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

Haohan Wang, Fen Pei, Michael M. Vanyukov, Ivet Bahar, Wei Wu, Eric P. Xing

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number50
JournalBMC bioinformatics
Volume22
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

Keywords

  • Deconfounding
  • Joint analysis
  • Mixed model

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets'. Together they form a unique fingerprint.

Cite this