CpGFilter: Model-based CpG probe filtering with replicates for epigenome-wide association studies

Jun Chen, Allan C. Just, Joel Schwartz, Lifang Hou, Nadereh Jafari, Zhifu Sun, Jean Pierre A. Kocher, Andrea Baccarelli, Xihong Lin

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Summary: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method.

Original languageEnglish (US)
Pages (from-to)469-471
Number of pages3
Issue number3
StatePublished - Feb 1 2016

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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