Title | Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Bae, H, Perls, T, Steinberg, M, Sebastiani, P |
Journal | Bayesian Anal |
Volume | 10 |
Issue | 1 |
Pagination | 53-74 |
Date Published | 03/2015 |
ISSN | 1936-0975 |
Abstract | 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 likelihood for normally distributed data, and evaluate the performance of the proposed method and existing method through simulated and real genome-wide data sets. |
DOI | 10.1214/14-BA880 |
Alternate Journal | Bayesian Anal |
PubMed ID | 26029316 |
PubMed Central ID | PMC4446790 |
Grant List | U19 AG023122 / AG / NIA NIH HHS / United States T32 GM074905 / GM / NIGMS NIH HHS / United States R21 HL114237 / HL / NHLBI NIH HHS / United States U01 AG023755 / AG / NIA NIH HHS / United States R01 HL087681 / HL / NHLBI NIH HHS / United States |