Predicting DNA methylation from genetic data lacking racial diversity using shared classified random effects.
Genomics
; 113(1 Pt 2): 1018-1028, 2021 01.
Article
em En
| MEDLINE
| ID: mdl-33161089
ABSTRACT
Public genomic repositories are notoriously lacking in racially and ethnically diverse samples. This limits the reaches of exploration and has in fact been one of the driving factors for the initiation of the All of Us project. Our particular focus here is to provide a model-based framework for accurately predicting DNA methylation from genetic data using racially sparse public repository data. Epigenetic alterations are of great interest in cancer research but public repository data is limited in the information it provides. However, genetic data is more plentiful. Our phenotype of interest is cervical cancer in The Cancer Genome Atlas (TCGA) repository. Being able to generate such predictions would nicely complement other work that has generated gene-level predictions of gene expression for normal samples. We develop a new prediction approach which uses shared random effects from a nested error mixed effects regression model. The sharing of random effects allows borrowing of strength across racial groups greatly improving predictive accuracy. Additionally, we show how to further borrow strength by combining data from different cancers in TCGA even though the focus of our predictions is DNA methylation in cervical cancer. We compare our methodology against other popular approaches including the elastic net shrinkage estimator and random forest prediction. Results are very encouraging with the shared classified random effects approach uniformly producing more accurate predictions - overall and for each racial group.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias do Colo do Útero
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Metilação de DNA
Tipo de estudo:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article