coefficient cut-off at 0.0884) was used to infer 369,968 unrelated participants, from whom the 100,000 participants of the reference sample were randomly drawn.To identify additional signals in regions of association, approximate joint and conditional association analysis was performed using the GCTA toolp < 5 × 10–5) in both sexes. In addition, each such identified variant was required to be independent of all variants in the lead SNP list (LD r2 < 0.1). The variant with the lowest p value among such identified variants was added to the list of lead variants. Iterations of the conditional analysis were run until no significant signal could be detected.A recursive conditional association analysis was performed. In each iteration, an approximate conditional analysis conditioned on the current list of lead variants was performed in each sex, followed by a between-sex meta-analysis. Again, an association was defined as significant if it achieved both a conditional meta-analysis GWS signal and a conditional suggestive signal  were extracted from the GWAS summary statistics. The significance level for the obesity traits was set at the conventional level of 5.0 × 10–8.GWAS summary statistics for three obesity traits, including BMIThe UKB participants from the South Asian population were analysed to replicate the findings identified in the white population. Specifically, self-reported South Asian participants  were collected. Quality control criteria were the same as those for the main analysis. The final sample consisted of 7452 participants. Phenotype modelling was the same as that for the main analysis, with the only exception that both sexes were analysed together, and sex was used as a covariate. Association analysis was performed again with BOLT-LMM.During the preparation of this manuscript, the UKB released exome-sequencing data on a selected subset of ~50,000 participants. While we are aware of the systematic assembly issue announced by the UKB, we used the current released SPB dataset to assess the imputation accuracy of the present GWAS data by comparing association signals from imputed genotypes to those from direct sequencing. BOLT-LMM involves sophisticated two-step implementation, where the first step fits the model to a set of directly typed variants and the second step examines the association in another set of imputed and/or typed variants. When using BOLT-LMM for comparison, the first-step model parameters fitted to imputed versus sequenced data may incur differences, which is nonrelevant to imputation accuracy. To make the comparison as fair as possible, we used a simple linear model instead. Specifically, we generated an unrelated sample consisting of participants who were both exome-sequenced and genotype-imputed.43 according to genotyped SNPs. The final sample consisted of 45,554 participants, including 24,740 females and 20,814 males.As the QC procedure, we removed participants who were not self-reported as white, whose self-reported sex was inconsistent with their genetic sex, and who withdrew their consent. KING software was again used to select unrelated participantshttp://genome.ucsc.edu/cgi-bin/hgLiftOver). For each participant, variants that were missing in the sequenced data were set to missing in the imputed data as well. In both datasets, genetic association with normalized phenotype residuals was analysed in R.Sequence variant coordinates, which were annotated to the GRCH38 assembly, were converted back to the GRCH37 assembly with Liftover /(mean chi^2−1), where intercept and mean chi^2 are estimates of confounding and the overall association inflation, respectively28.The LDSC method was used to estimate the amount of genomic inflation due to confounding factors such as population stratification and cryptic relatedness19. It was applied to raw genotypic data with or without removing all variants among the total identified loci (each defined as the lead SNP +500 kb flanking region to either side) to estimate respective values of heritability. The difference between the two measures provides an estimate of the heritability explained by the identified loci.BOLT-REML was used to estimate heritability tagged by all the analysed variantsf(1 − f)β2, where f is allele frequency and β is the regression coefficient associated with the variant. To account for the winner’s curse effect, the effect sizes were shrunk with a false discovery rate (FDR)-based method45.The variance explained by all lead variants was calculated as the sum of all individual variant effect sizes, which is defined as the percentage of phenotypic variance explained by the variant and estimated with the formula 2 adj. Specifically, the sample was divided into the following six age strata defined by bins of 5 years: 45 or less , 46–50 , 51–55 , 56–60 , 61–65  and 66 or more . Participants of both sexes within each age stratum were pooled together for analysis. Phenotype modelling within each age stratum was similar to that in the main association analysis, with the only exception that sex was added to the covariates. The association within each age stratum was examined by BOLT-LMM. The generated regression coefficients from all age strata were meta-regressed against the mean age of each stratum to examine the effect of age on the genetic effect. Meta-regression was implemented by a linear regression analysis weighted by the inverse variance of each regression coefficient. Evidence of significance was determined by the Bonferroni-corrected significance level.The identified lead variants were evaluated for their age-dependent effects on ALM20. The analysis was based on the ‘full baseline model’ created by Finucane et al.20 from 24 publicly available main annotations that are not specific to any cell type. The significance level of enrichment was set at p < 2.08 × 10–3 (0.05/24).Stratified LDSC was used to partition heritability from GWAS summary statistics into different functional categories21. This method analyses gene expression data together with GWAS summary statistics, for which the two precompiled gene expression datasets in LDSC were used. The first is the GTEx project dataset46, and the second is the Franke Laboratory dataset47. The GTEx dataset contains 53 tissues with an average of 161 samples per tissue. The Franke Laboratory dataset is an aggregation of publicly available microarray gene expression datasets comprising 37,427 human samples from 152 tissues. A total of 205 (=53 + 152) tissues were classified into nine categories for visualization. Again, evidence of significance was determined by a Bonferroni-corrected significance level of p < 2.44 × 10–4 (0.05/205).Stratified LDSC was also used to assess the enrichment of heritability in specific tissues and cell types2 > 0.8, including the lead variant itself). The LD r2 measure was estimated based on the above 100,000 unrelated reference samples with PLINK48. Six sources of information were used to evaluate a gene’s causality: (1) being nearest to the lead CRV; (2) containing a missense coding CRV; (3) being a target gene for a cis-eQTL CRV; (4) being a target gene for a cis-protein QTL (cis-pQTL) CRV; (5) being prioritized by DEPICT analysis49 and (6) being prioritized by SMR analysis50.At each associated locus, CRVs were defined as variants in strong LD with the lead variant 46. Cis-eQTL information is available for over 50 tissues. We selected skeletal muscle and whole blood for our analysis. Cis-eQTL was searched within a 500 kb distance of the target gene. Significant cis-eQTLs were declared at p < 5 × 10–5.Cis-eQTLs revealed by the GTEx (v7) project were accessed from the GTEx web portal  method53.The intersections of candidate genes prioritized from different sources were plotted using the R package UpSetR54, as implemented on the FUMA website (http://fuma.ctglab.nl/). GWAS meta-analysis summary statistics were mapped to 19,427 protein-coding genes, resulting in 17,788 genes that were covered by at least one SNP. A gene-based association test was performed, taking into account the LD between variants. The significance level was set at a stringent Bonferroni-corrected threshold of 2.81 × 10–6, i.e. 0.05/17,788.Gene-based association analysis was performed with MAGMA v1.6The generated gene-based summary statistics were further used to test for enrichment of associations with specific biological pathways or gene sets. A