Supplemental Digital Content is available in the text. Drug effects can be investigated through natural variation in the genes for their protein targets. The present study aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are among the most commonly used medications worldwide.Genetic proxies for the effect of antihypertensive drug classes were identified as variants in the genes for the corresponding targets that associated with systolic blood pressure at genome-wide significance. Mendelian randomization estimates for drug effects on coronary heart disease and stroke risk were compared with randomized, controlled trial results. A phenome-wide association study in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University biobank (BioVU) and in observational analysis of the UK Biobank.Suitable genetic proxies for angiotensin-converting enzyme inhibitors, β-blockers, and calcium channel blockers (CCBs) were identified. Mendelian randomization estimates for their effect on coronary heart disease and stroke risk, respectively, were comparable to results from randomized, controlled trials against placebo. A phenome-wide association study in the UK Biobank identified an association of the CCB standardized genetic risk score with increased risk of diverticulosis , with a consistent estimate found in BioVU . Cox regression analysis of drug use in the UK Biobank suggested that this association was specific to nondihydropyridine CCBs  but not dihydropyridine CCBs .Genetic variants can be used to explore the efficacy and side effects of antihypertensive medications. The identified potential effect of nondihydropyridine CCBs on diverticulosis risk could have clinical implications and warrants further investigation. This work identifies genetic variants that serve as proxies for the effect of angiotensin-converting enzyme inhibitor, β-blocker, and calcium channel blocker antihypertensive drugs.Mendelian randomization using the genetic proxies for each respective drug class provides estimates consistent with those of randomized, controlled trials against placebo for effects on risk of coronary heart disease and stroke.Phenome-wide association study identifies diverticulosis as a previously unreported possible side effect of calcium channel blockers, with observational analysis further supporting an association between nondihydropyridine calcium channel blocker use and increased risk of diverticulosis.Any increase in the risk of diverticulosis related to use of nondihydropyridine calcium channel blockers could have notable consequences and warrants further study.No other potential side effects of angiotensin-converting enzyme inhibitors, β-blockers, or calcium channel blockers were identified.1 making hypertension a leading cause of mortality and morbidity. Blood pressure lowering through lifestyle modification or pharmacological treatment can significantly decrease cardiovascular risk, with every 10 mm Hg reduction estimated to decrease risk of all-cause mortality by 13%.2In 2015, the 874 million adults worldwide estimated to have a systolic blood pressure (SBP) of ≥140 mm Hg accounted for 106 deaths per 100 000 and loss of 143 million disability-adjusted life-years,3 However, RCTs based on clinical outcomes have limitations4; they are largely restricted to older or high-risk patients and have a relatively short duration of follow-up, rarely beyond 5 years.5 Therefore, recommendations for treatment are often based on extrapolation of the available evidence, with known side effects frequently limited to relatively common outcomes captured in RCTs.6 At the same time, particular drug treatments for hypertension may have beneficial effects beyond their blood pressure–lowering properties,6 thus offering potential for repurposing. However, observational research used to study such opportunities suffers from well-characterized biases, including confounding by indication.7The pharmacological treatment of hypertension is founded on strong evidence, underpinned by a large number of outcome-based randomized, controlled trials (RCTs) that have identified several drug classes to be effective for lowering blood pressure.7 In this study, human genetic variants within genes corresponding to the targets of common pharmacological agents for hypertension were first identified to serve as a proxy for the effects of these treatments. Second, the validity of this approach for studying the effects of these drugs was investigated by exploring consistency in mendelian randomization (MR) estimates for their effect on coronary heart disease (CHD) and stroke risk with corresponding RCT findings. Finally, to offer insight into their adverse effect profiles and repurposing potential, phenome-wide association study (PheWAS) analyses were undertaken with replication in an external dataset, as well as further investigation in observational analysis of drug use.With the growing availability of genome-wide association study (GWAS) meta-analyses, it is becoming increasingly feasible to study drug effects by investigating genetic variants in the genes of their protein targets, as has previously been applied to lipid-lowering drugs.online-only Data Supplement, and the web links provided. UK Biobank data were accessed through application 236. Relevant ethical approval and participant consent were already obtained in all studies that contributed data to this work. Statistical analysis was undertaken with R version 3.4.1  and Stata 14.2 (StataCorp LP).All supporting data are available within the article, the 6: angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers, β-blockers (BB), calcium channel blockers (CCB) and thiazide diuretic agents. Genes encoding the targets of these drugs related to effects on blood pressure were identified using the DrugBank database,8 with promoter and enhancer regions identified using the GeneHancer database in the GeneCards online platform (version 4.7).9 Genetic association estimates for SBP were obtained from a GWAS meta-analysis of 757 601 individuals with European ancestry drawn from the UK Biobank and the International Consortium of Blood Pressure GWAS meta-analysis,10 where correction was made for antihypertensive medication use by adding 15 mm Hg to the SBP of participants receiving medication, with further adjustment for body mass index.10 In sensitivity analyses, a GWAS of SBP on ≈337 000 white British individuals in the UK Biobank was also used, without correction for medication use or adjustment for body mass index.11 Genetic variants to serve as proxies  for the effect of lower SBP through antihypertensive drug targets were selected as single-nucleotide polymorphisms (SNPs) in corresponding genes, promoter regions, or enhancers that were associated with SBP at genome-wide significance (P<5×10–8) and clumped to a linkage disequilibrium (LD) threshold of r2<0.1 using the 1000G European reference panel. This approach does not distinguish between selection of loss-of-function variants or those related to gene expression. The R2 and F statistics were used to estimate the variance in SBP explained and the strength of each SNP, respectively.12Common antihypertensive drugs were selected for study on the basis of recent consensus guidelines13 Estimates for stroke risk were obtained from the MEGASTROKE Consortium’s transethnic meta-analysis of 67 162 cases of any stroke and 454 450 control subjects.14 Details for the MR analyses are provided in the online-only Data Supplement Methods. To allow comparison with RCT results, all MR estimates were scaled to the SBP-lowering effect of their respective drug class as measured in these RCTs.3MR uses randomly allocated genetic variants related to an exposure of interest to study the effect of that exposure on a given outcome. In this study, the exposure of interest was SBP lowering through a particular antihypertensive drug class. All antihypertensive drug classes for which SNPs were identified as proxies using the larger SBP GWAS were taken forward to MR analysis investigating their effect on CHD and stroke risk. Genetic association estimates for CHD were obtained from the CARDIoGRAMplusC4D  Consortium’s 1000 Genomes–based transethnic meta-analysis of 60 801 case subjects and 123 504 control subjects.r2>0.8 (using a 1000G reference panel) were also associated at genome-wide significance (P<5×10−8) with traits that may potentially be exerting such pleiotropy,15 and any such SNPs were excluded in sensitivity analyses. PhenoScanner includes SNP-phenotype associations identified in analysis of UK Biobank data.11 Statistical evaluations of pleiotropy were also incorporated where multiple genetic variants were available to serve as proxies for the drug effect16 and are detailed in the Methods in the online-only Data Supplement.The MR estimates can be biased if any of the genetic variants used affect the outcome under consideration through a pleiotropic pathway that is independent of the drug effect for which they serve as proxies. The PhenoScanner curated database of publicly available SNP-phenotype associations  was used to explore whether any of the selected SNPs or proxies with LD 17 served as the cohort for the PheWAS investigating drug side effects and repurposing opportunities. The participants provided self-reported information, with blood samples collected for biochemical tests and genotyping and physical measurements performed as described previously.17 Individuals were linked retrospectively and prospectively to the National Health Service’s Hospital Episode Statistics database.The UK Biobank, a prospective study comprising approximately half a million middle-aged individuals,18 and standardized to have a mean of 0 and an SD of 1 across all individuals. The 9th and 10th revisions of the International Classification of Diseases were used to define cases based on inpatient Hospital Episode Statistics data. The phecode grouping system was used to align diagnoses used in clinical practice with genomic analysis.19 A series of case-control groups were generated for each phecode, with control subjects identified as individuals with no record of the respective outcome and its related phecodes.19 Analysis was performed with logistic regression after adjustment for age, sex, and first 4 genetic principal components. Only outcomes that had a minimum of 200 cases were considered, to maintain sufficient statistical power to identify associations with common variants.20 A 5% threshold with the false-discovery rate method was used in ascertaining the statistical significance of associations, to correct for multiple testing of correlated phenotypes. As for the MR analysis, sensitivity analyses were performed using genetic association estimates derived from the SBP GWAS that did not correct for medication use or adjust for body mass index, and after the exclusion of any SNPs with potentially pleiotropic associations at genome-wide significance that were identified with PhenoScanner.15PheWAS was restricted to participants of self-reported European descent, with random exclusion of 1 participant from each pair of relatives based on a kinship coefficient >0.0884. For antihypertensive drugs for which genetic variants were identified to serve as proxies, PLINK was used to construct a genetic risk score (GRS) for each individual, weighted for the SBP-lowering effect of each participating SNP,r2<0.001; Table I in the online-only Data Supplement). Compared with the investigation of antihypertensive drug targets, a more stringent LD threshold was used, because variants for SBP were selected from throughout the genome rather than any particular locus. The proportion of such permutation analyses that have a consistent direction of effect and P value lower than in the main PheWAS analysis would serve as an adjusted P value of the null hypothesis. Further study of any PheWAS associations significant at a false-discovery rate threshold of 5% for noncardiovascular conditions was also undertaken in the Vanderbilt University Biobank (BioVU), for which genetic data on ≈50 000 individuals are linked to a deidentified electronic health record system.21 Similar to the main PheWAS, a standardized GRS was constructed, and logistic regression with the outcome was performed after adjustment for age, sex, and first 3 principal components. The analysis was restricted to individuals identified as white, with control subjects based on the same exclusions as the main PheWAS. Results between the UK Biobank and BioVU analysis were pooled by use of a fixed-effects meta-analysis model.PheWAS associations for noncardiovascular conditions were investigated for relation to SBP more generally using a permutation-based approach that repeated association analyses 1000 times, with the standardized GRS created on each instance using a matched number of randomly sampled SBP-related SNPs from throughout the genome , angiotensin receptor blockers (AGTR1), BBs (ADRB1), and thiazide diuretic agents (SLC12A3), and 11 genes for CCBs  encoding the different calcium channel subunits related to effects on blood pressure. The CACNA1F gene is located on the X chromosome, and SNPs corresponding to this region were not available. Using the predefined selection criteria, there was 1 SNP identified for ACE inhibitors, 6 for BBs, and 24 for CCBs (Tables III through V in the online-only Data Supplement). The larger number of SNPs and correspondingly greater proportion of variation in SBP explained for CCBs was related to the availability of more genes from which to identify variants. The F statistic for SNPs ranged from 54 to 534 (Tables III through V in the online-only Data Supplement), consistent with a low risk of weak instrument bias.12The genes and enhancer and promoter regions corresponding to the targets of each antihypertensive drug class are shown in 3 MR analysis using the single genetic variant identified for ACE inhibitors showed a protective effect on stroke  but not CHD risk . The main MR analysis using the 6 variants for BBs identified a protective effect on CHD risk  but not stroke risk . For CCBs, the main MR analysis using the 24 SNPs identified a protective effect on both CHD risk  and stroke risk . Similar results for all drug classes were obtained when the incidence of CHD and stroke was considered to be 1%, 5%, and 10% (Table VI in the online-only Data Supplement). The MR estimates had overlapping 95% CIs to those from RCTs of these drugs versus placebo3 .To allow comparison with RCT meta-analysis effect estimates, MR results for each drug class were scaled to their respective SBP-lowering effect in these studies. Thus, for ACE inhibitors, MR estimates are given per 21.14 mm Hg decrease, for BBs per 9.51 mm Hg decrease, and for CCBs per 8.90 mm Hg decrease.International Classification of Diseases, 9th Revision and 10th Revision, to phecodes, data for 424 439 individuals across 909 distinct phenotypes were available for PheWAS analysis. Details of the number of phenotypes and cases per disease category are provided in the Table, with the number of cases and controls for each outcome in Tables X through XVI in the online-only Data Supplement. Using the ACE inhibitor, BB, and CCB standardized GRS, the respective PheWAS analyses revealed associations with hypertension and related cardiovascular disease . CCBs additionally showed an association with higher risk of diverticulosis . Similar results were obtained in PheWAS sensitivity analyses (Tables XIII through XVI in the online-only Data Supplement). Random sampling of 24 SBP SNPs from throughout the genome (Table I in the online-only Data Supplement) to create standardized GRSs and measurement of associations with diverticulosis risk in permutation analyses (N=1000) showed effect estimates centered close to the null . Of the 1000 permutation analyses, only 10 had a consistent direction of effect and P value lower than that observed for the association of the standardized CCB GRS with diverticulosis in PheWAS, thus generating an adjusted P value=0.01.After quality control and mapping of Table XVII in the online-only Data Supplement. The prevalence of diverticulosis in BioVU was 12%, comparable to the 10% observed in the UK Biobank. In BioVU, the CCB standardized GRS association with diverticulosis had an odds ratio per SD increase of 1.01 . The meta-analyses of UK Biobank and BioVU estimates had an odds ratio of 1.02 . Considering CCB subclasses, there was evidence for an association with risk of diverticulosis for nondihydropyridine CCB use  but not dihydropyridine CCB use  or any other antihypertensive drug class (Table XVIII in the online-only Data Supplement).For the observational analysis of antihypertensive drug use in the UK Biobank, there were 1408 incident diverticulosis diagnoses up to February 13, 2016, in the 54 612 individuals taking any of the considered antihypertensive drug classes at recruitment , with a mean follow-up of 2538 days. In adjusted Cox regression , there was no evidence for an association between use of any CCB and risk of diverticulosis  might have increased the number of variants available, this could also have reduced the sensitivity and specificity of the analysis because of the introduction of weak instrument bias and invalid instruments, respectively. Similarly, information on gene expression was not incorporated in this work, and although such an approach could offer an additional strategy for identifying genetic variants that serve as proxies for drug effects,7 this would be restricted to the cells or tissues in which gene expression was measured, limiting applicability for exploration of general side effects or repurposing opportunities. Although the PheWAS analysis was performed to explore clinically relevant outcomes identified using harmonized Hospital Episode Statistics data in UK Biobank participants, there is also the potential to extend this approach to other cohorts and summary-level genetic data.15 Finally, although the observational analysis of drug use in the UK Biobank did support an association between nondihydropyridine CCB use and risk of diverticulosis, it is not clear whether this finding may in part relate to ascertainment bias or residual confounding. Diverticulosis can be incidental in asymptomatic individuals, and as such, increased interaction with healthcare services could lead to a greater chance of diagnosis.Concerning the limitations of the study, the MR and PheWAS results estimate the cumulative effect of lifelong exposure to genetic variants, rather than the consequence of a clinical intervention. Furthermore, there may be unknown pleiotropic effects of the genetic variants that bias the association estimates.In conclusion, this work has identified genetic variants that serve as proxies for the effect of the ACE inhibitor, BB, and CCB classes of antihypertensive medication. In MR and PheWAS, our instrumental variable approaches corroborated the established associations of these agents with a range of traits related to hypertension. Additionally, this study identified an apparent, previously unreported detrimental effect of nondihydropyridine CCBs on risk of diverticulosis, a finding that requires further replication before it should alter clinical practice. No other potential side effects of any drug class were identified to suggest a lack of long-term safety. This study demonstrates that the use of genetic variants offers a powerful complement to existing RCT and observational approaches for investigating the efficacy, side effects, and repurposing potential of antihypertensive agents.D.G., M.K.G., M.D., and I.T. designed the study. D.G., M.K.G., F.K., and L.J. collectively had full access to the data and performed the analysis. All authors interpreted the results. D.G. and I.T. drafted the manuscript. All authors critically revised the manuscript for intellectual content. All authors approved the submitted version and are accountable for the integrity of the work.This work was funded by the Wellcome 4i Clinical PhD Program at Imperial College London (to D.G.); scholarships from the Deutscher Akademischer Austauschdienst and the Onassis Foundation (to M.K.G.); National Institute of Health grants R01 HL133786, R01 GM120523 and R01 LM010685 ; Cancer Research UK grants C31250/A22804 (to E.T.); the Medical Research Council and Public Health England (grant MR/L01341X/1) ; the National Institute for Health Research Imperial Biomedical Research Centre in collaboration with Imperial College Healthcare National Health Service Trust (to P.E.); the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK ; a consortium led by the UK Medical Research Council ; the European Union Horizon 2020 research and innovation programme “Small vessel diseases in a mechanistic perspective: Targets for Intervention” ; the German Research Foundation (DFG) as part of the “Munich Cluster for Systems Neurology”  and the Collaborative Research Center 1123 (B3) (to M.D.).None.


