Human Heredity最新文献_第9页

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies. 在测序关联研究中测试多个性状的基因-环境交互作用

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-05-16 DOI: 10.1159/000506008

Jianjun Zhang, Qiuying Sha, Han Hao, Shuanglin Zhang, Xiaoyi Raymond Gao, Xuexia Wang

{"title":"Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.","authors":"Jianjun Zhang, Qiuying Sha, Han Hao, Shuanglin Zhang, Xiaoyi Raymond Gao, Xuexia Wang","doi":"10.1159/000506008","DOIUrl":"10.1159/000506008","url":null,"abstract":"Motivation: The risk of many complex diseases is determined by an interplay of genetic and environmental factors. The examination of gene-environment interactions (G×Es) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease-associated genes. However, the methods for testing G×Es for multiple traits are very limited.Method: We developed novel approaches to test G×Es for multiple traits in sequencing association studies. We first perform a transformation of multiple traits by using either principal component analysis or standardization analysis. Then, we detect the effects of G×Es using novel proposed tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ Fisher's combination test to combine the p values.Results: Extensive simulation studies show that the type I error rates of the proposed methods are well controlled. Compared to the interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal G×Es. Application to the COPDGene Study demonstrates that our proposed methods are very effective.Conclusions: Our proposed methods are useful tools in the identification of G×Es for multiple traits. The proposed methods can be used not only to identify G×Es for common variants, but also for rare variants. Therefore, they can be employed in identifying G×Es in both genome-wide association studies and next-generation sequencing data analyses.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 4-5","pages":"170-196"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351593/pdf/nihms-1558071.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37943558","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

48th European Mathematical Genetics Meeting (EMGM) 2020. 第48届欧洲数学遗传学会议(EMGM) 2020。

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-04-08 DOI: 10.1159/000507248

Zoltan Kutalik

引用次数: 0

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