Regression-Based Multi-Marker Tests for Gene-Based Analysis of Genetic Association

• Activity Number: 588
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Yun Joo Yoo*+ and Shelley B. Bull and Lei Sun
• Companies: Seoul National University and Samuel Lunenfeld Research Institute and University of Toronto
• Keywords: Global test ; Genetic association ; Linear combination test ; Multiple regression ; Linkage disequilbrium ; Gene-based test

Abstract

Single-marker analysis of quantitative traits is predominant in practice, but joint analysis of multiple SNPs can improve power. We propose multi-marker tests called multi-bin linear combination (MLC) tests constructed using parameter estimates from regression of multiple gene-specific tag-SNP markers, with weights determined by the covariance matrix and bins determined by correlation structure of markers. Using power computations and simulations, we compare them with a multi-df joint (Hotelling) test, minimum P tests, tests using principal components (PC), and one-df linear combination tests (LC) under genetic association models specified according to international HapMap genotypes and one or more causal loci for a quantitative phenotype. The LC tests can be more powerful than other tests depending on relationships between causal loci and markers used in the analysis, but suffer loss of power when estimated marker effects have opposite direction, and are sensitive to the marker allele coding scheme. In constrast, MLC tests demonstrated overall stable power gain with improved robustness to the allele coding scheme.