Genome-wide association analyses identified significant maternal loci for p,p′-DDE (P = 7.8 × 10−11) in the CYP2B6 gene and for BDE-28 (P = 3.2 × 10−8) near the SH3GL2 gene, both involved in xenobiotic and lipid metabolism. Fetal genetic loci contributed to the levels of BDE-100 (P = 4.6 × 10−8) and PCB187 (P = 2.8 × 10−8), near the potential metabolic genes LOXHD1 and PTPRD, previously implicated in neurodevelopment. Negative associations were observed for BDE-100, BDE153, and the sum of PBDEs with ASD, partly explained by genome-wide additive genetic effects that predicted PBDE levels. Our results support genetic control of midgestational biomarkers for environmental exposures by nonoverlapping maternal and fetal genetic determinants, suggesting that future studies of environmental risk factors should take genetic variation into consideration. The independent influence of fetal genetics supports previous hypotheses that fetal genotypes expressed in placenta can influence maternal physiology and the transplacental transfer of organohalogens.Maternal exposure to environmental pollutants could affect fetal brain development and increase autism spectrum disorder (ASD) risk in conjunction with differential genetic susceptibility. Organohalogen congeners measured in maternal midpregnancy blood samples have recently shown significant, but negative associations with offspring ASD outcome. We report the first large-scale maternal and fetal genetic study of the midpregnancy serum levels of a set of 21 organohalogens in a subset of 790 genotyped women and 764 children collected in California by the Early Markers for Autism (EMA) Project. Levels of PCB (polychlorinated biphenyl) and PBDE (polybrominated diphenyl ether) congeners showed high maternal and fetal estimated SNP-based heritability ( Autism spectrum disorders (ASD) are complex disorders that affect brain development, and are characterized by repetitive behaviors, deficits in use of language and social communication, sensory dysfunction, and restricted interests that emerge during infancy. Available genetic data based on family studies and twin studies have highlighted high heritability for ASD between 40 and 90% . Recent et al. unpublished results, Several organohalogen compounds, such as persistent organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and flame retardants like polybrominated biphenyls (PBBs) and polybrominated diphenyl ethers (PBDEs) widely used in industrial and domestic settings in the past, have been phased out from use in the United States, but due to their persistence continue to be detected in the bloodstream, liver, breast milk, adipose tissue, and placenta , and dopOur primary hypothesis was that the circulating midgestational levels of organohalogens would be driven by common maternal genetic determinants, and that these results could shed light on the observed associations between the organohalogens and ASD. We carried out the first genome-wide maternal SNP-based heritability and association analyses of maternal serum levels of organohalogens and we assessed whether the genome-wide associated genetic factors were also associated with ASD outcome. Additionally, we hypothesized that fetal genetics might be associated with the midgestational maternal circulating levels of organohalogens, actively regulating the maternal–fetal chemical transfer and accumulation.trans-nonachlor (T-NONA)] were measured in maternal serum by gas chromatography isotope dilution high-resolution mass spectrometry as previously described in ng/g lipid weight subtracted for the analytical background detected in blank samples. All concentration data were corrected for the average amount present in blank samples, and three blanks were included in every set of 30 samples as previously described  as reported in P-value < 10−10) using the Hardy–Weinberg exact test statistic. Additionally, we extracted only common SNPs (MAF ≥ 1%). Two high-quality datasets were used in our analysis: the first dataset included 790 maternal samples  and 629,686 genotyped markers and the second dataset included 764 neonatal samples  and 622,716 genotyped markers. Most of these were related pairs of maternal–neonatal samples . The maternal dataset was a subset of those with chemical biomarker levels previously reported . We also carried out genome-wide multi-dimensional scaling (MDS) analysis separately on high-quality markers genotyped in women and in newborns included in our dataset using a pairwise distance genomic matrix and the multi-dimensional scale functions implemented in PLINK software (File S1). The genetic distances among different ancestry groups were highlighted in the PC1 and PC2 chart: most of the women were contained within a distinct ancestry cluster matching their self-reported group (Table S1 in File S1), but 10% of them were between clusters . In order to account for population stratification, we included the first 10 principal coordinates in our analysis. In addition, we calculated a two-sample Mann–Whitney Wilcoxon test in R 3.2.0 (File S1). Non-Hispanic clusters include well-matched proportions of mothers of ASD cases and mothers of controls. Hispanic controls exceed the number of cases, as expected based on lower ASD rates in Hispanic families reported in the literature , five PBDEs , and 11 PCB congeners  for which at least 60% of the study group had values above the limit of detection (LOD), except BDE-28 (only detected in 55% of the study group), were included in this study. Two additional variables for total PBDEs and PCBs were created by summing the individual concentrations of congeners with detection rates >60% (Sum PBDE and Sum PCB), as previously described . Following Lyall et al. (2015) we included several confounding factors, such as maternal country of birth  and age at midpregnancy (15–45 yr old) and offspring gender and month of birth, as well as offspring ASD affection status, maternal educational attainment , and genetic ancestry (the first 10 coordinates).After outlier exclusion, using a threshold of 3 or 4 SD from the mean , we analyzed the effect of available sociodemographic covariates on the levels of BB-153, OCPs, PBDEs, and PCBs with linear regression models in the subset of genotyped women using the R 3.2.0 environment  (Table SN = 284), Caucasian (N = 261), and Asian (N = 116) women were separately analyzed . After ancestry adjustment, the mean value of the residual levels of congeners did not show significant differences among ancestry groups (two-sample Mann–Whitney Wilcoxon test P > 0.1), suggesting that the first 10 principal coordinates accounted for bias due to population stratification (data not shown).We tested the correlation between organohalogens and the first 10 ancestry principal coordinates in three subsets of women selected from distinct ancestry groups. Hispanic  of organohalogen serum levels with a Restricted Maximum Likelihood  encodes the endophilin-1 protein. BDE-100, BDE-153, and SumPBDE, which were negatively and significantly associated with offspring ASD outcome, did not show genome-wide significant association but demonstrated suggestive or nominal association with two SNPs  that map to MICB and TACC1 genes, respectively (Table S6 in File S1).A second maternal SNP was associated with several PBDE congeners: low-frequency variant rs11789653 (MAF = 0.025) reached genome-wide significance for low-detected BDE-28  , with the most significant individual congener effect for PCB28 (P = 3.5 × 10−6). Other loci showed suggestive association (P < 5.0 × 10−7) mainly with PCB congeners (Tables S6 and S7 in File S1): rs75107653  maps near SETMAR and SUMF1 on chromosome 3p26.1; rs4945829  maps adjacent to the CCDC162P gene and near the CEP57L1 and CD164 genes on chromosome 6q21, and rs10500669  is located on chromosome 11p15.4 near the PRKCDBP gene . BB-153 is suggestively associated with a locus on chromosome 18p11.31 , which maps to the DLGAP1 gene .The sum of PCB congeners showed an association near genome-wide significance with the low-frequency SNP rs35560597 that maps near h2g = 0.58, SE = 0.34, P = 0.04) and one for PCB138/158   exceeded maternal estimates (39–93%) but similarly high SEs occurred due to the small sample size . There w = 0.04) . BDE-153 = 0.04) . However = 0.04) .P = 2.8 × 10−8) and suggestively associated across PCBs except PCB28 (File S1). This SNP is located on chromosome 9p24.1 and maps to the PTPRD gene. A second SNP, rs72913475, on chromosome 18q21.1 that maps to the LOXHD1 gene was significantly associated with BDE-100  and showed effects with other PBDEs (File S1). Maternal PBDEs and PCBs were also associated with several additional loci at the suggestive level in offspring DNA (Table S9 in File S1).Fetal genome-wide association of midgestational maternal levels of organohalogens identified one SNP, rs72692916, significantly associated with PCB187  that also showed nominal effects in fetal genetic associations of the same congener: rs7259965 in CYP2B6 , rs11789653 near ADAMTSL1-SH3GL2 , rs75107653 near SETMAR-SUMF1 , and rs10500669 near PRKCDBP  were used. Additionally, we selected one fetal top SNP rs72913475 in LOXDH1 (Table S5 in File S1) that showed nominal effects in maternal genetic associations of BDE-100 . We included the genotypes of each pair of maternal–fetal SNPs in linear models to assess whether maternal and/or fetal genetic factors showed an independent contribution to the association of each respective congener above. The maternal–fetal combined models that include genome-wide maternal-associated SNP and nominal fetal SNP showed attenuated or nonsignificant fetal association and vice versa; the combined model that includes the genome-wide fetal-associated SNP and the same nominal maternal SNP showed only significant fetal association , we selected two maternal SNPs  and one fetal SNP  most strongly associated with congeners related to ASD outcome to determine whether they might explain the negative relationship between PBDEs and ASD. We excluded Sum PBDE from our analysis because we are not able to easily interpret any finding that links the sum of congener levels with ASD outcome. These SNPs were not themselves associated in logistic models with ASD outcome, and they did not account for the association between maternal PBDE congeners and ASD outcome in offspring . No significant ASD by SNP interactions were identified (data not shown). However, given that the three associated SNPs accounted for a small proportion of each PBDE level variance , we calculated that our case–control sample size has a power at 80% (α = 0.05) of detecting an OR approximately below 0.3 (strong protective effect) for ASD per each PBDE unit. Our case–control analysis is thus underpowered to draw strong conclusions about individual SNP–congener relationships because the estimated ORs for ASD per unit were 0.82 and 0.84 for BDE-153 and -100, respectively ]. Additionally, some pairs of PBDE congeners showed genetic correlation significantly higher than linear correlation, indicating that even when the exposure sources and/or the contribution of diet and lifestyle to the pollutant accumulation in the body differ between organohalogens, shared genetic factors can affect the observed circulating levels, e.g., metabolism or lipid levels.We report here the first large-scale genetic study of the maternal peripheral blood levels of several environmentally persistent and potential neurotoxic organohalogen compounds measured in midpregnancy, among a diverse-ancestry population of women. We first assessed whether maternal circulating levels of organohalogens were regulated by maternal genetic factors. For the first time, we found evidence that a large proportion of maternal circulating levels of BB-153, BDE-47, -100, -153, and their sum was significantly controlled by common genetic factors [CYP2B6 locus were found. Our results suggest that the encoded P450 2B6 enzyme may influence the levels of circulating organohalogens, but the function of the associated SNP rs7259965 (or its LD proxies) on enzyme expression or function is unknown. The encoded protein is a member of the cytochrome P450 superfamily of enzymes that catalyze reactions involved in xenobiotic metabolism and drug pharmacokinetics, in synthesis of cholesterol, steroids, and other lipids. Previous evidence of association between CYP genes and ASD or autistic traits has been reported in studies motivated by sex-hormone biosynthesis but not including environmental biomarkers . We did not genotype the published SNPs but based on a 1000 Genome reference panel, our associated SNP (rs7259965) is in low LD with rs7260538 (r2 < 0.4) and in high LD with rs7255374, the second independent associated SNP (r2 between 0.6 and 0.8). Recently, SNPs in the CYP2B6 gene were also associated with the circulating levels of BDE-47 in 1500 elderly individuals  in CYP2B6 and PCB levels in the same Swedish cohort. We did not genotype the published SNP and we only found nominally significant association across PCBs with rs7246465 in the opposite direction (r2 > 0.4 linkage disequilibrium with rs8109848). The PBDE-associated CYP2B6 SNP rs7259965 was only nominally significant for the sum of PCB congeners . Our sample could be underpowered to find CYP2B6 association with PCB levels or the differences in enrolled subjects between the Swedish study and ours  might explain lack of replication, particularly given that PCB levels have shown differences across gender . FST or follistatin is secreted by the liver based on the glucagon-to-insulin ratio . The second BDE-100-associated locus maps to the LOXHD1 gene (Lipoxygenase Homology Domains 1) that encodes a protein that consists of PLAT  domains . In the genotyped subset of the EMA sample studied here, examining continuously modeled PCB levels, we did not find association between maternal PCB congeners and ASD risk (P > 0.1). The difference is due to the models used (continuous levels vs. quartiles), as we observed consistent results using the quartile approach in the genotyped EMA dataset (Table S1 in File S1). Additionally, we confirmed that the inclusion in our regression models of more than two genetic ancestry coordinates and the exclusion of the extreme values of distributions did not affect the association between PCBs and ASD.We confirmed that the serum levels of BDE-100, -153, and the total sum of PBDEs were significantly lower in mothers of ASD-affected children compared to mothers of control children, as reported in the full EMA study . Thus, transplacental transfer of organohalogens during pregnancy may be driven by the fetal genome expressed in placenta   . PreviouMajor strengths of this study include a mother–infant paired sample of varied ancestry, the measurement of chemical environmental pollutants in blood of pregnant women during a gestational time (15–20 wk) relevant to neurodevelopment, and diagnostic confirmation of ASD status. Additionally, the availability of maternal and neonatal genotypes, several confounding factors that predict the levels of exposure, and the ancestry genetic coordinates allowed us to analyze the maternal and neonatal genetic factors that may regulate the levels of circulating environmental chemicals in the maternal bloodstream. Limitations include the reduced power of our sample size to detect moderate heritability and to assess the causality between PBDEs and ASD, the generalization of our findings from the target populations to other populations, the replacement of measurements below the LOD, and the low limit of detection of BDE-28 as well as lack of measurement of other potential confounders such as BMI or diet. Although we have used significance thresholds appropriate for genome-wide testing per biomarker, we have performed testing across a large number of biomarkers and for both maternal and fetal genomes, so any individual results require independent replication.CYP2B6 may be involved in controlling PBDE levels and confirm the association with persistent organochlorine pesticides (OCP) levels. Additional candidate loci, such as PRKCDBP, SUMF1, NDUFS4, and FST, that encode proteins important for hormone, liver, and lipid function may be involved in PCB level regulation. This study also provides evidence that fetal genetic loci near potential metabolic genes such as LOXHD1 and genes previously implicated in neurodevelopment, such as PTPRD, contribute to maternal biomarker levels independent of maternal genotype. The functional relevance of these fetal loci or the second significant maternal locus, near SH3GL2, for circulating chemical levels is unknown. Additional work will be required to uncover novel roles for these genes or other functional elements in these regions. Future studies investigating associations between environmental exposures and metabolic disease or developmental outcomes should therefore consider maternal and fetal genetic determinants of what are thought to be environmental biomarkers.Our evidence suggests that maternal and fetal genetic make-up are important determinants of midgestational maternal circulating levels of some environmental organohalogens. Previous results showed that organohalogen levels were associated with metabolic dysfunction, including obesity (www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.039784/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.


LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.http://ldsc.broadinstitute.org/The web interface and instructions for using LD Hub are available at Bioinformatics online. There is now substantial empirical evidence demonstrating that the majority of complex traits and diseases in humans are influenced by hundreds if not thousands of genetic loci of small effect scattered across the genome as was first predicted a century ago . The advet al previously proposed a different method, LD score regression  of interest; and the web server will automatically test their results against GWAS results from (currently) 173 other traits/diseases. The proposed database and web interface calculates the SNP heritability for the uploaded phenotype(s), and a genetic correlation matrix across traits. LD Hub allows the user to conduct the analysis on specific phenotypes only or perform a hypothesis free screen across all traits in the database . Users hAs summarized in 2.1.1et al, in preparation).We cleaned and harmonized 963 publicly available GWAS summary-level datasets from 36 consortia, which included 82 diseases, 154 complex traits, 576 metabolites and 151 immune markers  that indicates the population ancestry of individuals in the corresponding meta-analysis, as well as a similar field in the LD Score regression results file .Meta-analyses using a GWAS backbone chip only Number of SNPs is large (N > 450 000)Number of individuals is large (N > 5000)Mean Chi-square of the test statistics is larger than 1From this database pool, we chose datasets that fit the following selection criteria:As shown in 2.1.2We pre-calculated LD scores for each SNP using individuals of European ancestry from the 1000 Genomes project . These LThe LD Hub web interface framework was developed using Python Django v1.8 as the LD score regression program is written using Python.2.2.1The LD Hub web interface provides an automatic LD score regression analysis pipeline for users. As shown in User login system: using a Google OAuth system (login by using a Google account)File upload system: To run the LD score analysis pipeline, LD Hub requires upload of a file containing summary results data. In the web interface, we provide an example GWAS results file to illustrate the file format required for successful upload and analysis by LD Hub. To save uploading time, each results file should be a white space delimited zipped text file (LD Hub accepts both tab and/or space delimited zipped text files) in which each row contains the results from a single SNP whilst the columns comprise the following fields:SNP ID (rs number)Effect allele of the SNPAlternate allele of the SNPSample size of each SNP Z-score)A signed summary statistic where the sign refers to the addition of the effect allele SNP Imputation quality Quality control and heritability analysis: To standardize the input file, quality control is automatically performed on the uploaded file.For studies that provide sample MAF, a filter to include SNPs with MAF above 1%.In order to restrict the analysis to well-imputed SNPs, we filter the uploaded SNPs to HapMap3 SNPs  and structural variants.Remove strand-ambiguous SNPs.Remove SNPs whose alleles do not match those in the 1000 Genomes data.Remove SNPs within the major histocompatibility complex (MHC) region (i.e. SNPs between 26Mb and 34Mb on chromosome six) since these often display extreme LD and/or effect sizes. Inclusion of these outlying SNPs would have the potential to bias results of SNP heritability and genetic correlation analyses similar to the inclusion of outliers in traditional regression analyses and would therefore be inappropriate.X12 > 80). The second part of this step is the SNP heritability analysis. The results of this analysis provide a useful indication of whether genetic correlation analysis is likely to be informative .Because outliers can unduly influence the regression, we also removed SNPs with extremely large effect sizes (Z scores (we recommend Z scores > 4 to be interpretable), and large standard errors around the genetic correlation estimate. We advise the user to treat these estimates as unreliable and discard them. In contrast, it is also possible for genetic correlation estimates to exceed one if the analysis involves two very similar traits from large GWAS that exhibit good power (e.g. GWAS of body mass index and obesity). In this case, the true genetic correlation is probably one and the user is advised to interpret the results accordingly.Genetic correlation analysis. The LD Hub pipeline will perform genetic correlation analysis on the uploaded GWAS results after the SNP heritability analysis. Users have the option of selecting which traits they want to include in the analysis. Occasionally LD Hub will produce estimates of the genetic correlation that exceed positive or minus one. Often these estimates will involve GWAS that are small in size, exhibit low SNP heritability Reporting of results. LD Hub returns three output (results) files to the users: (i) A log file with quality control information with regards to the uploaded GWAS summary data; (ii) A ‘h2.log’ file with the SNP heritability information about the uploaded GWAS data and (iii) A ‘rg.results.csv’ file with pair-wise genetic correlations between the uploaded GWAS results and the selected GWASs in LD Hub.2.2.2Another feature of the LD Hub web interface is the heritability and genetic correlation ‘lookup’ function for GWAS results which currently exist in the LD Hub database. In the current version (v1.0), we provide (i) SNP heritability and (ii) genetic correlation results. Both, tables and downloadable links can be found on the Lookup Center webpage.2.2.3We aim to promote sharing of summary GWAS results data and to this extent we have included web links to all the publicly available summary GWAS results data that we have incorporated into LD Hub . In the case of summary results GWAS data that are not publicly available outside LD Hub, users will need to get in touch with the authors of the study themselves to request the data. Users may find this feature useful in conducting other types of SNP comparative study outside the scope of LD Hub as well as following up interesting genetic correlations. We encourage users of LD Hub to upload their GWAS results for curation into the database. We will update the database regularly and allow other users to use the shared data for LD score regression analyses, which will then benefit the whole human genetics community.2 > 80) compared to the rest of the genome, we recommend the exclusion of SNPs in these regions as good practice when using LD Hub , since the inclusion of these SNPs could unduly leverage the regressions and consequently the estimates of genetic correlations and SNP heritability. However, with the exception of autoimmune diseases (SNPs in the MHC can have large effects on certain autoimmune diseases), it is unusual for common traits/diseases to exhibit a single locus of large effect, and thus this potential source of bias should not be an issue for a majority of diseases/traits. For traits that exhibit a single locus of disproportionately large effect (χ2 > 80), we recommend fine-mapping and direct evaluation of overlap in the particular region to assess whether genetic effects are shared, and LD score regression of the rest of the genome with this particular region excluded from analyses. After the abovementioned quality control steps, 1 215 002 SNPs were selected for upload.In order to illustrate the utility of LD Hub, we conduct an analysis using summary results data from a large GWAS of atopic dermatitis (AD) for 40 835  individuals of European ancestry (i.e. the whole discovery set except 23andMe results)  which is extremely heterogeneous, relying often on self-reported data or retrospective recall which will introduce substantial measurement error into the analysis (and hence decrease heritability estimates). Our results strongly suggest that reanalysis using a more precise definition of eczema would result in a cleaner phenotype and consequently increase the number of genome-wide significant loci detected.http://ldsc.broadinstitute.org/), a web-based utility that centralizes and harmonizes summary-level GWAS results data, and automates LD Score regression analysis   utilizes results from 173 different GWAS, which includes the majority of publicly available GWAS summary results suitable for LD Score regression  can return the systematic analysis results to the user within a few hours. A queuing system has been introduced to prevent the server from crashing.As users upload and share their own summary GWAS results, the resource becomes increasingly useful.There are four major advantages of using our database and web interface:www.mrbase.org/beta). This framework could be particularly useful in the dissection of high dimensional molecular networks where the number of possible pair-wise relationships may be extremely large.We envisage LD Hub as a useful hypothesis generating tool, providing an easy method of screening hundreds/thousands of traits for interesting genetic correlations that could subsequently be followed up in further detail by other approaches such as pathway analysis . For exaIn order for estimates of the genetic correlation to be reliable we suggest that traits uploaded meet the following criteria2) Z score is at least > 1.5 Heritability  to return null results.LD Hub is currently designed for GWAS studies involving European populations exclusively. As the number of publicly available GWAS involving other ethnicities increases we will extend LD Hub to include these.For LD Hub, we list a few suggestions/limitations here:In summary, due to the growing availability of summary-level data, our database together with the web interface will maximize the potential of GWAS summary-level data for heritability and genetic correlation analyses.This work was supported by the Medical Research Council (MC_UU_12013/4 and MC_UU_12013/8). This work was also supported by the following grants: 1R01MH101244-02  and 1R01MH107649-01 (Methods for linking GWAS peaks to function in psychiatric disease) D.M.E. is supported by an Australian Research Council Future Fellowship (FT130101709). This work was in part supported by Cancer Research UK programme grant number C18281/A19169 (the Integrative Cancer Epidemiology Programme). P.H. is a Cancer Research UK Population Research Fellow (grant number C52724/A20138).Conflict of Interest: none declared.Supplementary DataClick here for additional data file.


The variant is located in the gene encoding serine protease inhibitor, low levels of which are associated with alpha-1 antitrypsin deficiency which leads to liver disease. We identified novel associations  for dihydroepiandrosterone sulphate, a precursor to major sex-hormones, and for glycated haemoglobin . rs12819124 is nominally associated with risk of type 2 diabetes. Our study offers insights into the genetic architecture of well-known and less well-studied biomarkers.Serum biomarker levels are associated with the risk of complex diseases. Here, we aimed to gain insights into the genetic architecture of biomarker traits which can reflect health status. We performed genome-wide association analyses for twenty serum biomarkers involved in organ function and reproductive health. 9,961 individuals from the UK Household Longitudinal Study were genotyped using the Illumina HumanCoreExome array and variants imputed to the 1000 Genomes Project and UK10K haplotypes. We establish a polygenic heritability for all biomarkers, confirm associations of fifty-four established loci, and identify five novel, replicating associations at genome-wide significance. A low-frequency variant, rs28929474, (beta = 0.04, P = 2 × 10 Serum biomarker levels are associated with the risk of complex diseases and are therefore increasingly used in clinical practice to assist with diagnosis, status monitoring and disease management. Well-known examples include the measurement of lipid levels in the context of cardiovascular disease or liver enzymes and albumin to assess liver function.3. However, the genetic architecture of most health-related biomarkers has not been studied as extensively as for lipids. Alleles identified to be associated with protein biomarkers to date are predominantly common  >5%). This is primarily driven by genotyping technology and composition of arrays or imputation reference panels used to date6. Systematically evaluating the association of low frequency and rare variants can provide new insights regarding the genetic architecture of protein biomarkers.Serum biomarker levels have a polygenic basis. As demonstrated in the case of lipids, identifying genetic associations can provide new insights into disease aetiology which can in turn guide drug discovery and be useful for diagnosis and risk stratificationwww.understandingsociety.ac.uk), also known as Understanding Society. Involving a total of 40,000 households representative of the UK population, UKHLS is the largest panel survey in the world to support social research. A wide range of social, economic, environment, behavioural, attitudinal, physiological and biomedical variables, including a large panel of the most commonly used clinical biomarkers, have been measured for a representative selection of the sample. This study represents a large sample with very homogenous biomarker measurements, in which recruitment and processing have been carried out consistently and following strict protocols.The importance of studying the joint impact of genetic and non-genetic factors on health has been recognised by the UK Household Longitudinal Study , diabetic status (glycated haemoglobin [HbA1c]), insulin-like growth factor 1 [IGF-1]), inflammation (C-reactive protein [CRP]), iron homeostasis , lipid metabolism , liver function , liver and kidney function , and reproductive health  in 9,961 individuals from UKHLS. We also leverage the homogeneity of the sample and its size to estimate the narrow sense heritability which has not yet been quantified for many of these biomarkers.After quality control, genotype data for 525,314 variants were available for 9,961 individuals (Table −3) evidence for a heritable polygenic component (Table 2 = 27.7% (standard error (SE): 0.040) and h2 = 27.1% (SE: 0.084), respectively. Creatinine, GGT, HbA1c, HDL, IGF1, and triglycerides all had estimates larger than 0.20 while the lowest estimate was observed for ferritin . We found statistically significant (p < 5.5 × 10−4) evidence of genome-wide pleiotropy between different biomarkers . Triglyceride levels were also inversely genetically linked with DHEAS (rg = −0.53 p = 4.0 × 10−4). The genetic correlation between two markers of inflammation, C-reactive protein and fibrinogen, was also significant (rg = 0.60 p = 3.2 × 10−8). Finally, the genetic factors for creatinine and urea were positively correlated (rg = 0.56 p = 1.2 × 10−5).For all biomarkers except overall and LDL-cholesterol, alanine transaminase and ferritin there was significant  divided by the effective number of independent traits analysed . Across fifteen biomarkers, we observed associations of 54 previously reported loci at this threshold , associated with levels of fibrinogen, a glycoprotein that assists in the blood clot formation. This variant is a missense mutation and has been previously reported to be associated with fibrinogen levels7 as well as with hypofibrinogenemia and haemorrhage10.The genome-wide significance threshold of P < 3.56 × 10old Fig. . This in2 < 0.01) variants that were associated with biomarker levels at P < 1 × 10−5 and were located more than 500 kb away from any known index variant for the respective biomarker. Using data from up to 25,897 samples from 4 independent studies , a low-frequency variant associated with alanine transaminase (ALT), resides in the serpin family A member 1 (SERPINA1) gene , which is a serine protease inhibitor produced in the liver11. Low levels of this protein are the hallmark of a genetic disorder called alpha-1 antitrypsin deficiency (A1AD), which leads to liver disease12.rs28929474 at 14q32  is located 1 kb upstream of a pseudogene, peptidylprolyl isomerase A pseudogene 7 (PPIAP7). This gene shows a high degree of similarity to cyclophilin A (PPIA), the product of which is involved in a number of biological processes including signal transduction13, inflammation14 and apoptosis15. At the second novel locus associated with DHEAS the index variant, rs2911280 at 16q13 , is located in an intron of the gene encoding c-Maf inducing protein (CMIP), thought to play a role in the T-cell signalling pathway.16We identified two novel replicating associations for DHEAS Fig. . DHEAS i−9) lies in an intron of RP1-228P16.4, a long non-coding RNA. The index variant of the second novel locus, rs761772 at 17q25 , lies within a non-coding exon in the transmembrane channel-like 6 (TMC6) gene and has been shown to affect the expression of TMC6, as well as TNRC6C antisense RNA 1 (TNRC6C-AS1) and transmembrane channel like 8 (TMC8), in cardiac, thyroid, and vascular tissue, as well as whole blood in the GTEx database17.Two novel replicating associations with HbA1c levels were identified Fig. . HbA1c r18. For total and LDL cholesterol, the SNP-based heritability is less than 10% whilst for HDL it is higher at 23.2%. All these estimates represent a lower bound for the narrow sense heritability. Our estimate of the negative genetic correlation between levels of HDL-cholesterol with triglycerides of rg = −0.67 is similar to the estimate derived from an independent study (rg = −0.61)19. High levels of triglycerides are mechanistically related to low levels of HDL21, which could explain the reverse influence of the shared genetic factors on the biomarkers. We show for the first time that polygenic factors for triglyceride are also negatively correlated with DHEAS. There is a statistically significant genetic correlation between CRP and fibrinogen levels, which could be due to shared inflammation pathways. Finally, the genetic correlation we observe between creatinine and urea is a previously unreported and highly biologically plausible finding as both markers are increased in blood when glomerular filtration rate declines, reflecting impaired kidney function. Characterising the genetic architecture of health-related biomarkers in this way is informative with respect to their biology as well as the design of future association studies. While each known locus individually explains only a small proportion of the variance in biomarker levels, these analyses demonstrate that the joint effect of many variants can be much larger.We identify five new biomarker loci, across common and low frequency variants, associated with DHEAS, HbA1c and ALT. We demonstrate polygenic heritability of the majority of biomarkers included in this study and observe large differences in their polygenic component. To our knowledge this is the first report of SNP-based heritability estimates for DHEAS, insulin-like growth factor 1, testosterone and urea. The large sample set with homogeneous biomarker measurements afforded by UKHLS enables reliable estimation for this population. We also identify genetic correlations between several of the biomarkers. Genetic correlation between two traits is an indicator of shared genetic factors and consequently genome-wide pleiotropy. The patterns of heritability and