Multi-tissue transcriptome-wide association studies.
Genet Epidemiol
; 45(3): 324-337, 2021 04.
Article
em En
| MEDLINE
| ID: mdl-33369784
ABSTRACT
A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.
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Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Estudo de Associação Genômica Ampla
/
Transcriptoma
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Genet Epidemiol
Assunto da revista:
EPIDEMIOLOGIA
/
GENETICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Reino Unido