Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.
Elife
; 92020 01 27.
Article
en En
| MEDLINE
| ID: mdl-31985400
ABSTRACT
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict epigenome features from DNA sequence - to support inference concerning the regulatory effects of disease-associated variants. Here, we evaluate the advantages of training convolutional neural network (CNN) models on a broad set of epigenomic features collected in a single disease-relevant tissue - pancreatic islets in the case of type 2 diabetes (T2D) - as opposed to models trained on multiple human tissues. We report convergence of CNN-based metrics of regulatory function with conventional approaches to variant prioritization - genetic fine-mapping and regulatory annotation enrichment. We demonstrate that CNN-based analyses can refine association signals at T2D-associated loci and provide experimental validation for one such signal. We anticipate that these approaches will become routine in downstream analyses of GWAS.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Transducción de Señal
/
Islotes Pancreáticos
/
Diabetes Mellitus Tipo 2
/
Aprendizaje Profundo
/
Modelos Teóricos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Elife
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido