title: The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions.
abstract: Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered.
authors.affiliation: Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
authors.forename: Michelle M
authors.initials: MM
authors.lastname: Clark
authors.affiliation: South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas, Rio Grande Valley, Brownsville, TX, USA.
authors.forename: John
authors.initials: J
authors.lastname: Blangero
authors.affiliation: South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas, Rio Grande Valley, Brownsville, TX, USA.
authors.forename: Thomas D
authors.initials: TD
authors.lastname: Dyer
authors.affiliation: Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
authors.forename: Eric M
authors.initials: EM
authors.lastname: Sobel
authors.affiliation: Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.|Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.|Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
authors.forename: Janet S
authors.initials: JS
authors.lastname: Sinsheimer
mesh_terms: D003198:Computer Simulation; D005260:Female; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D019655:Quantitative Trait, Heritable
keywords: Maternal-fetal genotype incompatibility; family-based association; gene-gene interaction; intergenerational effects; measured genotype analysis; pedigree GWAS; quantitative traits; score test; variance components