Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data

Souvik Seal, Jeffrey A. Boatman, Matt McGue, Saonli Basu

Research output: Contribution to journalArticlepeer-review

Abstract

Genome-wide association studies (GWASs) are a popular tool for detecting association between genetic variants or single nucleotide polymorphisms (SNPs) and complex traits. Family data introduce complexity due to the non-independence of the family members. Methods for non-independent data are well established, but when the GWAS contains distinct family types, explicit modeling of between-family-type differences in the dependence structure comes at the cost of significantly increased computational burden. The situation is exacerbated with binary traits. In this paper, we perform several simulation studies to compare multiple candidate methods to perform single SNP association analysis with binary traits. We consider generalized estimating equations (GEE), generalized linear mixed models (GLMMs), or generalized least square (GLS) approaches. We study the influence of different working correlation structures for GEE on the GWAS findings and also the performance of different analysis method(s) to conduct a GWAS with binary trait data in families. We discuss the merits of each approach with attention to their applicability in a GWAS. We also compare the performances of the methods on the alcoholism data from the Minnesota Center for Twin and Family Research (MCTFR) study.

Original languageEnglish (US)
Pages (from-to)423-439
Number of pages17
JournalBehavior genetics
Volume50
Issue number6
DOIs
StatePublished - Nov 1 2020

Bibliographical note

Funding Information:
This research was supported by NIH Grant Nos. R01-DA033958 and R21-DA046188 (PI: Saonli Basu).

Funding Information:
This research was supported by NIH Grant Nos. R01-DA033958 and R21-DA046188 (PI: Saonli Basu).

Keywords

  • Family data
  • Generalized estimating equation
  • Generalized least squares
  • Generalized linear mixed effect model
  • Genome-wide scan
  • Population-based association analysis

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

Fingerprint Dive into the research topics of 'Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data'. Together they form a unique fingerprint.

Cite this