Trees Assembling Mann-Whitney approach for detecting genome-wide joint association among low-marginal-effect loci.

ABSTRACT: Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low marginal effects (LMEs) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high-dimensional data (e.g., genome-wide data) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann-Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood ratio-based

SUBMITTER: Wei C 

PROVIDER: S-EPMC3593719 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature