We present a coherent Bayesian framework for selection of the most likely model from the five genetic models (genotypic, additive, dominant, co-dominant, and recessive) commonly used in genetic association studies. The approach uses a polynomial parameterization of genetic data to simultaneously fit the five models and save computations. We provide a closed-form expression of the marginal likel...

In genetic association studies, due to the varying underlying genetic models, no single statistical test can be the most powerful test under all situations. Current studies show that if the underlying genetic models are known, trend-based tests, which outperform the classical Pearson χ² test, can be constructed. However, when the underlying genetic models are unknown, the χ² test is usually mor...

Microbes in the ocean dominate biogeochemical processes and are far more diverse than anticipated. Thus, in order to understand the ocean system, we need to delineate microbial populations with predictable ecological functions. Recent observations suggest that ocean communities comprise diverse groups of bacteria organized into genotypic (and phenotypic) clusters of closely related organisms. A...

We study a class of genetic models in which a quantitative trait determined by several additive loci is subject to temporally fluctuating selection. Selection on the trait is assumed to be stabilizing but with an optimum that varies periodically and might be perturbed stochastically. The population mates at random, is infinitely large and has discrete generations. We pursue a statistical and nu...