Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets
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Published:2021-02-05
Issue:1
Volume:22
Page:
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ISSN:1471-2105
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Container-title:BMC Bioinformatics
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language:en
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Short-container-title:BMC Bioinformatics
Author:
Wang Haohan,Pei Fen,Vanyukov Michael M.,Bahar Ivet,Wu Wei,Xing Eric P.
Abstract
Abstract
Background
In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge.
Results
In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratification, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimer’s disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM.
Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference60 articles.
1. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22. 2. Wu C, Wang Z, Song X, Feng X-S, Abnet CC, He J, Hu N, Zuo X-B, Tan W, Zhan Q, et al. Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations. Nat Genet. 2014;46(9):1001–6. 3. Mukherjee S, Thornton T, Naj A, Kim S, Kauwe J, Fardo D, Valladares O, Wijsman E, Schellenberg G, Crane P. GWAS of the joint ADGC data set identifies novel common variants associated with late-onset Alzheimer’s disease. Alzheimer’s Dement J Alzheimer’s Assoc. 2013;9(4):550. 4. Pain O, Dudbridge F, Cardno AG, Freeman D, Lu Y, Lundstrom S, Lichtenstein P, Ronald A. Genome-wide analysis of adolescent psychotic-like experiences shows genetic overlap with psychiatric disorders. bioRxiv; 2018. 265512. 5. Walters RK, Adams MJ, Adkins AE, Aliev F, Bacanu S-A, Batzler A, Bertelsen S, Biernacka J, Bigdeli TB, Chen L-S, et al. Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. bioRxiv; 2018. 257311.
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