Gene-based Association Study Research Articles

NetworkGWAS: a network-based approach to discover genetic associations.

While the search for associations between genetic markers and complex traits has led to the discovery of tens of thousands of trait-related genetic variants, the vast majority of these only explain a small fraction of the observed phenotypic variation. One possible strategy to overcome this while leveraging biological prior is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffer from a vast search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, a computationally efficient and statistically sound approach to network-based genome-wide association studies using mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated P-values, which are obtained through circular and degree-preserving network permutations. networkGWAS successfully detects known associations on diverse synthetic phenotypes, as well as known and novel genes in phenotypes from Saccharomycescerevisiae and Homo sapiens. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. https://github.com/BorgwardtLab/networkGWAS.git.

Gene-based association study reveals a distinct female genetic signal in primary hypertension.

Hypertension is a polygenic disease that affects over 1.2 billion adults aged 30-79 worldwide. It is a major risk factor for renal, cerebrovascular, and cardiovascular diseases. The heritability of hypertension is estimated to be high; nevertheless, our understanding of its underlying mechanisms remains scarce and incomplete. This study covered the entries from European ancestry from the UK-Biobank (UKB), with 74,090 cases diagnosed with essential (primary) hypertension and 200,734 controls. We compared the findings from large-scale genome-wide association studies (GWAS) to the gene-based method of proteome-wide association studies (PWAS). We focused on 70 statistically significant associated genes, most of which failed to reach significance in variant-based GWAS. A total of 30% of the PWAS-associated genes were validated against independent cohorts, including the Finnish Biobank. Furthermore, gene-based analyses that were performed on both sexes revealed sex-dependent genetics with a stronger genetic component associated with females. Analysis of systolic and diastolic blood pressure measurements confirms a strong genetic effect associated with females. We demonstrated that gene-based approaches provide insight into the underlying biology of hypertension. Specifically, the expression profiles of the identified genes exposed the enrichment of endothelial cells from multiple organs. Furthermore, females' top-ranked significant genes are involved in cellular immunity. We conclude that studying hypertension and blood pressure via gene-based association methods improves interpretability and exposes sex-dependent genetic effects, which enhances clinical utility.

GWAS across multiple environments and WGCNA suggest the involvement of ZmARF23 in embryonic callus induction from immature maize embryos.

Combined GWAS, WGCNA, and gene-based association studies identified the co-expression network and hub genes for maize EC induction. ZmARF23 bound to ZmSAUR15 promoter and regulated its expression, affecting EC induction. Embryonic callus (EC) induction in immature maize embryos shows high genotype dependence, which limits the application of genetic transformation in transgenic breeding and gene function elucidation in maize. Herein, we conducted a genome-wide association mapping (GWAS) for four EC induction-related traits, namely rate of embryonic callus induction (REC), increased callus diameter (ICD), ratio of shoot formation (RSF), and length of shoot (LS) across different environments. A total of 77 SNPs were significantly associated these traits under three environments and using the averages (across environments). Among these significant SNPs, five were simultaneously detected under multiple environments and 11 had respective phenotypic variation explained > 10%. A total of 257 genes were located in the linkage disequilibrium decay of these REC- and ICD-associated SNPs, of which 178 were responsive to EC induction. According to the expression values of the 178 genes, we performed a weighted gene co-expression network analysis (WGCNA) and revealed an EC induction-associated module and five hub genes. Hub gene-based association studies uncovered that the intragenic variations in GRMZM2G105473 and ZmARF23 influenced EC induction efficiency among different maize lines. Dual-luciferase reporter assay indicated that ZmARF23 bound to the promoter of a known causal gene (ZmSAUR15) for EC induction and positively regulated its expression on the transcription level. Our study will deepen the understanding of genetic and molecular mechanisms underlying EC induction and contribute to the use of genetic transformation in maize.

Exome-wide screening identifies novel rare risk variants for bone mineral density.

BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.

Cytokine- and Vascular Endothelial Growth Factor-Related Gene-Based Genome-Wide Association Study of Low-Dose Ketamine Infusion in Patients with Treatment-Resistant Depression.