geneset’s association signal was evaluated by integrating all signals from the genes in the set with MAGMA. A competitive geneset analysis model was used to test whether the genes in a geneset were more strongly associated with the phenotype than other genes.http://software.broadinstitute.org/gsea/msigdb/index.jsp)55. Each gene was assigned to a geneset as annotated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and BioCarta geneset databases and other gene sets curated by domain experts or from the biomedical literature55. A total of 15,481 gene sets were used in this analysis. Significance was set at a Bonferroni-corrected level of 0.05/15,481 = 3.23 × 10–6.Gene sets were obtained through the MSigDB website  were randomly selected as the training sample, and the remaining one quarter of the participants  were selected as the validation sample. Participants of both sexes were pooled together for analysis.56 because of its computational efficiency.Raw phenotypes were adjusted by age, age squared, sex, assessment centre, genotyping array, AFM and the top 10 PCs. The residuals were converted to the standard normal distribution quantiles for downstream analysis. Genetic association analysis was performed with PLINK 257. LDpred infers the posterior mean effect size of each marker by using a prior of effect sizes and LD information from an external reference panel. To save computer memory usage, only the variants achieving a p value of <1 × 10–5 in the training sample were selected and used for prediction in the validation sample. Specifically, the validation sample with original genotypes was used as a reference panel for LD estimation. The number of SNPs used to adjust LD from each side of the target SNP was set to 1000. Other software parameters were set to the default.PRS calculation was conducted with LDpredUsing the same approach, we also evaluated the capability of the GWAS finding to predict DEXA-derived ALM. To accomplish this, we divided the total 450,243 eligible participants into one training sample including participants who did not receive the DEXA scan and one validation sample including participants who received the DEXA scan. The correlation between the risk score and DEXA-derived ALM was examined in the validation sample.28. An online web tool, LDHub (http://ldsc.broadinstitute.org/ldhub/), was used to estimate the genetic correlations between ALMadj and 49 complex traits and diseases. The standalone version of the software was used to estimate the correlations between ALMadj and two additional traits, ALM and whole body lean mass, which are not available in the LDHub GWAS summary statistics collections and were downloaded from the GEFOS consortium website (http://www.gefos.org).To test whether lean mass has a shared genetic aetiology with other diseases and relevant traits, a genetic correlation analysis was performed with the LDSC method28. SNPs were further removed given the following conditions: MAF < 0.01, palindromic strand (A/T or C/G), duplicated ID, or reported sample size less than 60% of the total sample size. LD scores precomputed with the European participants in 1000 Genomes Project were used for calculation.Both the LDHub and standalone analyses adopted the same QC criteria. Specifically, only HapMap3 autosomal SNPs were included to minimize poor imputation qualityadj (as exposure) is causally associated with complex traits and diseases (as outcomes), a Mendelian randomization analysis was performed with GSMR29 on 10 diseases and 8 metabolic traits. The 10 diseases included fracture, type 2 diabetes, asthma, insomnia, inflammatory bowel disease, smoking addiction, coronary artery disease, amyotrophic lateral sclerosis, bipolar disorder and autistic spectrum disorder. Although some of the selected diseases were distantly related to lean mass, they could serve as negative controls for the MR analysis. The metabolic traits included high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, insulin, glucose, diastolic blood pressure and systolic blood pressure.To investigate whether ALMadj at the 5 × 10–9 level, qualified SNPs were included based on the following criteria: concordant alleles between exposure and outcome GWAS summary statistics, nonpalindromic SNPs with certain strands, a MAF > 1%, and allele frequency difference between exposure and outcome GWAS summary statistics <0.2.GWAS summary statistics for each trait were downloaded from the corresponding study’s website. From the list of SNPs associated with ALM56 by using an independence LD threshold r2 < 0.05 and a 1 MB window size. For each pair of studied traits, the clumped independent SNPs were examined for their pleiotropic effects on both exposure and outcome by the HEIDI test29. The significance level for the HEIDI test was set to α = 0.05. After removing pleiotropic SNPs on an outcome-by-outcome basis, the remaining independent SNPs were taken as instrumental variables to test for a causal effect of exposure on outcomes. The estimated causal effect coefficients are approximately equal to the natural log odds ratio for a case–control trait. The MR analysis significance level was set to 2.78 × 10–3 (0.05/18).Independent SNPs were further clumped with PLINK 2Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1–17Reporting Summary


Triticum aestivum L.) achieved over 8 yr of international Elite Spring Wheat Yield Trials (ESWYT), from 2006–2007 (27th ESWYT) to 2014–2015 (34th ESWYT). In total, 426 locations were classified within three main megaenvironments (MEs): ME1 , ME4 (drought-stressed environments), and ME5 (heat-stressed environments). By fitting a factor analytical structure for modeling the genotype × environment (G × E) interaction, we measured GY gains relative to the widely grown cultivar Attila (GYA) and to the local checks (GYLC). Genetic gains for GYA and GYLC across locations were 1.67 and 0.53% (90.1 and 28.7 kg ha–1 yr–1), respectively. In ME1, genetic gains were 1.63 and 0.72% (102.7 and 46.65 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME4, genetic gains were 2.7 and 0.41% (88 and 15.45 kg ha–1 yr–1) for GYA and GYLC, respectively. In ME5, genetic gains were 0.31 and 1.0% (11.28 and 36.6 kg ha–1 yr–1) for GYA and GYLC, respectively. The high GYA in ME1 and ME4 can be partially attributed to yellow rust races that affect Attila. When G × E interactions were not modeled, genetic gains were lower. Analyses showed that CIMMYT’s location at Ciudad Obregon, Mexico, is highly correlated with locations in other countries in ME1. Lines that were top performers in more than one ME and more than one country were identified. CIMMYT’s breeding program continues to deliver improved and widely adapted germplasm for target environments.We calculated the annual genetic gains for grain yield (GY) of wheat ( Such nurseries consist of newly developed lines targeted at certain environments  or at achieving certain breeding goals. Cooperators collect and send the data back to CIMMYT. This information allows breeders to make better crossing and selection decisions. One important key for the global wheat breeding program at CIMMYT is the concept of the ME, introduced in 1988 to describe target environments globally  model  to estimate the yield gains for ESWYT germplasm in ME1, ME4, and ME5; and (ii) to identify the most recent high-yielding lines developed by CIMMYT’s Global Wheat Program for ME1 in the 34th ESWYT.The ESWYT is a nursery annually distributed by CIMMYT to international collaborators, predominantly in developing countries. The main target of ESWYT-selected germplasm is ME1. However, because of CIMMYT’s international collaborative network, ESWYTs are distributed to regions classified within other MEs  because cooperators in those MEs also find locally adapted lines from the ESWYTs.http://www.cimmyt.org/international-wheat-improvement-network-iwin/, accessed 10 Oct. 2016), where the information is freely available or is free on request.The ESWYTs consist of 50 lines selected after 2 yr of testing under optimal irrigation conditions. The lines are tested for an additional 1 to 2 yr under drought and heat stressed conditions at CIMMYT’s experiment station in Ciudad Obregon, Mexico. The first slot of the entries is assigned to a local check (LC), which is often the best commercial variety or a new varietal candidate at each location where the ESWYTs are distributed. The LC entry is selected by the cooperators. Four CIMMYT checks are included in each location and they can change from year to year. However, from 2006 to 2014, spring wheat cultivar Attila  has been consistently included in the trials. The rest of the lines are new CIMMYT-derived cultivars released in different countries. The trial design is an α-lattice with two replicates. Cooperators follow the local management practices. From all the data returned and considered for the analysis, 5% of the trials were treated with fungicides and 74% were fertilized; in 45% of the trials, weeds were controlled with herbicides. For additional information on agronomic practices and the main characteristics of the local checks used for each site we encourage the reader to visit the webpage of the International Wheat Improvement Network (–1) for the 8 yr from 2006–2007 (27th ESWYT) to 2014–2015 (34th ESWYT). Throughout this period, yield and agronomic data for the trials were reported at 426 locations all over the world, which is about a 50% recovery rate for the results. The locations in each year were assigned to a specific ME according to their average historical climate data (minimum temperatures and precipitation). The trial management information provided by cooperators (if available) was also used to assign MEs . The ME assignment was then refined by performing a cluster analysis of each ESWYT to distinguish between high- and low-yielding environments. The locations used for the statistical analysis were those included in ME1, ME4, and ME5.We analyzed the yield data  is:The linear mixed model used for the individual location analysis of the iG is the fixed effects of the genotypes ; jR is the fixed effects of the replicates ; kSB denotes the random effects of the sub-blocks , which are assumed to be independently and identically normal distributed with a mean of zero and a variance of σ2sb(r); and εijk is a random residual assumed to be independently and identically normal distributed with a mean of zero and a variance of σ2ε. Heritability was estimated from the variance components of the individual site analyses to discard all locations with a heritability lower than 0.05 from the combined analyses.where μ is the general mean; g genotypes, s sites and r replicates in each site is shown in Eq. [1]:The mixed model used for fitting the data from X is the incidence matrix for the fixed effects of sites and r, ZgZ, and geZ are the incidence matrices of zeros and ones for the random effects of replicates within sites, genotypes, and G × E interactions, respectively. The vector b denotes the fixed effect of sites; the vectors r, g, ge, and e contain the random effects of replicates within sites, genotypes, G × E interactions, and residuals, respectively, and are assumed to be random and multivariate normally distributed with a zero mean vectors and the variance–covariance matrices R, G, GE, and E, respectively, that is,where Y is:where the variance of the response vector R, G, and E are assumed to have a simple variance–covariance component structure such that R = ∑r ⊗ rI, where ∑r = diag ; Ir is the identity matrix of order r, E=σ2eI ; and G= σ2gI, where σ2g is the genetic variance, assuming that there is no relationship between lines.The variance-covariance matrices GE = ∑ge ⊗ I interaction matrix variance–covariance can be represented as:The jth diagonal element of the s × s matrix σ2gejis the G × E variance σ2ge j in the jth site. The ijth element is the G × E variance covariance ρijσgei σge j between Sites i and j; thus ρij is the correlation of G × E variance effects between Sites i and j. The matrix I is of order g × g and assumes that the lines are not related and the breeding value of each genotype will be predicted only by the value of the empirical response of the genotype itself.where the GE. The most restrictive variance–covariance structure is to assume that GE variances within all sites are equal and that all pairwise correlations between genotypes and between sites are zero. On the other hand, the most liberal structure is the completely unstructured model which assumes matrix GE contains s(s-1)÷ 2 parameters. In this study, we used the FA structure that models covariance among observations in terms of a few hypothetical unobserved factors, which is useful for modeling the matrix GE. The FA model has been extensively used for modeling GE  were obtained for each genotype in the analysis. We expressed the BLUP of the GY of the 10% highest-yielding lines (HYL) of each ESWYT in terms of (i) the spring wheat cultivar Attila (GYA) and in terms of (ii) the LCs. These proportions were regressed on the years to estimate the annual yield gains in relation to Attila and the LC. We also performed multienvironment trial analyses by computing the BLUPs without modeling the G × E interactionand also performed the regression of the best 10% highest yielding lines in each ESWYT.Since the 34th ESWYT was the trial most recently distributed by CIMMYT at the time of this study, it was further analyzed to identify the most recent wheat lines with higher yield potential in the target environment. Site regression analysis, as described by Crossa and Cornelius  and CrosBased on all the data returned from 426 locations throughout the world, 305 locations were analyzed and classified as ME1, ME4, and ME5 on the basis of their climate data and agronomic practices, representing a wide range of environments ; Fig. 1.The average GY of Attila ranged from 2.0 t ha–1 in ME4 (33th ESWYT) to 6.31 t ha–1 in ME1 (31st ESWYT), whereas for LCs, the average GY ranged from 2.6 t ha–1 in ME4 (33rd ESWYT) to 6.76 t ha–1 in ME1 (29th ESWYT). Phenotypic correlations for GY were highly significant between ME1 and ME4 in the 28th, 29th, 31st, 33rd, and 34th ESWYTs . Correla–11 yr–11 compared with the cultivar Attila and 28.7 kg ha–11 yr–11 compared with the LCs, which represents an annual increase of 1.67 and 0.53%, respectively  for the GYLC. Estimates of the GY gains for GYA and GYLC without modeling the G × E interaction with the FA structure were 1.53 and 0.42%, respectively.Megaenvironment 1 had the highest number of locations, with yield data from 180 sites being classified as ME1 , represe–1 yr–1. The GYLC of the HYL had an increase of 0.41% (15.45 kg ha–1 yr–1). Estimates of the GY gains for GYA and GYLC without modeling the G × E interaction with the FA structure were 0.74 and 0.08%, respectively.Seventy locations represented ME4, which is 22.9% of the total analyzed . The HYL–1 yr–1, whereas in terms of the GYLC, the observed significant rate of increase was 1.0% or 36.6 kg ha–1 yr–1 , whereas the correlation between ME1 and ME5 was not significant . The HYL in ME1 showed a GY advantage of 22.2 to 26% and 6.9 to 10.2% over Attila and the LCs, respectively. In ME4, the top five lines displayed a GY of 18.4 to 23.2% and 13.8 to 18.4% higher than Attila and the LCs, respectively. In ME5, the GY advantage of the HYL over Attila was 5.0 to 9.1% and 8.2 to 12.4% over the LCs. Pedigrees and ranks of the 50 genotypes included in the 34th ESWYT can be found in the Supplementary Material. Phenotypic correlations were statistically significant between ME1 and ME4 , which are virulent to the resistance genes Yr9 and Yr27 . These contrasting results can be attributed to the fact that the evaluation periods of the two studies were different and also to the use of the FA model for assessing the GE matrix; the FA model increases the precision of the predicted GY estimates in multienvironmental trials. Nevertheless, another important factor that influences the differences in the results between Sharma et al. . However, Attila was outperformed by germplasm developed later, which was often Attila-derived , is highly correlated with other sites where wheat is grown  ancestry or direct CGIAR lines, creating an estimated economic benefit for CGIAR national partners’ wheat research of US$1.5 to 4.8 billion per year .CIMMYT’s international multilocation testing strategy allows periodic assessment of the yield progress achieved by breeding programs on a global scale. These evaluations of GY progress are a fundamental part of CIMMYT’s activities, since the global scenario requires breeders to set strategies to increase wheat yield potential as a high priority. More precise prediction of genetic values is achieved by modeling the G × E covariance structure by means of a parsimonious model . These analytical methods, together with the high GY performance of newly developed wheat lines, demonstrate the success of CIMMYT’s wheat breeding program in increasing GY genetic gains over the last 8 yr. To speed up cultivar release and information-sharing with cooperators, Singh et al.  proposedFrom our study, we concluded that CIMMYT continues to deliver improved and widely-adapted germplasm to its target environments in developing countries. The average rate of GY increase across the locations in which the ESWYT was distributed from 2006–2007 to 2014–2015 was 1.67% relative to Attila, and 0.53% relative to the LCs. Because Attila has become susceptible to new yellow rust races, it has been replaced by new resistant and high yielding lines in subsequent ESWYTs. In this way, future yield progress can be measured with reference to a genotype without the influence of rust infections.–1, and about 2.5 t ha–1 in developing economies (FAO, Global wheat production in 2014 was more than triple compared with that in 1961 in about the same harvested area FAO, , which iies FAO, . Howeveries FAO, . This yiCIMMYT’s international multilocation testing network is an unprecedented effort aimed at breeding wheat on a global scale (Trethowan and Crossa,


Drosophila melanogaster reared at standardized densities in three temperatures  from a set of lines of the Drosophila Genetic Reference Panel (DGRP). We found little phenotypic variation at the two lower temperatures with more variation at the highest temperature and for plasticity. Males were more affected than females. Despite reasonably large broad-sense heritabilities, a genome-wide association study found little evidence for additive genetic variance and no genetic variants were robustly linked with reproductive performance at specific temperatures or for phenotypic plasticity. We compared results on heat-induced male sterility with other DGRP results on relevant fitness traits measured after abiotic stress and found an association between male susceptibility to sterility and male lifespan reduction following oxidative stress. Our results suggest that sublethal stress during development has profound negative consequences on male adult reproduction, but despite phenotypic variation in a population for this response, there is limited evolutionary potential, either through adaptation to a specific developmental temperature or plasticity in response to developmental heat-induced sterility.Thermal tolerance range, based on temperatures that result in incapacitating effects, influences species’ distributions and has been used to predict species’ response to increasing temperature. Reproductive performance may also be negatively affected at less extreme temperatures, but such sublethal heat-induced sterility has been relatively ignored in studies addressing the potential effects of, and ability of species’ to respond to, predicted climate warming. The few studies examining the link between increased temperature and reproductive performance typically focus on adults, although effects can vary between life history stages. Here we assessed how sublethal heat stress during development impacted subsequent adult fertility and its plasticity, both of which can provide the raw material for evolutionary responses to increased temperature. We quantified phenotypic and genetic variation in fertility of  Drosophila species have latitudinal clines of CTmax  more pronounced at juvenile stages , a set of inbred D. melanogaster lines  used as mates across our experiments, using a wild-type Canton Special (CS) strain . CS and DGRP stock flies were maintained in standard culture vials using cornmeal medium  at 12−h light:12−h dark cycle at 25°C. Mates for experimental individuals were similarly reared at 25°C throughout development, under controlled density conditions , subsequently collected as virgins under CO2 anesthesia, transferred to vials in groups of about 20, stored at 25°C, and were 3–6 day old when used for experiments.We used isogenic, genome-sequenced lines from the DGRP, initiated from a natural population from Raleigh, North Carolina that underwent 20 generations of full-sib mating . ClimateWeather-us.com (2020)g agar, 57.5 g bread syrup, and 360 mL of water, seeded with 100 μL yeast paste upon drying) for 2–3 days prior to egg collection at 25°C. In the morning on the day of egg collections, flies were transferred onto fresh egg laying media for 2–4 h, eggs collected onto mesh screen  and because for each line we simultaneously tested fertility in response to three different temperatures, while strictly controlling egg density in vials generating experimental individuals. We ran 13 blocks, each consisting of 7–11 DGRP lines, with most blocks having 10 lines. To account for block variation, we assessed fertility of CS males at 25°C and 29°C in each block. The mean number of individual males/line/temperature was 24.8 (median 28). See For focal experimental males of each DGRP line, we standardized egg number by placing 2-week old adult flies onto egg laying media  of tested male lines. This subset represented the lines performing well across all temperatures (“high lines” – 19 lines) and lines performing poorly as temperature increases (“low lines” – 21 lines). Performance category was based on a variety of considerations, but firstly on males’ phenotypic response at 29°C and the slope of response (based on random intercepts and slope model described in the below section “Genome-wide association response variables: temperature-specific reproductive performance and plasticity”), then on other considerations such as the number of replicates contributing to the values, and whether we could obtain sufficient number of individuals before the experiment. We ran the female experiment three times, each consisting of 12–15 DGRP lines averaging 27.39 (median 28) females/line/temperature and CS. See To determine the consequences of developmental heat stress on female fertility we used a subset  ∼ Temperature + (1| Block) + (1| DGRP line)]. The model for female data set had an additional fixed factor, Line status, which indicated whether the line was classified as high or low performing based on the reproductive performance of males [cbind  ∼ Temperature × Line status + (1| Block) + (1| DGRP line)]. In these models we included CS to account for variation in each block but model fits with CS included did not perform better than model fits without CS included. This is likely because there was little variation across temperatures in CS performance. Thus, to assess the extent to which DGRP responses were repeatable , we analyzed male data from two subsequent experiments we have run only on the high and low lines, with the experimental design exactly the same as here. That is, we have measured male fertility, under the same conditions, in 40 lines, three different times. We used the corrplot R package to obtain a matrix of Pearson correlations and the Hmisc package to calculate the p-values of the correlation between fertility measures for DGRP lines across the three different sampling periods. We found consistent results at 27°C and especially at 29°C, with correlations across these experiments at the higher temperature ranging from 0.73 to 0.88; low repeatability at 25°C is likely a consequence of little variation across lines  or a failure (no larva produced), taking into account the number of males tested in each line  ∼ (1| Block) + (1| DGRP line)]. The variance components of line and block, along with the residual variance, assumed to be π2/3 (H2 = Vline/(Vline + Vblock + Vresid). We note that although the variance explained by line is assumed to be some form of genetic variance , it is not possible to partition the line variance between these different genetic components. We also note that part of the variance explained by block is likely to include some form of genetic variance but it is not possible to partition the part of block variance that would contribute to genetic components of line variance. Thus, the H2 estimate is likely conservative. We used the package lsmeans  ∼ Temperature + (1| Block) + (Temperature| DGRP line)]. Temperature was a continuous variable in this model and was centered at each temperature treatment  to extract treatment-specific intercepts. The random factor of the DGRP line (Temperature| DGRP line) allowed for the effect of temperature to vary between the lines and provided the line-specific slope estimate. We extracted the model intercepts and slopes with the coef command from the binomial random slope model with a logit link function fitted using the lme4 package . The rate of genotype missingness (–mind in Plink) for each line was set to be not more than 15%, which ensured the retention of all 127 phenotyped lines. A total of 1,465,358 variants were retained after quality control.Quality control of the genomic data was performed in Plink v1.9 . We set We estimated the proportion of variance for male phenotypes explained by all genetic variants, the SNP-based heritability sensu , using tgaussian and the link function set to identity. Wolbachia status of each DGRP line and 5 major inversions present in the DGRP panel  were included as fixed factors. To account for cryptic genetic relatedness, we fitted a centered GRM, created using the GEMMA package, as a random factor in our model. Only the autosomal markers were used to create this matrix. GMMAT performs the GWAS by adding each SNP, in turn, to the model to test for associations between genotype and phenotype. Because some of the genetic markers will be in linkage disequilibrium with one another, we estimated the effective number of tests (Me) in the GWAS, using the Genetic Type 1 error calculator  and DGRP line explained about 0.20 of variance  at 25°C, 0.94 at 27°C, and 0.59 at 29°C  indicating little or no association between male and female fertility in response to thermal stress. Probabilities of reproducing at different temperatures, derived from least square means, were 0.95 for 25°C, 0.93 for 27°C, 0.90 for 29°C, substantially higher at 29°C than for males. Broad-sense heritabilities returned singular fits except for 27°C treatment, estimated to be 0.01.Temperature also significantly affected female reproductive performance, although unlike males, females reproductive performance did not differ between 25°C and 27°C . ReproduSNP-based heritability analyses in GCTA revealed narrow-sense heritability and additive genetic variance of 0 for all traits analyzed , althougp value of P < 1 × 10–5 threshold for reporting significant SNPs  ∼ 7.16  ∼ 5.94. Thus there is no statistical support for the claim that any of the variants that passed the p < 10–5 threshold represents a true positive finding. It is therefore unsurprising that GO enrichment analysis  and fertility at 29°C but all other comparisons between male fertility during developmental heat stress across lines and other traits responsive to abiotic stressors in the DGRP were not significant .D. melanogaster. We found males were affected to a larger degree than females by higher developmental temperatures, and the difference was particularly striking at the highest temperature. The average male fertility for each DGRP line, for both 29°C and for the slope of fertility across all temperatures, was correlated with previous DGRP results on male survival after oxidative stress , or sensitive (low performing) to developmental temperature on female fertility, indicating male and female reproductive performance in response to developmental heat-stress is unlinked. While both sexes showed increased between-line variation after developing at 29°C, male reproductive performance was affected to a much larger degree, both with respect to estimates of thermal fertility limit and its phenotypic plasticity (slope of the reaction norm), than female reproductive performance. Similar sex-specific results were recently reported following adult heat stress in the red flour beetle astaneum .D. subobscura males had reduced sperm motility after experiencing sublethal heat stress during development . Low heritability estimates for thermal performance traits has been suggested to be a function of the intensity and duration of the thermal treatment . In someper se, a trait intimately related to fitness. Other life history traits closely linked to fitness exhibit lower narrow-sense heritabilities than morphological or physiological traits (per se (D. melanogaster outbred and inbred populations (D. serrata (C. elegans mapping panel of recombinant inbred lines found estimates of the heritability of fertility to not be significantly different from 0. Instead, around 40% of variance in fertility was explained by epistasis (Heritability estimates are also impacted by how close the trait is to fitness. We assayed fertility l traits . While l (per se , numerou (per se . This inulations . Low to  serrata , and a spistasis . The stupistasis , similarpistasis .p values below the 1 × 10–5 threshold, suggesting an enrichment of true positive associations (P < 1 × 10–5 in Genome-wide association study analyses did not identify any SNPs that were genome-wide significant for heat-induced sterility and Q-Q plots did not reveal an excess of nominally significant SNPs at lower thresholds. Many DGRP GWAS papers show evidence for a modest excess of loci with ciations . Howeverciations . While wIn summary, we showed that male fertility was less thermally tolerant than female fertility, and that males exhibited within-population variation in the response of fertility to sublethal heat stress during development and in phenotypic plasticity of this response. Lines in which males were susceptible to heat-induced sterility were also more susceptible to oxidative stress and oxidative stress has known negative consequences on sperm quantity and quality. Despite a moderate broad-sense heritability at 29°C, we found no evidence of additive genetic variation although some non-additive genetic variation may be present. Likewise, we observed no genetic variants that could be robustly associated with either temperature-specific fertility consequences, even at the most stressful temperature tested, or its plasticity. Future climate scenarios predict increased likelihood for temperatures that could result in at least portions of the population becoming sterile, at temperatures well below those resulting in reduced performance associated with survival, and our current measure of the impact of developmentally-induced sterility is conservative. Therefore, the impact of thermal fertility limits on population persistence under future climate scenarios will need to be considered to help predict responses to increased temperatures.https://su.figshare.com/articles/Zwoinska_et_al_2020_Heat-induced_sterility_data/12248576/1.All datasets generated and analyzed for this study are included in RS conceived the experiments. MZ and LR collected the data. The data was analyzed by ZW, JS, and LR. MZ and RS wrote the manuscript with contributions from LR and JS. All authors agreed on the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB)  and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder group (PGC-PTSD) . h2snp for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression. PRS was calculated using PRSice and mtCOJO was used to perform conditional analysis. Two genome-wide significant loci associated with childhood maltreatment  were identified in the discovery dataset but were not replicated in PGC-PTSD. h2snp for childhood maltreatment was ~6% and the PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC-PTSD . The most significant genetic correlation of childhood maltreatment was with depressive symptoms , although we show evidence that our top hits may be specific to childhood maltreatment. This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Speculatively, FOXP genes might influence externalizing traits and so be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of phenotypes, may ultimately be useful in in developing targeted treatment and prevention strategies.Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to (1) identify genetic variation associated with self-reported childhood maltreatment, (2) estimate SNP-based heritability ( In addition to being highly prevalent, such childhood abuse is associated with the development of mental disorders, including depression3, and physical ill health, including non-communicable diseases5. Although these associations are now well established, estimates of effect size vary considerably across epidemiological studies, likely reflecting methodological challenges, including uncertainty about how best to assess childhood maltreatment6.The lifetime prevalence of childhood physical, sexual, and emotional maltreatment ranges from 8% to 36%7. Although the idea that childhood maltreatment is heritable may seem counter-intuitive, work on behavior genetics has long documented the heritability of many exposures perceived as environmental. Such heritability is referred to as gene–environment correlation (rGE), and three potential rGE mechanisms to explain the heritability of childhood maltreatment may be posited. First, a “passive” rGE: parental genes affecting parental behavior may influence the childhood environment . Second, an “active” rGE: individuals with genetic variants associated with certain behavioral phenotypes may be more at risk of selecting or creating adverse situations (e.g. risk-taking is heritable and children who are high in risk-taking may be exposed to more trauma)10. Third, an “evocative” rGE: genetic variation may influence child behavior, which in turn is associated with responses to the child . The latter two rGEs are sometimes collectively referred to as non-passive correlations7.A twin-based study found that retrospective reports of childhood maltreatment has a heritability of 6%6, studies have seldom focused on associated genetic variation. The few genetic association studies of childhood maltreatment have only considered variants in candidate genes13 and have had insufficient power to detect the small polygenic effect sizes typically associated with behavioral phenotypes14. Also, there are no studies of the genetic overlap of childhood maltreatment with mental and physical health-related traits, using genome-wide single nucleotide polymorphism (SNP) data. Knowledge of specific genetic variation for childhood maltreatment, the heritability of this phenotype, the polygenic risk, and the genetic overlap with other traits may be useful in informing our understanding of the risk factors, the etiology, and the outcomes of childhood maltreatment. This, in turn, may have implications for the design of prevention and treatment programs for adverse health outcomes. For example, environmental exposures that play a causal role in impacting health outcomes are likely to mediate any observed associations between genetic variants and that health outcome . Thus, preventative strategies would focus on decreasing the risk conferred by the environmental exposure without needing to specifically consider the genetic influences on the health outcome9 (e.g. development of programs for children who have experienced early loss).While a number of key risk factors for childhood maltreatment, including child behavioral characteristics and parental mental health, have been investigated17, providing a unique opportunity to address knowledge gaps in the area of childhood maltreatment. This study aims to: (1) identify genetic variants associated with childhood maltreatment using a genome-wide association study (GWAS) design, (2) quantify the heritability of childhood maltreatment using SNP-based methods, (3) assess the predictive value of polygenic risk scores (PRS) for childhood maltreatment, and (4) assess the degree of genetic overlap of childhood maltreatment with mental and physical health-related phenotypes, and condition the top genome-wide hits from our analyses when such overlap is present.The PGC-PTSD has collaborated to obtain access to well-powered genetic studies of trauma and PTSD that have allowed a number of key genetic questions in this field to be investigated18, and the replication sample comprised 26,290 individuals—a subset of the PGC-PTSD Freeze 1.5 dataset (PGC1.5)17. The details of these studies, including the demographics and instruments used to assess maltreatment can be found in Supplementary Table Nineteen studies, comprising subjects of European ancestry only, were used in this analysis. The discovery dataset consisted of 124,711 individuals with available childhood maltreatment data from the UK Biobank (UKBB)19 were obtained from the participating studies. From this, an overall childhood maltreatment count score of 0–3 was constructed, based on a count of the three abuse categories listed above. An individual was considered to have endorsed a childhood abuse category if they scored in the moderate to extreme range for that particular category, per established cut-offs20  scores on physical, sexual, and emotional abusehttps://sites.google.com/a/broadinstitute.org/ricopili/ and https://github.com/orgs/Nealelab/teams/ricopili) as part of the PGC-PTSD Freeze 2 data analysis21. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. A detailed outline of these methods can be found in ref. 17. Briefly, ancestry was determined with pre-QC genotypes using a SNPweights panel of 10,000 ancestry informative markers from a reference panel comprising 2911 subjects from 71 diverse populations and six continental groups . Samples with estimated > 90% European ancestry were classified as European. Samples were excluded if they had call rates < 98%, deviated from the expected inbreeding coefficient (fhet < −0.2 or >0.2), or had a sex discrepancy between reported and genotypic sex . Markers were excluded if they had call rates < 98%, >2% difference in missing genotypes between PTSD cases and controls, or were monomorphic. Markers with a Hardy–Weinberg equilibrium (HWE) p < 1 × 10−6 in controls were excluded from all subjects. Principal components (PCs) were calculated using the smartPCA algorithm in EIGENSTRAT22. Pre-phasing and phasing was performed using SHAPEIT2 v2.r83723. Imputation was performed with IMPUTE2 v2.2.224 using the 1000 Genomes (1000G) phase 3 data25 as the reference.Study participants from the PGC-PTSD were genotyped with a number of different arrays . A two-stage imputation was performed using the Haplotype Reference Consortium (HRC)27 and the UK10K28 as the reference panels. Variants in the UKBB dataset were filtered to include only those with a minor allele frequency (MAF) of > 1% and an INFO score of > 0.4. Related individuals (third degree and closer) and those with a genotyping call rate < 98% were excluded. Ancestry was determined by 4-means clustering on the first two PCs provided by the UKBB29. Additional principal component analysis was conducted on the European-only data subset using flashpca230.Details regarding the QC, imputation, and ancestry determination of the UKBB dataset can be found in ref. 31 using the plug-in Rserve. To ensure computational efficiency, linear regression models were run for 4 of the larger contributing studies 17. For the NSS1; NSS2; and PPDS studies, age, sex, and 5 PCs were included as covariates in the regression model. For the UKBB dataset, the regression analysis was implemented in BGenie v1.232 with age, sex, 6 PCs, batch, and site included as covariates. All tests performed were two-sided.GWAS analysis was conducted separately for each study. Best-guess genotypes were tested for association to self-reported childhood maltreatment using an ordinal logistic regression model with age, sex, and the first five PCs included as covariates. Variants with a MAF < 0.5% and a genotyping rate < 98% were excluded, for all studies except the UKBB. These analyses were implemented in PLINK 1.9p-values and direction of effect, weighted according to the effective sample size as the analysis scheme, in METAL (v. March 25 2011)33. Effective sample sizes (Neff) for ordinal logistic regressions were calculated as Neff = harmonic mean*n levels of childhood maltreatment, and for linear regressions as Neff = ((1−probability of having a zero score) × mean of nonzero data)34. Heterogeneity across datasets was tested using the Cochran’s Q-test for heterogeneity, also implemented in METAL. Only variants with an INFO score of >0.8 and a conservative MAF of >5% were included in the meta-analysis, except where otherwise indicated in the results. Forest plots were generated for genome-wide significant hits using the R package meta35.As both linear and ordinal logistic models were implemented in the GWASs, which resulted in different effect statistics, fixed effects meta-analysis was conducted across studies using FUnctional Mapping and Annotation (FUMA) v1.3.4c36. Default settings were used and annotations were based on the human genome assembly GRCh37 (hg19). The SNP2GENE module was used to identify genomic risk loci and these were mapped to protein-coding genes within a 10 kb window. An r2 of ≥ 0.6 was used to identify variants in LD with lead SNPs. The 1000G European Phase 3 was used as the reference dataset. Variants were functionally annotated using ANNOVAR, combined dependent depletion (CADD), RegulomeDB (RDB), and chromatin states (only tissues/cells from brains were included). The NHGRI-EBI GWAS catalog was used to determine any previous associations with the identified risk variants. The GTEx v7 brain tissue, RNAseq data from the CommonMind Consortium and the BRAINEAC database were used to perform eQTL mapping for significant SNP–gene pairs (FDR q < 0.05).Genome-wide significant hits identified from the GWAS meta-analysis were annotated using the web-based tool p < 2.63 × 10−6. In addition, gene-based test statistics were used to determine whether specific biological pathways are associated with childhood maltreatment. This was performed for 10,678 curated gene sets and GO terms obtained from MsigDB, using MAGMA. The significance threshold was set at a Bonferroni-corrected threshold of p = 4.68 × 10−6 .A gene-based analysis was performed within FUMA using MAGMA whereby SNPs were mapped to 18,989 protein-coding genes. Genome-wide significance was set at a Bonferroni-corrected threshold 37. This is accomplished by evaluating the relationship between linkage disequilibrium (LD) scores  and SNP test statistics. Using this approach, the LDSR intercept was used to estimate the proportion of inflation in test statistics due to polygenic signal (rather than inflation due to population stratification and cryptic relatedness), with the Eq. (1)—(LDSR intercept−1)/(mean observed chi-square−1)17. Using GWAS summary statistics, SNP-based heritability (h2snp) was calculated, which is one of the applications of LDSR.Linkage disequilibrium score regression (LDSR) is a technique for quantifying polygenicity and confounding, such as population stratification, in GWAS summary statistics38, PRS were calculated in target samples (PGC1.5) based on SNP effect sizes from childhood maltreatment GWAS in non-overlapping discovery/training samples (UKBB). Multiple p-value thresholds (PT)  were generated using the best guess genotype data of target samples. Variants with a MAF < 5% were excluded from the discovery dataset. As a default in PRSice, LD pruning was performed whereby variants were pruned if they were nearby (within 250 kb) and in LD (r2 > 0.1) with the leading variant  in a given region. For this analysis, a rescaled childhood maltreatment phenotype was generated whereby the childhood maltreatment score for each individual was divided by the theoretical maximum score for a given study. Best-fit PRS (at PT = 0.0354) were used to predict childhood maltreatment status as a quantitative trait, adjusting for five PCs and dummy study indicator variables. As women in PGC1.5 experienced significantly more childhood maltreatment than men, we generated PRS in women and men separately. The proportion of variance explained by PRS was estimated as the difference in Nagelkerke’s R2 between the full model (which includes PRS plus covariates) and the null model (which only has the covariates). PRS prediction plots were based on quantiles of PRS, with effect sizes calculated in reference to the lowest quantile. p-values for PRS were derived from a likelihood ratio test comparing the two models. The significance threshold was set at a Bonferroni-corrected threshold of p = 0.006 (0.05/8).Using PRSice v2.1.3.beta39. Cross-cohort genetic correlation (rg) was calculated using LDSR. The web-based interface for LDSR, LD Hub, was used to further calculate pairwise genetic correlations between childhood maltreatment and 247 non-UKBB traits of interest including psychiatric, anthropomorphic, smoking behavior, reproductive, aging, education, autoimmune, and cardio-metabolic categories.Another application of LDSR is the measurement of genetic correlation, i.e. the degree and direction of shared genetic effects between different traits40 feature in GCTA41. Data for major depressive disorder (MDD)42 (from https://www.med.unc.edu/pgc/results-and-downloads) was used to minimize sample overlap with the UK Biobank data. The effect of the correlated trait on childhood maltreatment was estimated using a generalized summary-data based Mendelian randomization analysis of significant LD independent SNPs . The threshold for significance was set at p < 5 × 10−6, due to having less than the required 10 significant independent SNPs at the program default of p < 5 × 10−8, for the correlated trait.To evaluate if the effects of top variants in the UKBB GWAS and meta-analysis were specific to childhood maltreatment, we conditioned childhood maltreatment on genetically correlated traits using the multi-trait conditional and joint analysis (mtCOJO)n = 124,711) and meta-analysis . In our UKBB discovery dataset, we identified two genome-wide significant loci (p < 5 × 10−8) associated with childhood maltreatment (Table p = 4.35 × 10−8) and rs10262462 . These variants remained significant in the meta-analysis (Table p = 3.91 × 10−8) and chromosome 12   n = 24,711 anp-values across studies  plots indicate minimal inflation of FOXP1 and FOXP2 (Table KIF26B (p = 1.67 × 10−7), CNTNAP5 (p = 8.89 × 10−7), and EXOC2 (p = 2.04 × 10−6) from a total of 18,989 protein-coding genes. Gene-set analysis did not reveal any significant pathways associated with childhood maltreatment. Limited functionality of the two risk variants (rs142346759 and rs10262462) was observed  as rs10262462. The other hit observed in the meta-analysis, rs917577, was mapped to an intergenic region on chromosome 12. This variant obtained an RDB categorical score of 2B, indicating that it is likely to affect transcription factor binding. No eQTLs exist in the selected tissue types for this region  for the UKBB discovery dataset and 0.123 (p = 0.002) for PGC1.5. The h2snp for the meta-analysis was 0.057 (p = 4.48 × 10−46).GWAS summary statistics were used to estimate the SR Table . The h2sr2 = 0.0025 (p = 1.8 × 10−15). Participants in the 5th quantile of genetic risk had significantly higher childhood maltreatment scores than subjects in the 1st quantile , PRS were also calculated separately for women and men. When stratified by sex, PRS had significantly higher explanatory power in women (r2 = 0.0053) relative to men (r2 = 0.0015) . To determine whether there is significant genetic overlap between childhood maltreatment and other traits and disorders, pairwise genetic correlations were calculated using the web-based tool LD Hub. A total of 27 significant correlations  were found between childhood maltreatment in the meta-analysis and 247 non-UKBB traits. The top 10 highest genetic correlations are plotted in Fig. rg = 0.70, p = 4.65 × 10−40) having the most significant correlation with childhood maltreatment. There were also positive genetic correlations with “MDD” , “PGC cross-disorder analysis”  and “neuroticism” . Significant negative genetic correlations between childhood maltreatment and “age of first birth” , “subjective well-being” , and “mother’s age at death”  were also observed.All pairwise genetic correlations are listed in Supplementary Table 63 p = 3.8 × 10−6.rg = 0.70, p = 4.65 × 10−40) and MDD  were the most genetically correlated with childhood maltreatment, we conditioned the top hits from our meta-analysis for the effects of MDD using publicly available summary statistics for MDD42. We found that effect sizes for the four top hits for childhood maltreatment remained similar when adjusted for the effects of MDD  the SNP-based estimate of childhood maltreatment is ~6%, (3) PRS of self-reported childhood maltreatment derived from a discovery cohort can significantly predict this phenotype in a target cohort, with 0.25% of variance explained, and (4) childhood maltreatment is significantly genetically correlated with “depressive symptoms” and “MDD”, “neuroticism”, “age of first birth”, and “subjective well-being”, despite showing evidence that our top hits may be specific to childhood maltreatment when conditioning on MDD.The main findings of this study were that (1) variants located in the genes FOXP1 and rs10262462 (chr7q31.1) an intronic variant located in FOXP2. Both genes form part of the forkhead box superfamily of transcription factors which are widely expressed, and which play important roles during development and adulthood. FOXP1 and