Epigenome-wide association study of total nicotine equivalents in multiethnic current smokers from three prospective cohorts.

The impact of tobacco exposure on health varies by race and ethnicity and is closely tied to internal nicotine dose, a marker of carcinogen uptake. DNA methylation is strongly responsive to smoking status and may mediate health effects, but study of associations with internal dose is limited. We performed a blood leukocyte epigenome-wide association study (EWAS) of urinary total nicotine equivalents (TNEs; a measure of nicotine uptake) and DNA methylation measured using the MethylationEPIC v1.0 BeadChip (EPIC) in six racial and ethnic groups across three cohort studies. In the Multiethnic Cohort Study (discovery, n = 1994), TNEs were associated with differential methylation at 408 CpG sites across >250 genomic regions (p < 9 × 10-8). The top significant sites were annotated to AHRR, F2RL3, RARA, GPR15, PRSS23, and 2q37.1, all of which had decreasing methylation with increasing TNEs. We identified 45 novel CpG sites, of which 42 were unique to the EPIC array and eight annotated to genes not previously linked with smoking-related DNA methylation. The most significant signal in a novel gene was cg03748458 in MIR383;SGCZ. Fifty-one of the 408 discovery sites were validated in the Singapore Chinese Health Study (n = 340) and the Southern Community Cohort Study (n = 394) (Bonferroni corrected p < 1.23 × 10-4). Significant heterogeneity by race and ethnicity was detected for CpG sites in MYO1G and CYTH1. Furthermore, TNEs significantly mediated the association between cigarettes per day and DNA methylation at 15 sites (average 22.5%-44.3% proportion mediated). Our multiethnic study highlights the transethnic and ethnic-specific methylation associations with internal nicotine dose, a strong predictor of smoking-related morbidities.

Integrated DNA Methylation/RNA Profiling in Middle Temporal Gyrus of Alzheimer's Disease.

Alzheimer's disease is a neurodegenerative disorder clinically defined by gradual cognitive impairment and alteration in executive function. We conducted an epigenome-wide association study (EWAS) of a clinically and neuropathologically characterized cohort of 296 brains, including Alzheimer's disease (AD) and non-demented controls (ND), exploring the relationship with the RNA expression from matched donors. We detected 5246 CpGs and 832 regions differentially methylated, finding overlap with previous EWAS but also new associations. CpGs previously identified in ANK1, MYOC, and RHBDF2 were differentially methylated, and one of our top hits (GPR56) was not previously detected. ANK1 was differentially methylated at the region level, along with APOE and RHBDF2. Only a small number of genes showed a correlation between DNA methylation and RNA expression statistically significant. Multiblock partial least-squares discriminant analysis showed several CpG sites and RNAs discriminating AD and ND (AUC = 0.908) and strongly correlated with each other. Furthermore, the CpG site cg25038311 was negatively correlated with the expression of 22 genes. Finally, with the functional epigenetic module analysis, we identified a protein-protein network characterized by inverse RNA/DNA methylation correlation and enriched for "Regulation of insulin-like growth factor transport", with IGF1 as the hub gene. Our results confirm and extend the previous EWAS, providing new information about a brain region not previously explored in AD DNA methylation studies. The relationship between DNA methylation and gene expression is not significant for most of the genes in our sample, consistently with the complexities in the gene expression regulation.

Inter-individual variability in structural brain development from late childhood to young adulthood.

A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0-26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual- and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations.

Spatial mutation patterns as markers of early colorectal tumor cell mobility.