N = 60,552), African (N = 8429), Asian (N = 9959), and Hispanic/Latino  ethnicities. We identify over 50 additional loci at genome-wide significance in ancestry-specific or multiethnic meta-analyses. Using recent fine-mapping methods incorporating functional annotation, gene expression, and differences in linkage disequilibrium between ethnicities, we further shed light on potential causal variants and genes at known and newly identified loci. Several of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12. Our study highlights the utility of multiethnic and integrative genomics approaches to extend existing knowledge of the genetics of lung function and clinical relevance of implicated loci.Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European ( Pulmonary function is influenced by environmental factors, lifestyle, and genetics. Here, in a multiethnic GWAS meta-analysis for pulmonary function traits, the authors identify over 50 additional genetic loci, a subset of which are specific for European, African, Asian, or Hispanic/Latino ancestry. Further, even when within the normal range, these parameters are related to mortality, independently of standard risk factors3.Pulmonary function traits (PFTs), including forced expiratory volume in the first second (FEV6. Previous genome-wide association studies (GWAS) have identified nearly 100 loci associated with PFTs15. These analyses have been primarily conducted using HapMap imputed data among European ancestry populations12. Recently, the UK BiLEVE Study  and SpiroMeta Consortium  have also examined associations between 1000 Genomes imputed variants and PFTs, but only among Europeans15.In addition to lifestyle and environmental factors, such as smoking and air pollution, genetics influences pulmonary functionThe present cohorts for heart and aging research in genomic epidemiology (CHARGE) meta-analysis builds upon previous studies by examining PFTs in relation to the more comprehensive 1000 Genomes panel in a larger study population . Meta-analyses included approximately 11.1 million variants for European ancestry, 18.1 million for African ancestry, 4.2 million variants for Asian ancestry, and 13.8 million for Hispanic/Latino ethnicity (see Methods).Each study used linear regression to model the additive effect of variants on PFTs, adjusting for age, sex, height, cigarette smoking, weight (for FVC only), and center, ancestral principal components, and a random familial effect to account for family relatedness when appropriate. Ancestry-specific fixed-effects inverse-variance weighted meta-analyses of study-specific results, with genomic control correction, were conducted in METAL (16), which were significantly (defined as P < 5.0 × 10−817) associated with pulmonary function: two loci for FEV1 only, 6 loci for FVC only, 7 loci for FEV1/FVC only, and two loci for both FEV1 and FVC  in African ancestry and which were monomorphic or nearly monomorphic  in other ancestries 18. For FEV1/FVC among Hispanics/Latinos, all significant variants were in loci identified in previous studies of European ancestry populations. In the Asian ancestry meta-analysis, all variants significantly associated with PFTs were also in loci previously identified among European ancestry populations  in the remaining ancestry-specific meta-analyses  and had the following ancestry-specific results: Asian β = 28.29 ml, P = 4.56 × 10−7; European β = 17.35 ml, P = 1.35 × 10−5; Hispanic β = 19.86 ml, P = 0.002; African β = 29.14 ml, P = 0.03; I2 = 0 and Pheterogeneity = 0.40 across the four ancestry-specific results.In multiethnic fixed-effects meta-analyses of 10.9 million variants, we identified 47 novel loci significantly associated with pulmonary function. Thirteen of these loci were also identified in the ancestry-specific meta-analyses, while 34 were uniquely identified in the multiethnic meta-analysis: 11 loci for FEVs Tables . Althoug19 in METASOFT (http://genetics.cs.ucla.edu/meta/) as a sensitivity analysis. In instances where significant heterogeneity is present, the Han-Eskin method mitigates power loss19. In the Han-Eskin random-effects model, 37 of the 47 loci identified in the fixed-effects model at P < 5 × 10−8 had a P value below the same threshold  while only two did not (CRADD and CCDC41) , FRMD4A , ETFA/SCAPER , APP , A4GNT , UBASH3B  . Among these studies, fixed-effects inverse-variance weighted meta-analyses were conducted separately in males and females using METAL and the resulting sex-specific results were combined using a weighted sums approach. No X-chromosome variants were associated with PFTs at genome-wide significance in ancestry-specific or multiethnic meta-analyses. Although the absence of associations between X-chromosome variants and PFTs could reflect the reduced sample size, previous GWAS of pulmonary function have only identified one variant13.Imputed data for X-chromosome variants were available in 12 studies 14. Since this study only included individuals of European ancestry, we sought replication only for the 52 novel loci  identified in either the European ancestry or multiethnic discovery meta-analyses. Data for the lead variant was available in the UK BiLEVE study for 51 loci, including 49 loci with a consistent direction of effect between our results and those from UK BiLEVE (Supplementary Table P < 9.6 × 10−4 (0.05/52), 15 loci replicated for the same trait based on the lead variant from our analysis: DCBLD2/MIR548G, SUZ12P1, CRHR1, WNT3, ZNF337, ALX1/RASSF9, MED1/CDK12, EYA2, RBMS3, LINC00340, FLJ35282/ELAVL2, DDHD1/MIR5580, TSHZ3, KLHL22/MED15, FAM168A  from which the UK BiLEVE study is sampled. Unlike the UK BiLEVE results which were adjusted for age, age2, sex, height, pack-years of smoking, and ancestral principal components14, the publicly available UK BioBank results (https://sites.google.com/broadinstitute.org/ukbbgwasresults/home) are only adjusted for sex and ancestral principal components. In addition, only results for FEV1 and FVC (not the ratio FEV1/FVC) were currently available. Nevertheless, this secondary look-up yielded evidence of replication for the same trait for an additional 9 loci with a two-sided P < 9.6 × 10−4: NR5A2, PIK3C2B, OTUD4/SMAD1, AP3B1, CENPW/RSPO3, SMAD3, PDXDC2P, SOGA2, DCC (Supplementary Table P < 9.6 × 10−4 (DNAH12) and another discovered for FEV1/FVC also replicated for FEV1 and FVC (KCNJ3/NR4A2) in the UK Biobank data. In summary, we found evidence of replication in UK BiLEVE or UK Biobank for 30 novel loci.We also conducted a secondary look-up replication for European ancestry and multiethnic lead variants in the much larger UK Biobank study (N = 3219)22, SAPPHIRE (N = 1707)24, and SAGE 25. We did not find evidence of replication for most of the African ancestry loci identified in our study (Supplementary Table RYR2 (rs3766889). This variant was common (MAF = 0.18) and well imputed (r2 > 0.90) in CHARGE. The effect size was similar across CHARGE  and the two adult replication studies ; meta-analysis of these adult studies resulted in a significant combined association . In SAGE, which includes mostly children and examined percent predicted values, the result was in the opposite direction and not significant. In our Hispanic ethnicity/Latino meta-analysis, the lead variant from the single novel locus  did not replicate in two smaller external studies of Hispanics: MESA  and GALA II 26 , including some of the same individuals included in the present lung function analysis27. Ten lead variants representing eight novel loci were associated with COPD at P < 9.6 × 10−4: RBMS3, OTUD4/SMAD1, TMEM38B/ZNF462, NCOR2/SCARB1, SUZ12P1, WNT3, SOGA2, C20orf112  is in LD (R2 = 0.8) with the top variant for COPD as reported by the IGCG Consortium27.Pulmonary function measures are the basis for the diagnosis of COPD, an important clinical outcome; therefore, we also looked-up the 52 novel loci identified in the European ancestry or multiethnic meta-analyses in the International COPD Genetics Consortium (ICGC). This consortium recently published a meta-analysis of 1000 Genomes imputed variants and COPD primarily among individuals of European ancestry 28; (2) lung samples from 1111 participants in studies from the Lung eQTL Consortium including Laval University, the University of Groningen and the University of British Columbia31; (3) whole blood samples from 5257 Framingham Heart Study participants32; (4) peripheral blood samples from 5311 participants in EGCUT, InCHIANTI, Rotterdam Study, Fehrmann, HVH, SHIP-TREND and DILGOM33; and (5) peripheral blood samples from 2116 participants in four Dutch studies collectively known as BIOS35. We examined both whole blood and lung datasets to take advantage of the much larger size, and higher statistical power, of available blood eQTL datasets since we have previously found substantial overlap between lung and blood eQTLs for lung function GWAS loci, as well as complementary information from these two different tissues29. The Lung eQTL Consortium study used a 10% FDR cut-off, while all other studies used a 5% FDR cutoff (see Supplementary Note To query whether novel loci contained variants associated with gene expression (eQTLs), we looked-up variants from all 60 novel loci identified in any ancestry-specific or multiethnic meta-analyses in the following datasets: 1) lung samples from 278 individuals in genotype-tissue expression (GTEx) . Eleven loci had significant cis-eQTLs in both blood and lung samples based on lead variants, with one locus having a significant cis-eQTL across all five datasets (SMAD3) and another four loci having a significant cis-eQTL in four datasets . Significant trans-eQTLs in at least one dataset were found for seven lead variants representing four novel loci  and 1 lead variant (1 novel locus), respectively (Supplementary Table N = 597) and MESA (N = 233)36. In SAPPHIRE, none of the eight African ancestry variants identified in the meta-analysis indicated significant cis-eQTLs (FDR < 0.05), but rs180930492 was associated with a trans-eQTL among individuals without asthma and rs111793843 and rs139215025 were associated with trans-eQTLs among individuals with asthma at FDR < 0.05 (Supplementary Table None of the novel variants discovered for African and Hispanic ancestry indicated significant cis-eQTLs in GTex which includes some slight multiethnic representation (12% African American and 3% other races/ethnicities). Although few ancestry-specific eQTL datasets exist, we identified two such studies with blood samples from African American participants: SAPPHIRE N = 57 and MES37 to estimate the variance explained by genetic variants investigated in our European ancestry meta-analysis, also known as single nucleotide polymorphisms (SNP) heritability. Across the genome, the SNP heritability (narrow-sense) was estimated to be 20.7% (SE 1.5%) for FEV1, 19.9% (SE 1.4%) for FVC, and 17.5% (SE 1.4%) for FEV1/FVC.We used LD score regression38. We found significant enrichment in six functional categories for all three PFTs: conserved regions in mammals, DNase I hypersensitive sites (DHS), superenhancers, the histone methylation mark H3K4me1 and histone acetylation marks H3K9Ac and H3K27Ac  and between FEV1 and FEV1/FVC (P < 0.001), but not between FVC and FEV1/FVC  are highly correlated, and to lesser degree FEV1/FVC10, these results are not surprising. Using publicly available GWAS results for asthma42, we also found significant correlation between PFTs and asthma 43 and DDX1. Using Ensembl variant effect predictor (VEP)44, we found six missense variants in four loci outside of the MHC region and 22 missense variants in the MHC region (Supplementary Table AAGAB and chr6:30899524 in the MHC region) appear to be possibly damaging based on sorting intolerant from tolerant (SIFT)45 and Polymorphism Phenotyping v2 (PolyPhen-2)46 scores  49. Standard Ingenuity Pathway Analysis (IPA) run without excluding the MHC highlighted 16 enriched networks based on the European ancestry meta-analysis results, including three involved in inflammatory diseases or immunity; 33 of the 84 genes in these 3 networks are in the MHC region (Supplementary Table Due to extended LD across the MHC locus on chromosome 6 (positions 25000000 to 35000000), DEPICT excludes this region50, we examined 38 loci that contained at least five genome-wide significant variants in the European ancestry and multiethnic meta-analyses or at least one significant variant in the African ancestry or Hispanic/Latino ethnicity meta-analyses. We identified 15 variants representing 13 loci as having high posterior probabilities of causality (>0.8): 3 for FEV1, 3 for FVC, and 9 for FEV1/FVC , while 6 were not .Using a multiethnic fine-mapping analysis incorporating strength of association, variation in genetic background across major ethnic groups, and functional annotations in Probabilistic Annotation INtegraTOR (PAINTOR)51 to identify variants with a high posterior probability of causality (>0.6) independent of 118 lead variants in pulmonary function loci identified in the current or previous studies14. We identified 37 independent variants for 23 previously identified loci and one independent variant at each of two novel loci . In addition, we incorporated an IPA to identify potential upstream targets. Genes associated with pulmonary function, but not included in the drug target analysis performed by Wain et al.14, were evaluated, for a total of 139 genes outside of the MHC: 110 genes representing the 60 novel loci identified in our fixed-effects ancestry-specific and multiethnic meta-analysis, 13 genes representing the 6 novel loci identified in our random-effects meta-analysis19, 3 genes representing an additional 3 loci near significance in the African ancestry meta-analysis , 9 genes representing 2 loci identified in a recent CHARGE analysis of exome variants43, which were also significant in our 1000 Genomes analysis (LY86/RREB1 and SEC24C), and 4 genes representing one locus identified at genome-wide significance in a separate publication from one of our participating studies (HCHS/SOL)18, but also significant in our analysis (ADORA2B/ZSWIM7/TTC19/NCOR1). In the ChEMBL database, 17 of these genes encode proteins with predicted or known drug targets: NR5A2, KCNK2, EDAR, KCNJ3, NR4A2, BAZ2B, A4GNT, GSTO1, GSTO2, NCOR2, SMAD3, NCOR1, CDK12, WNT3, PYGB, NANP, EYA2 (Supplementary Table KCNK2 and CDK12) have approved drug targets. Using IPA, four additional genes were predicted as drug targets : 17 from European ancestry, 8 from African ancestry, 1 from Hispanic/Latino ethnicity, and 34 from multiethnic meta-analyses.14: DCBLD2/MIR548G, SUZ12P1, CRHR1, WNT3, ZNF337, ALX1/RASSF9, MED1/CDK12, EYA2, RBMS3, LINC00340, FLJ35282/ELAVL2, DDHD1/MIR5580, TSHZ3, KLHL22/MED15, FAM168A, RAB5B, JMJD1C, AGMO, and C20orf112. Based on a minimally adjusted publicly available analysis in a larger sample of Europeans from the UK Biobank, an additional 11 loci replicated: NR5A2, PIK3C2B, OTUD4/SMAD1, AP3B1, CENPW/RSPO3, SMAD3, PDXDC2P, SOGA2, DCC, DNAH12, and KCNJ3/NR4A2. Because UK BiLEVE is sampled from UK Biobank we are not able to perform a combined replication meta-analysis. Additionally, the studies adjusted for different covariates . Among those loci which did not directly replicate for PFTs in the UK BiLEVE or UK Biobank datasets, the lead variants in an additional two European or multiethnic loci were significantly associated in the ICGC Consortium with COPD, which was defined using PFT measures27: TMEM38B/ZNF462 and NCOR2/SCARB1. FINEMAP and S-PrediXcan also produced evidence for causality for three European ancestry and multiethnic loci which had not replicated in UK BiLEVE, UK Biobank or ICGC: DCAF8, AFAP1, and SLC25A51P1/BAI3.For 32 of the 52 loci novel loci identified in our European ancestry and multiethnic meta-analyses, we found evidence for look-up replication in the UK BiLEVE study, UK Biobank study, or ICGC COPD consortium. For an additional three loci, we found support for validation using new genomic methods such as PAINTOR, FINEMAP, or S-PrediXcan. Specifically, 19 novel variants replicated in look-up in a smaller independent sample of Europeans from the UK BiLEVE studyC2orf48/HPCAL1, EN1/MARCO, CADPS, HDC, LOC283867/CDH5, and CPT1C) (MAF < 0.02), including three not well imputed (r2 < 0.75), and monomorphic or nearly monomorphic in other ancestries . For the two novel African ancestry variants with MAF > 0.02 and well imputed (r2 > 0.90), we found the strongest evidence for replication for RYR2 (rs3766889). This variant had a similar effect estimate for FEV1 in CHARGE, COPDGene, and SAPPHIRE with a significant combined association across these adult studies. Although this particular variant did not quite meet genome-wide significance in the multiethnic meta-analysis for FEV1 (P = 6.56 × 10−4), another variant in this gene did for FVC .The few additional studies with 1000 Genomes imputed variants and pulmonary function in African ancestry individuals have smaller samples sizes making replication challenging for the eight novel loci identified in our African ancestry meta-analyses. Further, lead variants for six of the eight loci were low frequency in African Ancestry , we found CRHR1 as the strongest gene associated with FEV1 in this region. Although some variants in CRHR1 identified in our study are within 500kb of KANSL1 , a number of significant variants in this gene are more than 500 kb away from previously identified hits [our definition of novel] . In our multiethnic meta-analysis, several variants in CRHR1 were associated with FEV1 at smaller P values than variants in KANSL1. Definitive assessment of the causal variants at this locus, as well as other multigenic GWAS loci, will likely require additional data from ongoing large-scale sequencing studies to enable detailed fine mapping.Our analysis also sheds light on additional potential causal genes at a complex locus  previously discovered from GWAS of FEVWNT3 on chromosome 17 near position 44800000 (hg19) which is more than 500kb from KANSL1 or CRHR1 [our definition of novel]. We found that the top variant in WNT3 for FEV1 among individuals of European ancestry  had a high probability for causality based on PAINTOR, an analysis which integrates functional annotations along with association statistics and LD for each ethnicity50. We also found evidence that WNT3 may be the causal gene at this locus using S-PrediXcan, a gene level association test that prioritizes potentially causal genes while filtering out LD-induced false-positives53. Notably, S-PrediXcan implicated WNT3 as a likely mediating gene for FVC based on the top variant in our multiethnic meta-analyses , which is an eQTL SNP for WNT3 in lung and other tissues. Further, the lead WNT3 variants for both FEV1 and FVC (rs916888 and rs199525) were significantly associated with COPD in a look-up of a large published meta-analysis dataset27. In addition, other genes in the WNT signaling pathway, a fundamental development pathway, have been implicated as influencing pulmonary function54. This pathway was also one of the significant pathways identified in our analysis. In a previous pathway analysis of asthma, SMAD3 has been shown to interact with the WNT signaling pathway55. Finally, WNT3 also emerged as having a potential druggable target, and incorporation of pathway analysis to identify upstream regulators found an additional four drugs in clinical use for which WNT3 is a target molecule 56. Again, further evaluation of this interesting and complex locus which contains many significant variants in LD will benefit from data being generated in ongoing large-scale sequencing studies.In both our European and multiethnic meta-analyses we also noted a significant association with MARCO (macrophage receptor with collagenous structure) has been shown in murine models to be required for lung defense against pneumonia and inhaled particles58. SMAD3 is part of the SMAD family of proteins which are signal transducers and transcriptional modulators that mediate multiple signaling pathways. SMAD3 is activated by transforming growth factor beta (TGF-B) which plays a key role in airway remodeling. SMAD3 has a predicted drug target and SNPs in SMAD3 are significantly associated with asthma in GWAS59.Some genes identified in our study play key roles in inflammation, immunity, and pulmonary biology. For example, CDK12 and KCNK2. CDK12 drug targets include AT-7519, Roniciclib, AZD-5438, and PH.A-793887. Roniciclib has been used in clinical trials including lung cancer patients60. KCNK2 (potassium channel subfamily K member 2) is targeted by five inhalational anesthetic agents. These agents have antiinflammatory effects both systemically61 and in the lungs62 and meta-analysis of clinical studies shows protection against pulmonary complications after cardiac surgery63. A recent trial suggested that one of these inhalation agents, sevoflurane, offers promise for reducing epithelial injury and improving outcomes in patients with acute respiratory distress syndrome64.Other genes identified in our study that are targeted by approved drugs include 50. PAINTOR identified 15 putative causal variants from 13 loci, including seven loci uniquely identified in the multiethnic meta-analyses such as PMFBP1/ZFHX3 and COL8A1 (part of the DCBLD2 loci). Several of the putative causal variants from PAINTOR were the top SNPs from the fixed-effects meta-analysis . Similarly, FINEMAP has been shown to be an accurate and efficient tool for investigating whether lead SNPs for a given loci are driven by independent variants in the same region, especially when annotation information is not available51. Among previous and novel loci identified in individuals of European ancestry, we identified 37 independent variants for 23 previously identified loci and two lead variants for two novel loci (rs1928168 LINC00340 and rs9351637 SLC25A51P1/BAI3) with a high probability of causality. Finally, we ran S-PrediXcan a gene level association test that prioritizes potentially causal genes52. Seven of our novel loci contained putative causal genes based on S-PrediXcan for lung or whole blood tissues, including NRBF2 (part of the JMJD1C locus) and WNT3. S-PrediXcan also highlighted the region around chromosome 11 position 73280000 (hg19), noting strong evidence for both FAM168A and ARHGEF17 which was further supported by the colocalization analysis. Interestingly, DEPICT also prioritized ARHGEF17, a member of the guanine nucleotide exchange factor (GEF) family of genes which can mediate actin polymerization and contractile sensitization in airway smooth muscle66.In addition to querying commonly used genome databases for functional annotation of variants, we sought to narrow down causal variants in implicated loci using recently developed methods that incorporate LD, functional data and/or the multiethnic analysis done in this paper. In particular, PAINTOR is a useful tool to identify potential causal variants in our novel loci as it leverages LD across ancestral groups