Transfer learning with false negative control improves polygenic risk prediction.
PLoS Genet
; 19(11): e1010597, 2023 Nov.
Article
em En
| MEDLINE
| ID: mdl-38011285
ABSTRACT
Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps:
(1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Polimorfismo de Nucleotídeo Único
/
Estudo de Associação Genômica Ampla
Limite:
Humans
Idioma:
En
Revista:
PLoS Genet
Assunto da revista:
GENETICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos