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Research Abstracts Online
January - December 2011

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University of Minnesota Twin Cities
School of Public Health
Division of Biostatistics

PI: Saonli Basu

A Gene-Set Approach for Pathway Analysis of Genome-Wide SNP Data With Application to Type 2 Diabetes and Related Quantitative Traits

Current pathway-based methods are mostly limited to investigating the individual effects of the SNPs within a pathway, in order to avoid the estimation of a large number of parameters involved in joint modeling of effects of a large number of SNPs. Hence, most of these methods do not take into account the possibility of the interaction among the multiple SNPs within each pathway. Moreover, none of these approaches are likelihood-based. Hence, it is not possible to estimate and quantify the overall pathway effect on disease risk and assess its statistical uncertainty. This project offers a collection of novel statistical methods as well as a suite of user-friendly software to study the joint effects of a group of SNPs within a pathway on a complex multifactorial disease, incorporating the possibility of interaction among the SNPs. The model also offers a data reduction strategy that avoids the issues with estimation of a large number of parameters (main effects and interaction effects) for a large number of SNPs within a pathway. These researchers employ this model to study the pathway effects on type 2 diabetes and related quantitative traits in the Caucasian population of Atherosclerosis Risk in Communities (ARIC) cohort and compare the findings with the findings of single SNP association analyses. This work will have immediate impact on pathway-based GWAS analyses.

Group Members

Xiaohui Cui, Graduate Student
Sharmistha Guha, Graduate Student
Debashree Ray, Graduate Student
Taufik Saputera, Graduate Student