期刊
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
卷 12, 期 2, 页码 189-205出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/sagmb-2012-0057
关键词
AR1; autoregressive; Bayesian hierarchical model; epigenetic changes
资金
- NCI NIH HHS [P30 CA142543] Funding Source: Medline
- NIDA NIH HHS [R01 DA007359, P01 DA008227] Funding Source: Medline
- NIMH NIH HHS [P50 MH096890] Funding Source: Medline
Identification of genome-wide epigenetic changes, the stable changes in gene function without a change in DNA sequence, under various conditions plays an important role in biomedical research. High-throughput epigenetic experiments are useful tools to measure genome-wide epigenetic changes, but the measured intensity levels from these high-resolution genome-wide epigenetic profiling data are often spatially correlated with high noise levels. In addition, it is challenging to detect genome-wide epigenetic changes across multiple conditions, so efficient statistical methodology development is needed for this purpose. In this study, we consider ANOVA models with spatially varying coefficients, combined with a hierarchical Bayesian approach, to explicitly model spatial correlation caused by location-dependent biological effects (i.e., epigenetic changes) and borrow strength among neighboring probes to compare epigenetic changes across multiple conditions. Through simulation studies and applications in drug addiction and depression datasets, we find that our approach compares favorably with competing methods; it is more efficient in estimation and more effective in detecting epigenetic changes. In addition, it can provide biologically meaningful results.
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