Ketamine may work as an anti-inflammatory agent, and it increases the levels of vascular endothelial growth factor (VEGF) in patients with treatment-resistant depression. However, whether genes related to pro-inflammatory and anti-inflammatory cytokines and VEGF may predict the treatment response to ketamine remains unknown.Therefore the aim of this study was to analyze whether specific genes related to inflammatory processes and VEGF were associated with treatment response to low-dose ketamine in patients with treatment-resistant depression. Based on the genome data from our clinical trial, this study was a secondary analysis of candidate genes correlated with different timepoints of depressive symptoms. In total, 65 patients with treatment-resistant depression (n = 21 for ketamine 0.5 mg/kg, 20 for ketamine 0.2 mg/kg, and 24 for normal saline) were genotyped for 684,616 single nucleotide polymorphisms. Genes associated with 80 cytokines (i.e., interleukin [IL]-1, IL-6, tumor necrosis factor-α, and adiponectin) and VEGF (i.e., VEGF and VEGF receptors) were selected for the gene-based genome-wide association study on the antidepressant effect of a ketamine infusion. Specific single nucleotide polymorphisms, including rs2540315 and rs75746675 in IL1R1 and rs79568085 in VEGFC, were related to the rapid (within 240 min) antidepressant effect of a ketamine infusion; specific single nucleotide polymorphisms, such as Affx-20131665 in PIGF and rs8179353, rs8179353, and rs8179353 in TNFRSF8, were associated with the sustained (up to 2 weeks) antidepressant effect of low-dose (combined 0.5mg/kg and 0.2mg/kg) ketamine. Our findings further revealed that genes related to both anti-inflammatory and pro-inflammatory cytokines (i.e., IL-1, IL-2, IL-6, tumor necrosis factor-α, C-reactive protein, and adiponectin) and VEGF-FLK signaling predicted the treatment response to a ketamine infusion in patients with treatment-resistant depression. The synergic modulation of inflammatory and VEGF systems may contribute to the antidepressant effect of ketamine. University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) number: UMIN000016985.

A fast and efficient approach for gene-based association studies of ordinal phenotypes.

Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.

Association studies of genes in a Pb response-associated network in maize (Zea mays L.) reveal that ZmPIP2;5 is involved in Pb tolerance

Lead (Pb) in the soil affects the growth and development of plants and causes damages to the human body through the food chain. Here, we identified and cloned a Pb-tolerance gene ZmPIP2;5 based on a weighted gene co-expression network analysis and gene-based association studies. We showed that ZmPIP2;5 encodes a plasma membrane aquaporin and positively regulated Pb tolerance and accumulation in Arabidopsis and yeast. Overexpression of ZmPIP2;5 increased root length and fresh weight of Arabidopsis seedlings under Pb stress. Heterologous expression of ZmPIP2;5 in yeast caused the enhanced growth speed under Pb treatment and Pb accumulation in yeast cells. A (T/A) SNP in the ZmPIP2;5 promoter affected the expression abundance of ZmPIP2;5 and thereby led to the difference in Pb tolerance among different maize lines. Our study helps to understand the mechanism underlying plant tolerance to Pb stress and provides new ideas for breeding Pb-tolerance maize varieties via molecular marker-assisted selection.

Combined linkage mapping and association analysis uncovers candidate genes for 25 leaf-related traits across three environments in maize.

Combined linkage and association analysis revealed five co-localized genetic loci across multiple environments. The key gene Zm00001d026491 was further verified to influence leaf length by candidate gene association analysis. Leaf morphology and number determine the canopy structure and thus affect crop yield. Herein, the genetic basis and key genes for 25 leaf-related traits, including leaf lengths (LL), leaf widths (LW), and leaf areas (LA) of eight continuous leaves under the tassel, and the number of leaves above the primary ear (LAE), were dissected by using an association panel and a biparental population. Using an intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, 290 quantitative trait loci (QTL) controlling these traits were detected across different locations, among which 115 QTL were individually repeatedly identified in at least two environments. Using the association panel, 165 unique significant single-nucleotide polymorphisms (SNPs) were associated with target traits (P < 2.15E-06), of which 35 were separately detected across multiple environments. In total, 42 pleiotropic QTL/SNPs (pQTL/SNPs) were responsible for at least two of the LL, LW, LA, and LAE traits across multiple environments. Combining the QTL mapping and association study, five unique SNPs were located within the confidence intervals of seven QTL, and 77 genes were identified based on the linkage disequilibrium regions of co-localized SNP loci. Gene-based association studies verified that the intragenic variants in the candidate gene Zm00001d026491 influenced LL of the third leaf counted from the top node. These findings will provide vital information to understanding the genetic basis of leaf-related traits and help to cultivate maize varieties with ideal plant architecture.

