Identification of multiple rare variants associated with a disease

BMC Proceedings

Proceedings

Jessica Dantzer¹  Jeesun Jung²  Yunlong Liu³ 

[1] Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th Street, HS 5000, 46202, Indianapolis, IN, USA;Department of Medical and Molecular Genetics, Indiana University School of Medicine, IB 130, 975 West Walnut Street, 46202, Indianapolis, IN, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th Street, HS 5000, 46202, Indianapolis, IN, USA;Department of Medical and Molecular Genetics, Indiana University School of Medicine, IB 130, 975 West Walnut Street, 46202, Indianapolis, IN, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West 10th Street, HS 5000, 46202, Indianapolis, IN, USA;Center for Medical Genomics, Indiana University School of Medicine, Fairbanks Hall, 340 West 10th Street, Suite 6200, 46202-3082, Indianapolis, IN, USA;

关键词: Rare Variant;    Genetic Analysis Workshop;    Nonsynonymous SNPs;    Disease Trait;    Exonic SNPs;   

DOI  :  10.1186/1753-6561-5-S9-S103

来源: Springer

【 摘 要 】

Identifying rare variants that are responsible for complex disease has been promoted by advances in sequencing technologies. However, statistical methods that can handle the vast amount of data generated and that can interpret the complicated relationship between disease and these variants have lagged. We apply a zero-inflated Poisson regression model to take into account the excess of zeros caused by the extremely low frequency of the 24,487 exonic variants in the Genetic Analysis Workshop 17 data. We grouped the 697 subjects in the data set as Europeans, Asians, and Africans based on principal components analysis and found the total number of rare variants per gene for each individual. We then analyzed these collapsed variants based on the assumption that rare variants are enriched in a group of people affected by a disease compared to a group of unaffected people. We also tested the hypothesis with quantitative traits Q1, Q2, and Q4. Analyses performed on the combined 697 individuals and on each ethnic group yielded different results. For the combined population analysis, we found that UGT1A1, which was not part of the simulation model, was associated with disease liability and that FLT1, which was a causal locus in the simulation model, was associated with Q1. Of the causal loci in the simulation models, FLT1 and KDR were associated with Q1 and VNN1 was correlated with Q2. No significant genes were associated with Q4. These results show the feasibility and capability of our new statistical model to detect multiple rare variants influencing disease risk.

【 授权许可】

CC BY   
© Jung et al; licensee BioMed Central Ltd. 2011