A general semi-parametric approach to the analysis of genetic association studies in population-based designs

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A general semi-parametric approach to the analysis of genetic association studies in population-based designs

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Title: A general semi-parametric approach to the analysis of genetic association studies in population-based designs
Author: Lutz, Sharon; Yip, Wai-Ki; Hokanson, John; Laird, Nan M.; Lange, Christoph

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Citation: Lutz, Sharon, Wai-Ki Yip, John Hokanson, Nan Laird, and Christoph Lange. 2013. A general semi-parametric approach to the analysis of genetic association studies in population-based designs. BMC Genetics 14: 13.
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Abstract: Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
Published Version: doi:10.1186/1471-2156-14-13
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648382/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11181085
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