Whole-exome rare-variant analysis of Alzheimer's disease and related biomarker traits.

Despite increasing evidence of a role of rare genetic variation in the risk of Alzheimer's disease (AD), limited attention has been paid to its contribution to AD-related biomarker traits indicative of AD-relevant pathophysiological processes. We performed whole-exome gene-based rare-variant association studies (RVASs) of 17 AD-related traits on whole-exome sequencing (WES) data generated in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study (n=450) and whole-genome sequencing (WGS) data from ADNI (n=808). Mutation screening revealed a novel probably pathogenic mutation (PSEN1 p.Leu232Phe). Gene-based RVAS revealed the exome-wide significant contribution of rare coding variation in RBKS and OR7A10 to cognitive performance and protection against left hippocampal atrophy, respectively. The identification of these novel gene-trait associations offers new perspectives into the role of rare coding variation in the distinct pathophysiological processes culminating in AD, which may lead to identification of novel therapeutic and diagnostic targets.

Gene-Based Genome-Wide Association Study Identified Genes for Agronomic Traits in Maize.

A gene integrates the effects of all SNPs in its sequence span, which benefits the genome-wide association study. To explore gene-level variations affecting economic traits in maize, we extended the SNP-based GWAS analysis software Single-RunKing developed by our team to gene-based GWAS, which used the FaST-LMM algorithm to convert the linear mixed model into simple linear model association analysis. An F-test statistic was formulated to test and identify candidate genes. We compared the statistical efficiency of using 80% principal components (EPC), the first principal component (FPC), and all SNP markers (ALLSNP) as independent variables, which predecessors commonly used to integrate SNPs and represent genes. With a Huazhong Agricultural University (HAU) genomic dataset of 2.65M SNPs from 540 maize plants, 34,774 genes were annotated across the whole genome. Genome-wide association studies with 20 agronomic traits were performed using the software developed here. Another maize dataset from the Ames panel (AP) was also analyzed. The EPC method fits the model well and has good statistical efficiency. It not only overcomes the false negative problem when using all SNP markers for analysis (ALLSNP) but also solves the false positive problem of its corresponding simple linear model method EPCLM. Compared with FPC, the EPC method has higher statistical efficiency. A total of 132 quantitative trait genes (QTG) were identified for the 20 traits from HAU maize dataset and one trait of AP maize.

Distribution characteristics of selenium, cadmium and arsenic in rice grains and their genetic dissection by genome-wide association study.

High selenium (Se) and low cadmium (Cd) and arsenic (As) contents in rice grains were good for human health. The genetic basis and relationship of Se, Cd and As concentrations in rice grains are still largely unknown. In the present study, large variations were observed in Se, Cd and As concentrations in brown and milled rice in normal and Se treatment conditions in 307 rice accessions from 3K Rice Genomes Project. Se fertilizer treatment greatly increased Se concentrations but had no obvious changes in concentrations of Cd and As both in brown and milled rice. Total of 237 QTL were identified for Se, Cd and As concentrations in brown and milled rice in normal and Se treatment conditions as well as ratio of concentrations under Se treatment to normal conditions. Only 19 QTL (13.4%) were mapped for concentrations of Se and Cd, Se and As, and Se, Cd and As in the same or adjacent regions, indicating that most Se concentration QTL are independent of Cd and As concentration QTL. Forty-three favorable alleles were identified for 40 candidate genes by gene-based association study and haplotype analysis in 14 important QTL regions. Se-enriched rice variety will be developed by pyramiding favorable alleles at different Se QTL and excluding undesirable alleles at Cd and As QTL, or combining favorable alleles at Se QTL with the alleles at Se-sensitive QTL by marker-assisted selection.

Genome Wide Association Study with Imputed Whole Genome Sequence Data Identifies a 431 kb Risk Haplotype on CFA18 for Congenital Laryngeal Paralysis in Alaskan Sled Dogs.

