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bioRxiv2018

Towards automation of germline variant curation inclinical cancer genetics

Cancer care professionals are confronted with interpreting results from multiplexed gene sequencing of patients at hereditary risk for cancer. Assessments for variant classification now require orthogonal data searches, requiring aggregation of multiple lines of evidence from diverse resources. The burden of evidence for each variant to meet thresholds for pathogenicity or actionability now poses a growing challenge for those seeking to counsel patients and families following germline genetic testing. A computational algorithm that automates, provides uniformity and significantly accelerates this interpretive process is needed. The tool described here, Pathogenicity of Mutation Analyzer (PathoMAN) automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein and disease specific information from public sources. We compared expert manually curated variant data from studies on (i) prostate cancer (ii) breast cancer and (iii) ClinVar to assess performance. PathoMAN achieves high concordance (83.1% pathogenic, 75.5% benign) and negligible discordance (0.04% pathogenic, 0.9% benign) when contrasted against expert curation. Some loss of resolution (8.6% pathogenic, 23.64% benign) and gain of resolution (6.6% pathogenic, 1.6% benign) was also observed. We highlight the advantages and weaknesses related to the programmable automation of variant classification. We also propose a new nosology for the five ACMG classes to facilitate more accurate reporting to ClinVar. The proposed refinements will enhance utility of ClinVar to allow further automation in cancer genetics. PathoMAN will reduce the manual workload of domain level experts. It provides a substantial advance in rapid classification of genetic variants by generating robust models using a knowledge-base of diverse genetic data https://pathoman.mskcc.org.

genetics↗

bioRxiv2018

Genome-wide association study of social genetic effects on 170 phenotypes in laboratory mice

The phenotype of one individual can be affected not only by the individuals own genotypes (direct genetic effects, DGE) but also by genotypes of interacting partners (indirect genetic effects, IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genome-wide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from "peers" and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation. We leveraged a dataset of 170 behavioural, physiological and morphological phenotypes measured in 1,812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated laboratory mice housed in the same cage. We developed methods for igeGWAS in this context and identified 24 significant IGE loci for 17 phenotypes (FDR < 10%). There was no overlap between IGE loci and DGE loci for the same phenotype, which was consistent with the moderate genetic correlations between DGE and IGE for the same phenotype estimated using polygenic models. Finally, we fine-mapped seven significant IGE loci to individual genes and confirmed, in an experiment with a knockout model, that Epha4 gives rise to IGE on stress-coping strategy and wound healing. Our results demonstrate the potential for igeGWAS to identify IGE genes and shed some light into the mechanisms of peer influence.

genetics↗

bioRxiv2018

Genetic contributions to ectopic sperm cell migration in Caenorhabditis nematodes

Reproductive barriers involving gametic incompatibilities can act to enhance population divergence and promote the persistence of species boundaries. Observing gametic interactions in internal fertilizing organisms, however, presents a considerable practical challenge to characterizing mechanisms of such gametic isolation. Here we exploit the transparency of Caenorhabditis nematodes to investigate gametic isolation mediated by sperm that can migrate to ectopic locations, with this sperm invasion capable of inducing female sterility and premature death. As a step toward identifying genetic factors and mechanisms associated with female susceptibility to sperm invasion, we characterized a panel of 25 C. elegans genetic mutants to test for effects on the incidence and severity of sperm invasion in both conspecific and interspecies matings. We found genetic perturbations to contribute to distinct patterns of susceptibility that identify ovulation dynamics and sperm guidance cues as modulators of ectopic sperm migration incidence and severity. Genotypes confer distinctive phenotypic sensitivities to the sperm from conspecific C. elegans males versus heterospecific C. nigoni males, implicating evolution of functional divergence in the history of these species for components of sperm-reproductive tract interactions. Sexually-antagonistic co-evolution within species that drives divergent trait and molecular evolution between species provides a working model to explain mismatched species-specific gametic interactions that promote or mitigate ectopic sperm migration.\n\nArticle SummaryHere we exploit the transparency of C. elegans roundworms to investigate reproductive barriers between species resulting from sperm-cell invasion into ectopic tissues, causing female sterility and premature death. We quantify female sensitivity to sperm invasion from conspecific and interspecific matings in a panel of 25 C. elegans genetic mutants, to demonstrate how ovulation dynamics and sperm guidance cues act as modulators of ectopic sperm-cell migration incidence and severity. We observe both conserved and divergent responses to different species, implicating evolution of functional divergence for components of sperm-reproductive tract interactions consistent with the outcome of sexually-antagonistic co-evolution.

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bioRxiv2018

Subset-Based Analysis using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes

ObjectivesClassical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. We aim to enhance the discovery of novel associated loci in the presence of heterogeneity of genetic effects in sub-groups defined by an environmental factor.\n\nMethodsWe present a p-value Assisted Subset Testing for Associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure. We conduct simulation studies and provide two data examples.\n\nResultsSimulation studies show that our proposal is more powerful than methods based on marginal associations in the presence of G-E interactions and maintains comparable power even in their absence. Both data examples demonstrate that our method can increase power to detect overall genetic associations and identify novel studies/phenotypes that contribute to the association.\n\nConclusionsOur proposed method can be a useful screening tool to identify candidate single nucleotide polymorphisms (SNPs) that are potentially associated with the trait(s) of interest for further validation. It also allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.

genetics↗

bioRxiv2018

Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.\n\nMethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration\n\nResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimers disease - outcomes for which large-scale trial data were unavailable.\n\nConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.\n\nFundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.\n\nCondensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimers disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

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bioRxiv2018

Genetic Modifiers of Pathogenic LRRK2 G2019S Neurodegeneration in Drosophila

Disease phenotypes can be highly variable among individuals with the same pathogenic mutation. There is increasing evidence that background genetic variation is a strong driver of disease variability in addition to the influence of environment. To understand the genotype-phenotype relationship that determines the expressivity of a pathogenic mutation, a large number of backgrounds must be studied. This can be efficiently achieved using model organism collections such as the Drosophila Genetic Reference Panel (DGRP). Here, we used the DGRP to assess the variability of locomotor dysfunction in a LRRK2 G2019S Drosophila melanogaster model of Parkinsons disease. We find substantial variability in the LRRK2 G2019S locomotor phenotype in different DGRP backgrounds. A genome-wide association study for candidate genetic modifiers reveals 177 genes that drive wide phenotypic variation, including 19 top association genes. Genes involved in the outgrowth and regulation of neuronal projections are enriched in these candidate modifiers. RNAi functional testing of the top association and neuronal projection-related genes reveals that pros, pbl, ct and CG33506 significantly modify age-related dopamine neuron loss and associated locomotor dysfunction in the Drosophila LRRK2 G2019S model. These results demonstrate how natural genetic variation can be used as a powerful tool to identify genes that modify disease-related phenotypes. We report novel candidate modifier genes for LRRK2 G2019S that may be used to interrogate the link between LRRK2, neurite regulation and neuronal degeneration in Parkinsons disease.

genetics↗

bioRxiv2018

Analysis of genetic control and QTL mapping of essential wheat grain quality traits in a recombinant inbred population

Wheat cultivars are genetically crossed for improving end use quality for apt traits as per need of baking industry and broad consumers preferences. The processing and baking qualities of bread wheat underlie into genetic make-up of a variety and influence by environmental factors and their interactions. WL711 and C306 derived recombinant inbred lines (RILs) population of 206 was used for phenotyping of quality related traits in three different environmental conditions. The genetic analysis of quality traits showed considerable variation for measurable quality traits with normal distribution and transgressive segregation across the years. From the 206 RIL, few RILs found to be superior to those of the parental cultivars for key quality traitsindicating their potential usefor improvement of end use quality and also suggestingprobability of finding new alleles and allelic combinations from the RIL population. A genetic linkage map including 346 markers was constructed withtotal map distance of 4526.8cM andinterval distance between adjacent markersof 12.9cM. Mapping analysis identified 38 putative QTLs for 13 quality related traits with QTLs explaining 7.9% - 16.8% phenotypic variation spanning over 14 chromosomes i.e. 1A, 1B, 1D, 2A, 2D, 3B, 3D, 4A, 4B, 4D, 5D, 6A, 7A and 7B. Major novel QTLs regions for quality traits have been identified on several chromosome in studied RIL population posing their potential role in marker assisted selection for better bread making quality after validation.

genetics↗

bioRxiv2018

Longevity defined as top 10% survivors is transmitted as a quantitative genetic trait: results from large three-generation datasets

