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PMID: 16354752 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Logistic regression protects against population structure in genetic association studies.

Genome research ·Vol. 16 ·No. 2 ·2006-02-00 ·Pages 290-6

Setakis E, Stirnadel H, Balding DJ

Abstract

We conduct an extensive simulation study to compare the merits of several methods for using null (unlinked) markers to protect against false positives due to cryptic substructure in population-based genetic association studies. The more sophisticated "structured association" methods perform well but are computationally demanding and rely on estimating the correct number of subpopulations. The simple and fast "genomic control" approach can lose power in certain scenarios. We find that procedures based on logistic regression that are flexible, computationally fast, and easy to implement also provide good protection against the effects of cryptic substructure, even though they do not explicitly model the population structure.

MeSH Terms
Animals Computational Biology/methods Genetics, Population/methods Humans Logistic Models Models, Genetic
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Setakis Efrosini
Department of Epidemiology and Public Health, Imperial College, St. Mary's Campus, London W2 1PG, United Kingdom. e.setakis@imperial.ac.uk
Stirnadel Heide
Balding David J
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Article Info
Journal
Genome research
Abbr.
Genome Res
ISSN
1088-9051
DOI
10.1101/gr.4346306
Email
e.setakis@imperial.ac.uk
Published
2006-02-00
Epub
2005-00-14
Pages
290-6
Language
English
Region
United States
NLM ID
9518021
PMCID
PMC1361725
Subset
IM
External Links
PubMed source DOI: 10.1101/gr.4346306 PMC Full Text View journal details
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