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The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts.

Original publication

DOI

10.1002/gepi.20486

Type

Journal article

Journal

Genet Epidemiol

Publication Date

05/2010

Volume

34

Pages

335 - 343

Keywords

Chromosomes, Computer Simulation, Diabetes Mellitus, Type 2, Gene Frequency, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Heterozygote, Humans, Models, Genetic, Models, Statistical, Obesity, Odds Ratio, Phenotype, Regression Analysis