Survival to extreme ages clusters within families. However, identifying genetic loci conferring longevity and low morbidity in such longevous families is challenging. There is debate concerning the survival percentile that best isolates the genetic component in longevity. Here, we use three-generational mortality data from two large datasets, UPDB (US) and LINKS (Netherlands). We studied 21,046 unselected families containing index persons, their parents, siblings, spouses, and children, comprising 321,687 individuals. Our analyses provide strong evidence that longevity is transmitted as a quantitative genetic trait among survivors up to the top 10% of their birth cohort. We subsequently showed a survival advantage, mounting to 31%, for individuals with top 10% surviving first and second-degree relatives in both databases and across generations, even in the presence of non-longevous parents. To guide future genetic studies, we suggest to base case selection on top 10% survivors of their birth cohort with equally long-lived family members.

genetics↗

bioRxiv2018

Systematic classification of shared components of genetic risk for common human diseases

Disease classification is fundamental to clinical practice, but current taxonomies do not necessarily reflect the pathophysiological processes that are common or unique to different disorders, such as those determined by genetic risk factors. Here, we use routine healthcare data from the 500,000 participants in the UK Biobank to map genome-wide associations across 19,628 diagnostic terms. We find that 3,510 independent genetic risk loci affect multiple clinical phenotypes, which we cluster into 629 distinct disease association profiles. We use multiple approaches to link clusters to different underlying biological pathways and show how these clusters define the genetic architecture of common medical conditions, including hypertension and immune-mediated diseases. Finally, we demonstrate how clusters can be utilised to re-define disease relationships and to inform therapeutic strategies.\n\nOne sentence summarySystematic classification of genetic risk factors reveals molecular connectivity of human diseases with clinical implications

genetics↗

bioRxiv2018

Genome Wide Meta-Analysis identifies new loci associated with cardiac phenotypes and uncovers a common genetic signature shared by heart function and Alzheimer’s disease

AimsEchocardiography has become an indispensable tool for the study of heart performance, improving the monitoring of individuals with cardiac diseases. Diverse genetic factors associated with echocardiographic measures of heart structure and functions have been previously reported. The impact of several apoptotic genes in heart development identified in experimental models prompted us to assess their potential association with indicators of human cardiac function. This study started with the aim to investigate the possible association of variants of apoptotic genes with echocardiographic traits and to identify new genetic markers associated with cardiac function.\n\nMethods and resultsGenome wide data from different studies were obtained from public repositories. After quality control and imputation, association analyses confirm the role of caspases and other apoptosis related genes with cardiac phenotypes. Moreover, enrichment analysis showed an over-representation of genes, including some apoptotic regulators, associated with Alzheimers disease (AD). We further explored this unexpected observation which was confirmed by genetic correlation analyses.\n\nConclusionsOur findings show the association of apoptotic gene variants with echocardiographic indicators of heart function and reveal a novel potential genetic link between echocardiographic measures in healthy populations and cognitive decline later on in life. These findings may have important implications for preventative strategies combating Alzheimers disease.

genetics↗

bioRxiv2018

Genome-Wide Control of Population Structure and Relatedness in Genetic Association Studies via Linear Mixed Models with Orthogonally Partitioned Structure

Linear mixed models (LMMs) have become the standard approach for genetic association testing in the presence of sample structure. However, the performance of LMMs has primarily been evaluated in relatively homogeneous populations of European ancestry, despite many of the recent genetic association studies including samples from worldwide populations with diverse ancestries. In this paper, we demonstrate that existing LMM methods can have systematic miscalibration of association test statistics genome-wide in samples with heterogenous ancestry, resulting in both increased type-I error rates and a loss of power. Furthermore, we show that this miscalibration arises due to varying allele frequency differences across the genome among populations. To overcome this problem, we developed LMM-OPS, an LMM approach which orthogonally partitions diverse genetic structure into two components: distant population structure and recent genetic relatedness. In simulation studies with real and simulated genotype data, we demonstrate that LMM-OPS is appropriately calibrated in the presence of ancestry heterogeneity and outperforms existing LMM approaches, including EMMAX, GCTA, and GEMMA. We conduct a GWAS of white blood cell (WBC) count in an admixed sample of 3,551 Hispanic/Latino American women from the Womens Health Initiative SNP Health Association Resource where LMM-OPS detects genome-wide significant associations with corresponding p-values that are one or more orders of magnitude smaller than those from competing LMM methods. We also identify a genome-wide significant association with regulatory variant rs2814778 in the DARC gene on chromosome 1, which generalizes to Hispanic/Latino Americans a previous association with reduced WBC count identified in African Americans.

genetics↗

bioRxiv2018

Genome-wide association studies in Samoans give insight into the genetic etiology of fasting serum lipid levels.

The current understanding of the genetic architecture of lipids has largely come from genome-wide association studies. To date, few studies have examined the genetic architecture of lipids in Polynesians, and none have in Samoans, whose unique population history, including many population bottlenecks, may provide insight into the biological foundations of variation in lipid levels. Here we performed a genome-wide association study of four fasting serum lipid levels: total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglycerides (TG) in a sample of 2,849 Samoans, with validation genotyping for associations in a replication cohort comprising 1,798 Samoans and American Samoans. We identified multiple genome-wide significant associations (P < 5 x 10-8) previously seen in other populations - APOA1 with TG, CETP with HDL, and APOE with TC and LDL - and several suggestive associations (P < 1 x 10-5), including an association of variants downstream of MGAT1 and RAB21 with HDL. However, we observed different association signals for variants near APOE than what has been previously reported in non-Polynesian populations. The association with several known lipid loci combined with the newly-identified associations with variants near MGAT1 and RAB21 suggest that while some of the genetic architecture of lipids is shared between Samoans and other populations, part of the genetic architecture may be Polynesian-specific.

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bioRxiv2018

Genetic And Epigenetic Fine Mapping Of Complex Trait Associated Loci In The Human Liver

Deciphering the impact of genetic variation on gene regulation is fundamental to understanding common, complex human diseases. Although histone modifications are important markers of gene regulatory regions of the genome, any specific histone modification has not been assayed in more than a few individuals in the human liver. As a result, the impacts of genetic variation that direct histone modification states in the liver are poorly understood. Here, we generate the most comprehensive genome-wide dataset of two epigenetic marks, H3K4me3 and H3K27ac, and annotate thousands of putative regulatory elements in the human liver. We integrate these findings with genome-wide gene expression data collected from the same human liver tissues and high-resolution promoter-focused chromatin interaction maps collected from human liver-derived HepG2 cells. We demonstrate widespread functional consequences of natural genetic variation on putative regulatory element activity and gene expression levels. Leveraging these extensive datasets, we fine-map a total of 77 GWAS loci that have been associated with at least one complex phenotype. Our results contribute to the repertoire of genes and regulatory mechanisms governing complex disease development and further the basic understanding of genetic and epigenetic regulation of gene expression in the human liver tissue.

genetics↗

bioRxiv2018

Variably methylated regions in the newborn epigenome: environmental, genetic and combined influences

BackgroundEpigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. We examined the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs), defined as consecutive CpGs showing the highest variability of DNAm in 4 independent cohorts (PREDO, DCHS, UCI, MoBa, N=2,934).\n\nResultsWe used Akaikes information criterion to test which factors best explained variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E) including maternal demographic, psychosocial and metabolism related phenotypes, genotypes in cis (G), or their additive (G+E) or interaction (GxE) effects. G+E and GxE models consistently best explained variability in DNAm of VMRs across the cohorts, with G explaining the remaining sites best. VMRs best explained by G, GxE or G+E, as well as their associated functional genetic variants (predicted using deep learning algorithms), were located in distinct genomic regions, with different enrichments for transcription and enhancer marks. Genetic variants of not only G and G+E models, but also of variants in GxE models were significantly enriched in genome wide association studies (GWAS) for complex disorders.\n\nConclusionGenetic and environmental factors in combination best explain DNAm at VMRs. The CpGs best explained by G, G+E or GxE are functionally distinct. The enrichment of GxE variants in GWAS for complex disorders supports their importance for disease risk.

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bioRxiv2018

Genetic networks underlying natural variation in basal and induced activity levels in Drosophila melanogaster

Exercise is recommended by health professionals across the globe as part of a healthy lifestyle to prevent and/or treat the consequences of obesity. While overall, the health benefits of exercise and an active lifestyle are well understood, very little is known about how genetics impacts an individuals inclination for and response to exercise. To address this knowledge gap, we investigated the genetic architecture underlying natural variation in activity levels in the model system Drosophila melanogaster. Activity levels were assayed in the Drosophila Genetics Reference Panel 2 fly strains at baseline and in response to a gentle exercise treatment using the Rotational Exercise Quantification System. We found significant, sex-dependent variation in both activity measures and identified over 100 genes that contribute to basal and induced exercise activity levels. This gene set was enriched for genes with functions in the central nervous system and in neuromuscular junctions and included several candidate genes with known activity phenotypes such as flightlessness or uncoordinated movement. Interestingly, there were also several chromatin proteins among the candidate genes, two of which were validated and shown to impact activity levels. Thus, the study described here reveals the complex genetic architecture controlling basal and exercise-induced activity levels in D. melanogaster and provides a resource for exercise biologists.

genetics↗

bioRxiv2018

Genetics of human plasma lipidome: Understanding lipid metabolism and its link to diseases beyond traditional lipids

AimGenetic investigation of human plasma lipidome to get insights into lipid-related disorders beyond traditional lipid measures.\n\nMethods and ResultsWe performed a genome-wide association study (GWAS) of 141 lipid species (n=2,181 individuals), followed by phenome-wide scans (PheWAS) with 44 clinical endpoints related to cardiometabolic, psychiatric and gastrointestinal disorders (n=456,941 individuals). SNP-based heritability for lipid species ranged from 0.10-0.54. Lipids with long-chain polyunsaturated fatty acids showed higher heritability and genetic sharing, suggesting considerable genetic regulation at acyl chains levels. We identified 35 genomic regions associated with at least one lipid species (P<5x10-8), revealing 37 new SNP-lipid species pair associations e.g. new association between ABCG5/8 and CE(20:2;0). PheWAS of lipid-species-associated loci suggested new associations of BLK with obesity, FADS2 with thrombophlebitis, and BLK and SPTLC3 with gallbladder disease (false discovery rate <0.05). The association patterns of lipid-species-associated loci supplied clues to their probable roles in lipid metabolism e.g. suggestive role of SYNGR1, MIR100HG, and PTPRN2 in desaturation and/or elongation of fatty acids. At known lipid loci (FADS2, APOA5 and LPL), genetic associations provided detailed insights to their roles in lipid biology and diseases. We also show that traditional lipid measures may fail to capture lipids such as lysophospatidylcholines (LPCs) and phosphatidylcholines (PCs) that are potential disease risk factors, but are not included in routine screens. The full genome-wide association statistics are available on the web-based database (http://35.205.141.92).\n\nConclusionOur study reveals genetic regulation of plasma lipidome and highlights the potential of lipidomic profiling in disease gene mapping.

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bioRxiv2018

A SIMPLE APPROXIMATION TO THE BIAS IN THE GENETIC EFFECT ESTIMATES WHEN MULTIPLE DISEASE STATES SHARE A CLINICAL DIAGNOSIS

Case-control genome-wide association (CC-GWAS) studies might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease, cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished in a biomarker evaluation that might not be feasible on the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or non-genetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a logistic regression model with the clinical diagnosis as an outcome variable to the estimates in the relationship to the true disease state of interest. We performed simulation studies to assess accuracy of the approximation that weve derived. We next applied the derived approximation to the analysis of the genetic basis of innate immune system of Alzheimers disease.

genetics↗

bioRxiv2018

Integrative analysis of Dupuytren’s disease identifies novel risk locus and reveals a shared genetic etiology with BMI

Dupuytrens disease is the common inherited tissue-specific fibrotic disorder. Its characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand, with an onset typically in the sixth decade of life. Although genome-wide association studies (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytrens disease by integrating the most recent, largest GWAS (ncases = 3,871, ncontrols = 4,686) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome-wide association study (TWAS). We identify 43 tissue-specific gene associations with Dupuytrens risk, one of which resides at least 0.5 Mb away from the 24 risk regions previously identified. We also estimate the genome-wide genetic correlation between Dupuytrens disease and 45 complex traits and find significant genetic correlations between Dupuytrens disease and body mass index ([Formula], P = 1.6 x 10-6), type II diabetes ([Formula], P = 1.7 x 10-4), triglycerides ([Formula], P = 3.5 x 10-4), and high-density lipoprotein ([Formula], P = 4.1 x 10-4), which suggests a shared genetic etiology. We further refine the genome-wide genetic correlation signal to identify 8 regions significantly negatively correlated with BMI and 3 regions significantly correlated (1 positively and 2 negatively correlated) with HDL; none of these regions contained the novel gene association identified by TWAS. Our results are consistent with previous epidemiological findings which show that lower BMI increases risk for Dupuytrens disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytrens disease and serve as a starting point for functional validation.

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