title: Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method.
abstract: Family-based association studies are commonly used in genetic research because they can be robust to population stratification (PS). Recent advances in high-throughput genotyping technologies have produced a massive amount of genomic data in family-based studies. However, current family-based association tests are mainly focused on evaluating individual variants one at a time. In this article, we introduce a family-based generalized genetic random field (FB-GGRF) method to test the joint association between a set of autosomal SNPs (i.e., single-nucleotide polymorphisms) and disease phenotypes. The proposed method is a natural extension of a recently developed GGRF method for population-based case-control studies. It models offspring genotypes conditional on parental genotypes, and, thus, is robust to PS. Through simulations, we presented that under various disease scenarios the FB-GGRF has improved power over a commonly used family-based sequence kernel association test (FB-SKAT). Further, similar to GGRF, the proposed FB-GGRF method is asymptotically well-behaved, and does not require empirical adjustment of the type I error rates. We illustrate the proposed method using a study of congenital heart defects with family trios from the National Birth Defects Prevention Study (NBDPS).
authors.affiliation: Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, Indiana, United States of America.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
authors.forename: Jingyun
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Zihuai
authors.initials: Z
authors.lastname: He
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
authors.affiliation: Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, United States of America.
authors.forename: John S
authors.initials: JS
authors.lastname: Witte
authors.affiliation: Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
authors.forename: Stewart L
authors.initials: SL
authors.lastname: Macleod
authors.affiliation: Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
authors.forename: Charlotte A
authors.initials: CA
authors.lastname: Hobbs
authors.affiliation: Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
authors.forename: Mario A
authors.initials: MA
authors.lastname: Cleves
mesh_terms: D000483:Alleles; D016022:Case-Control Studies; D005190:Family; D056726:Genetic Association Studies / Q000379:methods*; D005838:Genotype; D006330:Heart Defects, Congenital / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*
keywords: allele distortion; congenital heart defects; family-based association test; generalized genetic random field; genetic similarity; population stratification