Addition of multiple rare SNPs to known common variants improves the association between disease and gene in the Genetic Analysis Workshop 17 data

BMC Proceedings

Proceedings

Geoffrey Liu¹  Lu Cheng²  Jenna Sykes²  Wei Xu³  Melania Pintilie³  Ming-Sound Tsao⁴ 

[1] Dalla Lana School of Public Health, University of Toronto, 155 College Street, M5T 3M7, Toronto, ON, Canada;Division of Molecular Genomics, Princess Margaret Hospital, 610 University Avenue, M5G 2M9, Toronto, ON, Canada;Department of Biostatistics, Princess Margaret Hospital, 610 University Avenue, M5G 2M9, Toronto, ON, Canada;Department of Biostatistics, Princess Margaret Hospital, 610 University Avenue, M5G 2M9, Toronto, ON, Canada;Dalla Lana School of Public Health, University of Toronto, 155 College Street, M5T 3M7, Toronto, ON, Canada;Laboratory of Medicine and Pathobiology, Princess Margaret Hospital, 610 University Avenue, M5G 2M9, Toronto, ON, Canada;

关键词: Common Variant;    Rare Variant;    Multivariate Logistic Regression Model;    Common SNPs;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-5-S9-S97

来源: Springer

【 摘 要 】

The upcoming release of new whole-genome genotyping technologies will shed new light on whether there is an associative effect of previously immeasurable rare variants on incidence of disease. For Genetic Analysis Workshop 17, our team focused on a statistical method to detect associations between gene-based multiple rare variants and disease status. We added a combination of rare SNPs to a common variant shown to have an influence on disease status. This method provides us with an enhanced ability to detect the effect of these rare variants, which, modeled alone, would normally be undetectable. Adjusting for significant clinical parameters, several genes were found to have multiple rare variants that were significantly associated with disease outcome.

【 授权许可】

Unknown   
© Sykes 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.