Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Feb 5;22(1):50.
doi: 10.1186/s12859-021-03959-2.

Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

Haohan Wang  1 , Fen Pei  2 , Michael M Vanyukov  3 , Ivet Bahar  2 , Wei Wu  4 , Eric P Xing  5
Affiliations

Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

Haohan Wang et al. BMC Bioinformatics. .

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 .

Keywords: Deconfounding; Joint analysis; Mixed model.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Illustration of the existing challenges when conducting a joint analysis on two independently collected data sets with two different phenotypes
Fig. 2
Fig. 2
The ROC curves of the compared methods in terms of identifying the SNPs that are jointly associated with both phenotypes
Fig. 3
Fig. 3
The interactions between TRPV1 and 9 SUD-related drugs. Violet ellipses represent drugs of abuse; black solid edges represent known interactions in DrugBank; and red dashed edges represent predicted interactions using the PMF model
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    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. doi: 10.1016/j.ajhg.2017.06.005. - DOI - PMC - PubMed
    1. 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):1001–1006. doi: 10.1038/ng.3064. - DOI - PMC - PubMed
    1. 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. doi: 10.1016/j.jalz.2013.05.1071. - DOI
    1. 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. - PMC - PubMed
    1. 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. - PMC - PubMed

LinkOut - more resources