Genetic Epidemiology最新文献_第6页

Using parent-offspring pairs and trios to estimate indirect genetic effects in education 利用父母-后代配对和三人组合估算教育的间接遗传效应。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-03-12 DOI: 10.1002/gepi.22554

Victória Trindade Pons, Annique Claringbould, Priscilla Kamphuis, Albertine J. Oldehinkel, Hanna M. van Loo

{"title":"Using parent-offspring pairs and trios to estimate indirect genetic effects in education","authors":"Victória Trindade Pons, Annique Claringbould, Priscilla Kamphuis, Albertine J. Oldehinkel, Hanna M. van Loo","doi":"10.1002/gepi.22554","DOIUrl":"10.1002/gepi.22554","url":null,"abstract":"We investigated indirect genetic effects (IGEs), also known as genetic nurture, in education with a novel approach that uses phased data to include parent-offspring pairs in the transmitted/nontransmitted study design. This method increases the power to detect IGEs, enhances the generalizability of the findings, and allows for the study of effects by parent-of-origin. We validated and applied this method in a family-based subsample of adolescents and adults from the Lifelines Cohort Study in the Netherlands (N = 6147), using the latest genome-wide association study data on educational attainment to construct polygenic scores (PGS). Our results indicated that IGEs play a role in education outcomes in the Netherlands: we found significant associations of the nontransmitted PGS with secondary school level in youth between 13 and 24 years old as well as with education attainment and years of education in adults over 25 years old (β = 0.14, 0.17 and 0.26, respectively), with tentative evidence for larger maternal IGEs. In conclusion, we replicated previous findings and showed that including parent-offspring pairs in addition to trios in the transmitted/nontransmitted design can benefit future studies of parental IGEs in a wide range of outcomes.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 4","pages":"190-199"},"PeriodicalIF":2.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109831","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

Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses 解释多基因风险评分与乳腺癌关联的因果关系和家族混杂因素：来自创新型 ICE FALCON 和 ICE CRISTAL 分析的证据。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-03-12 DOI: 10.1002/gepi.22556

Shuai Li, Gillian S. Dite, Robert J. MacInnis, Minh Bui, Tuong L. Nguyen, Vivienne F. C. Esser, Zhoufeng Ye, James G. Dowty, Enes Makalic, Joohon Sung, Graham G. Giles, Melissa C. Southey, John L. Hopper

{"title":"Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses","authors":"Shuai Li, Gillian S. Dite, Robert J. MacInnis, Minh Bui, Tuong L. Nguyen, Vivienne F. C. Esser, Zhoufeng Ye, James G. Dowty, Enes Makalic, Joohon Sung, Graham G. Giles, Melissa C. Southey, John L. Hopper","doi":"10.1002/gepi.22556","DOIUrl":"10.1002/gepi.22556","url":null,"abstract":"A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 8","pages":"401-413"},"PeriodicalIF":1.7,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109830","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

Are trait-associated genes clustered together in a gene network? 与性状相关的基因是否在基因网络中聚集在一起？

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-03-12 DOI: 10.1002/gepi.22557

Hyun Jung Koo, Wei Pan

{"title":"Are trait-associated genes clustered together in a gene network?","authors":"Hyun Jung Koo, Wei Pan","doi":"10.1002/gepi.22557","DOIUrl":"10.1002/gepi.22557","url":null,"abstract":"Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 5","pages":"203-213"},"PeriodicalIF":1.7,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109761","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

Unveiling challenges in Mendelian randomization for gene–environment interaction 揭示基因与环境相互作用的孟德尔随机化所面临的挑战。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-29 DOI: 10.1002/gepi.22552

Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu

{"title":"Unveiling challenges in Mendelian randomization for gene–environment interaction","authors":"Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu","doi":"10.1002/gepi.22552","DOIUrl":"10.1002/gepi.22552","url":null,"abstract":"Gene–environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 4","pages":"164-189"},"PeriodicalIF":2.1,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139989797","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

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region 利用药物靶基因的表型异质性深入了解机理：顺式多变量孟德尔随机化在 GLP1R 基因区域的应用。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-20 DOI: 10.1002/gepi.22551

Ashish Patel, Dipender Gill, Dmitry Shungin, Christos S. Mantzoros, Lotte Bjerre Knudsen, Jack Bowden, Stephen Burgess

{"title":"Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region","authors":"Ashish Patel, Dipender Gill, Dmitry Shungin, Christos S. Mantzoros, Lotte Bjerre Knudsen, Jack Bowden, Stephen Burgess","doi":"10.1002/gepi.22551","DOIUrl":"10.1002/gepi.22551","url":null,"abstract":"Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 4","pages":"151-163"},"PeriodicalIF":2.1,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139912418","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

Making sense of breast cancer risk estimates 合理估算乳腺癌风险。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-09 DOI: 10.1002/gepi.22550

John O'Quigley

引用次数: 0

Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements 揭示基因组异质性和共性：一种考虑到测量邻接结构的惩罚性综合分析方法。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-05 DOI: 10.1002/gepi.22549

Xindi Wang, Yu Jiang, Yifan Sun

{"title":"Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements","authors":"Xindi Wang, Yu Jiang, Yifan Sun","doi":"10.1002/gepi.22549","DOIUrl":"10.1002/gepi.22549","url":null,"abstract":"Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate these patterns to accurately detect markers and comprehensively understand the diseases. Integrative analysis has emerged as a promising approach to address this challenge. However, existing studies have been limited by ignoring the adjacency structure of genomic measurements, such as single nucleotide polymorphisms (SNPs) and DNA methylations. In this study, we propose a structured integrative analysis method that incorporates a spline type penalty to accommodate this adjacency structure. We utilize a fused lasso type penalty to identify both heterogeneity and commonality across the groups. Extensive simulations demonstrate its superiority compared to several direct competing methods. The analysis of The Cancer Genome Atlas melanoma data with DNA methylation measurements and GENEVA diabetes data with SNP measurements exhibit that the proposed analysis lead to meaningful findings with better prediction performance and higher selection stability.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 3","pages":"114-140"},"PeriodicalIF":2.1,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691643","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

Gene-based association tests in family samples using GWAS summary statistics 使用 GWAS 概要统计在家族样本中进行基于基因的关联测试。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-05 DOI: 10.1002/gepi.22548

Peng Wang, Xiao Xu, Ming Li, Xiang-Yang Lou, Siqi Xu, Baolin Wu, Guimin Gao, Ping Yin, Nianjun Liu

{"title":"Gene-based association tests in family samples using GWAS summary statistics","authors":"Peng Wang, Xiao Xu, Ming Li, Xiang-Yang Lou, Siqi Xu, Baolin Wu, Guimin Gao, Ping Yin, Nianjun Liu","doi":"10.1002/gepi.22548","DOIUrl":"10.1002/gepi.22548","url":null,"abstract":"Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 3","pages":"103-113"},"PeriodicalIF":2.1,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22548","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139691642","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

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype–environment interaction in polygenic risk score models 缓解 GxE PRS 中的 1 型错误膨胀和功率损失：多基因风险评分模型中基因型与环境的相互作用。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-02-01 DOI: 10.1002/gepi.22546

Dovini Jayasinghe, Md. Moksedul Momin, Kerri Beckmann, Elina Hyppönen, Beben Benyamin, S. Hong Lee

{"title":"Mitigating type 1 error inflation and power loss in GxE PRS: Genotype–environment interaction in polygenic risk score models","authors":"Dovini Jayasinghe, Md. Moksedul Momin, Kerri Beckmann, Elina Hyppönen, Beben Benyamin, S. Hong Lee","doi":"10.1002/gepi.22546","DOIUrl":"10.1002/gepi.22546","url":null,"abstract":"The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype–environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype–environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 2","pages":"85-100"},"PeriodicalIF":2.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139671559","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

Interval estimate of causal effect in summary data based Mendelian randomization in the presence of winner's curse 在存在赢家诅咒的情况下，基于孟德尔随机化的汇总数据中因果效应的区间估计。

IF 2.1 4区医学

Genetic Epidemiology Pub Date : 2024-01-28 DOI: 10.1002/gepi.22545

Kai Wang

引用次数: 0