Genetic Epidemiology最新文献_第4页

The 2024 Annual Meeting of the International Genetic Epidemiology Society 国际遗传流行病学学会 2024 年年会。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-09-23 DOI: 10.1002/gepi.22598

引用次数: 0

Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores 用多基因风险评分预测韩国从不吸烟者的肺癌发病率

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-09-23 DOI: 10.1002/gepi.22586

Juyeon Kim, Young Sik Park, Jin Hee Kim, Yun-Chul Hong, Young-Chul Kim, In-Jae Oh, Sun Ha Jee, Myung-Ju Ahn, Jong-Won Kim, Jae-Joon Yim, Sungho Won

{"title":"Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores","authors":"Juyeon Kim, Young Sik Park, Jin Hee Kim, Yun-Chul Hong, Young-Chul Kim, In-Jae Oh, Sun Ha Jee, Myung-Ju Ahn, Jong-Won Kim, Jae-Joon Yim, Sungho Won","doi":"10.1002/gepi.22586","DOIUrl":"10.1002/gepi.22586","url":null,"abstract":"<div>\u0000 \u0000 In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer and these loci have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs); PRSs were calculated using a pruning-thresholding-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31–2.23) using the 40%–60% percentile group as the reference, and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747–0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLong's test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142284342","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

Exploring and Accounting for Genetically Driven Effect Heterogeneity in Mendelian Randomization 探索和解释孟德尔随机化中基因驱动的效应异质性。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-09-22 DOI: 10.1002/gepi.22587

Annika Jaitner, Krasimira Tsaneva-Atanasova, Rachel M. Freathy, Jack Bowden

{"title":"Exploring and Accounting for Genetically Driven Effect Heterogeneity in Mendelian Randomization","authors":"Annika Jaitner, Krasimira Tsaneva-Atanasova, Rachel M. Freathy, Jack Bowden","doi":"10.1002/gepi.22587","DOIUrl":"10.1002/gepi.22587","url":null,"abstract":"Mendelian randomization (MR) is a framework to estimate the causal effect of a modifiable health exposure, drug target or pharmaceutical intervention on a downstream outcome by using genetic variants as instrumental variables. A crucial assumption allowing estimation of the average causal effect in MR, termed homogeneity, is that the causal effect does not vary across levels of any instrument used in the analysis. In contrast, the science of pharmacogenetics seeks to actively uncover and exploit genetically driven effect heterogeneity for the purposes of precision medicine. In this study, we consider a recently proposed method for performing pharmacogenetic analysis on observational data—the Triangulation WIthin a STudy (TWIST) framework—and explore how it can be combined with traditional MR approaches to properly characterise average causal effects and genetically driven effect heterogeneity. We propose two new methods which not only estimate the genetically driven effect heterogeneity but also enable the estimation of a causal effect in the genetic group with and without the risk allele separately. Both methods utilise homogeneity-respecting and homogeneity-violating genetic variants and rely on a different set of assumptions. Using data from the ALSPAC study, we apply our new methods to estimate the causal effect of smoking before and during pregnancy on offspring birth weight in mothers whose genetics mean they find it (relatively) easier or harder to quit smoking.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142284341","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

Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity from a common risk factor 利用孟德尔随机化中的遗传变异聚类，从一个共同的风险因素出发，探究多病致病的因果途径。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-08-13 DOI: 10.1002/gepi.22582

Xiaoran Liang, Ninon Mounier, Nicolas Apfel, Sara Khalid, Timothy M. Frayling, Jack Bowden

