Genetic Epidemiology最新文献

{"title":"Multivariable MR Can Mitigate Bias in Two-Sample MR Using Covariable-Adjusted Summary Associations","authors":"Joe Gilbody,&nbsp;Maria Carolina Borges,&nbsp;George Davey Smith,&nbsp;Eleanor Sanderson","doi":"10.1002/gepi.22606","DOIUrl":"10.1002/gepi.22606","url":null,"abstract":"<p>Genome-wide association studies (GWAS) are hypothesis-free studies that estimate the association between polymorphisms across the genome with a trait of interest. To increase power and to estimate the direct effects of these single-nucleotide polymorphisms (SNPs) on a trait GWAS are often conditioned on a covariate (such as body mass index or smoking status). This adjustment can introduce bias in the estimated effect of the SNP on the trait. Two-sample Mendelian randomisation (MR) studies use summary statistics from GWAS estimate the causal effect of a risk factor (or exposure) on an outcome. Covariate adjustment in GWAS can bias the effect estimates obtained from MR studies conducted using covariate adjusted GWAS data. Multivariable MR (MVMR) is an extension of MR that includes multiple traits as exposures. Here we propose the use of MVMR to correct the bias in MR studies from covariate adjustment. We show how MVMR can recover unbiased estimates of the direct effect of the exposure of interest by including the covariate used to adjust the GWAS within the analysis. We apply this method to estimate the effect of systolic blood pressure on type-2 diabetes and the effect of waist circumference on systolic blood pressure. Our analytical and simulation results show that MVMR provides unbiased effect estimates for the exposure when either the exposure or outcome of interest has been adjusted for a covariate. Our results also highlight the parameters that determine when MR will be biased by GWAS covariate adjustment. The results from the applied analysis mirror these results, with equivalent results seen in the MVMR with and without adjusted GWAS. When GWAS results have been adjusted for a covariate, biasing MR effect estimates, direct effect estimates of an exposure on an outcome can be obtained by including that covariate as an additional exposure in an MVMR estimation. However, the estimated effect of the covariate obtained from the MVMR estimation is biased.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals","authors":"Amarise Little,&nbsp;Ni Zhao,&nbsp;Anna Mikhaylova,&nbsp;Angela Zhang,&nbsp;Wodan Ling,&nbsp;Florian Thibord,&nbsp;Andrew D. Johnson,&nbsp;Laura M. Raffield,&nbsp;Joanne E. Curran,&nbsp;John Blangero,&nbsp;Jeffrey R. O'Connell,&nbsp;Huichun Xu,&nbsp;Jerome I. Rotter,&nbsp;Stephen S. Rich,&nbsp;Kenneth M. Rice,&nbsp;Ming-Huei Chen,&nbsp;Alexander Reiner,&nbsp;Charles Kooperberg,&nbsp;Thao Vu,&nbsp;Lifang Hou,&nbsp;Myriam Fornage,&nbsp;Ruth J.F. Loos,&nbsp;Eimear Kenny,&nbsp;Rasika Mathias,&nbsp;Lewis Becker,&nbsp;Albert V. Smith,&nbsp;Eric Boerwinkle,&nbsp;Bing Yu,&nbsp;Timothy Thornton,&nbsp;Michael C. Wu","doi":"10.1002/gepi.22610","DOIUrl":"10.1002/gepi.22610","url":null,"abstract":"<div>\u0000 \u0000 <p>Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high-dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi-omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi-ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups","authors":"Inti Pagnuco,&nbsp;Stephen Eyre,&nbsp;Magnus Rattray,&nbsp;Andrew P. Morris","doi":"10.1002/gepi.22611","DOIUrl":"10.1002/gepi.22611","url":null,"abstract":"<p>Transcriptome-wide association studies (TWAS) investigate the links between genetically regulated gene expression and complex traits. TWAS involves imputing gene expression using expression quantitative trait loci (eQTL) as predictors and testing the association between the imputed expression and the trait. The effectiveness of TWAS depends on the accuracy of these imputation models, which require genotype and gene expression data from the same samples. However, publicly accessible resources, such as the Genotype Tissue Expression (GTEx) Project, are biased toward individuals of European ancestry, potentially reducing prediction accuracy into other ancestry groups. This study explored eQTL transferability across ancestry groups by comparing two imputation models: PrediXcan (tissue-specific) and UTMOST (cross-tissue). Both models were trained on tissues from the GTEx Project using European ancestry individuals and then tested on data sets of European ancestry and African American individuals. Results showed that both models performed best when the training and testing data sets were from the same ancestry group, with the cross-tissue approach generally outperforming the tissue-specific approach. This study underscores that eQTL detection is influenced by ancestry and tissue context. Developing ancestry-specific reference panels across tissues can improve prediction accuracy, enhancing TWAS analysis and our understanding of the biological processes contributing to complex traits.