Shared genetic correlations between kidney diseases and sepsis.

Background: Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated. Methods: Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis. Results: In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them. Conclusions: This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

CoPheScan: phenome-wide association studies accounting for linkage disequilibrium.

Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.

The loci of environmental adaptation in a model eukaryote.

While the underlying genetic changes have been uncovered in some cases of adaptive evolution, the lack of a systematic study prevents a general understanding of the genomic basis of adaptation. For example, it is unclear whether protein-coding or noncoding mutations are more important to adaptive evolution and whether adaptations to different environments are brought by genetic changes distributed in diverse genes and biological processes or concentrated in a core set. We here perform laboratory evolution of 3360 Saccharomyces cerevisiae populations in 252 environments of varying levels of stress. We find the yeast adaptations to be primarily fueled by large-effect coding mutations overrepresented in a relatively small gene set, despite prevalent antagonistic pleiotropy across environments. Populations generally adapt faster in more stressful environments, partly because of greater benefits of the same mutations in more stressful environments. These and other findings from this model eukaryote help unravel the genomic principles of environmental adaptation.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits.

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

A robust cis-Mendelian randomization method with application to drug target discovery.

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.

Genomic correlation, shared loci, and causal relationship between insomnia and psoriasis: a large-scale genome-wide cross-trait analysis.

Psoriasis and insomnia have co-morbidities, however, their common genetic basis is still unclear. We analyzed psoriasis and insomnia with summary statistics from genome-wide association studies. We first quantified overall genetic correlations, then ascertained multiple effector loci and expression-trait associations, and lastly, we analyzed the causal effects between psoriasis and insomnia. A prevalent genetic link between psoriasis and insomnia was found, four pleiotropic loci affecting psoriasis and insomnia were identified, and 154 genes were shared, indicating a genetic link between psoriasis and insomnia. Yet, there is no causal relationship between psoriasis and insomnia by two-sample Mendelian randomization. We discovered a genetic connection between insomnia and psoriasis driven by biological pleiotropy and unrelated to causation. Cross-trait analysis indicates a common genetic basis for psoriasis and insomnia. The results of this study highlight the importance of sleep management in the pathogenesis of psoriasis.

The Waxy Gene Has Pleiotropic Effects on Hot Water-Soluble and -Insoluble Amylose Contents in Rice (Oryza sativa) Grains.

Rice (Oryza sativa) is a cereal crop with a starchy endosperm. Starch is composed of amylose and amylopectin. Amylose content (AC) is the principal determinant of rice quality, but varieties with similar ACs can still vary substantially in their quality. In this study, we analyzed the total AC (TAC) and its constituent fractions, the hot water-soluble amylose content (SAC) and hot water-insoluble amylose content (IAC), in two sets of related chromosome segment substitution lines of rice with a common genetic background grown in two years. We searched for quantitative trait loci (QTLs) associated with SAC, IAC, and TAC and identified one common QTL (qSAC-6, qIAC-6, and qTAC-6) on chromosome 6. Map-based cloning revealed that the gene underlying the trait associated with this common QTL is Waxy (Wx). An analysis of the colors of soluble and insoluble starch-iodine complexes and their λmax values (wavelengths at the positions of their peak absorbance values) as well as gel permeation chromatography revealed that Wx is responsible for the biosynthesis of amylose, comprising a large proportion of the soluble fractions of the SAC. Wx is also involved in the biosynthesis of long chains of amylopectin, comprising the hot water-insoluble fractions of the IAC. These findings highlight the pleiotropic effects of Wx on the SAC and IAC. This pleiotropy indicates that these traits have a positive genetic correlation. Therefore, further studies of rice quality should use rice varieties with the same Wx genotype to eliminate the pleiotropic effects of this gene, allowing the independent relationship between the SAC or IAC and rice quality to be elucidated through a multiple correlation analysis. These findings are applicable to other valuable cereal crops as well.

Pleiotropy of positive selection in ancient ACE2 suggests an alternative hypothesis for bat-specific adaptations to host coronaviruses.