{"title":"Using clustering of genetic variants in Mendelian randomization to interrogate the causal pathways underlying multimorbidity from a common risk factor","authors":"Xiaoran Liang, Ninon Mounier, Nicolas Apfel, Sara Khalid, Timothy M. Frayling, Jack Bowden","doi":"10.1002/gepi.22582","DOIUrl":"10.1002/gepi.22582","url":null,"abstract":"Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as instrumental variables to estimate the causal effect of an exposure on a health outcome. This paper investigates an MR scenario in which genetic variants aggregate into clusters that identify heterogeneous causal effects. Such variant clusters are likely to emerge if they affect the exposure and outcome via distinct biological pathways. In the multi-outcome MR framework, where a shared exposure causally impacts several disease outcomes simultaneously, these variant clusters can provide insights into the common disease-causing mechanisms underpinning the co-occurrence of multiple long-term conditions, a phenomenon known as multimorbidity. To identify such variant clusters, we adapt the general method of agglomerative hierarchical clustering to multi-sample summary-data MR setup, enabling cluster detection based on variant-specific ratio estimates. Particularly, we tailor the method for multi-outcome MR to aid in elucidating the causal pathways through which a common risk factor contributes to multiple morbidities. We show in simulations that our “MR-AHC” method detects clusters with high accuracy, outperforming the existing methods. We apply the method to investigate the causal effects of high body fat percentage on type 2 diabetes and osteoarthritis, uncovering interconnected cellular processes underlying this multimorbid disease pair.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975524","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

Exploring pleiotropy in Mendelian randomisation analyses: What are genetic variants associated with ‘cigarette smoking initiation’ really capturing? 探索孟德尔随机分析中的多义性：与 "开始吸烟 "相关的基因变异到底在捕捉什么？

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-08-04 DOI: 10.1002/gepi.22583

Zoe E. Reed, Robyn E. Wootton, Jasmine N. Khouja, Tom G. Richardson, Eleanor Sanderson, George Davey Smith, Marcus R. Munafò

{"title":"Exploring pleiotropy in Mendelian randomisation analyses: What are genetic variants associated with ‘cigarette smoking initiation’ really capturing?","authors":"Zoe E. Reed, Robyn E. Wootton, Jasmine N. Khouja, Tom G. Richardson, Eleanor Sanderson, George Davey Smith, Marcus R. Munafò","doi":"10.1002/gepi.22583","DOIUrl":"10.1002/gepi.22583","url":null,"abstract":"Genetic variants used as instruments for exposures in Mendelian randomisation (MR) analyses may have horizontal pleiotropic effects (i.e., influence outcomes via pathways other than through the exposure), which can undermine the validity of results. We examined the extent of this using smoking behaviours as an example. We first ran a phenome-wide association study in UK Biobank, using a smoking initiation genetic instrument. From the most strongly associated phenotypes, we selected those we considered could either plausibly or not plausibly be caused by smoking. We examined associations between genetic instruments for smoking initiation, smoking heaviness and lifetime smoking and these phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). We conducted negative control analyses among never smokers, including children. We found evidence that smoking-related genetic instruments were associated with phenotypes not plausibly caused by smoking in UK Biobank and (to a lesser extent) ALSPAC. We observed associations with phenotypes among never smokers. Our results demonstrate that smoking-related genetic risk scores are associated with unexpected phenotypes that are less plausibly downstream of smoking. This may reflect horizontal pleiotropy in these genetic risk scores, and we would encourage researchers to exercise caution this when using these and genetic risk scores for other complex behavioural exposures. We outline approaches that could be taken to consider this and overcome issues caused by potential horizontal pleiotropy, for example, in genetically informed causal inference analyses (e.g., MR) it is important to consider negative control outcomes and triangulation approaches, to avoid arriving at incorrect conclusions.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888953","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

Use of genetic correlations to examine selection bias 利用基因相关性研究选择偏差。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-07-30 DOI: 10.1002/gepi.22584

Chin Yang Shapland, Apostolos Gkatzionis, Gibran Hemani, Kate Tilling

引用次数: 0

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model 利用考克斯比例危险模型的套索，建立预测阿尔茨海默氏症患者自由生存期的多基因危险评分模型。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-07-09 DOI: 10.1002/gepi.22581

Georg Hahn, Dmitry Prokopenko, Julian Hecker, Sharon M. Lutz, Kristina Mullin, Rudolph E. Tanzi, Stacia DeSantis, Christoph Lange

