Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

BMC Proceedings

Proceedings

Inchi Hu¹  Chien Hsun Huang²  Shaw-Hwa Lo²  Ying Liu²  Tian Zheng² 

[1] Department of Information Systems, Business Statistics, and Operations Management (ISOM), Hong Kong University of Science and Technology, Kowloon, Hong Kong;Department of Statistics, Columbia University, 10027, New York, NY, USA;

关键词: Similarity Score;    Rare Variant;    Genetic Analysis Workshop;    Causal SNPs;    Nonsynonymous SNPs;   

DOI  :  10.1186/1753-6561-5-S9-S106

来源: Springer

【 摘 要 】

Both common variants and rare variants are involved in the etiology of most complex diseases in humans. Developments in sequencing technology have led to the identification of a high density of rare variant single-nucleotide polymorphisms (SNPs) on the genome, each of which affects only at most 1% of the population. Genotypes derived from these SNPs allow one to study the involvement of rare variants in common human disorders. Here, we propose an association screening approach that treats genes as units of analysis. SNPs within a gene are used to create partitions of individuals, and inverse-probability weighting is used to overweight genotypic differences observed on rare variants. Association between a phenotype trait and the constructed partition is then evaluated. We consider three association tests (one-way ANOVA, chi-square test, and the partition retention method) and compare these strategies using the simulated data from the Genetic Analysis Workshop 17. Several genes that contain causal SNPs were identified by the proposed method as top genes.

【 授权许可】

Unknown   
© Liu et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.