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Statistical Applications in Genetics and Molecular Biology

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Multiple Imputation of Missing Phenotype Data for QTL Mapping

Jennifer F Bobb1 / Daniel O. Scharfstein2 / Michael J. Daniels3 / Francis S Collins4 / Samir Kelada5

1Johns Hopkins Bloomberg School of Public Health

2Johns Hopkins Bloomberg School of Public Health

3University of Florida

4National Human Genome Research Institute, National Institutes of Health

5National Human Genome Research Institute, National Institutes of Health

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 10, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: 10.2202/1544-6115.1676, June 2011

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Published Online:

Missing phenotype data can be a major hurdle to mapping quantitative trait loci (QTL). Though in many cases experiments may be designed to minimize the occurrence of missing data, it is often unavoidable in practice; thus, statistical methods to account for missing data are needed. In this paper we describe an approach for conjoining multiple imputation and QTL mapping. Methods are applied to map genes associated with increased breathing effort in mice after lung inflammation due to allergen challenge in developing lines of the Collaborative Cross, a new mouse genetics resource. Missing data poses a particular challenge in this study because the desired phenotype summary to be mapped is a function of incompletely observed dose-response curves. Comparison of the multiple imputation approach to two naive approaches for handling missing data suggest that these simpler methods may yield poor results: ignoring missing data through a complete case analysis may lead to incorrect conclusions, while using a last observation carried forward procedure, which does not account for uncertainty in the imputed values, may lead to anti-conservative inference. The proposed approach is widely applicable to other studies with missing phenotype data.

Keywords: multiple imputation; missing data; quantitative trait loci

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