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Activity Number: 703
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #320202
Title: Modeling Maternal-Offspring Gene-Gene Interactions at Multiple Loci Using the Quantitative-MFG Test with an Application to Human Birth Weight
Author(s): Michelle M. Clark* and Olympe Chazara and Eric M. Sobel and Ashley Moffett and Janet S. Sinsheimer
Companies: University of California at Los Angeles and University of Cambridge and University of California at Los Angeles and University of Cambridge and University of California at Los Angeles
Keywords: linear mixed models ; family based association ; gene-gene interaction ; genomics
Abstract:

Joint maternal and offspring genetic effects, including interactions, can impact the conditions in which a fetus develops and in tern, offspring traits. Until recently, association testing for maternal-fetal genotype (MFG) interactions with quantitative traits was limited to case-parent trios. To address this limitation, the Quantitative-MFG (QMFG) test was developed. This linear mixed effects modeling approach can test for various scenarios of MFG interactions at a single locus using families of various sizes. One example of a potential MFG interaction involves two genes, KIR and HLA-C, which regulate human birth weight. This example prompts us to extend the QMFG test to multiple loci. Through simulation studies, we demonstrate that the statistical properties of the QMFG test remain sound. We also show that if a standard model, which considers only offspring genotypes, is fit to data with an underlying MFG effect, associations can be missed or identified incorrectly. In some cases, power for the standard model relies heavily on population allele frequencies, which may limit the ability to replicate significant findings unless the QMFG test is used.


Authors who are presenting talks have a * after their name.

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