Human Heredity最新文献_第9页

Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design. 估计分层两阶段病例对照设计中的加性相互作用效应

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2019-10-21 DOI: 10.1159/000502738

Ai Ni, Jaya M Satagopan

{"title":"Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.","authors":"Ai Ni, Jaya M Satagopan","doi":"10.1159/000502738","DOIUrl":"10.1159/000502738","url":null,"abstract":"Background and aims: There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified two-phase case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.Methods: Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.Results: Simulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.Conclusion: When estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"90-108"},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925975/pdf/nihms-1053034.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46172932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Front & Back Matter 正面和背面事项

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-11-01 DOI: 10.1159/000495776

引用次数: 0

Front & Back Matter 正面和背面

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-06-01 DOI: 10.1159/000490860

W. Kiess, C. Bornehag, C. Gennings

引用次数: 0

SAGES 2018, Symposium on Advances in Genomics, Epidemiology and Statistics 2018, Philadelphia, PA, USA, June 1, 2018: Abstracts. SAGES 2018，2018 年基因组学、流行病学和统计学进展研讨会，美国宾夕法尼亚州费城，2018 年 6 月 1 日：摘要。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-06-01 DOI: 10.1159/000490340

引用次数: 0

46th European Mathematical Genetics Meeting (EMGM) 2018, Cagliari, Italy, April 18-20, 2018: Abstracts. 2018年第46届欧洲数学遗传学会议（EMGM），意大利卡利亚里，2018年4月18-20日：摘要。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-04-18 DOI: 10.1159/000488519

引用次数: 0

Next-Generation Sequencing in Human Genetic Studies: Genome Technologies and Applications to Human Genetic Studies. 人类遗传研究中的下一代测序:基因组技术及其在人类遗传研究中的应用。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-01-01 Epub Date: 2019-01-09 DOI: 10.1159/000494818

Junwen Wang, Kai Wang, Xiaoming Liu, Pak Sham, Zhongming Zhao

引用次数: 0

The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions. 在分析基因与体力活动的相互作用时，从极端暴露中选择个体的前景。

IF 1.1 4区生物学

Human Heredity Pub Date : 2018-01-01 Epub Date: 2019-06-05 DOI: 10.1159/000499711

Oyomoare L Osazuwa-Peters, Karen Schwander, R J Waken, Lisa de las Fuentes, Tuomas O Kilpeläinen, Ruth J F Loos, Susan B Racette, Yun Ju Sung, D C Rao

{"title":"The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions.","authors":"Oyomoare L Osazuwa-Peters, Karen Schwander, R J Waken, Lisa de las Fuentes, Tuomas O Kilpeläinen, Ruth J F Loos, Susan B Racette, Yun Ju Sung, D C Rao","doi":"10.1159/000499711","DOIUrl":"10.1159/000499711","url":null,"abstract":"Background: Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci.Objectives: This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification.Method: For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error.Results: In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power.Conclusion: SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"83 6","pages":"315-332"},"PeriodicalIF":1.1,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662918/pdf/nihms-1022057.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37310949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Assessment of Genotype Imputation Performance. 基因型代入性能的综合评价。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-01-01 Epub Date: 2019-01-22 DOI: 10.1159/000489758

Shuo Shi, Na Yuan, Ming Yang, Zhenglin Du, Jinyue Wang, Xin Sheng, Jiayan Wu, Jingfa Xiao

{"title":"Comprehensive Assessment of Genotype Imputation Performance.","authors":"Shuo Shi, Na Yuan, Ming Yang, Zhenglin Du, Jinyue Wang, Xin Sheng, Jiayan Wu, Jingfa Xiao","doi":"10.1159/000489758","DOIUrl":"https://doi.org/10.1159/000489758","url":null,"abstract":"Genotype imputation is a process of estimating missing ge-notypes from the haplotype or genotype reference panel. It can effectively boost the power of detecting single nucleotide polymorphisms (SNPs) in genome-wide association studies, integrate multi-studies for meta-analysis, and be applied in fine-mapping studies. The performance of genotype imputation is affected by many factors, including software, reference selection, sample size, and SNP density/sequencing coverage. A systematical evaluation of the imputation performance of current popular software will benefit future studies. Here, we evaluate imputation performances of Beagle4.1, IMPUTE2, MACH+Minimac3, and SHAPEIT2+ IM-PUTE2 using test samples of East Asian ancestry and references of the 1000 Genomes Project. The result indicated the accuracy of IMPUTE2 (99.18%) is slightly higher than that of the others (Beagle4.1: 98.94%, MACH+Minimac3: 98.51%, and SHAPEIT2+IMPUTE2: 99.08%). To achieve good and stable imputation quality, the minimum requirement of SNP density needs to be > 200/Mb. The imputation accuracies of IMPUTE2 and Beagle4.1 were under the minor influence of the study sample size. The contribution extent of reference to genotype imputation performance relied on software selection. We assessed the imputation performance on SNPs generated by next-generation whole genome sequencing and found that SNP sets detected by sequencing with 15× depth could be mostly got by imputing from the haplotype reference panel of the 1000 Genomes Project based on SNP data detected by sequencing with 4× depth. All of the imputation software had a weaker performance in low minor allele frequency SNP regions because of the bias of reference or software. In the future, more comprehensive reference panels or new algorithm developments may rise up to this challenge.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"83 3","pages":"107-116"},"PeriodicalIF":1.8,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000489758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36875552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 44

A Family-Based Rare Haplotype Association Method for Quantitative Traits. 基于家族的数量性状稀有单倍型关联方法。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-01-01 Epub Date: 2019-02-21 DOI: 10.1159/000493543

Ananda S Datta, Shili Lin, Swati Biswas

{"title":"A Family-Based Rare Haplotype Association Method for Quantitative Traits.","authors":"Ananda S Datta, Shili Lin, Swati Biswas","doi":"10.1159/000493543","DOIUrl":"https://doi.org/10.1159/000493543","url":null,"abstract":"Background: The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this \"missing heritability\" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect.Methods: We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods.Results: We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure.Conclusion: FamQBL can help uncover rHTVs associated with quantitative traits.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"83 4","pages":"175-195"},"PeriodicalIF":1.8,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000493543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36994355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes. 利用基因-环境相互作用发现跨多种研究或表型的遗传关联的基于子集的分析。

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-01-01 Epub Date: 2019-05-27 DOI: 10.1159/000496867

Youfei Yu, Lu Xia, Seunggeun Lee, Xiang Zhou, Heather M Stringham, Michael Boehnke, Bhramar Mukherjee

{"title":"Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes.","authors":"Youfei Yu, Lu Xia, Seunggeun Lee, Xiang Zhou, Heather M Stringham, Michael Boehnke, Bhramar Mukherjee","doi":"10.1159/000496867","DOIUrl":"https://doi.org/10.1159/000496867","url":null,"abstract":"Objectives: Classical methods for combining summary data from genome-wide association studies only use marginal genetic effects, and power can be compromised in the presence of heterogeneity. We aim to enhance the discovery of novel associated loci in the presence of heterogeneity of genetic effects in subgroups defined by an environmental factor.Methods: We present a pvalue-assisted subset testing for associations (pASTA) framework that generalizes the previously proposed association analysis based on subsets (ASSET) method by incorporating gene-environment (G-E) interactions into the testing procedure. We conduct simulation studies and provide two data examples.Results: Simulation studies show that our proposal is more powerful than methods based on marginal associations in the presence of G-E interactions and maintains comparable power even in their absence. Both data examples demonstrate that our method can increase power to detect overall genetic associations and identify novel studies/phenotypes that contribute to the association.Conclusions: Our proposed method can be a useful screening tool to identify candidate single nucleotide polymorphisms that are potentially associated with the trait(s) of interest for further validation. It also allows researchers to determine the most probable subset of traits that exhibit genetic associations in addition to the enhancement of power.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"83 6","pages":"283-314"},"PeriodicalIF":1.8,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000496867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37001409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8