Fast and robust adjustment of cell mixtures in epigenome-wide association studies with SmartSVA

BMC Genomics

Methodology Article

Xihong Lin¹  Liming Liang²  Ehsan Behnam³  Jun Chen³  Daniel J. Schaid³  Miriam F. Moffatt⁴  Jinyan Huang⁵ 

[1] Department of Biostatistics, Harvard T.H. School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA;Department of Epidemiology, Harvard T.H. School of Public Health, Boston, 677 Huntington Ave, 02115, Boston, MA, USA;Department of Biostatistics, Harvard T.H. School of Public Health, 677 Huntington Ave, 02115, Boston, MA, USA;Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA;Faculty of Medicine, National Heart & Lung Institute, Imperial College London, Dovehouse St, SW3 6LY, London, UK;State Key Laboratory of Medical Genomics, Rui-jin Hospital & Shanghai Jiao Tong University School of Medicine, 197 Rui Jin Er Road, 200025, Shanghai, China;

关键词: Epigenome-wide association;    cell mixture;    surrogate variable analysis;    DNA methylation;   

DOI  :  10.1186/s12864-017-3808-1

 received in 2017-03-23, accepted in 2017-05-18,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundOne problem that plagues epigenome-wide association studies is the potential confounding due to cell mixtures when purified target cells are not available. Reference-free adjustment of cell mixtures has become increasingly popular due to its flexibility and simplicity. However, existing methods are still not optimal: increased false positive rates and reduced statistical power have been observed in many scenarios.MethodsWe develop SmartSVA, an optimized surrogate variable analysis (SVA) method, for fast and robust reference-free adjustment of cell mixtures. SmartSVA corrects the limitation of traditional SVA under highly confounded scenarios by imposing an explicit convergence criterion and improves the computational efficiency for large datasets.ResultsCompared to traditional SVA, SmartSVA achieves an order-of-magnitude speedup and better false positive control. It protects the signals when capturing the cell mixtures, resulting in significant power increase while controlling for false positives. Through extensive simulations and real data applications, we demonstrate a better performance of SmartSVA than the existing methods.ConclusionsSmartSVA is a fast and robust method for reference-free adjustment of cell mixtures for epigenome-wide association studies. As a general method, SmartSVA can be applied to other genomic studies to capture unknown sources of variability.

【 授权许可】

CC BY   
© The Author(s). 2017