| Literature DB >> 27061818 |
Ming Li1, Jingyun Li2, Zihuai He3, Qing Lu4, John S Witte5, Stewart L Macleod2, Charlotte A Hobbs2, Mario A Cleves2.
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).Entities:
Keywords: allele distortion; congenital heart defects; family-based association test; generalized genetic random field; genetic similarity; population stratification
Mesh:
Year: 2016 PMID: 27061818 PMCID: PMC5061344 DOI: 10.1002/gepi.21970
Source DB: PubMed Journal: Genet Epidemiol ISSN: 0741-0395 Impact factor: 2.135