A pseudolikelihood approach for assessing genetic association in case–control studies with unmeasured population structure

Yong Chen, Kung Yee Liang, Pan Tong, Terri H. Beaty, Kathleen C. Barnes, W. H. Linda Kao

Research output: Contribution to journalArticlepeer-review

Abstract

The case–control study design is one of the main tools for detecting associations between genetic markers and diseases. It is well known that population substructure can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we propose a novel statistical method to estimate the association in case–control studies with unmeasured population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer the population substructure; second, the association between the disease and the test marker adjusting for the population substructure is modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to the existing methods, on bias, coverage probability and computational time, is assessed through simulations. The method is applied to an end-stage renal disease study in African Americans population.

Original languageEnglish (US)
Pages (from-to)3153-3165
Number of pages13
JournalStatistical Methods in Medical Research
Volume29
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • Case–control study
  • latent class model
  • population substructure
  • pseudolikelihood
  • two stage estimation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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