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refinement of a Published Gene-Physical Activity Interaction Impacting HDL-Cholesterol: Role of Sex and Lipoprotein Subfractions","authors":"Kenneth E. Westerman,&nbsp;Tuomas O. Kilpeläinen,&nbsp;Magdalena Sevilla-Gonzalez,&nbsp;Margery A. Connelly,&nbsp;Alexis C. Wood,&nbsp;Michael Y. Tsai,&nbsp;Kent D. Taylor,&nbsp;Stephen S. Rich,&nbsp;Jerome I. Rotter,&nbsp;James D. Otvos,&nbsp;Amy R. Bentley,&nbsp;Samia Mora,&nbsp;Hugues Aschard,&nbsp;D. C. Rao,&nbsp;Charles Gu,&nbsp;Daniel I. Chasman,&nbsp;Alisa K. Manning,&nbsp;The CHARGE Gene-Lifestyle Interactions Working Group","doi":"10.1002/gepi.22607","DOIUrl":"10.1002/gepi.22607","url":null,"abstract":"<div>\u0000 \u0000 <p>Large-scale gene–environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). We explored this GxE in the Women's Genome Health Study (WGHS; <i>N</i> = 23,294; the strongest cohort-specific signal in the original meta-analysis), the UK Biobank (UKB; <i>N</i> = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; <i>N</i> = 4587), using self-reported PA (MET-min/wk) and genotypes at rs295849 (nearest gene: <i>LHX1</i>). As originally reported, minor allele carriers of rs295849 in WGHS had a stronger positive association between PA and HDL-C (<i>p</i>_int = 0.002). When testing available NMR metabolites to refine the HDL-C outcome, we found a stronger interaction effect on medium-sized HDL particle concentrations (M-HDL-P; <i>p</i>_int = 1.0 × 10⁻⁴) than HDL-C. Meta-regression revealed a systematically larger interaction effect in cohorts from the original meta-analysis with a greater fraction of women (<i>p</i> = 0.018). In the UKB, GxE effects were stronger in women and using M-HDL-P as the outcome. In MESA, the primary interaction for HDL-C showed nominal significance (<i>p</i>_int = 0.013), but without clear sex differences and with a greater magnitude for large HDL-P. Our work provides additional insights into a known gene-PA interaction while illustrating the importance of phenotype and model refinement toward understanding and replicating GxEs.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies","authors":"Daniel J. M. Crouch,&nbsp;Jamie R. J. Inshaw,&nbsp;Catherine C. Robertson,&nbsp;Esther Ng,&nbsp;Jia-Yuan Zhang,&nbsp;Wei-Min Chen,&nbsp;Suna Onengut-Gumuscu,&nbsp;Antony J. Cutler,&nbsp;Carlo Sidore,&nbsp;Francesco Cucca,&nbsp;Flemming Pociot,&nbsp;Patrick Concannon,&nbsp;Stephen S. Rich,&nbsp;John A. Todd","doi":"10.1002/gepi.22608","DOIUrl":"10.1002/gepi.22608","url":null,"abstract":"<p>Biological datasets often consist of thousands or millions of variables, e.g. genetic variants or biomarkers, and when sample sizes are large it is common to find many associated with an outcome of interest, for example, disease risk in a GWAS, at high levels of statistical significance, but with very small effects. The False Discovery Rate (FDR) is used to identify effects of interest based on ranking variables according to their statistical significance. Here, we develop a complementary measure to the FDR, the priorityFDR, that ranks variables by a combination of effect size and significance, allowing further prioritisation among a set of variables that pass a significance or FDR threshold. Applying to the largest GWAS of type 1 diabetes to date (15,573 cases and 158,408 controls), we identified 26 independent genetic associations, including two newly-reported loci, with qualitatively lower priorityFDRs than the remaining 175 signals. We detected putatively causal type 1 diabetes risk genes using Mendelian Randomisation, and found that these were located disproportionately close to low priorityFDR signals (<i>p</i> = 0.005), as were genes in the IL-2 pathway (<i>p</i> = 0.003). Selecting variables on both effect size and significance can lead to improved prioritisation for mechanistic follow-up studies from genetic and other large biological datasets.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Family History Data to Improve the Power of Association Studies: Application to Cancer in UK Biobank","authors":"Naomi Wilcox,&nbsp;Jonathan P. Tyrer,&nbsp;Joe Dennis,&nbsp;Xin Yang,&nbsp;John R. B. Perry,&nbsp;Eugene J. Gardner,&nbsp;Douglas F. Easton","doi":"10.1002/gepi.22609","DOIUrl":"10.1002/gepi.22609","url":null,"abstract":"<p>In large cohort studies the number of unaffected individuals outnumbers the number of affected individuals, and the power can be low to detect associations for outcomes with low prevalence. We consider how including recorded family history in regression models increases the power to detect associations between genetic variants and disease risk. We show theoretically and using Monte-Carlo simulations that including a family history of the disease, with a weighting of 0.5 compared with true cases, increases the power to detect associations. This is a powerful approach for detecting variants with moderate effects, but for larger effect sizes a weighting of &gt; 0.5 can be more powerful. We illustrate this both for common variants and for exome sequencing data for over 400,000 individuals in UK Biobank to evaluate the association between the burden of protein-truncating variants in genes and risk for four cancer types.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142921513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additional article of this Special Issue was previously published in another issue of Genetic Epidemiology. That is:","authors":"","doi":"10.1002/gepi.22604","DOIUrl":"https://doi.org/10.1002/gepi.22604","url":null,"abstract":"<p>Gorfine, M., Qu, C.,Peters, U., &amp; Hsu, L. (2024). Unveiling challenges in Mendelian randomization for gene-environment interaction. Genetic Epidemiology, 48, 164–189. https://doi.org/10.1002/gepi.22552</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142714718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects","authors":"Janaka S. S. Liyanage,&nbsp;Jane S. Hankins,&nbsp;Jeremie H. Estepp,&nbsp;Deokumar Srivastava,&nbsp;Sara R. Rashkin,&nbsp;Clifford Takemoto,&nbsp;Yun Li,&nbsp;Yuehua Cui,&nbsp;Motomi Mori,&nbsp;Mitchell J. Weiss,&nbsp;Guolian Kang","doi":"10.1002/gepi.22602","DOIUrl":"10.1002/gepi.22602","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions. In simulations, our proposed approaches demonstrate robustness to the violations, delivering valid estimates, and interpretable type-I errors and statistical power. This increases the practical applicability of the models. We applied the proposed approaches to evaluate the causal effect of fetal hemoglobin (HbF) on the vaso-occlusive crisis and acute chest syndrome (ACS) events in patients with sickle cell disease (SCD) and revealed the causal relation between HbF and ACS events in these patients. We also developed a user-friendly Shiny web application to facilitate researchers' exploration of causal relations.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142582841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Causal Effects on a Disease Progression Trait Using Bivariate Mendelian Randomisation","authors":"Siyang Cai,&nbsp;Frank Dudbridge","doi":"10.1002/gepi.22600","DOIUrl":"10.1002/gepi.22600","url":null,"abstract":"<p>Genome-wide association studies (GWAS) have provided large numbers of genetic markers that can be used as instrumental variables in a Mendelian Randomisation (MR) analysis to assess the causal effect of a risk factor on an outcome. An extension of MR analysis, multivariable MR, has been proposed to handle multiple risk factors. However, adjusting or stratifying the outcome on a variable that is associated with it may induce collider bias. For an outcome that represents progression of a disease, conditioning by selecting only the cases may cause a biased MR estimation of the causal effect of the risk factor of interest on the progression outcome. Recently, we developed instrument effect regression and corrected weighted least squares (CWLS) to adjust for collider bias in observational associations. In this paper, we highlight the importance of adjusting for collider bias in MR with a risk factor of interest and disease progression as the outcome. A generalised version of the instrument effect regression and CWLS adjustment is proposed based on a multivariable MR model. We highlight the assumptions required for this approach and demonstrate its utility for bias reduction. We give an illustrative application to the effect of smoking initiation and smoking cessation on Crohn's disease prognosis, finding no evidence to support a causal effect.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrative Multi-Omics Approach for Improving Causal Gene Identification","authors":"Austin King,&nbsp;Chong Wu","doi":"10.1002/gepi.22601","DOIUrl":"10.1002/gepi.22601","url":null,"abstract":"<div>\u0000 \u0000 <p>Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}