Genetic Epidemiology最新文献

{"title":"Gene–environment interaction analysis via deep learning","authors":"Shuni Wu,&nbsp;Yaqing Xu,&nbsp;Qingzhao Zhang,&nbsp;Shuangge Ma","doi":"10.1002/gepi.22518","DOIUrl":"10.1002/gepi.22518","url":null,"abstract":"<p>Gene–environment (G–E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G–E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G–E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G–E interactions), while uniquely respecting the “main effects, interactions” variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G–E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"261-286"},"PeriodicalIF":2.1,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9944213","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":"New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables","authors":"Claudia Coscia,&nbsp;Esther Molina-Montes,&nbsp;Raquel Benítez,&nbsp;Evangelina López de Maturana,&nbsp;Alfonso Muriel,&nbsp;Núria Malats,&nbsp;Teresa Pérez","doi":"10.1002/gepi.22519","DOIUrl":"10.1002/gepi.22519","url":null,"abstract":"<p>The application of causal mediation analysis (CMA) considering the mediation effect of a third variable is increasing in epidemiological studies; however, this requires fitting strong assumptions on confounding bias. To address this limitation, we propose an extension of CMA combining it with Mendelian randomization (MRinCMA). We applied the new approach to analyse the causal effect of obesity and diabetes on pancreatic cancer, considering each factor as potential mediator. To check the performance of MRinCMA under several conditions/scenarios, we used it in different simulated data sets and compared it with structural equation models. For continuous variables, MRinCMA and structural equation models performed similarly, suggesting that both approaches are valid to obtain unbiased estimates. When noncontinuous variables were considered, MRinCMA presented, overall, lower bias than structural equation models. By applying MRinCMA, we did not find any evidence of causality of obesity or diabetes on pancreatic cancer. With this new methodology, researchers would be able to address CMA hypotheses by appropriately accounting for the confounding bias assumption regardless of the conditions used in their studies in different settings.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"287-300"},"PeriodicalIF":2.1,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9121098","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":"MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method","authors":"Dapeng Shi,&nbsp;Yuquan Wang,&nbsp;Ziyong Zhang,&nbsp;Yunlong Cao,&nbsp;Yue-Qing Hu","doi":"10.1002/gepi.22520","DOIUrl":"10.1002/gepi.22520","url":null,"abstract":"<p>Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for exploring binary outcome cannot take the effect of confounding factors into account, which leads to biased estimate of the causal effect. In this article, we propose an integrated likelihood method MR-BOIL to investigate causal relationships for binary outcomes by treating confounders as latent variables in one-sample Mendelian randomization. Under the assumption of a joint normal distribution of the confounders, we use expectation maximization algorithm to estimate the causal effect. Extensive simulations demonstrate that the estimator of MR-BOIL is asymptotically unbiased and that our method improves statistical power without inflating type I error rate. We then apply this method to analyze the data from Atherosclerosis Risk in Communications Study. The results show that MR-BOIL can better identify plausible causal relationships with high reliability, compared with the unreliable results of existing methods. MR-BOIL is implemented in R and the corresponding R code is provided for free download.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 4","pages":"332-357"},"PeriodicalIF":2.1,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9665586","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 fast linkage method for population GWAS cohorts with related individuals","authors":"Gregory J. M. Zajac,&nbsp;Sarah A. Gagliano Taliun,&nbsp;Carlo Sidore,&nbsp;Sarah E. Graham,&nbsp;Bjørn O. Åsvold,&nbsp;Ben Brumpton,&nbsp;Jonas B. Nielsen,&nbsp;Wei Zhou,&nbsp;Maiken Gabrielsen,&nbsp;Anne H. Skogholt,&nbsp;Lars G. Fritsche,&nbsp;David Schlessinger,&nbsp;Francesco Cucca,&nbsp;Kristian Hveem,&nbsp;Cristen J. Willer,&nbsp;Gonçalo R. Abecasis","doi":"10.1002/gepi.22516","DOIUrl":"10.1002/gepi.22516","url":null,"abstract":"<p>Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional linkage methods are computationally inefficient for larger datasets. Here, we describe Population Linkage, a novel application of Haseman–Elston regression as a method of moments estimator of variance components and their standard errors. We achieve additional computational efficiency by using modern methods for detection of IBD segments and variance component estimation, efficient preprocessing of input data, and minimizing redundant numerical calculations. We also refined variance component models to account for the biases in population-scale methods for IBD segment detection. We ran Population Linkage on four blood lipid traits in over 70,000 individuals from the HUNT and SardiNIA studies, successfully detecting 25 known genetic signals. One notable linkage signal that appeared in both was for low-density lipoprotein (LDL) cholesterol levels in the region near the gene <i>APOE</i> (LOD = 29.3, variance explained = 4.1%). This is the region where the missense variants rs7412 and rs429358, which together make up the ε2, ε3, and ε4 alleles each account for 2.4% and 0.8% of variation in circulating LDL cholesterol. Our results show the potential for linkage analysis and other large-scale applications of method of moments variance components estimation.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"231-248"},"PeriodicalIF":2.1,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9496203","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":"Bayesian multivariant fine mapping using the Laplace prior","authors":"Kevin Walters,&nbsp;Hannuun Yaacob","doi":"10.1002/gepi.22517","DOIUrl":"10.1002/gepi.22517","url":null,"abstract":"<p>Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the corresponding Gaussian prior and FINEMAP. Our results indicate that, for the simulation scenarios we consider here, the Laplace prior can lead to higher PIPs than either the Gaussian prior or FINEMAP, particularly for moderately sized fine-mapping studies. The Laplace prior also appears to have better worst-case scenario properties. We reanalyse the iCOGS case–control data from the CASP8 region on Chromosome 2. Even though this study has a total sample size of nearly 90,000 individuals, there are still some differences in the top few ranked SNPs if the Laplace prior is used rather than the Gaussian prior. R code to implement the Laplace (and Gaussian) prior is available at https://github.com/Kevin-walters/lapmapr.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"249-260"},"PeriodicalIF":2.1,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9120129","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":"Study of effect modifiers of genetically predicted CETP reduction","authors":"Marc-André Legault,&nbsp;Amina Barhdadi,&nbsp;Isabel Gamache,&nbsp;Audrey Lemaçon,&nbsp;Louis-Philippe Lemieux Perreault,&nbsp;Jean-Christophe Grenier,&nbsp;Marie-Pierre Sylvestre,&nbsp;Julie G. Hussin,&nbsp;David Rhainds,&nbsp;Jean-Claude Tardif,&nbsp;Marie-Pierre Dubé","doi":"10.1002/gepi.22514","DOIUrl":"10.1002/gepi.22514","url":null,"abstract":"<p>Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"198-212"},"PeriodicalIF":2.1,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9406442","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":"Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies","authors":"Hongjing Xie,&nbsp;Xuewei Cao,&nbsp;Shuanglin Zhang,&nbsp;Qiuying Sha","doi":"10.1002/gepi.22513","DOIUrl":"10.1002/gepi.22513","url":null,"abstract":"<p>In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the <i>p</i> value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated <i>p</i> value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"185-197"},"PeriodicalIF":2.1,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9906667","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":"Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank","authors":"Zihuan Liu,&nbsp;Wei Dai,&nbsp;Shiying Wang,&nbsp;Yisha Yao,&nbsp;Heping Zhang","doi":"10.1002/gepi.22515","DOIUrl":"10.1002/gepi.22515","url":null,"abstract":"<p>Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, <i>p</i> = 5.47 × 10⁻⁹) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, <i>p</i> = 2.87 × 10⁻⁸) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 3","pages":"215-230"},"PeriodicalIF":2.1,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9184940","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":"Weak and pleiotropy robust sex-stratified Mendelian randomization in the one sample and two sample settings","authors":"Vasilios Karageorgiou,&nbsp;Jess Tyrrell,&nbsp;Trevelyan J. Mckinley,&nbsp;Jack Bowden","doi":"10.1002/gepi.22512","DOIUrl":"10.1002/gepi.22512","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Mendelian randomization (MR) leverages genetic data as an instrumental variable to provide estimates for the causal effect of an exposure <i>X</i> on a health outcome <i>Y</i> that is robust to confounding. Unfortunately, horizontal pleiotropy—the direct association of a genetic variant with multiple phenotypes—is highly prevalent and can easily render a genetic variant an invalid instrument.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Building on existing work, we propose a simple method for leveraging sex-specific genetic associations to perform weak and pleiotropy-robust MR analysis. This is achieved by constructing an MR estimator in which pleiotropy is perfectly removed by cancellation, while placing it within the powerful machinery of the robust adjusted profile score (MR-RAPS) method. Pleiotropy cancellation has the attractive property that it removes heterogeneity and therefore justifies a statistically efficient fixed effects model. We extend the method from the typical two-sample summary-data MR setting to the one-sample setting by adapting the technique of Collider-Correction. Simulation studies and applied examples are used to assess how the sex-stratified MR-RAPS estimator performs against other common approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sex-stratified MR-RAPS method is shown to be robust to pleiotropy even in cases where all genetic variants violated the standard Instrument Strength Independent of Direct Effect assumption. In some cases where the strength of the pleiotropic effect additionally varied by sex (and so perfect cancellation was not achieved), over-dispersed MR-RAPS implementations can still consistently estimate the true causal effect. In applied analyses, we investigate the causal effect of waist-hip ratio (WHR), an important marker of central obesity, on a range of downstream traits. While the conventional approaches suggested paradoxical links between WHR and height and body mass index, the sex-stratified approach obtained a more realistic null effect. Nonzero effects were also detected for systolic and diastolic blood pressure as well as high-density and low-density lipoprotein cholesterol.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>We provide a simple but attractive method for weak and pleiotropy robust causal estimation of sexually dimorphic traits on downstream outcomes, by combining several existing approaches in a novel fashion.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"135-151"},"PeriodicalIF":2.1,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10816122","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":"Improved two-step testing of genome-wide gene–environment interactions","authors":"Eric S. Kawaguchi,&nbsp;Andre E. Kim,&nbsp;Juan Pablo Lewinger,&nbsp;W. James Gauderman","doi":"10.1002/gepi.22509","DOIUrl":"10.1002/gepi.22509","url":null,"abstract":"<p>Two-step tests for gene–environment (<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 \u0000 <mo>×</mo>\u0000 \u0000 <mi>E</mi>\u0000 </mrow>\u0000 <annotation> $Gtimes E$</annotation>\u0000 </semantics></math>) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to “bin” SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by “displacing” true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>G</mi>\u0000 \u0000 <mo>×</mo>\u0000 \u0000 <mi>E</mi>\u0000 </mrow>\u0000 <annotation> $Gtimes E$</annotation>\u0000 </semantics></math> testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a <i>G</i> × Sex interaction located near the SMAD7 gene.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 2","pages":"152-166"},"PeriodicalIF":2.1,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10811874","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}