Welcome to CDC Stacks | Testing Allele Transmission of a SNP-Set using a Family-based Generalized Genetic Random Field Method - 41725 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Testing Allele Transmission of a SNP-Set using a Family-based Generalized Genetic Random Field Method
  • Published Date:
    Apr 7 2016
  • Source:
    Genet Epidemiol. 40(4):341-351.


Public Access Version Available on: May 01, 2017 information icon
Please check back on the date listed above.
Details:
  • Corporate Authors:
    National Birth Defect Prevention Study
  • Pubmed ID:
    27061818
  • Pubmed Central ID:
    PMC5061344
  • Funding:
    UL1 TR000039/TR/NCATS NIH HHS/United States
    UL1TR000039/TR/NCATS NIH HHS/United States
    5U01DD000491/DD/NCBDD CDC HHS/United States
    KL2 TR000063/TR/NCATS NIH HHS/United States
    5R01HD039054/HD/NICHD NIH HHS/United States
    KL2TR000063/TR/NCATS NIH HHS/United States
    U01 DD001039/DD/NCBDD CDC HHS/United States
    R01 HD039054/HD/NICHD NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    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).

  • Supporting Files:
    No Additional Files