FOXP2 fall under the FOXP sub-family  which has functions in oncogenic and tumor suppressive pathways43. FOXP2 contains highly conserved genomic sites, including an intronic region within this gene, located about 107 kb downstream from our risk variant44. FOXP1 and FOXP2 have ~60% homology at the amino acid level (https://www.ncbi.nlm.nih.gov/books/NBK7023/) and both proteins have been implicated in cognitive disorders, including expressive language impairment45. In the meta-analysis, we observed an additional genome-wide variant, located in an intergenic region on chromosome 12, but as this variant does not map to a particular gene, its possible biological mechanism is unclear.Two genome-wide loci for childhood maltreatment identified in our discovery dataset were also significant in the meta-analysis: rs142346759 (chr3p13), an intronic variant in FOXP1 has been found to have associations with language impairment, internalizing symptoms, and externalizing symptoms46. FOXP2 has mainly been investigated in regards to speech and language development47, but has also been found to be associated with depression48 and attention deficit hyperactivity disorder (ADHD)49. Further, an intronic variant in the FOXP2 gene, rs727644, has been associated with risk-taking behavior51. While most work on childhood maltreatment has emphasized subsequent risk for mental and physical disorders, it is possible that externalizing behaviors increase risk for childhood trauma52, consistent with a non-passive rGE mechanism. Alternatively, phenotypes such as depression or neuroticism may increase the likelihood of individuals recalling childhood maltreatment54.Notably, variation within FOXP variants and externalizing symptoms, is that genetic factors influence environmental factors indirectly through temperament and behavior9. A second possibility, consistent with the link of FOXP variants to internalizing symptoms and depression, is that genetic factors influence the recall of childhood maltreatment. In particular, retrospective assessment of childhood maltreatment may be limited by recall bias and the respondent’s subjective assessment of the event56. Indeed, a recent systematic review found very low concordance between prospective and retrospective measures of childhood maltreatment57 and those who retrospectively report childhood adversity were at greater risk for having psychopathology than those who prospectively reported childhood maltreatment58.In this study we estimated SNP-based heritability for childhood maltreatment to be ~6%. A first possibility, in line with a link between 7, the same as our SNP-based estimate. As twin-based studies capture latent heritability across the entire genome, these heritability estimates are generally higher than SNP-based heritability estimates, which are limited to common variation and by the number of markers present and tagged on the genotyping array used15. However, in this twin study, when considering each maltreatment category separately, the heritability of childhood physical maltreatment, sexual maltreatment, and neglect was 28%, 0%, and 24%, respectively. This suggests that only physical abuse and neglect are heritable and that sexual abuse is not genetically influenced. It is notable that these twin data, then, do not support an rGE for some abuse types .A twin-based study estimated the heritability of reported childhood maltreatment  to be 6%We show that PRS derived from the UKBB was significantly predictive of childhood maltreatment in PGC1.5, explaining 0.25% of the variance for this exposure. Although the variance explained is relatively modest, we expect greater prediction accuracy with future larger sample sizes. When stratifying by sex, PRS had significantly higher explanatory power in women relative to men. This is expected as women had higher mean self-reported childhood maltreatment scores than men in PGC1.5.60. Depressed individuals with and without trauma exposure differ in associated genetic variation, with trauma-exposed individuals having greater SNP-based heritability, supporting this hypothesis61. On the other hand, polygenic scores for MDD were associated with greater reporting of stressful life events in individuals with MDD62. Indeed, current mood can influence the recall of childhood experiences, and individuals with current depression are at an increased likelihood of reporting early life adversity63. Notably, although we show that childhood maltreatment is significantly genetically correlated with depression, results from our conditional GWAS analysis indicates that our top four hits are specific to self-reported childhood maltreatment, favoring a non-passive rGE mechanism for childhood maltreatment.The PRS results suggests a polygenic architecture for self-reported childhood maltreatment but does not indicate the mechanism by which genetic factors are able to influence this exposure. However, our finding of positive genetic correlations between childhood maltreatment, depressive symptoms, and MDD provides support for the hypothesis that genetic factors predisposing to reporting early life maltreatment overlap with those underlying depression. Genetic correlations between depression, stressful life events, and lifetime trauma have led to the hypothesis that genes increasing risk for the development of depression predispose individuals to entering into adverse environments72. Further investigation is required to delineate the mechanisms that play a role in the relationship between childhood maltreatment and these outcomes.In addition to depression, we found significant positive genetic correlations between childhood maltreatment and “neuroticism” and “PGC cross-disorder analysis” . We observed negative genetic correlations of childhood maltreatment with “age of first birth” and “subjective well-being”. Associations between early life maltreatment and each of these phenotypes have previously been observed73, and the findings reported here may not be generalizable to the general population. However, it is also worth noting that the top hits were significant in the meta-analysis, where additional hits for childhood maltreatment were detected in an intergenic region on chromosome 12. Second, although many of the study sites included in the final meta-analysis utilized the well-validated CTQ, childhood maltreatment was measured in a diversity of ways across the different studies. Thus, our main phenotype was not homogenous and may reflect different aspects of childhood maltreatment in different contributing studies.Our study had a number of limitations that deserve emphasis. First, the genetic correlation between the UKBB and PGC1.5 datasets was only 0.63, indicating differences between the datasets, which possibly explains the non-replication of our top hit and of greater SNP heritability in PGC1.5. The UKBB dataset comprises healthy volunteers who are typically of a higher socioeconomic status and in better overall health than the general population of comparable ageFOXP genes and the polygenic architecture associated with childhood maltreatment may put individuals at greater risk for maltreatment. Alternatively, however, these variants may be associated with a greater likelihood of reporting maltreatment, given the high genetic correlation between childhood maltreatment and depression, and neuroticism. Using the available data, we are unable to indicate definitively which of these explanations is a better one, and it is possible that different mechanisms have more robust explanatory power in accounting for different abuse subtypes as well as different associated psychopathologies. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with a range of different phenotypes, may ultimately be useful in developing targeted treatment and prevention strategies.This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. Variation in The views, opinions, and/or findings contained in this report are those of the authors and should not be construed as official Department of the Army position, policy, or decision, unless so designated by other official documentation. Citations of commercial organizations or trade names in this report do not constitute an official Department of the Army endorsement or approval of the products or services of these organizations.Supplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary FiguresSupplementary Note