along with association statistics and functional annotations49. In addition to identifying potential causal variants, this approach revealed a number of fundamental development processes, including pathways related to lung development, growth regulation, and organ morphogenesis. The WNT signaling pathway was also highlighted along with processes relevant to the pathogenesis of COPD including extracellular matrix structure and collagen networks. Tissue/cell type enrichment results highlighted smooth muscle which is highly relevant for lung function. DEPICT excludes the MHC due to extended LD in this region, which likely explains the relative paucity of inflammation-related pathways identified compared to previous pathway analyses in GWAS of PFTs54. Indeed, standard IPA analysis of our data including the MHC region, found that 33 of 84 genes (39%) in the 3 (out of 16) enriched networks involved in immune or inflammatory processes are in the MHC. The predominance of fundamental pathways related to lung growth, differentiation and structure is consistent with recent work67 that has rekindled interest in the observation made 40 years ago68 that individuals can cross the threshold for diagnosis of COPD either by rapid decline in adulthood or by starting from a lower baseline of maximal pulmonary function attained during growth. Within this context, understanding the genetic  factors that influence the variability in maximal lung function attained during the first three decades of life is essential to reducing the public health burden of COPD69.Rather than conducting a standard gene-based pathway analysis, we performed a newer integrative method, DEPICT, that incorporates cell and tissue-specific functional data into a pathway analysis to prioritize genes within implicated lociIn summary, our study extends existing knowledge of the genetic landscape of PFTs by utilizing the more comprehensive 1000 Genomes imputed variants, increasing the sample size, including multiple ancestries and ethnicities, and employing newly developed computational applications to interrogate implicated loci. We discovered 60 novel loci associated with pulmonary function and found evidence of replication in UK BiLEVE, UK Biobank, or ICGC for 32 novel loci and validation for another 3 loci. We found evidence that several variants in these loci were missense mutations and had possible deleterious or regulatory effects, and many had significant eQTLs. Further, using new genomic methods that incorporate LD, functional data and the multiethnic structure of our data, we shed light on potential causal genes and variants in implicated loci. Finally, several of the newly identified genes linked to lung function are druggable targets, highlighting the clinical relevance of our integrative genomics approach.8.Member and affiliate studies from The CHARGE consortium with pulmonary function and 1000 Genomes imputed genetic data were invited to participate in the present meta-analysis. Participating studies included: AGES, ALHS, ARIC, CARDIA, CHS, FamHS, FHS, GOYA, HCHS/SOL, HCS, Health ABC, Healthy Twin, JHS, KARE3, LifeLines, LLFS, MESA, NEO, 1982 PELOTAS, RSI, RSII, RIII. Characteristics of these studies are provided in Supplementary Table 1, FVC, and the ratio FEV1/FVC) were collected by trained staff in each study according to American Thoracic Society or European Respiratory Society guidelines. See cohort descriptions in Supplementary Note Spirometry measures of pulmonary function , pack-years of smoking, center (if multicenter study), and ancestral principal components, including a random familial effect to account for family relatedness when appropriate70. Models of FVC were additionally adjusted for weight. Analyses were conducted using ProbAbel, PLINK, FAST, or the R kinship package as described in the cohort descriptions of the Supplementary Note Within each study, linear regression was used to model the additive effect of variants on PFTs. FEVhttp://www.sph.umich.edu/csg/abecasis/metal/). Multiethnic random-effects meta-analyses using the four ancestry-specific fixed-effects meta-analysis results were conducted using the Han-Eskin model19 in METASOFT (http://genetics.cs.ucla.edu/meta/). Only variants with p-values for association <0.05 or P values for heterogeneity <0.1 from fixed-effects models were included in the random-effects models.Ancestry-specific and multiethnic fixed-effects meta-analyses using inverse variance weighting of study-specific results with genomic control correction were conducted in Meta-Analysis Helper  less than 0.3 and/or a minor allele count (MAC) less than 20 were excluded from each study prior to meta-analysis. Following meta-analysis, we also excluded variants with less than one-third the total sample size or less than the sample size of the largest study for a given meta-analysis to achieve the following minimal sample sizes: 20,184 for European ancestry; 2810 for African ancestry; 7862 for Asian ancestry; 4435 for Hispanic/Latino ethnicity; and 30,238 for Multiethnic.Variants with imputation quality scores  from quantile–quantile plots of observed and expected https://github.com/bulik/ldsc)37. Partitioned heritability was also calculated using the method described by Finucane et al.38. In total, 28 functional annotation classes were used for this analysis, including coding regions, regions conserved in mammals, CCCTC-binding factor, DNase genomic foot printing, DHS, fetal DHS, enhancer regions; including superenhancers and active enhancers from the FANTOM5 panel of samples, histone marks including two versions of acetylation of histone H3 at lysine 27 (H3K27ac and H3K27ac2), histone marks monomethylation (H3K4me1), trimethylation of histone H3 at lysine 4 (H3K4me), and acetylation of histone H3 at lysine 9 (H3K9ac5). In addition to promotor and intronic regions, transcription factor binding site, transcription start site, and untranslated regions (UTR3 and UTR5). A P value of 0.05/28 classes <1.79 × 10−3 was considered statistically significant. Genetic correlation between our pulmonary function  results and publicly available GWAS of ever smoking40 and height41 was also investigated using LD score regression39.The SNP heritability, i.e., the variance explained by genetic variants, was calculated from the European ancestry GWAS summary statistics (with genomic control off) using LD score regression (44 (Accessed 17 Jan 2017) and obtained mapped genes, transcripts, consequence of variants on protein sequence, SIFT45 scores, and PolyPhen-246 scores. We checked if there were deleterious variants using CADD v1.347, which integrates multiple annotations, compares each variant with possible substitutions across the human genome, ranks variants, and generates raw and scaled C-scores. A variant having a scaled C-score of 10 or 20 indicates that it is predicted to be in the top 10% or 1% deleterious changes in human genome, respectively. We used a cutoff of 15 to provide deleterious variants since it is the median for all possible splice site changes and nonsynonymous variants . To find potential regulatory variants, we used RegulomeDB48 (Accessed 17 Jan 2017), which integrates DNA features and regulatory information including DNAase hypersensitivity, transcription factor binding sites, promoter regions, chromatin states, eQTLs, and methylation signals based on multiple high-throughput datasets and assign a category to each variant. Variants having RegulomeDB categories 1 or 2, meaning “likely to affect binding and linked to expression of a gene target” or “likely to affect binding,” were considered as regulatory variants.To find functional elements in novel genome-wide significant signals, we annotated SNPs using various databases. We used Ensembl VEP49 with association results for FEV1, FVC, and FEV1/FVC. We used association results from our European ancestry meta-analysis and the LD structure from 1000 Genomes European  reference panel. The software excludes the MHC region on chromosome 6 due to extended LD structure in the region. We ran a version of DEPICT for 1000 Genomes imputed meta-analysis results using its default parameters with an input file containing chromosomal location and P values for variants having unadjusted P < 1 × 10−5. For gene set enrichment analyses, DEPICT utilizes 14,461 reconstituted gene sets generated by genes’ coregulation patterns in 77,840 gene expression microarray data. For tissue/cell type enrichment analysis, mapped genes were tested if they are highly expressed in 209 medical subject headings annotations using 37,427 microarray data. Gene prioritization analysis using cofunctionality of genes can provide candidate causal genes in associated loci even if the loci are poorly studied or a gene is not the closest gene to a genome-wide significant variant. We chose FDR < 0.05 as a cutoff for statistical significance in these enrichment analyses and gene prioritization results. Because DEPICT excludes the MHC, we also ran a pathway analysis with IPA  on genes to which variants with P < 1 × 10−5 annotated.For gene prioritization and identification of enriched pathways and tissues/cell types, we used DEPICT50 implemented in PAINTOR . This method utilizes functional annotations along with association statistics (Z-scores) and LD information for each locus for each ancestry. We included our ancestry-specific meta-analysis results and used the African, American, European, and East Asian individuals from 1000 Genomes to calculate LD72. For PAINTOR we focused on 22 novel loci identified in our European ancestry and multiethnic fixed-effects meta-analyses which had at least five genome-wide significant variants as well as all nine African or Hispanic loci which had at least one genome-wide significant variant. In addition, we included six loci which overlapped with the UK BiLEVE 1000 Genomes paper14 and one locus with the CHARGE exome paper43, since we ran PAINTOR prior to those publications. To reduce computational burden, we limited flanking regions to ±100 kilobase (kb) from the top SNPs and included variants with absolute value of Z-score greater than 1.96.To identify causal variants in novel genome-wide significant loci, we used a transethnic functional fine mapping method73, superenhancers74, Fantom5 enhancer and transcription start site regions75, immune cell enhancers76, and methylation and acetylation marks ENCODE77. We ran PAINTOR for each phenotype separately to prioritize annotations based on likelihood-ratio statistics79. We included minimally correlated top annotations (less than five for each phenotype) to identify causal variants.We used 269 publicly available annotations relevant to “lung”, “bronch”, or “pulmo” from the following: hypersensitivity sitesFor the 38 loci from the fixed-effects meta-analysis, we used PAINTOR to construct credible sets of causal variants using a Bayesian meta-analysis framework. To obtain 95% credible sets for each locus, we ranked SNPs based on posterior probabilities of causality (high to low) and then took the SNPs filling in 95% of the summed posterior probability. We computed the median number of SNPs in the credible sets for ancestry-specific and multiethnic analyses of each trait.51 to identify signals independent of lead variants for pulmonary function loci identified in the current or previous studies14. The Rotterdam Study (N = 6291), one of the larger cohort studies included in our meta-analysis, was used as a reference population. SNPs with MAF of <1% were excluded, leaving 118 SNPs for analysis. Ten SNPs for FEV1 and FVC and 20 SNPs for FEV1/FVC were further excluded because the LD matrix of the reference file from the Rotterdam Study did not represent the correlation matrix of the total study population. We allowed up to 10 causal SNPs per loci in FINEMAP analyses. To reduce the chance of false positive findings, we also conducted sensitivity analyses allowing up to 15 causal SNPs for loci with more than four SNPs with posterior probabilities of >0.8.We used FINEMAP52 that was derived as an extension of the PrediXcan method for integration of GWAS and reference transcriptome data80. We used the S-PrediXcan approach to prioritize potentially causal genes, coupled with a Bayesian colocalization procedure53 used to filter out LD-induced false-positives. S-PrediXcan was used to analyze both European ancestry and multiethnic GWAS summary data for pulmonary function tests from the current study.S-PrediXcan is a summary statistics based approach for gene-based analysishttp://predictdb.org) from the GTEx project v6p28: (1) GTEx Lung (278 samples) and (2) GTEx whole blood (338 samples). Approximately, 85% of participants in GTEx are white, 12% African American, and 3% of other races/ethnicities. Gene-based S-PrediXcan results were filtered on the following: (1) proportion of SNPs used = (n SNPs available in GWAS summary data)/(n SNPs in prediction model) > 0.6, and (2) prediction performance R-squared > 0.01. Following application of S-PrediXcan to each of the GWAS summary data sets, we computed Bonferroni-corrected P values derived as the nominal P value for each gene-based test divided by the number of genes passing specified filters in each analysis to test whether genetically regulated gene expression was associated with the trait of interest. The genome-wide S-PrediXcan results were then merged with novel loci from the current GWAS study by identifying all matches in which the novel locus SNP was within 500kb of the start of the gene.S-PrediXcan analysis was performed using the following publicly available tissue-specific expression models  has no association with neither trait, (H1) associated with PFT phenotype but not gene expression, (H2) associated with gene expression but not PFT phenotype, (H3) associated with both traits, due to two independent SNPs, and (H4) associated with both traits, due to one shared SNP.We further incorporated a Bayesian colocalization approachwww.ingenuity.com, content of 2017-06-22) to identify drug targets and upstream regulators of the gene lists. We reported the upstream regulators in the following categories, biologic drug, chemical—endogenous mammalian, chemical—kinase inhibitor, chemical—other, chemical drug, chemical reagent, and chemical toxicant.We searched annotated gene lists against the ChEMBL database  to identify genes as targets of approved drugs or drugs in development. In addition, we used the Ingenuity Pathway Analysis  who will forward them to the relevant party. Requests for METAL code can be directed to Stephanie London.The complete meta-analysis results have been deposited in the database of Genotypes and Phenotypes (dbGaP) under the CHARGE acquisition number [Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


However, the underlying mechanisms of depression in Parkinson’s disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson’s disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson’s disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson’s disease appropriately (adjusted R Parkinson’s disease (PD) is the second most common neurodegenerative disorder . PD is cThe criteria involved in general depression and DPD are subject to bias as they are either psychiatric scales or clinical interviews. Thus, neuroimaging techniques have been used to reduce subjective bias and better understand DPD or general depression. Many studies that have used single photon emission tomography (SPECT) and positron emission tomography (PET) were mainly focused on the dopaminergic and serotonergic systems ,12. MagnHowever, several factors impede our ability to diagnose a person with a psychiatric condition including depression based on neuroimaging: 1) there is considerable variation in brain imaging among people with the same diagnosis, 2) psychiatric conditions can present quite differently in different individuals, 3) different psychiatric conditions often share similar symptoms, and, finally, 4) similar groups of brain areas are involved in diverse psychiatric conditions. Psychiatric disorders such as depression are difficult to characterize using neuroimaging alone. We tried to overcome this limitation by adopting an imaging genetics approach that uses both neuroimaging and gene data.Both genetic and environmental factors contribute to individual differences in brain function and behavior . AlthougThe aim of this study was to identify genetic features affecting DPD using the imaging genetics approach and to construct an analytical model for predicting the degree of depression in PD. We hypothesized that a neuroimaging genetics approach would be sensitive enough to identify genetic features as biomarkers that could explain the degree of DPD. Furthermore, we constructed other models using imaging features only and genetic features based on references and compared them against our image genetics model.This study was a retrospective analysis of anonymized data and institutional review board (IRB) approval was obtained at Sungkyunwkan University. All data were obtained with informed written consent in accordance with established human subject research procedures expressed in the Declaration of Helsinki. Our study was performed in full accordance with the local IRB guidelines. We used diffusion MRI, T1-weighted MRI and DNA genotyping data of 81 patients with PD obtained from the Parkinson’s Progression Markers Initiative (PPMI) database . Patient3). Diffusion MRI were obtained using the following parameters on a 3T scanner . Our study considered two types of information  and many procedures are necessary to process them. The schematic of the overall processing steps is given in Diffusion MRI and T1-weighted MRI data were obtained from the PPMI database . T1-weigWe obtained DNA samples genotyped by NeuroX genotyping arrays from the PPMI. The following procedure pertains to the genetic data of the PPMI. All available DNA samples were genotyped by using NeuroX as genotyping arrays. Briefly, NeuroX was proposed to cover more than 240,000 exonic variants so that neurodegenerative disease could be studied effectively ,26. Thes−6) were filtered out of the dataset  0.242). The second model used only imaging features and showed a moderate correlation and RMS error  between the predicted and real GDS. The model using only the reference-based genetic features achieved the correlation RMS error that were similar to the model with only neuroimaging features . The prediction plots of the three models are given in We identified SNPs that could explain GDS well using the imaging genetics approach limited to PD patients. It is still possible that the identified SNPs could also be present in NC participants using the same approach. We applied the same imaging genetics approach to NC participants (n = 69) from the PPMI database. Unfortunately, no imaging features that explained GDS well were computed from the NC group. This led to applying the imaging genetics approach infeasible as there were no intermediate phenotypes available. If the SNPs identified in the PD group were indeed present in the NC group, the SNPs should be able to explain GDS well in the NC group as well. We applied the linear model of the identified SNPs learned from PD to the NC group. The results showed that the identified SNPs were not effective at explaining the degree of depression in the NC group with a very low adjusted R2 value . Taken together, we believe the identified SNP were only effective at explain GDS within PD group and thus the features could be specific to DPD.Psychological phenomena are difficult to characterize using neuroimaging or genetic analysis alone as the given diagnosis spans a wide spectrum of symptoms. Some argue that diagnosis is becoming less relevant due to this problem ,42. The Imaging genetics analysis is better than conventional analysis because imaging observations are used as intermediate phenotypes . Others We identified 18 SNPs related to eight regional structural connectivity measures that contributed to predicting the depression-related score  in PD patients. The SNPs were matched with genes. Among the identified genes, several genes were overexpressed in the identified ROIs as intermediate phenotypes. NRXN1 and LOC284395 are known to be related to the anterior cingulate cortex . LOC2843Krug et al. found that the left precuneus was more activated in healthy subjects carrying a specific gene found to be overrepresented in patients suffering from bipolar disorder, depression, or schizophrenia [In addition to the regions in the corticolimbic system, we identified postcentral gyrus, precuneus, and temporal pole. These regions were also reported to be related to DPD patients ,12,58. Tophrenia ,61. The ophrenia  that is ophrenia . Based oWe used DC of structural connectivity because psychiatric disorders such as depression were thought to be related to the complex interactions of the brain regions, and thus we considered a more network-oriented complex measure such as DC as the intermediate phenotype compared to simple measures such as fractional anisotropy (FA) and mean diffusivity (MD). In the current study, we found larger DC values in DPD patients compared to the nDPD patients . Among tWe conducted additional experiments to see if the imaging features were related to clinical scores provided by the PPMI database. If the imaging features were affected by other psychiatric disorders, they could be used to the predict clinical score related to the psychiatric disorder. We predicted STAI, MoCA and QUIP scores using the same imaging features in a regression framework. The models that predicted MoCA and QUIP scores showed very low correlations  between the predicted and actual scores. When STAI was predicted with the same imaging features, we observed a slightly lower correlation  than the existing correlation with GDS . This is consistent with literature that depression and anxiety are linked strongly, and they co-occur frequently . This coImaging genetics is a sensitive analysis for identifying additional genetic information that impacts brain function . Still, We combined two types of distinct information to predict the degree of depression. The genetic information is rather fixed, but imaging could be performed depending on the patient’s condition. Our model combining imaging and genetics information could be applied whenever a patient undergoes new imaging and thus could be used for the early prediction of depression. If detected, patients could be directed to many non-drug therapy options that are only available in the early stages of depression. Our study is also the first to propose a model that can explain the degree of depression (determined by GDS) in PD using diffusion MRI and SNP data.S1 FileThis S1 File included the validation and prediction results of constructed additional models by integrating different combinations of our proposed model using imaging genetic features, the model using only neuroimaging features, and the model using only conventional genetic features.(DOCX)Click here for additional data file.


Most people have left‐hemisphere dominance for various aspects of language processing, but only roughly 1% of the adult population has atypically reversed, rightward hemispheric language dominance (RHLD). The genetic‐developmental program that underlies leftward language laterality is unknown, as are the causes of atypical variation. We performed an exploratory whole‐genome‐sequencing study, with the hypothesis that strongly penetrant, rare genetic mutations might sometimes be involved in RHLD. This was by analogy with situs inversus of the visceral organs , which is sometimes due to monogenic mutations. The genomes of 33 subjects with RHLD were sequenced and analyzed with reference to large population‐genetic data sets, as well as 34 subjects (14 left‐handed) with typical language laterality. The sample was powered to detect rare, highly penetrant, monogenic effects if they would be present in at least 10 of the 33 RHLD cases and no controls, but no individual genes had mutations in more than five RHLD cases while being un‐mutated in controls. A hypothesis derived from invertebrate mechanisms of left‐right axis formation led to the detection of an increased mutation load, in RHLD subjects, within genes involved with the actin cytoskeleton. The latter finding offers a first, tentative insight into molecular genetic influences on hemispheric language dominance. Study design of the genome sequencing for rightward hemispheric language dominance. Images are shown from an example subject with typical left‐hemisphere language dominance, and an example subject with atypical rightward hemispheric language dominance, as assessed by functional magnetic resonance imaging. Genomic analysis was focused on rare, protein‐altering variants within genes and candidate gene‐sets. We also tested for an increased rate of rare mutations in RHLD within specific candidate gene sets, in case an increased load of mutations affecting specific biological processes might increase the chance of having RHLD. The candidate sets included genes involved in visceral laterality or the actin cytoskeleton, as well as a set of 18 genes which have been tentatively associated with human brain laterality in previous studies.22.1A total of 67 participants (33 with RHLD) were included in the present study, all of whom gave written informed consent. All RHLD subjects except one were left‐handed (Edinburgh Handedness Inventory [EHI] median = −87.50), while the controls included 14 left‐handed and 20 right‐handed participants . Summary statistics for language laterality measures and handedness are provided in Table The subjects in this study were recruited from two separate sources, that is, the BIL&GIN data set (France) and the GOAL data set (Belgium).2.2Seventeen RHLD subjects and 22 controls were drawn from a larger data set of healthy, young adults, balanced for handedness .2*‐weighted echo planar imaging acquisition  = 2 seconds; echo time (TE) = 35 ms; flip angle = 80°; 31 axial slices; 3.75mm3 isotropic voxel size).We studied hemispheric lateralization for three language tasks, namely production, reading and listening, using fMRI to calculate Global Hemispheric Functional Laterality Indexes (HFLIs), as described previously.http://www.fil.ion.ucl.ac.uk/spm/). Scans of each participant and each run were normalized to our site‐specific template, corrected for motion during the run, and then warped into the standard montreal neurological institute (MNI) space using a tri‐linear interpolation, with subsequent smoothing using a 6‐mm full width at half maximum (FWHM) Gaussian filtering. We then computed for each participant the BOLD signal difference maps and associated t‐maps corresponding to the “sentence vs word‐list” contrast for the production, reading and listening runs. For each individual and each language task, we computed a HFLI using the LI‐toolbox applied to the individual contrast t‐map of the considered language task.fMRI data analysis was performed using the SPM5 software .32*‐weighted gradient‐echo EPI sequence. Forty axial slices covering the whole brain were acquired .Sixteen RHLD participants were selected from a larger data set of healthy left‐handers N = 250)502 PartiThe 16 strongly right‐lateralized individuals all met a stringent criterion for RHLD (HFLI for language production < −50).Informed consent was obtained from all participants, and ethical approval for the study was obtained from the Ethics Committee of the Ghent University Hospital.44.1https://emea.illumina.com/systems.html). Thirteen additional subjects of European descent, who were not part of the present study, were also sequenced at the same time, and their data processed together with the 39 through preprocessing and variant calling stages  of the 39 BIL&GIN subjects was performed using Illumina's HiSeq technology by the genomics research organization and service company BGI (Hong Kong/Shenzhen)  paired‐end reads for 11 of the RHLD subjects and 14 controls, and 150 bp paired‐end reads for six RHLD subjects and eight controls. Raw reads were cleaned by excluding adapter sequences, reads with low‐quality bases for more than 50% of their lengths, and reads with unknown bases for more than 10% of their lengths. Clean reads were mapped onto the human reference genome (hg19) using the software Burrows‐Wheeler Aligner.These variants were then normalized, and variants belonging to any VQSR sensitivity tier over 99% were excluded. For the 39 BIL&GIN subjects of this study, the variant calling of SNPs and indels identified on average 4 165 806 variants per subject for the 90 bp protocol (range: 4 079 049‐4 330 101), and 4 484 638 per subject for the 150 bp protocol (range: 4 354 345‐4 657 333).5The genomics company Novogene (Hong Kong/Shenzhen) performed WGS on the 28 samples of the GOAL data set using Illumina's HiSeq Xten technology, and paired‐end sequencing with reads of 150 base pairs and 30x sequence depth. The same pipeline as that applied to the BIL&GIN data was used for alignment (build 37), variant calling, annotation and filtering . The variant calling and VQSR steps were carried out together with data from 34 European‐descent subjects who were not part of the present study, again because these steps benefit from a larger number of subjects. These variants were then normalized using the software tool vt normalize (v0.5772‐60f436c3)5.1P > .33). Inbreeding was assessed with the F coefficient estimate within each data set using PLINK (v1.9).P > .39).Within the BIL&GIN and GOAL data sets separately, population structure was assessed by calling genotypes from the sequence data for selected sets of common variants  spanning the autosomes. These were high‐confidence single‐nucleotide polymorphism (SNP) sites identified by the 1000 Genomes Project, 1000G_phase1.snps.high_confidence.hg19.vcf.gz with minor allele frequencies (MAFs) > 10% in each data set,Note that common genetic variants were only used for the purposes of assessing population stratification and inbreeding within the data sets, whereas the rest of the study was focused on rare genetic variation, which has the potential to involve highly penetrant effects.5.2http://evs.gs.washington.edu/EVS/, http://www.internationalgenome.org/home). Within the BIL&GIN and GOAL data sets separately, any variants present in at least 19 participants (case or control) were excluded as they are likely to be platform‐specific errors or else common variants not previously detected by other sequencing platforms or protocols, and would necessarily be present in at least two control subjects in BIL&GIN or three controls in GOAL (hence unlikely to be high‐penetrance mutations for RHLD).SNPs and indels were annotated using Annovar5.3Recessive: Here, we considered only homozygous or compound heterozygous mutations as potentially trait‐causal. For screening purposes, compound heterozygosity was assigned when a given gene had at least two different mutations, although allelic phase information was not usually available due to the limited sequence read lengths. Variants were excluded when they had MAF ≥ 10% on the basis of on‐line population databases (see above). At 10% MAF, assuming Hardy‐Weinberg equilibrium, the variant would be present in homozygous form at 1% in the population, that is, roughly equal to the RHLD frequency in the population. In the case that 50% penetrance might arise from L‐R randomization, as has been observed for mutations which cause situs inversus with PCD,Dominant: Here, we considered heterozygous or homozygous mutations as potentially trait‐causative. Variants were excluded as potentially causative when they had population MAF ≥ 1% in the population databases, on a similar logic as for the recessive model above, but appropriate for allelic dominance and the frequency of RHLD in the population (roughly 1%). Variants not present or with no MAF information in the population databases were retained. There were on average 196 genes per subject for the BIL&GIN‐90 bp protocol (range: 154‐215), 240 per subject for the BIL&GIN‐150 bp protocol (range: 208‐268), and 262 per subject for the GOAL data set (range: 229‐300).5.4The BIL&GIN and GOAL data sets were combined for subsequent analysis.t tests, all P > .10). Significance for single‐gene analysis was then assessed separately for individual genes and models (recessive or dominant), using the one‐tailed Fisher's exact test for a 2 × 2 contingency table, for the categories “mutated” and “not mutated” in 33 RHLD subjects and 34 controls. The minimum number of mutated RHLD subjects to achieve a nominally significant P value  was 5, that is, if a gene would be mutated in five out of the 33 RHLD subjects and none of the 34 controls, that gene would show a nominally significant P value of association with RHLD, as a putative major‐genetic effect . This approach allows for allelic heterogeneity, that is, the unit of testing is the gene, within which a variety of different mutations can be present. Note that the power and sample size considerations when modeling highly penetrant effects are different to typical genome‐wide association studies of common traits, in which large samples are screened for common variants of small effect. Here, we focus only on rare variants and interrogate the data with respect to the possibility of high penetrance. Note also that the Fisher's exact test is robust for the sample size, because the significance is assessed with respect to all of the actual possibilities that might have arisen in the contingency table in this set of subjects.We first verified that the total number of mutated genes per subject did not differ significantly between RHLD and control subjects, under either the dominant or recessive model  or 10 (recessive) of the 33 RHLD subjects, and no controls, leading to nominal P = .000186 (dominant) or P = .000373 (recessive) in the Fisher's exact test, that is, the gene would need to be a monogenic cause for roughly one third of the instances of RHLD. For these calculations, we counted how many individual genes, y, have mutations in at least x subjects, for every value of x from 1 to 67 subjects. For each value of x, we then calculated the minimum number of RHLD subjects with mutations in a given gene that would be required to produce a P value less than .05/y in the Fisher's exact test.We calculated that for an individual gene to be significant at We performed a post hoc filtering step in which we further excluded from consideration, as potentially monogenic effects, all genes which were mutated in at least one control subject, as these genes were unlikely to be causal monogenically for RHLD. Note that this filter was only applied after the statistical analysis, in order not to bias the multiple testing correction.5.5http://geneontology.org/gene-associations/goa_human.gaf.gz downloaded 16‐Nov‐2017), were “cilium” (GO:0005929), “left‐right axis specification” (GO:0070986), “actin cytoskeleton” (GO:0015629), plus two sets defined on the basis of visceral laterality phenotypes or disorders: 58 genes related to PCD and asymmetry disordersWe tested whether the RHLD cases had an increased mutational load in specific candidate gene‐sets . These candidate sets, based on the gene ontology (GO) as defined within AmiGO's direct annotationhttp://geneontology.org/gene-associations/goa_human.gaf.gz, downloaded 16‐Nov‐2017). Additional sets were investigated post hoc as child sets of the actin cytoskeleton set  per subject within the set was compared between RHLD subjects and controls by means of the one‐tailed exact binomial test, that is, considering the sum of mutated genes per subject in RHLD subjects only, the total sum across RHLD and controls combined, and the proportion of all subjects who were RHLD (33/67). Again, as an exact test, the binomial is robust for the subject sample size, and does not require assumptions on the number of mutations per individual.5.6TCTN1, rs188817098, which we initially considered a potential candidate for causing RHLD in some subjects (see P = 1), and the minor allele C had a frequency of 0.001305. Handedness (UK biobank field ID: 1707.0.0) was self‐reported and coded for the present purposes as “left‐handed” or “right‐handed”, as described elsewhere.Because the large majority of people with RHLD are left‐handed, any monogenic contributions to RHLD would likely also be strongly penetrant for left‐handedness. We checked whether a specific mutation of interest in the gene ects see , is also66.1We focused on mutations in the 33 RHLD cases which are known to be relatively rare in the general population on the basis of large‐scale genetic databases and predicted to disruptively affect protein sequence, while not being mutated in a set of 34 control subjects see . As noteP < .05), which could have arisen from being mutated in as few as five RHLD cases and no controls.Under a recessive model, no gene was even nominally significant , with five RHLD cases and no controls having heterozygous mutations . Recessive mutations in TCTN1 cause Joubert syndrome , a ciliopathy characterized by cerebellar and brainstem malformations.In the dominant model, TCTN1 missense variant , which has a maximum population frequency of 0.001199 (in ExAC non‐Finnish Europeans). This variant is present in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) as a variant of uncertain significance with potential relevance to Joubert syndrome/Meckel‐Gruber syndrome patients (SCV000634600.1). The other two TCTN1 mutations were a missense variant  and an in‐frame deletion (chr12:111070349 GATA/G), each present in one RHLD case only, and with maximum population frequencies of 0.0008 and 0.0033, respectively.Three of the five RHLD cases shared the same P = .034), with the minor allele C (frequency 0.001305) associated with left‐handedness (odd ratio = 1.24). However, this modest effect does not seem compatible with a role of this variant as a highly penetrant cause of RHLD and left‐handedness.Rs188817098 was also associated with handedness in the UK biobank data set , “left‐right axis specification” (GO:0070986), or sets defined on the basis of visceral laterality phenotypes or disorders,We investigated subsets of genes defined as belonging to specific components of the actin cytoskeleton, which included “actin filament” (GO:0005884), “myosin complex” (GO:0016459), and “cortical actin cytoskeleton” (GO:0030864), but saw no significant increase in mutation rates in RHLD in these sets Table . This maP = .04), RHLD vs left‐handed controls only (P = .004), right‐handed controls vs left‐handed controls (P = .88)  Table . This paP = .0006), while the effect was not significant in the GOAL data set (P = .4) despite having a similar trend of increased mutational load in RHLD cases  Although the TCTN1 variant showed a significant association with left‐handedness, in the expected direction , the effect size was not compatible with a highly penetrant effect. Therefore, this finding remains ambiguous.As RHLD is mostly found in left‐handed people,AR . For most of these genes, there is no clear mechanism that might link them to left‐right axis determination through chiral properties.In the present study, candidate genes that have been tentatively associated with human brain laterality in previous studies showed no evidence for an increase in mutation load in RHLD. The only gene among these that had more mutations in RHLD cases than those in controls was situs inversus of the viscera, when it occurs together with PCD, have shown normal population rates of left‐handedness and left hemisphere language dominance.We also found no evidence that candidate gene sets involved in visceral laterality or PCD have an enrichment of rare, protein‐altering mutations in RHLD. This finding is consistent with the fact that people with MAP2 have recently been associated with left‐handedness by large‐scale GWAS,Intriguingly, it may be that situs inversus of the visceral organs does associate with left‐handedness when not due to mutations affecting the nodal ciliary pathway,The possible link of RHLD to actin cytoskeleton genes will need to be replicated in larger independent data sets. Within the present study, we combined the BIL&GIN and GOAL data sets to maximize the power to detect genetic effects on RHLD, although the functional tasks used to define RHLD differed between these two data sets: hemispheric dominance was defined using a contrast at the sentence level in BIL&GIN, and a word‐level contrast in GOAL see . HoweverGiven the sample size for the present study, we focused on rare, protein‐altering mutations which had the potential to be highly penetrant effects. Whole genome sequence data, of the type produced in the present study, also contain information on noncoding variation. Rare noncoding variation has recently been implicated in neurodevelopmental disorders such as autism,Data sets based on hundreds of thousands of participants, such as the UK biobank,The authors declare no potential conflict of interest.Table S1. All putative mutations in actin cytoskeleton genes. Chr, chromosome; Ref, reference allele; Alt, alternative allele; MAF, maximum minor allele frequency across 1KG; ExAC, gnomAD populations; RS ID, refers the variant identity in dbSNP; AA, amino acid; PrD, probably damaging; PosD, possibly damaging. The RHLD and Ctrl columns show the numbers of these mutations in cases and controls . The 113 remaining genes in the actin cytoskeleton gene set, which had no mutations in this data set, were as follows: ABCB4, ABL2, ACACA, ACTA2, ACTB, ACTR2, ACTR3, ADAM17, AIF1L, ALDOA, AMPH, ANLN, ARC, ARHGAP21, ARHGAP35, ARHGAP6, ARPC1A, ARPC2, ARPC3, AUTS2, BAIAP2, BAIAP2L1, BAIAP2L2, BIN1, C10orf90, CALD1, CAPZA1, CAPZA2, CAPZB, CARMIL2, CASK, CATIP, CD2AP, CDC42EP3, CDH1, CENPQ, CFL2, CLIC4, CNN3, CNR1, CORO1C, CORO2A, CORO2B, CORO6, CRK, CTDP1, CTNNA2, DAPK1, DDX58, DHX9, DMTN, DSTN, FGR, FOXA3, FSCN1, FYB1, GABARAP, GAS2L3, H1F0, HAP1, HAX1, HNRNPC, HSPB7, IFIT5, INTS6, IPP, IQGAP2, IVNS1ABP, KANSL2, KLHL14, KLHL2, KLHL20, MARCKS, MPRIP, MSRB1, MYL2, MYLK3, MYOT, MYOZ1, MYOZ2, MYOZ3, NCOA5, ONECUT2, OPHN1, PGM1, PKNOX2, POU6F1, PPP1R12A, RAB22A, RAB5A, RARA, RND1, SEPT2, SEPT9, SORBS2, STK17B, STK38L, STOML2, TAF5, TARS, TAX1BP3, TLN2, TMEM63B, TOPBP1, TPM1, TPM2, TPM3, TRMT10A, TTC17, USH1G, WAS, WIPF1, ZNF174.Table S2. Mutation analysis of GO:0015629 “actin cytoskeleton” in relation to its child sets, and negative control gene sets of similar sizes to GO:0015629. Set size: number of genes within set. RHLD: instances of genes carrying mutations within RHLD cases; Total: instances of genes carrying mutations in RHLD cases and controls combined. The P‐value is shown from the exact binomial test, where the null probability was .4925 (33/67) participants being RHLD) and alternative hypothesis = “greater”. Negative control sets were identified which had set sizes similar to the actin cytoskeleton set, and no known links to the biology of left‐right asymmetry.Table S3. Mutation analysis of GO:0015629 “actin cytoskeleton” by different handedness groups. Test: instances of genes carrying mutations within the test group; Total: instances of genes carrying mutations in the test and contrast groups. Proportion = Test/Total. Null: null probability, given the proportion of test subjects for the contrast being tested. The P‐value is shown from the exact binomial test, with the alternative hypothesis = “greater”.Table S4. Mutation analysis of GO:0015629 “actin cytoskeleton” within each data set. Test: instances of genes carrying mutations within the test group; Total: instances of genes carrying mutations in the test and contrast groups. Proportion = Test/Total. Null: null probability, given the proportion of test subjects for the contrast being tested. The P‐value is shown from the exact binomial test, with the alternative hypothesis = “greater”.Figure S1. Multidimensional scaling (MDS) to capture overall genomic diversity among the study samples , in relation to the 1000 Genomes populations of known geographic ancestries (dots). 1KG super population codes: AFR, African; EAS, east asian; AMR, Ad Mixed American; EUR, European.Figure S2. Histogram distribution of the manual preference strength variable assessed by the Edinburgh Handedness Inventory (EHI) score across RHLD cases and controls, per data set.Figure S3. Distribution of mutations within the “actin cytoskeleton” gene set.Click here for additional data file.