Congenital laryngeal paralysis (CLP) is an inherited disorder that affects the ability of the dog to exercise and precludes it from functioning as a working sled dog. Though CLP is known to occur in Alaskan sled dogs (ASDs) since 1986, the genetic mutation underlying the disease has not been reported. Using a genome-wide association study (GWAS), we identified a 708 kb region on CFA 18 harboring 226 SNPs to be significantly associated with CLP. The significant SNPs explained 47.06% of the heritability of CLP. We narrowed the region to 431 kb through autozygosity mapping and found 18 of the 20 cases to be homozygous for the risk haplotype. Whole genome sequencing of two cases and a control ASD, and comparison with the genome of 657 dogs from various breeds, confirmed the homozygous status of the risk haplotype to be unique to the CLP cases. Most of the dogs that were homozygous for the risk allele had blue eyes. Gene annotation and a gene-based association study showed that the risk haplotype encompasses genes implicated in developmental and neurodegenerative disorders. Pathway analysis showed enrichment of glycoproteins and glycosaminoglycans biosynthesis, which play a key role in repairing damaged nerves. In conclusion, our results suggest an important role for the identified candidate region in CLP.

Association mapping uncovers maize ZmbZIP107 regulating root system architecture and lead absorption under lead stress.

Lead (Pb) is a highly toxic contaminant to living organisms and the environment. Excessive Pb in soils affects crop yield and quality, thus threatening human health via the food chain. Herein, we investigated Pb tolerance among a maize association panel using root bushiness (BSH) under Pb treatment as an indicator. Through a genome-wide association study of relative BSH, we identified four single nucleotide polymorphisms (SNPs) and 30 candidate genes associated with Pb tolerance in maize seedlings. Transcriptome analysis showed that four of the 30 genes were differentially responsive to Pb treatment between two maize lines with contrasting Pb tolerance. Among these, the ZmbZIP107 transcription factor was confirmed as the key gene controlling maize tolerance to Pb by using gene-based association studies. Two 5’ UTR_variants in ZmbZIP107 affected its expression level and Pb tolerance among different maize lines. ZmbZIP107 protein was specifically targeted to the nucleus and ZmbZIP107 mRNA showed the highest expression in maize seedling roots among different tissues. Heterologous expression of ZmbZIP107 enhanced rice tolerance to Pb stress and decreased Pb absorption in the roots. Our study provided the basis for revelation of the molecular mechanism underlying Pb tolerance and contributed to cultivation of Pb-tolerant varieties in maize.

The Genetic Architecture of the Etiology of Lower Extremity Peripheral Artery Disease: Current Knowledge and Future Challenges in the Era of Genomic Medicine.

Lower extremity artery disease (LEAD), caused by atherosclerotic obstruction of the arteries of the lower limb extremities, has exhibited an increase in mortality and morbidity worldwide. The phenotypic variability of LEAD is correlated with its complex, multifactorial etiology. In addition to traditional risk factors, it has been shown that the interaction between genetic factors (epistasis) or between genes and the environment potentially have an independent role in the development and progression of LEAD. In recent years, progress has been made in identifying genetic variants associated with LEAD, by Genome-Wide Association Studies (GWAS), Whole Exome Sequencing (WES) studies, and epigenetic profiling. The aim of this review is to present the current knowledge about the genetic factors involved in the etiopathogenic mechanisms of LEAD, as well as possible directions for future research. We analyzed data from the literature, starting with candidate gene-based association studies, and then continuing with extensive association studies, such as GWAS and WES. The results of these studies showed that the genetic architecture of LEAD is extremely heterogeneous. In the future, the identification of new genetic factors will allow for the development of targeted molecular therapies, and the use of polygenic risk scores (PRS) to identify individuals at an increased risk of LEAD will allow for early prophylactic measures and personalized therapy to improve their prognosis.

Genome-wide analysis of the serine carboxypeptidase-like (SCPL) proteins in Brassica napus L.