A growing body of evidence suggests that a subset of human cancers grows as single clonal expansions. In such a nearly neutral evolution scenario, it is possible to infer the early ancestral tree of a full-grown tumor. We hypothesized that early tree reconstruction can provide insights into the mobility phenotypes of tumor cells during their first few cell divisions. We explored this hypothesis by means of a computational multiscale model of tumor expansion incorporating the glandular structure of colorectal tumors. After calibrating the model to multiregional and single gland data from 19 human colorectal tumors using approximate Bayesian computation, we examined the role of early tumor cell mobility in shaping the private mutation patterns of the final tumor. The simulations showed that early cell mixing in the first tumor gland can result in side-variegated patterns where the same private mutations could be detected on opposite tumor sides. In contrast, absence of early mixing led to nonvariegated, sectional mutation patterns. These results suggest that the patterns of detectable private mutations in colorectal tumors may be a marker of early cell movement and hence the invasive and metastatic potential of the tumor at the start of the growth. In alignment with our hypothesis, we found evidence of early abnormal cell movement in 9 of 15 invasive colorectal carcinomas ("born to be bad"), but in none of 4 benign adenomas. If validated with a larger dataset, the private mutation patterns may be used for outcome prediction among screen-detected lesions with unknown invasive potential.

Epigenetic plasticity potentiates a rapid cyclical shift to and from an aggressive cancer phenotype.

Highly tumorigenic, drug-resistant cancer stem-like cells drive cancer progression. These aggressive cells can arise repeatedly from bulk tumor cells independently of mutational events, suggesting an epigenetic mechanism. To test this possibility, we studied bladder cancer cells as they cyclically shifted to and from a cancer stem-like phenotype, and we discovered that these two states exhibit distinct DNA methylation and chromatin accessibility. Most differential chromatin accessibility was independent of methylation and affected the expression of driver genes such as E2F3, a cell cycle regulator associated with aggressive bladder cancer. Cancer stem-like cells exhibited increased E2F3 promoter accessibility and increased E2F3 expression that drove cell migration, invasiveness and drug resistance. Epigenetic interference using a DNA methylation inhibitor blocked the transition to a cancer stem-like state and reduced E2F3 expression. Our findings indicate that epigenetic plasticity plays a key role in the transition to and from an aggressive, drug-resistant phenotype.

Deregulation of Biologically Significant Genes and Associated Molecular Pathways in the Oral Epithelium of Electronic Cigarette Users.

We have investigated the regulation of genes and associated molecular pathways, genome-wide, in oral cells of electronic cigarette (e-cigs) users and cigarette smokers as compared to non-smokers. Interrogation of the oral transcriptome by RNA-sequencing (RNA-seq) analysis showed significant number of aberrantly expressed transcripts in both e-cig users (vapers) and smokers relative to non-smokers; however, smokers had ~50% more differentially expressed transcripts than vapers (1726 versus 1152). Whereas the deregulated transcripts in smokers were predominately from protein-coding genes (79% versus 53% in vapers), nearly 28% of the aberrantly expressed transcripts in vapers (versus 8% in smokers) belonged to regulatory non-coding RNAs, including long intergenic non-coding, antisense, small nucleolar and misc RNA (P < 0.0001). Molecular pathway and functional network analyses revealed that "cancer" was the top disease associated with the deregulated genes in both e-cig users and smokers (~62% versus 79%). Examination of the canonical pathways and networks modulated in either e-cig users or smokers identified the "Wnt/Ca⁺ pathway" in vapers and the "integrin signaling pathway" in smokers as the most affected pathways. Amongst the overlapping functional pathways impacted in both e-cig users and smokers, the "Rho family GTPases signaling pathway" was the top disrupted pathway, although the number of affected targets was three times higher in smokers than vapers. In conclusion, we observed deregulation of critically important genes and associated molecular pathways in the oral epithelium of vapers that bears both resemblances and differences with that of smokers. Our findings have significant implications for public health and tobacco regulatory science.

Data integration by multi-tuning parameter elastic net regression.

