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

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 freque...

Full description

Saved in:
Bibliographic Details
Published in:Statistical methods in medical research 2020-11, Vol.29 (11), p.3153-3165
Main Authors: Chen, Yong, Liang, Kung-Yee, Tong, Pan, Beaty, Terri H, Barnes, Kathleen C, Linda Kao, WH
Format: Article
Language:English
Subjects:
African Americans
Bias
Case-Control Studies
Computer simulation
Computing time
Disease
Gene Frequency
Genetic Markers
Genomics
Humans
Kidney diseases
Latent class analysis
Markers
Population
Probabilistic methods
Probability
Statistical analysis
Substructures
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0962-2802
1477-0334
1477-0334
DOI:10.1177/0962280220921212