Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning.
Journal
European journal of human genetics : EJHG
ISSN: 1476-5438
Titre abrégé: Eur J Hum Genet
Pays: England
ID NLM: 9302235
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
received:
19
08
2020
accepted:
21
06
2021
revised:
23
05
2021
pubmed:
20
7
2021
medline:
24
3
2022
entrez:
19
7
2021
Statut:
ppublish
Résumé
A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.
Identifiants
pubmed: 34276057
doi: 10.1038/s41431-021-00930-w
pii: 10.1038/s41431-021-00930-w
pmc: PMC8484264
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1527-1535Informations de copyright
© 2021. The Author(s), under exclusive licence to European Society of Human Genetics.
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