Genetic Epidemiology最新文献_第9页

Bias correction for inverse variance weighting Mendelian randomization 方差逆加权孟德尔随机化的偏差校正

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-04-10 DOI: 10.1002/gepi.22522

Ninon Mounier, Zoltán Kutalik

{"title":"Bias correction for inverse variance weighting Mendelian randomization","authors":"Ninon Mounier, Zoltán Kutalik","doi":"10.1002/gepi.22522","DOIUrl":"10.1002/gepi.22522","url":null,"abstract":"Inverse-variance weighted two-sample Mendelian randomization (IVW-MR) is the most widely used approach that utilizes genome-wide association studies (GWAS) summary statistics to infer the existence and the strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to the use of weak instruments and winner's curse, which can change as a function of the overlap between the exposure and outcome samples. We developed a method (MRlap) that simultaneously considers weak instrument bias and winner's curse while accounting for potential sample overlap. Assuming spike-and-slab genomic architecture and leveraging linkage disequilibrium score regression and other techniques, we could analytically derive, reliably estimate, and hence correct for the bias of IVW-MR using association summary statistics only. We tested our approach using simulated data for a wide range of realistic settings. In all the explored scenarios, our correction reduced the bias, in some situations by as much as 30-fold. In addition, our results are consistent with the fact that the strength of the biases will decrease as the sample size increases and we also showed that the overall bias is also dependent on the genetic architecture of the exposure, and traits with low heritability and/or high polygenicity are more strongly affected. Applying MRlap to obesity-related exposures revealed statistically significant differences between IVW-based and corrected effects, both for nonoverlapping and fully overlapping samples. Our method not only reduces bias in causal effect estimation but also enables the use of much larger GWAS sample sizes, by allowing for potentially overlapping samples.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 4","pages":"314-331"},"PeriodicalIF":2.1,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10040176","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}

引用次数: 38

Effect of case and control definitions on genome-wide association study (GWAS) findings 病例和对照定义对全基因组关联研究(GWAS)结果的影响

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-04-06 DOI: 10.1002/gepi.22523

Monica Isgut, Kijoung Song, Margaret G. Ehm, May Dongmei Wang, Jonathan Davitte

{"title":"Effect of case and control definitions on genome-wide association study (GWAS) findings","authors":"Monica Isgut, Kijoung Song, Margaret G. Ehm, May Dongmei Wang, Jonathan Davitte","doi":"10.1002/gepi.22523","DOIUrl":"10.1002/gepi.22523","url":null,"abstract":"Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or different levels of granularity (i.e., specificity of inclusion criteria) to define cases and controls. The extent to which this variability in cohort definitions impacts the end-results of a GWAS study is unclear. In this study, we systematically evaluated the effect of the data sources used for case and control definitions on GWAS findings. Using the UK Biobank, we selected three diseases—glaucoma, migraine, and iron-deficiency anemia. For each disease, we designed 13 GWAS, each using different combinations of data sources to define cases and controls, and then calculated the pairwise genetic correlations between all GWAS for each disease. We found that the data sources used to define cases for a given disease can have a significant impact on GWAS end-results, but the extent of this depends heavily on the disease in question. This suggests the need for greater scrutiny on how case cohorts are defined for GWAS.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 5","pages":"394-406"},"PeriodicalIF":2.1,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10022267","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}

引用次数: 0

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes 评估多基因风险评分以区分1型和2型糖尿病

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-23 DOI: 10.1002/gepi.22521

Muhammad Shoaib, Qiang Ye, Heidi IglayReger, Meng H. Tan, Michael Boehnke, Charles F. Burant, Scott A. Soleimanpour, Sarah A. Gagliano Taliun

{"title":"Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes","authors":"Muhammad Shoaib, Qiang Ye, Heidi IglayReger, Meng H. Tan, Michael Boehnke, Charles F. Burant, Scott A. Soleimanpour, Sarah A. Gagliano Taliun","doi":"10.1002/gepi.22521","DOIUrl":"10.1002/gepi.22521","url":null,"abstract":"Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the potential to address possible misclassification of T1D and T2D. Here we evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC] = 0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC = 0.792) and separation in MGI (AUC = 0.686). In contrast, the best T2D model did not discriminate between T1D and T2D cases (AUC = 0.527). Our analysis suggests that a T1D PRS model based on independent single nucleotide polymorphisms may help differentiate between T1D, T2D, and controls in individuals of European genetic ancestry.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"47 4","pages":"303-313"},"PeriodicalIF":2.1,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9708272","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}

