DNA methylation plays an important role in disease etiology. The Illumina Infinium HumanMethylation450 (450 K) BeadChip is a widely used platform in large-scale epidemiologic studies. This platform can efficiently and simultaneously measure methylation levels at ˜480,000 CpG sites in the human genome in multiple study samples. Due to the intrinsic chip design of two types of chemistry probes, data normalization or preprocessing is a critical step to consider before data analysis. To date, numerous methods and pipelines have been developed for this purpose, and some studies have been conducted to evaluate different methods. However, validation studies have often been limited to a small number of CpG sites to reduce the variability in technical replicates. In this study, we measured methylation on a set of samples using both whole-genome bisulfite sequencing (WGBS) and 450 K chips. We used WGBS data as a gold standard of true methylation states in cells to compare the performances of eight normalization methods for 450 K data on a genome-wide scale. Analyses on our dataset indicate that the most effective methods are peak-based correction (PBC) and quantile normalization plus β-mixture quantile normalization (QN.BMIQ). To our knowledge, this is the first study to systematically compare existing normalization methods for Illumina 450 K data using novel WGBS data. Our results provide a benchmark reference for the analysis of DNA methylation chip data, particularly in white blood cells.