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Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method.
Li, Ming; Li, Jingyun; He, Zihuai; Lu, Qing; Witte, John S; Macleod, Stewart L; Hobbs, Charlotte A; Cleves, Mario A.
Afiliação
  • Li M; Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Bloomington, Indiana, United States of America.
  • Li J; Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
  • He Z; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.
  • Lu Q; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America.
  • Witte JS; Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, California, United States of America.
  • Macleod SL; Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
  • Hobbs CA; Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
  • Cleves MA; Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
Genet Epidemiol ; 40(4): 341-51, 2016 May.
Article em En | MEDLINE | ID: mdl-27061818
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).
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Família / Polimorfismo de Nucleotídeo Único / Alelos / Estudos de Associação Genética / Cardiopatias Congênitas Tipo de estudo: Clinical_trials / Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Família / Polimorfismo de Nucleotídeo Único / Alelos / Estudos de Associação Genética / Cardiopatias Congênitas Tipo de estudo: Clinical_trials / Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article