Predicting DNA methylation from genetic data lacking racial diversity using shared classified random effects
Predicting DNA methylation from genetic data lacking racial diversity using shared classified random effects
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DOI：
10.1016/j.ygeno.2020.10.036
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发表时间：
2021-01-25
期刊：
影响因子：
4.4
通讯作者：
Conway，Douglas
中科院分区：
文献类型：
--
作者：
Rao，J. Sunil;Zhang，Hang;Conway，Douglas
众所周知，公共基因库缺乏种族和民族多样性的样本。这限制了探索的范围，事实上也是启动“我们大家”项目的驱动因素之一。我们在这里特别关注的是提供一个基于模型的框架，用于使用种族稀疏的公共存储库数据从遗传数据中准确预测DNA甲基化。表观遗传学改变在癌症研究中具有很大的意义，但公共存储库数据提供的信息有限。然而，基因数据更加丰富。我们感兴趣的表型是癌症基因组图谱（TCGA）库中的宫颈癌。能够产生这样的预测将很好地补充其他工作，已产生基因水平的预测基因表达的正常samples.We开发了一种新的预测方法，它使用共享的随机效应从嵌套误差混合效应回归模型。随机效应的共享允许跨种族群体借用力量，大大提高了预测的准确性。此外，我们展示了如何通过结合TCGA中不同癌症的数据来进一步借用力量，即使我们预测的重点是宫颈癌中的DNA甲基化。我们比较我们的方法对其他流行的方法，包括弹性净收缩估计和随机森林预测。结果是非常令人鼓舞的共享分类随机效应方法统一产生更准确的预测-整体和每个种族群体。
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.