{"title":"Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model","authors":"Georg Hahn, Dmitry Prokopenko, Julian Hecker, Sharon M. Lutz, Kristina Mullin, Rudolph E. Tanzi, Stacia DeSantis, Christoph Lange","doi":"10.1002/gepi.22581","DOIUrl":"10.1002/gepi.22581","url":null,"abstract":"The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000–100,000 subjects in less than 1 h.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563235","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

Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes 在基于疾病亚型的家族测序研究中优先考虑罕见变异的统计方法。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-06-28 DOI: 10.1002/gepi.22579

Christina Nieuwoudt, Fabiha Binte Farooq, Angela Brooks-Wilson, Alexandre Bureau, Jinko Graham

{"title":"Statistics to prioritize rare variants in family-based sequencing studies with disease subtypes","authors":"Christina Nieuwoudt, Fabiha Binte Farooq, Angela Brooks-Wilson, Alexandre Bureau, Jinko Graham","doi":"10.1002/gepi.22579","DOIUrl":"10.1002/gepi.22579","url":null,"abstract":"Family-based sequencing studies are increasingly used to find rare genetic variants of high risk for disease traits with familial clustering. In some studies, families with multiple disease subtypes are collected and the exomes of affected relatives are sequenced for shared rare variants (RVs). Since different families can harbor different causal variants and each family harbors many RVs, tests to detect causal variants can have low power in this study design. Our goal is rather to prioritize shared variants for further investigation by, for example, pathway analyses or functional studies. The transmission-disequilibrium test prioritizes variants based on departures from Mendelian transmission in parent–child trios. Extending this idea to families, we propose methods to prioritize RVs shared in affected relatives with two disease subtypes, with one subtype more heritable than the other. Global approaches condition on a variant being observed in the study and assume a known probability of carrying a causal variant. In contrast, local approaches condition on a variant being observed in specific families to eliminate the carrier probability. Our simulation results indicate that global approaches are robust to misspecification of the carrier probability and prioritize more effectively than local approaches even when the carrier probability is misspecified.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 7","pages":"324-343"},"PeriodicalIF":1.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467490","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

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study 利用顺式和反式变异进行全蛋白质组关联研究，并将其应用于妇女健康倡议研究中的血细胞和血脂相关特征。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-06-28 DOI: 10.1002/gepi.22578

Brian D. Chen, Chanhwa Lee, Amanda L. Tapia, Alexander P. Reiner, Hua Tang, Charles Kooperberg, JoAnn E. Manson, Yun Li, Laura M. Raffield

{"title":"Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study","authors":"Brian D. Chen, Chanhwa Lee, Amanda L. Tapia, Alexander P. Reiner, Hua Tang, Charles Kooperberg, JoAnn E. Manson, Yun Li, Laura M. Raffield","doi":"10.1002/gepi.22578","DOIUrl":"10.1002/gepi.22578","url":null,"abstract":"In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 7","pages":"310-323"},"PeriodicalIF":1.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141467489","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

Hierarchical joint analysis of marginal summary statistics—Part II: High-dimensional instrumental analysis of omics data 边际汇总统计的层次联合分析--第二部分：omics 数据的高维工具分析。

IF 1.7 4区医学

Genetic Epidemiology Pub Date : 2024-06-17 DOI: 10.1002/gepi.22577

Lai Jiang, Jiayi Shen, Burcu F. Darst, Christopher A. Haiman, Nicholas Mancuso, David V. Conti

{"title":"Hierarchical joint analysis of marginal summary statistics—Part II: High-dimensional instrumental analysis of omics data","authors":"Lai Jiang, Jiayi Shen, Burcu F. Darst, Christopher A. Haiman, Nicholas Mancuso, David V. Conti","doi":"10.1002/gepi.22577","DOIUrl":"10.1002/gepi.22577","url":null,"abstract":"Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants—situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"48 7","pages":"291-309"},"PeriodicalIF":1.7,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.22577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418544","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