title: CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies.
abstract: CONCLUSIONS
  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.
  
  
  METHODS
  CpGFilter is implemented in R and publicly available under CRAN via the R package 'CpGFilter'.
  
  
  BACKGROUND
  chen.jun2@mayo.edu or xlin@hsph.harvard.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.
authors.forename: Jun
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.
authors.forename: Allan C
authors.initials: AC
authors.lastname: Just
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.
authors.forename: Joel
authors.initials: J
authors.lastname: Schwartz
authors.affiliation: Department of Preventive Medicine and the Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208 and.
authors.forename: Lifang
authors.initials: L
authors.lastname: Hou
authors.affiliation: Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60208, USA.
authors.forename: Nadereh
authors.initials: N
authors.lastname: Jafari
authors.affiliation: Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905.
authors.forename: Zhifu
authors.initials: Z
authors.lastname: Sun
authors.affiliation: Division of Biomedical Statistics and Informatics and Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905.
authors.forename: Jean-Pierre A
authors.initials: JP
authors.lastname: Kocher
authors.affiliation: Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115.
authors.forename: Andrea
authors.initials: A
authors.lastname: Baccarelli
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D018899:CpG Islands; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods*; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software