PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-2 (2)
 

Clipboard (0)
None
Journals
Authors
Year of Publication
Document Types
1.  A Powerful Approach to Sub-Phenotype Analysis in Population-Based Genetic Association Studies 
Genetic Epidemiology  2009;34(4):335-343.
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.
doi:10.1002/gepi.20486
PMCID: PMC2964510  PMID: 20039379
multinomial regression; sub-phenotype analysis; genome-wide association study; type 2 diabetes; obesity
2.  Rapid Testing of Gene-Gene Interactions in Genome-Wide Association Studies of Binary and Quantitative Phenotypes 
Genetic Epidemiology  2011;35(8):800-808.
Genome-wide association (GWA) studies have been extremely successful in identifying novel loci contributing effects to a wide range of complex human traits. However, despite this success, the joint marginal effects of these loci account for only a small proportion of the heritability of these traits. Interactions between variants in different loci are not typically modelled in traditional GWA analysis, but may account for some of the missing heritability in humans, as they do in other model organisms. One of the key challenges in performing gene-gene interaction studies is the computational burden of the analysis. We propose a two-stage interaction analysis strategy to address this challenge in the context of both quantitative traits and dichotomous phenotypes. We have performed simulations to demonstrate only a negligible loss in power of this two-stage strategy, while minimizing the computational burden. Application of this interaction strategy to GWA studies of T2D and obesity highlights potential novel signals of association, which warrant follow-up in larger cohorts. Genet. Epidemiol. 2011.© 2011 Wiley Periodicals, Inc.35: 800-808, 2011
doi:10.1002/gepi.20629
PMCID: PMC3410530  PMID: 21948692
genome-wide association study; gene-gene interaction; computational efficiency

Results 1-2 (2)