Literature DB >> 18497429

Population stratification bias in the case-only study for gene-environment interactions.

Liang-Yi Wang1, Wen-Chung Lee.   

Abstract

The case-only study is a convenient approach and provides increased statistical efficiency in detecting gene-environment interactions. The validity of a case-only study hinges on one well-recognized assumption: The susceptibility genotypes and the environmental exposures of interest are independent in the population. Otherwise, the study will be biased. The authors show that hidden stratification in the study population could also ruin a case-only study. They derive the formulas for population stratification bias. The bias involves three terms: 1) the coefficient of variation of the exposure prevalence odds, 2) the coefficient of variation of the genotype frequency odds, and 3) the correlation coefficient between the exposure prevalence odds and the genotype frequency odds. The authors perform simulation to investigate the magnitude of bias over a wide range of realistic scenarios. It is found that the estimated interaction effect is frequently biased by more than 5%. For a rarer gene and a rarer exposure, the bias becomes even larger (>30%). Because of the potentially large bias, researchers conducting case-only studies should use the boundary formula presented in this paper to make more prudent interpretations of their results, or they should use stratified analysis or a modeling approach to adjust for population stratification bias in their studies.

Mesh:

Year:  2008        PMID: 18497429     DOI: 10.1093/aje/kwn130

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  15 in total

1.  Allowing for population stratification in case-only studies of gene-environment interaction, using genomic control.

Authors:  Pankaj Yadav; Sandra Freitag-Wolf; Wolfgang Lieb; Astrid Dempfle; Michael Krawczak
Journal:  Hum Genet       Date:  2015-08-22       Impact factor: 4.132

2.  Interaction of cardiac implantable electronic device and patent foramen ovale in ischemic stroke: A case-only study.

Authors:  Kolade M Agboola; Jin-Moo Lee; Xiaoyan Liu; Eric Novak; Phillip S Cuculich; Daniel H Cooper; Amit Noheria
Journal:  Pacing Clin Electrophysiol       Date:  2019-01-31       Impact factor: 1.976

3.  An exploratory case-only analysis of gene-hazardous air pollutant interactions and the risk of childhood medulloblastoma.

Authors:  Philip J Lupo; Laura J Lee; M Fatih Okcu; Melissa L Bondy; Michael E Scheurer
Journal:  Pediatr Blood Cancer       Date:  2012-03-02       Impact factor: 3.167

4.  Maternal-fetal metabolic gene-gene interactions and risk of neural tube defects.

Authors:  Philip J Lupo; Laura E Mitchell; Mark A Canfield; Gary M Shaw; Andrew F Olshan; Richard H Finnell; Huiping Zhu
Journal:  Mol Genet Metab       Date:  2013-11-18       Impact factor: 4.797

Review 5.  Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.

Authors:  Hugues Aschard; Sharon Lutz; Bärbel Maus; Eric J Duell; Tasha E Fingerlin; Nilanjan Chatterjee; Peter Kraft; Kristel Van Steen
Journal:  Hum Genet       Date:  2012-07-04       Impact factor: 4.132

6.  Sample size requirements to detect gene-environment interactions in genome-wide association studies.

Authors:  Cassandra E Murcray; Juan Pablo Lewinger; David V Conti; Duncan C Thomas; W James Gauderman
Journal:  Genet Epidemiol       Date:  2011-02-09       Impact factor: 2.135

7.  Detecting gene-environment interactions in human birth defects: Study designs and statistical methods.

Authors:  Caroline G Tai; Rebecca E Graff; Jinghua Liu; Michael N Passarelli; Joel A Mefford; Gary M Shaw; Thomas J Hoffmann; John S Witte
Journal:  Birth Defects Res A Clin Mol Teratol       Date:  2015-05-23

8.  Gene-gene interactions in the folate metabolic pathway and the risk of conotruncal heart defects.

Authors:  Philip J Lupo; Elizabeth Goldmuntz; Laura E Mitchell
Journal:  J Biomed Biotechnol       Date:  2010-01-12

9.  Case-only genome-wide interaction study of disease risk, prognosis and treatment.

Authors:  Brandon L Pierce; Habibul Ahsan
Journal:  Genet Epidemiol       Date:  2010-01       Impact factor: 2.135

Review 10.  Design and analysis issues in gene and environment studies.

Authors:  Chen-yu Liu; Arnab Maity; Xihong Lin; Robert O Wright; David C Christiani
Journal:  Environ Health       Date:  2012-12-19       Impact factor: 5.984

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