Duan, Qing Xu, Zheng Raffield, Laura M Chang, Suhua Wu, Di Lange, Ethan M Reiner, Alex P Li, Yun
Published in
Genetic epidemiology
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. Thes...
Ray, Debashree Boehnke, Michael
Published in
Genetic epidemiology
Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycer...
Keys, Kevin L Chen, Gary K Lange, Kenneth
Published in
Genetic epidemiology
Source code is freely available at https://github.com/klkeys/IHT.jl.
Liu, Mengque Fan, Xinyan Fang, Kuangnan Zhang, Qingzhao Ma, Shuangge
Published in
Genetic epidemiology
In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic ...
Zhang, Yilong Han, Sung Won Cox, Laura M Li, Huilin
Published in
Genetic epidemiology
Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial d...
Carlson, Jenna C Standley, Jennifer Petrin, Aline Shaffer, John R Butali, Azeez Buxó, Carmen J Castilla, Eduardo Christensen, Kaare Deleyiannis, Frederic W-D Hecht, Jacqueline T
...
Published in
Genetic epidemiology
Orofacial clefts (OFCs) are common, complex birth defects with extremely heterogeneous phenotypic presentations. Two common subtypes-cleft lip alone (CL) and CL plus cleft palate (CLP)-are typically grouped into a single phenotype for genetic analysis (i.e., CL with or without cleft palate, CL/P). However, mounting evidence suggests there may be un...
Wang, Lu Damrauer, Scott M Zhang, Hong Zhang, Alan X Xiao, Rui Moore, Jason H Chen, Jinbo
Published in
Genetic epidemiology
The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide assoc...
He, Zihuai Lee, Seunggeun Zhang, Min Smith, Jennifer A Guo, Xiuqing Palmas, Walter Kardia, Sharon L R Ionita-Laza, Iuliana Mukherjee, Bhramar
Published in
Genetic epidemiology
Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene-based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one-at-a-time single...
Park, Danny S Eskin, Itamar Kang, Eun Yong Gamazon, Eric R Eng, Celeste Gignoux, Christopher R Galanter, Joshua M Burchard, Esteban Ye, Chun J Aschard, Hugues
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Published in
Genetic epidemiology
We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.
Ainsworth, Holly F Shin, So-Youn Cordell, Heather J
Published in
Genetic epidemiology
Genome wide association studies (GWAS) have been very successful over the last decade at identifying genetic variants associated with disease phenotypes. However, interpretation of the results obtained can be challenging. Incorporation of further relevant biological measurements (e.g. 'omics' data) measured in the same individuals for whom we have ...
