Exploring Bayesian Approaches to eQTL Mapping Through Probabilistic Programming.
Methods Mol Biol
; 2082: 123-146, 2020.
Article
em En
| MEDLINE
| ID: mdl-31849012
ABSTRACT
The discovery of genomic polymorphisms influencing gene expression (also known as expression quantitative trait loci or eQTLs) can be formulated as a sparse Bayesian multivariate/multiple regression problem. An important aspect in the development of such models is the implementation of bespoke inference methodologies, a process which can become quite laborious, when multiple candidate models are being considered. We describe automatic, black-box inference in such models using Stan, a popular probabilistic programming language. The utilization of systems like Stan can facilitate model prototyping and testing, thus accelerating the data modeling process. The code described in this chapter can be found at https//github.com/dvav/eQTLBookChapter .
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Expressão Gênica
/
Teorema de Bayes
/
Mapeamento Cromossômico
/
Biologia Computacional
/
Locos de Características Quantitativas
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Methods Mol Biol
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Reino Unido