genetic correlations we observe for lipid biomarkers are consistent with previous reports in independent samplesCMIP). CMIP is a highly pleiotropic gene, and is associated with several metabolic traits such as adiponectin and HDL cholesterol levels. Cholesterol is a precursor of DHEA in its synthesis process22.We examined less-well studied health-related biomarkers in addition to routine blood measures used in clinical practice. This made it possible to identify novel associations of common and low frequency variants with DHEAS, HbA1c and ALT. These associations could provide novel biological insights. rs2911280, which we found to be associated with DHEAS, is located in an intron of the gene encoding c-Maf inducing protein , which is largely produced in the liver. Associations of variants in this gene were previously found for cortisol23 and height24. Mutations of this gene can cause alpha-1 antitrypsin deficiency (A1AD) which can lead to an accumulation of aberrant proteins in hepatocytes causing liver damage25. This in turn may elevate levels of ALT, warranting future assessment of the association between this signal and liver-related clinical endpoints.rs28929474 at 14q32 is associated with levels of ALT, which is used in clinical practice to assess liver damage. This variant is located in 26, rs12819124 was associated with HbA1c levels with P = 1.8 × 10−6. The direction of effect was consistent with our findings. rs12819124 was also nominally associated with risk of type 2 diabetes at P = 0.025 using data from the DIAGRAM study27. Moreover, association results from published cohorts suggest a possible pleiotropic association with mental disorders and wellbeing . No HbA1c association results were available for rs761772 in MAGIC. For a proxy SNP, rs429216 (r2 = 0.75), the p-value for the association with HbA1c was in the same direction and reached P = 2.7 × 10−3.We identify two novel associations with HbA1c levels. In a lookup using published data from an independent large-scale meta-analysis by the MAGIC consortium26). The relative gain in power leading to novel locus identification in this study can be attributed to several factors. Two of the newly reported signals have relatively low allele frequency . These were captured here through use of the Illumina HumanCoreExome array and imputation to a comprehensive reference panel consisting of 1000 Genomes combined with the UK10K haplotypes30. A further power advantage was afforded by the homogeneous measurement of biomarkers in UKHLS and in two of the replication studies. Each biomarker was measured using the same assay for each sample, and processed on the same machine, avoiding loss of information due to diverse biomarker assays with different sensitivity, dynamic range and detection limit, potentially leading to power reductions31.The UKHLS sample size is modest compared to some of the previous large-scale GWAS meta-analysis efforts .Understanding Society (https://www.understandingsociety.ac.uk) is a longitudinal panel survey of 40,000 UK households from England, Scotland, Wales and Northern Ireland). Participants are surveyed annually since 2009 and contribute information relating to their socioeconomic circumstances, attitudes, and behaviours via a computer assisted interview. As recruitment was household based, the study contains related individuals. The study includes phenotypical data for a representative sample of participants for a wide range of social and economic indicators as well as a biological sample collection encompassing biometric, physiological, biochemical, and haematological measurements and self-reported medical history and medication use . For each participant non-fasting blood samples were collected through venepuncture, were centrifuged to separate plasma and serum, aliquoted and frozen at −80 °C. DNA has been extracted and stored for genetic analyses.The United Kingdom Household Longitudinal Study, also known as 32, 9888 from Fenland33 34, 2859 from NCDS35. These studies have been described in detail elsewhere. Sample collection were carried out consistently and analysed by the same laboratories for UKHLS, ELSA and NCDS.For replication, data were available for 5533 individuals from ELSAhttps://www.understandingsociety.ac.uk/d/154/7251-UnderstandingSociety-Biomarker-UserGuide-2014.pdf?1418057881). All biomarkers were measured from serum (non-fasting), using a variety of suitable assays, and the majority analysed on a single Roche P module analyser36. On each machine Internal Quality Controls (IQC) were at regular intervals per day. External Quality Assurance (EQA) systems were in place to monitor all tests.In total, biomarker data were successfully obtained from 13,107 eligible individuals who gave consent to give blood samples to be stored for future analysis  were excluded, leaving 525,314 variants passing QC. For typed variants in our GWAS analyses that were brought forward for replication we inspected cluster plots manually using Scattershot 0.75 beta . This array contains a set of >250,000 highly informative genome-wide tagging single nucleotide polymorphisms as well as a panel of functional  exonic markers, including a large proportion of low-frequency (MAF 1–5%) and rare (MAF <1%) variants. Genotype calling was performed with the gencall algorithm using GenomeStudio (Illumina Inc.). For quality control (QC) we excluded individuals based on the following criteria: sample call rate <98%, autosomal heterozygosity outliers (>3SD), gender mismatches, duplicates as established by identity by descent (IBD) analysis (PI_HAT > 0.9). Individuals with non-European ancestry were also excluded. For this we estimated the genomic kinship between all pairs of individuals along with 1000 Genomes Project data. These were converted to distances and subjected to multidimensional scaling. Prior to variant QC, we first mapped all 538,448 variants to the human reference genome build 37. Variants with Hardy-Weinberg equilibrium p-value < 1 × 1037. Prior to imputation, we first pre-phased using SHAPEIT (v2.r). Data were then imputed using IMPUTE2 (v2.3.0), resulting in an initial set of 38,310,212 variants. Variants with an IMPUTE info score <0.4, and variants with a Hardy-Weinberg p-value < 1 × 10−4 were excluded, leaving 26,851,013 variants for analysis.We imputed our genotype data using a combined reference panel consisting of 7,562 haplotypes from the UK10K project and 2,184 haplotypes from 1000 Genomes phase 3. Details regarding the creation of this combined imputation panel are described elsewhereEGAD00010000918. ELSA EGA accession number is EGAC00001000270. NCDS accession number is EGAC00000000001. HRS is available through dbGAP, Study Accession number phs000428.v1.p1. Genotype-phenotype data access for UKHLS, ELSA and NCDS is available by application to Metadac (www.metadac.ac.uk).The UKHLS EGA accession number is 39 (v1.26). We included all variants with minor allele frequency (MAF) > 0.01. We excluded variants with imputation accuracy less than 0.4. We computed the genetic relationship matrix (GRM) for each autosome and then used GCTA to combine them into one matrix. We excluded relatives from the estimation by filtering based on the GRM using a threshold of 0.1 after inspecting the distribution. This led to the exclusion of 672 individuals for this analysis. We also performed bivariate REML analysis in order to estimate genetic correlations between different biomarkers40. We applied a Bonferroni adjusted significance threshold using the effective number of traits for the heritability analyses and using the number of pairs based on the effective number of traits for the genetic correlation analyses.The proportion of trait variance explained by the genotyped and imputed variants was estimated using the GREML method as implemented in the GCTA software−8 was then adjusted for this using the Bonferroni approach: 5 × 10−8/14.05 = 3.56 × 10−9.The association analyses were carried out using a multivariate linear mixed model to account for relatedness as implemented in GEMMA (v0.95). QQ plots and genomic inflation factors, as well as Manhattan plots for traits where we identified novel associations are displayed in Supplementary Fig. −9, two-sided, for testosterone levels, whereas the largest replication sample of 25,000 to 60,000 would be needed for eGFR , for discrete per-variant frequency and (standardised) effect sizes combinations, representative of variants identified in the discovery. Per-trait and per selected variant power analyses showed that we would minimally need 5,000 to 15,000 samples to replicate our variants with P < 3.56 × 10, for dis−5 and at least > 500 Kb away from the nearest known reported index SNP for a given trait. We also took forward independent rare variants with a MAF ≤ 0.01 that were typed and reached P < 1 × 10−5, regardless whether they represented known associations for a given trait. Known index SNPs for all biomarkers analysed in this study were obtained through the GWAS catalog41  > , supplemented by manual searches in PubMed.For replication we selected independent SNPs (LD r2 < 0.1), with MAF > 0.01 and a discovery p-value of P < 1 × 1042, including variant function, the nearest gene IDs within < 500Kb from a given variant, transcript and protein IDs for these genes, as well as conservation scores. It also calculates GWAVA43 scores for non-genic variants amongst other annotations.For annotation of our lead variants we used an in-house annotation script that automatically retrieves variant annotations from ENSEMBLAll methods were performed in accordance with the relevant guidelines and regulations.Supplementary material


P < 1 × 10−6), though none were significant at P < 2.5 × 10−8 after correcting for multiple testing. The most significant SNP was identified in the non-stratified analysis , and is an intronic SNP in TMEM132C. The EQ had a modest but significant narrow-sense heritability . As predicted, based on earlier work, we confirmed a significant female advantage on the EQ . We identified similar SNP heritability and high genetic correlation between the sexes. Also, as predicted, we identified a significant negative genetic correlation between autism and the EQ . We also identified a significant positive genetic correlation between the EQ and risk for schizophrenia , risk for anorexia nervosa , and extraversion . This is the first GWAS of self-reported empathy. The results suggest that the genetic variations associated with empathy also play a role in psychiatric conditions and psychological traits.Empathy is the ability to recognize and respond to the emotional states of other individuals. It is an important psychological process that facilitates navigating social interactions and maintaining relationships, which are important for well-being. Several psychological studies have identified difficulties in both self-report and performance-based measures of empathy in a range of psychiatric conditions. To date, no study has systematically investigated the genetic architecture of empathy using genome-wide association studies (GWAS). Here we report the results of the largest GWAS of empathy to date using a well-validated self-report measure of empathy, the Empathy Quotient (EQ), in 46,861 research participants from 23andMe, Inc. We identify 11 suggestive loci ( It plays an important role in social interaction by facilitating both making sense of other people’s behaviour and in responding appropriately to their behaviour. For these reasons, it is considered a key component of prosocial behaviour, social cooperation, and social cognition2. Aspects of empathy are observed in humans and other animals and is thought to have evolved to support a range of prosocial behaviour and cooperative behaviour2.Empathy is the ability to identify other people’s thoughts, intentions, desires, and feelings, and to respond to others’ mental states with an appropriate emotion1, bipolar disorder3, schizophrenia6, and major depressive disorder8. Two major fractions of empathy include affective empathy  and cognitive empathy . These differences vary between psychiatric conditions: for example, individuals with schizophrenia are more likely to report higher personal distress and emotional contagion6, whereas individuals with autism are likely to show difficulties with cognitive empathy but not affective empathy9. These may reflect causal risk mechanisms where alterations in empathy contribute to higher risk for developing a psychiatric condition. Equally, differences in empathy may also be due to the presence of a psychiatric condition, which may not allow individuals to understand and respond to another person’s mental state effectively.Differences in various fractions of empathy have been observed in several psychiatric conditions including autism12, and a few candidate gene association studies have investigated the role of various genes in empathy15. In addition, several studies have identified a role for the oxytocinergic and the foetal testosterone systems in modulating empathy18. Neuroimaging studies have identified distinct brain regions implicated in different aspects of empathy including the amygdala and the ventromedial prefrontal cortex20. Empathy also shows a marked sex difference: females, on average, score higher on different measures of empathy21. A longitudinal study suggests that this female advantage grows larger with age22. Sex differences in the mind arise from a combination of innate biological differences, cultural, and environmental differences. Studies in infant humans have identified sex differences in the developmental precursors to empathy, such as neonatal preferences to faces over objects23 when environmental and cultural influences are minimal, lending support to the idea that sex differences in empathy are at least partly biological24.Whilst empathy is clearly shaped by early experience, parenting, and other social factors, different lines of evidence suggest that empathy is partly biological. Empathy is modestly heritable (approximately a third of the variance is heritable)25, creativity26, and cognitive ability27. We investigate the genetic architecture of self-reported empathy using the Empathy Quotient (EQ)1. The EQ is listed in the Research Domain Criteria (RDoC)28 as a self-report measure under the domain of ‘Understanding Mental States’ (https://www.nimh.nih.gov/research-priorities/rdoc/units/self-reports/151133.shtml). The EQ is widely used, has excellent test–retest reliability 29, high internal consistency (Cronbach’s α ~0.9)31 and is significantly correlated with factors in the Interpersonal Reactivity Index and the Toronto Empathy Questionnaire, two other measures of empathy, suggesting good concurrent validity32. Psychometric analysis of the EQ in 3334 individuals from the general population suggests that the EQ is a good measure of empathy which can be measured across a single dimension33. By focusing on a self-report measure of empathy, we were able to obtain phenotypic and genetic data from a large number of participants, increasing the statistical power of the study. Previous work from our lab investigated the genetic correlates of cognitive empathy using a specific performance measure—the ‘Reading the Mind in the Eyes’ Test (the Eyes Test)34. The Eyes Test has a low correlation with the EQ (r ~ 0.10)35 and measures only one facet of empathy, namely cognitive empathy. Cognitive empathy is also referred to as employing a ‘theory of mind’, or ‘mentalizing’. The EQ includes items that measure cognitive empathy, but others that measure affective empathy. Yet, other items on the EQ involve both cognitive and affective empathy.Because empathy difficulties are found in a range of psychiatric conditions, it is an important phenotype for investigation. Understanding the biological networks that partly determine empathy may help us understand how it contributes to psychiatric phenotypes, an approach that has been used for other traits such as neuroticismIn this study, we aim to answer three questions: 1. What is the polygenic architecture of empathy? 2. Is empathy genetically correlated to various psychiatric conditions, psychological traits, and education? 3. Is there a genetic contribution to sex differences in empathy? We performed sex-stratified and non-stratified genome-wide association analyses of empathy in research participants from 23andMe, a personalized genetics company. We calculated the narrow-sense heritability explained by all the single-nucleotide polymorphisms (SNPs) tested, and investigated sex differences. Finally, we conducted genetic correlation analyses with six psychiatric conditions , autism, bipolar disorder, major depressive disorder, and schizophrenia), psychological traits, and educational attainment.37. There were 46,861 participants . All participants included in the analyses provided informed consent and answered surveys online according to a human subjects research protocol, which was reviewed and approved by Ethical & Independent Review Services, an AAHRPP -accredited private institutional review board (http://www.eandireview.com). All participants completed the online version of the questionnaire accessible via the research tab of their password-protected 23andMe personal online account. Only participants who were primarily of European ancestry (97% European Ancestry) were selected for the analysis using existing methods38. Unrelated individuals were selected using a segmental identity-by-descent algorithm39.Research participants were drawn from the customer base of 23andMe, Inc., a personal genetics company, and are described in detail elsewhere1 is a self-report measure of empathy, and includes items relevant to both cognitive and affective empathy. It comprises 60 questions and has a good test–retest reliability29. Of these, 20 questions are filler questions, and of the remaining 40 questions, participants can score a maximum of 2 points and a minimum of 0 point per question. Therefore, in this study, participants scored a maximum of 80 and a minimum of 0.The Empathy Quotient (EQ)40 to phase batches of 8000–9000 individuals across chromosomal segments of no more than 10,000 genotyped SNPs, with overlaps of 200 SNPs. SNPs were excluded if they were not in Hardy–Weinberg equilibrium (P < 10−20), had a genotype call rate less than 95%, or had discrepancies in allele frequency compared to the reference European 1000 Genomes data (χ2 P < 10−15). We then imputed each phased segment against all-ethnicity 1000 Genomes haplotypes (excluding monomorphic and singleton sites) using Minimac241, using 5 rounds and 200 states for parameter estimation. We restricted the analyses to only SNPs that had a minor allele frequency of at least 1%. For genotyped SNPs, those present only on platform V1 or in chromosome Y and mitochondrial chromosomes were excluded due to small sample sizes and unreliable genotype calling respectively. Next, using trio data from all research participants in the 23andMe dataset, where available, SNPs that failed a parent offspring transmission test were excluded. For imputed SNPs, we excluded SNPs with average r2 < 0.5 or minimum r2 < 0.3 in any imputation batch, as well as SNPs that had strong evidence of an imputation batch effect. The batch effect test is an F-test from an analysis of variance of the SNP dosages against a factor representing imputation batch; we excluded results with P < 10−50. After quality control, 9,955,952 SNPs were analysed. Genotyping, imputation, and preliminary quality control were performed by 23andMe.DNA extraction and genotyping were performed on saliva samples by the National Genetic Institute, USA. Participants were genotyped on one of four different platforms: V1, V2, V3, and V4. The V1 and V2 platforms have a total of 560,000 SNPs largely based on the Illumina HumanHap550+BeadChip. The V3 platform has 950,000 SNPs based on the Illumina OmniExpress+Beadchip and has custom content to improve the overlap with the V2 platform. The V4 platform is a fully customized array and has about 570,000 SNPs. All samples had a call rate greater than 98.5%. A total of 1,030,430 SNPs (including Insertion/Deletion or InDels) were genotyped across all platforms. Imputation was performed using the March 2012 (v3) release of the 1000 Genomes Phase 1 reference haplotypes. First, we used Beagle (version 3.3.1)P < 2.5 × 10−8 (5 × 10−8/2) to identify significant SNPs. Leading SNPs in each locus were identified after pruning for linkage disequilibrium  using Plink version 1.9. We calculated the variance explained by the top SNPs using a previously used formula42:We performed a linear regression assuming an additive model of genetic effects. Age and sex along with the first five ancestry principal components were included as covariates. Additionally, we performed a male-only and a female-only linear regression analysis to identify sex-specific loci. Since we were performing two independent tests for each trait ), we used a threshold of t is the t-statistic of the regression coefficient, k is the number of covariates, and n is the sample size. Winner’s curse correction was conducted using false discovery rate (FDR) Inverse Quantile Transformation43.44 (https://github.com/bulik/ldsc). Heritability and genetic correlation was performed using extended methods in LDSR45. Difference in heritability between males and females was quantified using46:Z is the Z-score for the difference in heritability for a trait,  is the difference in SNP heritability estimate in males and females, and SE is the standard errors for heritability. Two-tailed P-values were calculated, and reported as significant if P < 0.05. We identified enrichment in genomic functional elements for the traits by partitioning heritability performed in LDSR47. In addition to the baseline partitions we conducted four additional enrichment analyses. Enrichment for central nervous system (CNS)-specific histone marks was conducted using cell type-specific partitioned heritability analysis. For genes that are intolerant to loss-of-function mutations, we identified gene boundaries of genes with probability of loss-of-function intolerance scores >0.9 from the Exome Aggregation Consortium48, and conducted partitioned heritability analysis for all common SNPs within the gene boundaries identified. Similarly, for sex differentially enriched genes, we identified gene boundaries of genes with sex differential expression in cerebral cortex and associates structures (Brain Other)49  coefficient to calculate genomic inflation due to population stratification50 for schizophrenia51, bipolar disorder, major depressive disorder, depressive symptoms, educational attainment (years of schooling), NEO-openness to experience, NEO-conscientiousness, subjective well-being, and neuroticism. For anorexia nervosa52 and ADHD53, we used the data available from the Psychiatric Genomics Consortium (PGC) webpage (https://www.med.unc.edu/pgc/results-and-downloads) to conduct genetic correlation analyses as these are in larger samples and, consequently, have greater statistical power than the datasets available on LD Hub. For autism, we used summary statistics from the PGC-iPSYCH meta-analysis54, details of which are provided in the Supplementary Note. We also conducted genetic correlation for extraversion55 separately as the data were unavailable on LD Hub at the time of analysis. For the anorexia nervosa, autism, and extraversion analyses, the North West European LD scores were used and the intercepts were not constrained as the extent of participant overlap was unknown. We report significant lists if they Bonferroni corrected P < 0.05, which we acknowledge is conservative. For anorexia nervosa and autism, we also conducted genetic correlation analyses using the sex-stratified EQ dataset due to the significant sex differences observed in these conditions. We correct for these using Bonferroni correction, and report significant correlations at P < 0.05.LDSR was also used to calculate genetic correlations. We restricted our primary analyses to only the non-stratified GWAS dataset due to the unavailability of sex-stratified GWAS data in the phenotypes investigated. We calculated initial genetic correlations using LD Hub56, which integrates LD information between SNPs to prioritize genes. Genes were significant if they had Bonferroni corrected P < 0.05. In addition, we also investigated enrichment in Gene Ontology (GO) terms using MAGMA.Gene-based analyses for the non-stratified GWAS were performed using MAGMA57 by dividing the genomes into segments containing approximately 5000 SNPs each. We considered the posterior probability of model 3, i.e., the model wherein SNPs in the same locus influence both the traits. We used a rigorous threshold of posterior probability >0.95 to identify significant loci that influenced both the traits. We conducted pairwise colocalization for empathy (non-stratified), and schizophrenia51, anorexia nervosa52, and autism.Pairwise genome-wide colocalization analyses were conducted using GWAS-PWhttps://ghfc.pasteur.fr/eq/.Summary statistics for the EQ GWAS can be requested directly from 23andMe, and will be made available to qualified researchers subject to the terms of a data transfer agreement with 23andMe that protects the privacy of the 23andMe research participants. Please contact David Hinds (dhinds@23andMe.com) for more information. Top SNPs can be visualized here: n = 46,861) using the EQ, which was normally distributed. A flowchart of the study protocol is shown in Fig. 1. The mean age of the participants was 48.9 (sd = 15.7). Females scored higher than males on the EQ   and a significant sex effect with females scoring higher than males .To understand the genetic architecture of empathy, we collaborated with 23andMe to conduct a GWAS of empathy n = 46,86 using thP = 2.5 × 10−8. LDSR coefficient suggested non-significant genomic inflation due to population stratification  in the three GWAS. The most significant SNP was identified in the non-stratified analysis , and is an intronic SNP in TMEM132C. Regional association plots for all suggestive loci are provided in Supplementary Figure We conducted three GWAS analyses: a male-only analysis, a female-only analysis, and a non-stratified analysis, using a linear regression model with age and the first four ancestry principal components as covariates (Methods). We corrected for the three different tests using a conservative threshold of P = 0.54; two-sided binomial sign test).To investigate if the top SNPs from the EQ also contribute to cognitive empathy as measured by the Eyes Test, we conducted SNP lookup of all the 11 suggestive loci in the Eyes Test GWAS. None of the 11 loci were significant in the Eyes Test GWAS, and only 7 out of the 11 SNPs had concordant effect directions in the two traits  and FBN2 (P = 1.68 × 10−6)  and identified a heritability of 0.11 ± 0.014 for the EQ (P = 1.7 × 10−14) .We used LDSR14) Fig. . Partiti14) Fig. . We also58, autism60, and developmental disorders61, conditions that are often accompanied by difficulties in social behaviour and empathy. We investigated if there was a significant enrichment in GWAS signal for the EQ in these genes that are extremely intolerant to loss-of-function variation. We did not identify a significant enrichment after correction for multiple testing .Recent studies have identified an enrichment of associations in or near genes that are extremely intolerant to loss-of-function variation in schizophrenia62 may reflect genetic as well as non-genetic factors 63. In our dataset, there was a significant female advantage on the EQ (P < 2 × 10−16 Cohen’s d = 0.65) , indicating a high degree of similarity in the genetic architecture of the traits in males and females. This was not significantly different from 1 . We investigated the heterogeneity in the 11 SNPs of suggestive significance in both the sexes using Cochran’s Q-test, and did not identify significant heterogeneity  or females .Sex differences may also arise by differential expression of specific genes in different neural tissues at different developmental stagesrg = −0.27 ± 0.07, P = 1.63 × 10−4), EQ-schizophrenia  and EQ-anorexia nervosa   with six psychiatric conditions  and schizophrenia), six psychological traits , and educational attainment   and the EQ in males (EQ-M) and autism .As anorexia nervosa is primarily diagnosed in women, and autism is primarily diagnosed in men, we further tested sex-specific correlations. After Bonferroni correction, we identified significant genetic correlations between the EQ in females (EQ-F) and anorexia . Additionally, we identified two nominally significant correlations: the EQ with subjective well-being  and NEO-conscientiousness  34, mirroring previous reported estimates of phenotypic correlation in the general population35 and estimates in our database from 916 neurotypical adults .With psychological traits, we identified one significant correlation after Bonferroni correction: EQ with extraversion . We did not identify any expression quantitative trait loci in this region in neural tissues. These results are preliminary and a cautious interpretation is warranted as the probability is influenced by the modest power of both the GWAS57.As there were significant genetic correlations between the EQ, anorexia nervosa schizophrenia, and autism, we investigated if there are genomic regions that influence both empathy and one of the psychiatric conditions  by estimating the Bayesian posterior probability. We did not identify any regions associated with empathy and the three conditions. The most significant region identified in this analysis was in Chr11p12, posterior probability = 0.78 . Males and females perform differently on the tests, but there was limited evidence of sex-specific genetic architecture.This is the first GWAS to investigate the genetic architecture of self-reported empathy. We identified four significant genetic correlations with the EQ and psychiatric conditions and psychological traits , providing insights into the shared genetic architecture. Although we did not identify any significant SNPs after correcting for multiple testing at 68. This is likely to be driven by difficulties in understanding the mental states of others rather than responding to them. We also identified significant genetic correlations for the EQ with schizophrenia and anorexia nervosa. The empirical literature in general report deficits in cognitive empathy69, but preserved or stronger affective empathy69 and emotional contagion/personal distress6 in individuals with schizophrenia compared to controls. Studies with anorexia nervosa, on the other hand, have yielded mixed results. Some studies suggest preserved empathy70, some identify reduced cognitive empathy73, and others identify greater emotional contagion/personal distress74 in individuals with anorexia nervosa compared to controls. These studies are typically conducted in small samples, which may explain the different results in these heterogeneous conditions. Our results suggest that genetic variants associated with self-reported empathy slightly increase the risk for schizophrenia and anorexia nervosa; the latter remained significant even after using the females-only EQ dataset. A previous study34 identified a significant genetic correlation between cognitive empathy (measured using the Eyes Test75) and anorexia nervosa, underscoring the importance of empathy as a genetic risk factor in anorexia nervosa. However, both cognitive empathy and anorexia nervosa are positively correlated with educational attainment76, and it is possible that the correlation between cognitive empathy and anorexia nervosa may be mediated by educational attainment.We identified a significant negative genetic correlation between the EQ and autism. Several studies have identified lower self-reported empathy in individuals with autism, and our results mirror these studiesrg = 0.23 ± 0.06)76, and it is possible that the pleiotropy between these two conditions may, in part, be mediated by genetic variants that contribute to empathy. This needs to be tested. Together with the GWAS on cognitive empathy34, this study provides evidence for the distinct roles of different social processes in various psychiatric conditions.Here, self-reported empathy is not genetically correlated with educational attainment. Further, while cognitive empathy was not correlated with schizophrenia, self-reported empathy was positively and significantly correlated with schizophrenia, suggesting distinct roles for the two phenotypes in neuropsychiatry. Schizophrenia and anorexia share significant positive genetic correlation 78. Our analysis therefore suggests that a third of the heritability can be attributed to common genetic variants. Like IQ79, the heritability of empathy and prosociality behaviour changes with age10. We did not investigate the effect of age on heritability in our study.This is also the first study to provide estimates of additive heritability explained by all the SNPs tested for self-reported empathy, and approximately 11% of variance was explained by SNPs. One study, investigating the heritability of the reduced EQ (18 items) in 250 twin pairs, identified a heritability of 0.3264. Investigating across different tissues and developmental time points in well-powered gene expression datasets will help better understand sex differences in empathy.We did not find any significant differences in heritability between males and females. Further, the male–female genetic correlation was high and not statistically different from 1. Despite the high genetic correlation, sex-specific correlations with anorexia nervosa were significant only for the females-only GWAS dataset. This suggests that the sex-specific genetic component of empathy can contribute differentially to psychiatric conditions. Several other factors may explain the observed phenotypic sex difference. For example, genetic variants for empathy may be enriched in sex-specific gene expression pathways. We conducted preliminary analysis by investigating if there is an enrichment in sex differentially expressed genes in cortical tissue samples, but did not find significant enrichment. However, sex-specific gene expression is a dynamic process with both spatial and developmental differencesThere are a few limitations that need to be taken into consideration in interpreting our results. The EQ is a self-report measure, and while it has excellent psychometric properties and construct validity, it is unclear how much of the intrinsic biological variation in this trait is captured by it. Further, while this is the largest GWAS to date of self-reported empathy, it still is only modestly powered, reflected in our inability to identify genome-wide significant loci. This modest statistical power influences subsequent analysis, and we highlight several nominally significant results for further investigation in larger datasets.In conclusion, the current study provides the first narrow-sense heritability for empathy. While there is a highly significant difference on the EQ between males and females, heritability is similar, with a high genetic correlation between the sexes. We also identified significant genetic correlations between empathy and some psychiatric conditions and psychological traits, including autism. This global view of the genomic architecture of empathy will allow us to better understand psychiatric conditions, and improve our knowledge of the biological bases of neurodiversity in humans.Supplementary InformationSupplementary Tables