A latent variable partial least squares path modeling approach to regional association and polygenic effect with applications to a human obesity study.

ABSTRACT: Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a

SUBMITTER: Xue F 

PROVIDER: S-EPMC3288051 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature