Genetic Association Analysis under Complex Survey Sampling: The Hispanic Community Health Study/Study of Latinos

The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants...

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Published inAmerican journal of human genetics Vol. 95; no. 6; pp. 675 - 688
Main Authors Lin, Dan-Yu, Tao, Ran, Kalsbeek, William D., Zeng, Donglin, Gonzalez, Franklyn, Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Koch, Gary G., North, Kari E., Heiss, Gerardo
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 04.12.2014
Cell Press
Elsevier
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ISSN0002-9297
1537-6605
1537-6605
DOI10.1016/j.ajhg.2014.11.005

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Summary:The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related. Analysis that ignores the complex sampling design can yield biased estimation of the genetic association and inflation of the type I error. Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the performance of the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States aimed at identifying risk factors for various diseases and determining the role of genes and environment in the occurrence of diseases. We provide guidelines on the use of weighted and unweighted estimators, as well as the relevant software.
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ISSN:0002-9297
1537-6605
1537-6605
DOI:10.1016/j.ajhg.2014.11.005