NDLI: SNP set analysis for detecting disease association using exon sequence data

Content Provider	PubMed Central
Author	Wang, Ru Peng, Jie Wang, Pei
Copyright Year	2011
Abstract	Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes (FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.
Related Links	http://dx.doi.org/10.1186/1753-6561-5-s9-s91
Starting Page	91
File Format	PDF
ISSN	17536561
e-ISSN	17536561
Journal	BMC Proceedings
Issue Number	Suppl 9
Volume Number	5
Language	English
Publisher	BioMed Central
Publisher Date	2011-01-01
Access Restriction	Open
Rights Holder	BioMed Central
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology

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