Angiotensin-convertingenzyme 2 (ACE2) has dual functions, regulating cardiovascular physiology and serving as the receptor for coronaviruses. Bats, the only true flying mammals and natural viral reservoirs, have evolved positive alterations in traits related to both functions of ACE2. This suggests significant evolutionary changes in ACE2 during bat evolution. To test this hypothesis, we examine the selection pressure in ACE2 along the ancestral branch of all bats (AncBat-ACE2), where powered flight and bat-coronavirus coevolution occurred, and detect a positive selection signature. To assess the functional effects of positive selection, we resurrect AncBat-ACE2 and its mutant (AncBat-ACE2-mut) created by replacing the positively selected sites. Compared to AncBat-ACE2-mut, AncBat-ACE2 exhibits stronger enzymatic activity, enhances mice's performance in exercise fatigue, and shows lower affinity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our findings indicate the functional pleiotropy of positive selection in the ancient ACE2 of bats, providing an alternative hypothesis for the evolutionary origin of bats' defense against coronaviruses.

Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis.

OBJECTIVE: This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms. METHODS: This genome-wide pleiotropic association study uses multiple statistical methods to systematically analyse the shared genetic basis between five respiratory diseases (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung cancer and snoring) using the largest publicly available genome wide association studies summary statistics. The missions of this study are to evaluate global and local genetic correlations, to identify pleiotropic loci, to elucidate biological pathways at the multiomics level and to explore causal relationships between respiratory diseases. Data were collected from 27 November 2022 to 30 March 2023 and analysed from 14 April 2023 to 13 July 2023. MAIN OUTCOMES AND MEASURES: The primary outcomes are shared genetic loci, pleiotropic genes, biological pathways and estimates of genetic correlations and causal effects. RESULTS: Significant genetic correlations were found for 10 paired traits in 5 respiratory diseases. Cross-Phenotype Association identified 12 400 significant potential pleiotropic single-nucleotide polymorphism at 156 independent pleiotropic loci. In addition, multitrait colocalisation analysis identified 15 colocalised loci and a subset of colocalised traits. Gene-based analyses identified 432 potential pleiotropic genes and were further validated at the transcriptome and protein levels. Both pathway enrichment and single-cell enrichment analyses supported the role of the immune system in respiratory diseases. Additionally, five pairs of respiratory diseases have a causal relationship. CONCLUSIONS AND RELEVANCE: This study reveals the common genetic basis and pleiotropic genes among respiratory diseases. It provides strong evidence for further therapeutic strategies and risk prediction for the phenomenon of respiratory disease comorbidity.

Pleiotropy alleviates the fitness costs associated with resource allocation trade-offs in immune signalling networks.

Many genes and signalling pathways within plant and animal taxa drive the expression of multiple organismal traits. This form of genetic pleiotropy instigates trade-offs among life-history traits if a mutation in the pleiotropic gene improves the fitness contribution of one trait at the expense of another. Whether or not pleiotropy gives rise to conflict among traits, however, likely depends on the resource costs and timing of trait deployment during organismal development. To investigate factors that could influence the evolutionary maintenance of pleiotropy in gene networks, we developed an agent-based model of co-evolution between parasites and hosts. Hosts comprise signalling networks that must faithfully complete a developmental programme while also defending against parasites, and trait signalling networks could be independent or share a pleiotropic component as they evolved to improve host fitness. We found that hosts with independent developmental and immune networks were significantly more fit than hosts with pleiotropic networks when traits were deployed asynchronously during development. When host genotypes directly competed against each other, however, pleiotropic hosts were victorious regardless of trait synchrony because the pleiotropic networks were more robust to parasite manipulation, potentially explaining the abundance of pleiotropy in immune systems despite its contribution to life history trade-offs.

Identification of pleiotropic and specific therapeutic targets for cardio-cerebral diseases: A large-scale proteome-wide mendelian randomization and colocalization study.

BACKGROUND: The cardiac-brain connection has been identified as the basis for multiple cardio-cerebral diseases. However, effective therapeutic targets for these diseases are still limited. Therefore, this study aimed to identify pleiotropic and specific therapeutic targets for cardio-cerebral diseases using Mendelian randomization (MR) and colocalization analyses. METHODS: This study included two large protein quantitative trait loci studies with over 4,000 plasma proteins were included in the discovery and replication cohorts, respectively. We initially used MR to estimate the associations between protein and 20 cardio-cerebral diseases. Subsequently, Colocalization analysis was employed to enhance the credibility of the results. Protein target prioritization was based solely on including highly robust significant results from both the discovery and replication phases. Lastly, the Drug-Gene Interaction Database was utilized to investigate protein-gene-drug interactions further. RESULTS: A total of 46 target proteins for cardio-cerebral diseases were identified as robust in the discovery and replication phases by MR, comprising 7 pleiotropic therapeutic proteins and 39 specific target proteins. Followed by colocalization analysis and prioritization of evidence grades for target protein, 6 of these protein-disease pairs have achieved the highly recommended level. For instance, the PILRA protein presents a pleiotropic effect on sick sinus syndrome and Alzheimer's disease, whereas GRN exerts specific effects on the latter. APOL3, LRP4, and F11, on the other hand, have specific effects on cardiomyopathy and ischemic stroke, respectively. CONCLUSIONS: This study successfully identified important therapeutic targets for cardio-cerebral diseases, which benefits the development of preventive or therapeutic drugs.

Alleles on locus chromosome 4B from different parents confer tiller number and the yield-associated traits in wheat.

Pleiotropy is frequently detected in agronomic traits of wheat (Triticum aestivum). A locus on chromosome 4B, QTn/Ptn/Sl/Sns/Al/Tgw/Gl/Gw.caas-4B, proved to show pleiotropic effects on tiller, spike, and grain traits using a recombinant inbred line (RIL) population of Qingxinmai × 041133. The allele from Qingxinmai increased tiller numbers, and the allele from line 041133 produced better performances of spike traits and grain traits. Another 52 QTL for the eight traits investigated were detected on 18 chromosomes, except for chromosomes 5D, 6D, and 7B. Several genes in the genomic interval of the locus on chromosome 4B were differentially expressed in crown and inflorescence samples between Qingxinmai and line 041133. The development of the KASP marker specific for the locus on chromosome 4B is useful for molecular marker-assisted selection in wheat breeding.

shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores.

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Genome-wide association analysis reveals potential genetic correlation and causality between circulating inflammatory proteins and amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS), a fatal neurodegenerative disease, continues to elude complete comprehension of its pathological underpinnings. Recent focus on inflammation in ALS pathogenesis prompts this investigation into the genetic correlation and potential causal relationships between circulating inflammatory proteins and ALS. METHODS: Genome-wide association study (GWAS) data encompassing 91 circulating inflammatory protein measures from 14,824 individuals of European ancestry, alongside records from 27,205 ALS cases and 110,881 controls, were employed. Assessment of genetic correlation and overlap utilized LD score regression (LDSC), high-definition likelihood (HDL), and genetic analysis integrating pleiotropy and annotation (GPA) methodologies. Identification of shared genetic loci involved pleiotropy analysis, functional mapping and annotation (FUMA), and co-localization analysis. Finally, Mendelian randomization was applied to probe causal relationships between inflammatory proteins and ALS. RESULTS: Our investigation revealed significant genetic correlation and overlap between ALS and various inflammatory proteins, including C-C motif chemokine 28, Interleukin-18, C-X-C motif chemokine 1, and Leukemia inhibitory factor receptor (LIFR). Pleiotropy analysis uncovered shared variations at specific genetic loci, some of which bore potential harm. Mendelian randomization analysis suggested that alterations in specific inflammatory protein levels, notably LIFR, could impact ALS risk. CONCLUSIONS: Our findings uncover a genetic correlation between certain circulating inflammatory proteins and ALS, suggesting their possible causal involvement in ALS pathogenesis. Moreover, the identification of LIFR as a crucial protein may yield new insights into ALS pathomechanisms and offer a promising avenue for therapeutic interventions. These discoveries provide novel perspectives for advancing the comprehension of ALS pathophysiology and exploring potential therapeutic avenues.

A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

Exploring the interconnected between type 2 diabetes mellitus and nonalcoholic fatty liver disease: Genetic correlation and Mendelian randomization analysis.

Epidemiological and clinical studies have indicated a higher risk of nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM), implying a potentially shared genetic etiology, which is still less explored. Genetic links between T2DM and NAFLD were assessed using linkage disequilibrium score regression and pleiotropic analysis under composite null hypothesis. European GWAS data have identified shared genes, whereas SNP-level pleiotropic analysis under composite null hypothesis has explored pleiotropic loci. generalized gene-set analysis of GWAS data determines pleiotropic pathways and tissue enrichment using eQTL mapping to identify associated genes. Mendelian randomization analysis was used to investigate the causal relationship between NAFLD and T2DM. Linkage disequilibrium score regression analysis revealed a strong genetic correlation between T2DM and NAFLD, and identified 24 pleiotropic loci. These single-nucleotide polymorphisms are primarily involved in biosynthetic regulation, RNA biosynthesis, and pancreatic development. generalized gene-set analysis of GWAS data analysis revealed significant enrichment in multiple brain tissues. Gene mapping using these 3 methods led to the identification of numerous pleiotropic genes, with differences observed in liver and kidney tissues. These genes were mainly enriched in pancreas, brain, and liver tissues. The Mendelian randomization method indicated a significantly positive unidirectional causal relationship between T2DM and NAFLD. Our study identified a shared genetic structure between NAFLD and T2DM, providing new insights into the genetic pathogenesis and mechanisms of NAFLD and T2DM comorbidities.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

BACKGROUND: Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown. METHODS: Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases. RESULTS: LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases. CONCLUSIONS: This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Integrated Pleiotropic Gene Set Unveils Comorbidity Insights across Digestive Cancers and Other Diseases.

Comorbidities are prevalent in digestive cancers, intensifying patient discomfort and complicating prognosis. Identifying potential comorbidities and investigating their genetic connections in a systemic manner prove to be instrumental in averting additional health challenges during digestive cancer management. Here, we investigated 150 diseases across 18 categories by collecting and integrating various factors related to disease comorbidity, such as disease-associated SNPs or genes from sources like MalaCards, GWAS Catalog and UK Biobank. Through this extensive analysis, we have established an integrated pleiotropic gene set comprising 548 genes in total. Particularly, there enclosed the genes encoding major histocompatibility complex or related to antigen presentation. Additionally, we have unveiled patterns in protein-protein interactions and key hub genes/proteins including TP53, KRAS, CTNNB1 and PIK3CA, which may elucidate the co-occurrence of digestive cancers with certain diseases. These findings provide valuable insights into the molecular origins of comorbidity, offering potential avenues for patient stratification and the development of targeted therapies in clinical trials.

The pleiotropic phenotype of FlbA of Aspergillus niger is explained in part by the activity of seven of its downstream-regulated transcription factors.

Inactivation of flbA in Aspergillus niger results in thinner cell walls, increased cell lysis, abolished sporulation, and an increased secretome complexity. A total of 36 transcription factor (TF) genes are differentially expressed in ΔflbA. Here, seven of these genes (abaA, aslA, aslB, azf1, htfA, nosA, and srbA) were inactivated. Inactivation of each of these genes affected sporulation and, with the exception of abaA, cell wall integrity and protein secretion. The impact on secretion was strongest in the case of ΔaslA and ΔaslB that showed increased pepsin, cellulase, and amylase activity. Biomass was reduced of agar cultures of ΔabaA, ΔaslA, ΔnosA, and ΔsrbA, while biomass was higher in liquid shaken cultures of ΔaslA and ΔaslB. The ΔaslA and ΔhtfA strains showed increased resistance to H2O2, while ΔaslB was more sensitive to this reactive oxygen species. Together, inactivation of the seven TF genes impacted biomass formation, sporulation, protein secretion, and stress resistance, and thereby these genes explain at least part of the pleiotropic phenotype of ΔflbA of A. niger.

Identifying latent genetic interactions in genome-wide association studies using multiple traits.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).