BACKGROUND: To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally. RESULTS: We propose an Elastic net (EN) model with separate tuning parameter penalties for each platform that is fit using standard software. In a comprehensive simulation study, we evaluated the performance of EN logistic regression with multiple tuning penalties. We found that when the number of informative features differs among the platforms, and when there is no notable correlation between the features from different platforms, the multi-tuning parameter EN yields more predictive models. Moreover, the multi-tuning parameter EN is robust, in the sense that there is no loss of predictivity relative to a single tuning parameter EN when features across all platforms have similar effects. We also investigated the performance of multi-tuning parameter EN using real cancer datasets. CONCLUSION: The proposed multi-tuning parameter EN model, fit using standard penalized regression software, can achieve better prediction in sample classification when integrating multiple genomic platforms, compared to the traditional method where a single penalty parameter is used for all features in different platforms.

Comprehensive meta-analysis reveals association between multiple imprinting disorders and conception by assisted reproductive technology.

PURPOSE: To determine whether a history of conception by assisted reproductive technology (ART) is associated with occurrence of one or more imprinting disorders of either maternal or paternal origin. METHODS: We implemented a systematic review of scholarly literature followed by comprehensive meta-analysis to quantitatively synthesize data from reports relating to use of ART to occurrence of any imprinting disorder of humans, including Beckwith-Wiedemann (BWS), Angelman (AS), Prader-Willi (PWS), and Silver-Russell (SRS) syndromes, as well as transient neonatal diabetes mellitus (TNDB) and sporadic retinoblasoma (RB). RESULTS: The systematic review identified 13 reports presenting unique data from 23 studies that related conception following ART to occurrence of imprinting disorders. Multiple studies of four disorder were identified, for which meta-analysis yielded the following summary estimates of associations with a history of ART: AS, summary odds ratio (sOR) = 4.7 (95% confidence interval (CI) 2.6-8.5, 4 studies); BWS, sOR = 5.8 (95% CI 3.1-11.1, 8 studies); PWS, sOR = 2.2 (95% CI 1.6-3.0, 6 studies); SRS, sOR = 11.3 (95% CI 4.5-28.5, 3 studies). Only one study reported on each of TNDB and RB. CONCLUSION: Published data reveal positive associations between history of ART conception and each of four imprinting disorders. Reasons for these associations warrant further investigation.

Cross-Species Transcriptome Profiling Identifies New Alveolar Epithelial Type I Cell-Specific Genes.

Diseases involving the distal lung alveolar epithelium include chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and lung adenocarcinoma. Accurate labeling of specific cell types is critical for determining the contribution of each to the pathogenesis of these diseases. The distal lung alveolar epithelium is composed of two cell types, alveolar epithelial type 1 (AT1) and type 2 (AT2) cells. Although cell type-specific markers, most prominently surfactant protein C, have allowed detailed lineage tracing studies of AT2 cell differentiation and the cells' roles in disease, studies of AT1 cells have been hampered by a lack of genes with expression unique to AT1 cells. In this study, we performed genome-wide expression profiling of multiple rat organs together with purified rat AT2, AT1, and in vitro differentiated AT1-like cells, resulting in the identification of 54 candidate AT1 cell markers. Cross-referencing with genes up-regulated in human in vitro differentiated AT1-like cells narrowed the potential list to 18 candidate genes. Testing the top four candidate genes at RNA and protein levels revealed GRAM domain 2 (GRAMD2), a protein of unknown function, as highly specific to AT1 cells. RNA sequencing (RNAseq) confirmed that GRAMD2 is transcriptionally silent in human AT2 cells. Immunofluorescence verified that GRAMD2 expression is restricted to the plasma membrane of AT1 cells and is not expressed in bronchial epithelial cells, whereas reverse transcription-polymerase chain reaction confirmed that it is not expressed in endothelial cells. Using GRAMD2 as a new AT1 cell-specific gene will enhance AT1 cell isolation, the investigation of alveolar epithelial cell differentiation potential, and the contribution of AT1 cells to distal lung diseases.

An evaluation of processing methods for HumanMethylation450 BeadChip data.

BACKGROUND: Illumina's HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis. As with other types of microarray platforms, technical artifacts are a concern, including background fluorescence, dye-bias from the use of two color channels, bias caused by type I/II probe design, and batch effects. Several approaches and pipelines have been developed, either targeting a single issue or designed to address multiple biases through a combination of methods. We evaluate the effect of combining separate approaches to improve signal processing. RESULTS: In this study nine processing methods, including both within- and between- array methods, are applied and compared in four datasets. For technical replicates, we found both within- and between-array methods did a comparable job in reducing variance across replicates. For evaluating biological differences, within-array processing always improved differential DNA methylation signal detection over no processing, and always benefitted from performing background correction first. Combinations of within-array procedures were always among the best performing methods, with a slight advantage appearing for the between-array method Funnorm when batch effects explained more variation in the data than the methylation alterations between cases and controls. However, when this occurred, RUVm, a new batch correction method noticeably improved reproducibility of differential methylation results over any of the signal-processing methods alone. CONCLUSIONS: The comparisons in our study provide valuable insights in preprocessing HumanMethylation450 BeadChip data. We found the within-array combination of Noob + BMIQ always improved signal sensitivity, and when combined with the RUVm batch-correction method, outperformed all other approaches in performing differential DNA methylation analysis. The effect of the data processing method, in any given data set, was a function of both the signal and noise.

Recommendations for the design and analysis of epigenome-wide association studies.

Epigenome-wide association studies (EWAS) hold promise for the detection of new regulatory mechanisms that may be susceptible to modification by environmental and lifestyle factors affecting susceptibility to disease. Epigenome-wide screening methods cover an increasing number of CpG sites, but the complexity of the data poses a challenge to separating robust signals from noise. Appropriate study design, a detailed a priori analysis plan and validation of results are essential to minimize the danger of false positive results and contribute to a unified approach. Epigenome-wide mapping studies in homogenous cell populations will inform our understanding of normal variation in the methylome that is not associated with disease or aging. Here we review concepts for conducting a stringent and powerful EWAS, including the choice of analyzed tissue, sources of variability and systematic biases, outline analytical solutions to EWAS-specific problems and highlight caveats in interpretation of data generated from samples with cellular heterogeneity.

At least two well-spaced samples are needed to genotype a solid tumor.

BACKGROUND: Human cancers are often sequenced to identify mutations. However, cancers are spatially heterogeneous populations with public mutations in all cells and private mutations in some cells. Without empiric knowledge of how mutations are distributed within a solid tumor it is uncertain whether single or multiple samples adequately sample its heterogeneity. METHODS: Using a cohort of 12 human colorectal tumors with well-validated mutations, the abilities to correctly classify public and private mutations were tested (paired t-test) with one sample or two samples obtained from opposite tumor sides. RESULTS: Two samples were significantly better than a single sample for correctly identifying public (99% versus 97%) and private mutations (85% versus 46%). Confounding single sample accuracy was that many private mutations appeared "clonal" in individual samples. Two samples detected the most frequent private mutations in 11 of the 12 tumors. CONCLUSIONS: Two spatially-separated samples efficiently distinguish public from private mutations because private mutations common in one specimen are usually less frequent or absent in another sample. The patch-like private mutation topography in most colorectal tumors inherently limits the information in single tumor samples. The correct identification of public and private mutations may aid efforts to target mutations present in all tumor cells.

Many private mutations originate from the first few divisions of a human colorectal adenoma.

Intratumoural mutational heterogeneity (ITH) or the presence of different private mutations in different parts of the same tumour is commonly observed in human tumours. The mechanisms generating such ITH are uncertain. Here we find that ITH can be remarkably well structured by measuring point mutations, chromosome copy numbers, and DNA passenger methylation from opposite sides and individual glands of a 6 cm human colorectal adenoma. ITH was present between tumour sides and individual glands, but the private mutations were side-specific and subdivided the adenoma into two major subclones. Furthermore, ITH disappeared within individual glands because the glands were clonal populations composed of cells with identical mutant genotypes. Despite mutation clonality, the glands were relatively old, diverse populations when their individual cells were compared for passenger methylation and by FISH. These observations can be organized into an expanding star-like ancestral tree with co-clonal expansion, where many private mutations and multiple related clones arise during the first few divisions. As a consequence, most detectable mutational ITH in the final tumour originates from the first few divisions. Much of the early history of a tumour, especially the first few divisions, may be embedded within the detectable ITH of tumour genomes.

Integrated transcriptomic and epigenomic analysis of primary human lung epithelial cell differentiation.

Elucidation of the epigenetic basis for cell-type specific gene regulation is key to gaining a full understanding of how the distinct phenotypes of differentiated cells are achieved and maintained. Here we examined how epigenetic changes are integrated with transcriptional activation to determine cell phenotype during differentiation. We performed epigenomic profiling in conjunction with transcriptomic profiling using in vitro differentiation of human primary alveolar epithelial cells (AEC). This model recapitulates an in vivo process in which AEC transition from one differentiated cell type to another during regeneration following lung injury. Interrogation of histone marks over time revealed enrichment of specific transcription factor binding motifs within regions of changing chromatin structure. Cross-referencing of these motifs with pathways showing transcriptional changes revealed known regulatory pathways of distal alveolar differentiation, such as the WNT and transforming growth factor beta (TGFB) pathways, and putative novel regulators of adult AEC differentiation including hepatocyte nuclear factor 4 alpha (HNF4A), and the retinoid X receptor (RXR) signaling pathways. Inhibition of the RXR pathway confirmed its functional relevance for alveolar differentiation. Our incorporation of epigenetic data allowed specific identification of transcription factors that are potential direct upstream regulators of the differentiation process, demonstrating the power of this approach. Integration of epigenomic data with transcriptomic profiling has broad application for the identification of regulatory pathways in other models of differentiation.

Testicular germ cell tumor susceptibility associated with the UCK2 locus on chromosome 1q23.

Genome-wide association studies (GWASs) have identified multiple common genetic variants associated with an increased risk of testicular germ cell tumors (TGCTs). A previous GWAS reported a possible TGCT susceptibility locus on chromosome 1q23 in the UCK2 gene, but failed to reach genome-wide significance following replication. We interrogated this region by conducting a meta-analysis of two independent GWASs including a total of 940 TGCT cases and 1559 controls for 122 single-nucleotide polymorphisms (SNPs) on chromosome 1q23 and followed up the most significant SNPs in an additional 2202 TGCT cases and 2386 controls from four case-control studies. We observed genome-wide significant associations for several UCK2 markers, the most significant of which was for rs3790665 (PCombined = 6.0 × 10(-9)). Additional support is provided from an independent familial study of TGCT where a significant over-transmission for rs3790665 with TGCT risk was observed (PFBAT = 2.3 × 10(-3)). Here, we provide substantial evidence for the association between UCK2 genetic variation and TGCT risk.

Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression.

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma is its most common histological subtype. Clinical and molecular evidence indicates that lung adenocarcinoma is a heterogeneous disease, which has important implications for treatment. Here we performed genome-scale DNA methylation profiling using the Illumina Infinium HumanMethylation27 platform on 59 matched lung adenocarcinoma/non-tumor lung pairs, with genome-scale verification on an independent set of tissues. We identified 766 genes showing altered DNA methylation between tumors and non-tumor lung. By integrating DNA methylation and mRNA expression data, we identified 164 hypermethylated genes showing concurrent down-regulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis indicates that these genes are involved in cell differentiation, epithelial to mesenchymal transition, RAS and WNT signaling pathways, and cell cycle regulation, among others. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never-smokers showed modest differences, identifying only LGALS4 as significantly hypermethylated and down-regulated in smokers. LGALS4, encoding a galactoside-binding protein involved in cell-cell and cell-matrix interactions, was recently shown to be a tumor suppressor in colorectal cancer. Unsupervised analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and to a lesser extent, with smoking. Our analysis lays the groundwork for further molecular studies of lung adenocarcinoma by identifying novel epigenetically deregulated genes potentially involved in lung adenocarcinoma development/progression, and by describing an epigenetic subgroup of lung adenocarcinoma associated with characteristic molecular alterations.

Low-level processing of Illumina Infinium DNA Methylation BeadArrays.

We propose a novel approach to background correction for Infinium HumanMethylation data to account for technical variation in background fluorescence signal. Our approach capitalizes on a new use for the Infinium I design bead types to measure non-specific fluorescence in the colour channel opposite of their design (Cy3/Cy5). This provides tens of thousands of features for measuring background instead of the much smaller number of negative control probes on the platforms (n = 32 for HumanMethylation27 and n = 614 for HumanMethylation450, respectively). We compare the performance of our methods with existing approaches, using technical replicates of both mixture samples and biological samples, and demonstrate that within- and between-platform artefacts can be substantially reduced, with concomitant improvement in sensitivity, by the proposed methods.

G9a functions as a molecular scaffold for assembly of transcriptional coactivators on a subset of glucocorticoid receptor target genes.

Histone H3 lysine-9 methyltransferase G9a/EHMT2/KMT1C is a key corepressor of gene expression. However, activation of a limited number of genes by G9a (independent of its catalytic activity) has also been observed, although the precise molecular mechanisms are unknown. By using RNAi in combination with gene expression microarray analysis, we found that G9a functions as a positive and a negative transcriptional coregulator for discrete subsets of genes that are regulated by the hormone-activated Glucocorticoid Receptor (GR). G9a was recruited to GR-binding sites (but not to the gene body) of its target genes and interacted with GR, suggesting recruitment of G9a by GR. In contrast to its corepressor function, positive regulation of gene expression by G9a involved G9a-mediated enhanced recruitment of coactivators CARM1 and p300 to GR target genes. Further supporting a role for G9a as a molecular scaffold for its coactivator function, the G9a-specific methyltransferase inhibitor UNC0646 did not affect G9a coactivator function but selectively decreased G9a corepressor function for endogenous target genes. Overall, G9a functioned as a coactivator for hormone-activated genes and as a corepressor in support of hormone-induced gene repression, suggesting that the positive or negative actions of G9a are determined by the gene-specific regulatory environment and chromatin architecture. These findings indicate distinct mechanisms of G9a coactivator vs. corepressor functions in transcriptional regulation and provide insight into the molecular mechanisms of G9a coactivator function. Our results also suggest a physiological role of G9a in fine tuning the set of genes that respond to glucocorticoids.

CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer.

Aberrant DNA methylation of CpG islands has been widely observed in human colorectal tumors and is associated with gene silencing when it occurs in promoter areas. A subset of colorectal tumors has an exceptionally high frequency of methylation of some CpG islands, leading to the suggestion of a distinct trait referred to as 'CpG island methylator phenotype', or 'CIMP'. However, the existence of CIMP has been challenged. To resolve this continuing controversy, we conducted a systematic, stepwise screen of 195 CpG island methylation markers using MethyLight technology, involving 295 primary human colorectal tumors and 16,785 separate quantitative analyses. We found that CIMP-positive (CIMP+) tumors convincingly represent a distinct subset, encompassing almost all cases of tumors with BRAF mutation (odds ratio = 203). Sporadic cases of mismatch repair deficiency occur almost exclusively as a consequence of CIMP-associated methylation of MLH1 . We propose a robust new marker panel to classify CIMP+ tumors.

Non-specific filtering of beta-distributed data.

BACKGROUND: Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias. RESULTS: We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets. CONCLUSIONS: We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered.