An empirical Bayes approach for multiple tissue eQTL analysis.
Biostatistics
; 19(3): 391-406, 2018 07 01.
Article
em En
| MEDLINE
| ID: mdl-29029013
ABSTRACT
Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this article, we develop a hierarchical Bayesian model (MT-eQTL) for multi-tissue eQTL analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We devise an efficient Expectation-Maximization (EM) algorithm for model fitting. Inferences concerning eQTL detection and the configuration of eQTL across tissues are derived from the adaptive thresholding of local false discovery rates, and maximum a posteriori estimation, respectively. We also provide theoretical justification of the adaptive procedure. We investigate the MT-eQTL model through an extensive analysis of a 9-tissue data set from the GTEx initiative.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Expressão Gênica
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Bioestatística
/
Modelos Estatísticos
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Genômica
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Locos de Características Quantitativas
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Técnicas de Genotipagem
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Biostatistics
Ano de publicação:
2018
Tipo de documento:
Article