引用次数: 0

Gene–environment interaction analysis via deep learning 基于深度学习的基因-环境相互作用分析

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-19 DOI: 10.1002/gepi.22518

Shuni Wu, Yaqing Xu, Qingzhao Zhang, Shuangge Ma

{"title":"Gene–environment interaction analysis via deep learning","authors":"Shuni Wu, Yaqing Xu, Qingzhao Zhang, Shuangge Ma","doi":"10.1002/gepi.22518","DOIUrl":"10.1002/gepi.22518","url":null,"abstract":"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.","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}

引用次数: 1

New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables 通过使用遗传变异作为工具变量来解决调解分析询问的新建议

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-19 DOI: 10.1002/gepi.22519

Claudia Coscia, Esther Molina-Montes, Raquel Benítez, Evangelina López de Maturana, Alfonso Muriel, Núria Malats, Teresa Pérez

{"title":"New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables","authors":"Claudia Coscia, Esther Molina-Montes, Raquel Benítez, Evangelina López de Maturana, Alfonso Muriel, Núria Malats, Teresa Pérez","doi":"10.1002/gepi.22519","DOIUrl":"10.1002/gepi.22519","url":null,"abstract":"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.","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}

引用次数: 0

MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method MR-BOIL:用综合似然法对二元结果进行单样本孟德尔随机化的因果推理

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-19 DOI: 10.1002/gepi.22520

Dapeng Shi, Yuquan Wang, Ziyong Zhang, Yunlong Cao, Yue-Qing Hu

{"title":"MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method","authors":"Dapeng Shi, Yuquan Wang, Ziyong Zhang, Yunlong Cao, Yue-Qing Hu","doi":"10.1002/gepi.22520","DOIUrl":"10.1002/gepi.22520","url":null,"abstract":"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.","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}

引用次数: 0

A fast linkage method for population GWAS cohorts with related individuals GWAS群体相关个体的快速连锁分析方法

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-05 DOI: 10.1002/gepi.22516

Gregory J. M. Zajac, Sarah A. Gagliano Taliun, Carlo Sidore, Sarah E. Graham, Bjørn O. Åsvold, Ben Brumpton, Jonas B. Nielsen, Wei Zhou, Maiken Gabrielsen, Anne H. Skogholt, Lars G. Fritsche, David Schlessinger, Francesco Cucca, Kristian Hveem, Cristen J. Willer, Gonçalo R. Abecasis

{"title":"A fast linkage method for population GWAS cohorts with related individuals","authors":"Gregory J. M. Zajac, Sarah A. Gagliano Taliun, Carlo Sidore, Sarah E. Graham, Bjørn O. Åsvold, Ben Brumpton, Jonas B. Nielsen, Wei Zhou, Maiken Gabrielsen, Anne H. Skogholt, Lars G. Fritsche, David Schlessinger, Francesco Cucca, Kristian Hveem, Cristen J. Willer, Gonçalo R. Abecasis","doi":"10.1002/gepi.22516","DOIUrl":"10.1002/gepi.22516","url":null,"abstract":"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 APOE (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.","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}

引用次数: 0

Bayesian multivariant fine mapping using the Laplace prior 利用拉普拉斯先验的贝叶斯多变量精细映射

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-02-05 DOI: 10.1002/gepi.22517

Kevin Walters, Hannuun Yaacob

引用次数: 0

Study of effect modifiers of genetically predicted CETP reduction 基因预测CETP降低效应修饰因子的研究

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-01-26 DOI: 10.1002/gepi.22514

Marc-André Legault, Amina Barhdadi, Isabel Gamache, Audrey Lemaçon, Louis-Philippe Lemieux Perreault, Jean-Christophe Grenier, Marie-Pierre Sylvestre, Julie G. Hussin, David Rhainds, Jean-Claude Tardif, Marie-Pierre Dubé

{"title":"Study of effect modifiers of genetically predicted CETP reduction","authors":"Marc-André Legault, Amina Barhdadi, Isabel Gamache, Audrey Lemaçon, Louis-Philippe Lemieux Perreault, Jean-Christophe Grenier, Marie-Pierre Sylvestre, Julie G. Hussin, David Rhainds, Jean-Claude Tardif, Marie-Pierre Dubé","doi":"10.1002/gepi.22514","DOIUrl":"10.1002/gepi.22514","url":null,"abstract":"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.","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}

引用次数: 0

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies 对极端不平衡病例-对照关联研究的多种表型进行联合分析

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2023-01-24 DOI: 10.1002/gepi.22513

Hongjing Xie, Xuewei Cao, Shuanglin Zhang, Qiuying Sha

{"title":"Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies","authors":"Hongjing Xie, Xuewei Cao, Shuanglin Zhang, Qiuying Sha","doi":"10.1002/gepi.22513","DOIUrl":"10.1002/gepi.22513","url":null,"abstract":"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 p 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 p 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.","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}

引用次数: 1