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ARS Home » Plains Area » Houston, Texas » Children's Nutrition Research Center » Research » Publications at this Location » Publication #307948
Title: Detecting local haplotype sharing and haplotype association

Author
item XU, HANLI - Children'S Nutrition Research Center (CNRC)
item GUAN, YONGTAO - Children'S Nutrition Research Center (CNRC)

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/5/2014
Publication Date: 7/1/2014
Citation: Xu, H., Guan, Y. 2014. Detecting local haplotype sharing and haplotype association. Genetics. 197(3):823-838.

Interpretive Summary: A haplotype is the unit of inheritance that can directly or indirectly affect an individual's risk for diseases. Humans are diplotypes that inherit one copy of haplotype from each parent. We used haplotype similarity to quantify genetic relatedness between two individuals. By examining if more related individuals are more likely (or less likely) to have certain disease, we can deduce whether a gene is associated with risks for diseases. We demonstrated the advantages of our newly developed statistical method through both superior power in simulation studies and novel findings in real data analysis. This new statistical software method is available online for use by the research community in conducting scientific research.

Technical Abstract: A novel haplotype association method is presented, and its power is demonstrated. Relying on a statistical model for linkage disequilibrium (LD), the method first infers ancestral haplotypes and their loadings at each marker for each individual. The loadings are then used to quantify local haplotype sharing between individuals at each marker. A statistical model was developed to link the local haplotype sharing and phenotypes to test for association. We devised a novel method to fit the LD model, reducing the complexity from putatively quadratic to linear (in the number of ancestral haplotypes). Therefore, the LD model can be fitted to all study samples simultaneously, and, consequently, our method is applicable to big data sets. Compared to existing haplotype association methods, our method integrated out phase uncertainty, avoided arbitrariness in specifying haplotypes, and had the same number of tests as the single-SNP analysis. We applied our method to data from the Wellcome Trust Case Control Consortium and discovered eight novel associations between seven gene regions and five disease phenotypes. Among these, GRIK4, which encodes a protein that belongs to the glutamate-gated ionic channel family, is strongly associated with both coronary artery disease and rheumatoid arthritis. A software package implementing methods described in this article is freely available at http://www.haplotype.org.