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Activity Number:
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171
- SPAAC Poster Competition
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biometrics Section
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Abstract #310969
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Title:
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Predicting DNA Methylation from Genetic Data Lacking Racial Diversity Using Shared Classified Random Effects
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Author(s):
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Hang Zhang* and J. Sunil Rao and Erin Kobetz and Melinda Aldrich and Douglas Conway
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Companies:
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University of Miami and University of Miami and University of Miami and Vanderbilt University and Vanderbilt University
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Keywords:
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DNA methylation;
prediction;
mixed effects models;
racial diversity
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Abstract:
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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. Results are very encouraging when we compare against other popular approaches including the elastic net shrinkage estimator and random forest prediction.
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Authors who are presenting talks have a * after their name.