The serine carboxypeptidase-like protein (SCPL) family plays a key part in plant growth, development and stress responses. However, the serine carboxypeptidase-like (SCPL) proteins in Brassica napus L. (B. napus) have not been reported yet. Here, we identified a total of 117 putative SCPL genes in B. napus, which were unevenly distributed on all 19 chromosomes and were divided into three groups (carboxypeptidase Ⅰ to Ⅲ) according to their phylogenetic relationships. Synteny and duplication analysis revealed that the SCPL gene family of B. napus was amplified during allopolyploidization, in which the whole genome triplication and dispersed duplication played critical roles. After the separation of Brassica and Arabidopsis lineages, orthologous gene analysis showed that many SCPL genes were lost during the evolutionary process in B. rapa, B. oleracea and B. napus. Subsequently, the analyses of the gene structure, conserved motifs, cis-element and expression patterns showed that the members in the same group were highly conserved. Furthermore, candidate gene based association study suggested the role of BnSCPL52 in controlling seed number per silique, seed weight and silique length and a CAPS marker was developed to distinguish different haplotypes. Our results provide an overview of rapeseed SCPL genes that enable us for further functional research and benefit the marker-assisted breeding in Brassica napus.

The impact of rare germline variants on human somatic mutation processes

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data.

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis figure based on UK Biobank data for asthma shows that the proposed method is applicable to GWAS.

Etiologic Puzzle of Coronary Artery Disease: How Important Is Genetic Component?

In the modern era, coronary artery disease (CAD) has become the most common form of heart disease and, due to the severity of its clinical manifestations and its acute complications, is a major cause of morbidity and mortality worldwide. The phenotypic variability of CAD is correlated with the complex etiology, multifactorial (caused by the interaction of genetic and environmental factors) but also monogenic. The purpose of this review is to present the genetic factors involved in the etiology of CAD and their relationship to the pathogenic mechanisms of the disease. Method: we analyzed data from the literature, starting with candidate gene-based association studies, then continuing with extensive association studies such as Genome-Wide Association Studies (GWAS) and Whole Exome Sequencing (WES). The results of these studies revealed that the number of genetic factors involved in CAD etiology is impressive. The identification of new genetic factors through GWASs offers new perspectives on understanding the complex pathophysiological mechanisms that determine CAD. In conclusion, deciphering the genetic architecture of CAD by extended genomic analysis (GWAS/WES) will establish new therapeutic targets and lead to the development of new treatments. The identification of individuals at high risk for CAD using polygenic risk scores (PRS) will allow early prophylactic measures and personalized therapy to improve their prognosis.

SumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics.

Gene-based association analysis is an effective gene-mapping tool. Many gene-based methods have been proposed recently. However, their power depends on the underlying genetic architecture, which is rarely known in complex traits, and so it is likely that a combination of such methods could serve as a universal approach. Several frameworks combining different gene-based methods have been developed. However, they all imply a fixed set of methods, weights and functional annotations. Moreover, most of them use individual phenotypes and genotypes as input data. Here, we introduce sumSTAAR, a framework for gene-based association analysis using summary statistics obtained from genome-wide association studies (GWAS). It is an extended and modified version of STAAR framework proposed by Li and colleagues in 2020. The sumSTAAR framework offers a wider range of gene-based methods to combine. It allows the user to arbitrarily define a set of these methods, weighting functions and probabilities of genetic variants being causal. The methods used in the framework were adapted to analyse genes with large number of SNPs to decrease the running time. The framework includes the polygene pruning procedure to guard against the influence of the strong GWAS signals outside the gene. We also present new improved matrices of correlations between the genotypes of variants within genes. These matrices estimated on a sample of 265,000 individuals are a state-of-the-art replacement of widely used matrices based on the 1000 Genomes Project data.

ExPheWas: a platform for cis-Mendelian randomization and gene-based association scans.

Establishing the relationship between protein-coding genes and phenotypes has the potential to inform on the molecular etiology of diseases. Here, we describe ExPheWas (exphewas.ca), a gene-based phenome-wide association study browser and platform that enables the conduct of gene-based Mendelian randomization. The ExPheWas data repository includes sex-stratified and sex-combined gene-based association results from 26 616 genes with 1746 phenotypes measured in up to 413 133 individuals from the UK Biobank. Interactive visualizations are provided through a browser to facilitate data exploration supported by false discovery rate control, and it includes tools for enrichment analysis. The interactive Mendelian randomization module in ExPheWas allows the estimation of causal effects of a genetically predicted exposure on an outcome by using genetic variation in a single gene as the instrumental variable.