A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies.
Biom J
; 62(1): 191-201, 2020 01.
Article
en En
| MEDLINE
| ID: mdl-31482590
ABSTRACT
Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome-wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two-phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome-wide application involving colorectal cancer.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Fenotipo
/
Biometría
/
Estudio de Asociación del Genoma Completo
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Biom J
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos