Developmental epigenomic effects of maternal financial problems.

Early-life adversity as neglect or low socioeconomic status is associated with negative physical/mental health outcomes and plays an important role in health trajectories through life. The early-life environment has been shown to be encoded as changes in epigenetic markers that are retained for many years.We investigated the effect of maternal major financial problems (MFP) and material deprivation (MD) on their children's epigenome in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Epigenetic aging, measured with epigenetic clocks, was weakly accelerated with increased MFP. In subsequent EWAS, MFP, and MD showed strong, independent programing effects on children's genomes. MFP in the period from birth to age seven was associated with genome-wide epigenetic modifications on children's genome visible at age 7 and partially remaining at age 15.These results support the hypothesis that physiological processes at least partially explain associations between early-life adversity and health problems later in life. Both maternal stressors (MFP/MD) had similar effects on biological pathways, providing preliminary evidence for the mechanisms underlying the effects of low socioeconomic status in early life and disease outcomes later in life. Understanding these associations is essential to explain disease susceptibility, overall life trajectories and the transition from health to disease.

Epigenetic Association Analyses and Risk Prediction of RLS.

BACKGROUND: As opposed to other neurobehavioral disorders, epigenetic analyses and biomarkers are largely missing in the case of idiopathic restless legs syndrome (RLS). OBJECTIVES: Our aims were to develop a biomarker for RLS based on DNA methylation in blood and to examine DNA methylation in brain tissues for dissecting RLS pathophysiology. METHODS: Methylation of blood DNA from three independent cohorts (n = 2283) and post-mortem brain DNA from two cohorts (n = 61) was assessed by Infinium EPIC 850 K BeadChip. Epigenome-wide association study (EWAS) results of individual cohorts were combined by random-effect meta-analysis. A three-stage selection procedure (discovery, n = 884; testing, n = 520; validation, n = 879) established an epigenetic risk score including 30 CpG sites. Epigenetic age was assessed by Horvath's multi-tissue clock and Shireby's cortical clock. RESULTS: EWAS meta-analysis revealed 149 CpG sites linked to 136 genes (P < 0.05 after Bonferroni correction) in blood and 23 CpG linked to 18 genes in brain (false discovery rate [FDR] < 5%). Gene-set analyses of blood EWAS results suggested enrichments in brain tissue types and in subunits of the kainate-selective glutamate receptor complex. Individual candidate genes of the brain EWAS could be assigned to neurodevelopmental or metabolic traits. The blood epigenetic risk score achieved an area under the curve (AUC) of 0.70 (0.67-0.73) in the validation set, comparable to analogous scores in other neurobehavioral disorders. A significant difference in biological age in blood or brain of RLS patients was not detectable. CONCLUSIONS: DNA methylation supports the notion of altered neurodevelopment in RLS. Epigenetic risk scores are reliably associated with RLS but require even higher accuracy to be useful as biomarkers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes.

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82-0.89, p = 1.22 × 10-29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66-0.76, p = 9.07 × 10-17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.

Epigenome-wide association study of incident type 2 diabetes: a meta-analysis of five prospective European cohorts.

AIMS/HYPOTHESIS: Type 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts. METHODS: We conducted a meta-analysis of EWASs in blood collected 7-10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK). RESULTS: The meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values <1.1 × 10-7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values <0.05 in an independent cohort of Indian Asians. However, on adjustment for baseline BMI only four CpG sites remained genome-wide significant, and addition of the 76 CpG methylation risk score to a prediction model including established predictors of type 2 diabetes (age, sex, BMI and HbA1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation. CONCLUSIONS/INTERPRETATION: By combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development.

Altered DNA methylation pattern characterizes the peripheral immune cells of patients with autoimmune hepatitis.

BACKGROUND AND AIMS: Little is known about the impact of DNA methylation modifications on autoimmune hepatitis (AIH) pathogenesis and therapeutic response. We investigated the potential alterations of DNA methylation in AIH peripheral lymphocytes at diagnosis and remission. METHODS: Ten AIH patients at diagnosis (time-point 1; AIH-tp1), 8/10 following biochemical response (time-point 2; AIH-tp2), 9 primary biliary cholangitis (PBC) and 10 healthy controls (HC) were investigated. Peripheral CD19(+) and CD4(+) cells were isolated. Global DNA methylation (5m C)/hydroxymethylation (5hm C) was studied by ELISAs. mRNA of DNA methylation (DNMT1/3A/3B) and their counteracting hydroxymethylation enzymes (TET1/2/3) was determined by quantitative RT-PCR. Epigenome wide association study (EWAS) was performed in CD4(+) cells (Illumina HumanMethylation 850 K array) in AIH and HC. Total 5m C/5hm C was also assessed by immunohistochemistry (IHC) on paraffin-embedded liver sections. RESULTS: Reduced TET1 and increased DNMT3A mRNA levels characterized CD19(+) and CD4(+)-lymphocytes from AIH-tp1 compared to HC and PBC, respectively, without affecting global DNA 5m C/5hm C. In AIH-tp1, CD4(+) DNMT3A expression was negatively correlated with serum IgG (P = .03). In remission, DNMT3A decreased in both CD19(+) and CD4(+) cells compared to AIH-tp1 (P = .02, P = .03 respectively). EWAS in CD4(+) cells from AIH patients confirmed important modifications in genes implicated in immune responses (HLA-DP, TNF, lnRNAs and CD86). IHC showed increased 5hm C staining of periportal infiltrating lymphocytes in AIH-tp1 compared to HC and PBC. CONCLUSION: Altered TET1 and DNMT3A expressions, characterize peripheral lymphocytes in AIH. DNMT3A was associated with disease activity and decreased following remission. Gene DNA methylation modifications affect immunological pathways that may play an important role in AIH pathogenesis.

An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.

Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.

A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets.

Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

Meta-analysis of epigenome-wide association studies of carotid intima-media thickness.

Common carotid intima-media thickness (cIMT) is an index of subclinical atherosclerosis that is associated with ischemic stroke and coronary artery disease (CAD). We undertook a cross-sectional epigenome-wide association study (EWAS) of measures of cIMT in 6400 individuals. Mendelian randomization analysis was applied to investigate the potential causal role of DNA methylation in the link between atherosclerotic cardiovascular risk factors and cIMT or clinical cardiovascular disease. The CpG site cg05575921 was associated with cIMT (beta = -0.0264, p value = 3.5 × 10-8) in the discovery panel and was replicated in replication panel (beta = -0.07, p value = 0.005). This CpG is located at chr5:81649347 in the intron 3 of the aryl hydrocarbon receptor repressor gene (AHRR). Our results indicate that DNA methylation at cg05575921 might be in the pathway between smoking, cIMT and stroke. Moreover, in a region-based analysis, 34 differentially methylated regions (DMRs) were identified of which a DMR upstream of ALOX12 showed the strongest association with cIMT (p value = 1.4 × 10-13). In conclusion, our study suggests that DNA methylation may play a role in the link between cardiovascular risk factors, cIMT and clinical cardiovascular disease.

Mendelian Randomization Analysis Identifies CpG Sites as Putative Mediators for Genetic Influences on Cardiovascular Disease Risk.

The extent to which genetic influences on cardiovascular disease risk are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits. We identified ten genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits after multiple-testing correction. Bivariate fine mapping provided evidence that the individual variants responsible for the observed effects on cardiovascular traits at the ADCY3 and ADIPOQ loci were potentially mediated through changes in DNA methylation, although we highlight that we are unable to reliably separate causality from horizontal pleiotropy. Estimates of causal effects were replicated with results from large-scale consortia. Genetic variants and CpG sites identified in this study were enriched for histone mark peaks in relevant tissue types and gene promoter regions. Integrating our results with expression quantitative trait loci data, we provide evidence that variation at these regulatory regions is likely to also influence gene expression levels at these loci.

Orthognathic surgery induces genomewide changes longitudinally in DNA methylation in saliva.

OBJECTIVE: Orthognathic surgery dramatically changes morphology of the maxillofacial deformity and improves the malocclusion morphologically and functionally. We investigated the influence of orthognathic surgery on genomewide DNA methylation in saliva. METHODS: Saliva was obtained from nine patients undergoing orthognathic surgery and two healthy reference individuals before and 3 months after orthognathic surgery. Genomewide DNA methylation profiling of saliva (341,482 CpG dinucleotides) was conducted using Infinium HumanMethylation450 BeadChips. RESULTS: Comparison between pre- and postsurgery saliva samples revealed significant changes in DNA methylation patterns at 2,381 CpG sites (p < 0.01) with suggestive significance. The differentially methylated probe sets were significantly associated with the cancer pathway (p = 2.8 × 10-7 ; a false discovery rate q-value = 3.7 × 10-4 ) and PI3K-Akt signalling pathway (p = 2.4 × 10-5 ; a false discovery rate q-value = 3.1 × 10-2 ). CONCLUSION: Pathway enrichment analysis of genes with suggestive significance demonstrated that altered DNA methylation in saliva of patients undergoing orthognathic surgery, possibly as a response to surgical stress or bone injury. Further studies with a large sample size and long-term observation are needed to validate the phenomena identified in this study.

DNA methylation markers associated with type 2 diabetes, fasting glucose and HbA1c levels: a systematic review and replication in a case-control sample of the Lifelines study.

AIMS/HYPOTHESIS: Epigenetic mechanisms may play an important role in the aetiology of type 2 diabetes. Recent epigenome-wide association studies (EWASs) identified several DNA methylation markers associated with type 2 diabetes, fasting glucose and HbA1c levels. Here we present a systematic review of these studies and attempt to replicate the CpG sites (CpGs) with the most significant associations from these EWASs in a case-control sample of the Lifelines study. METHODS: We performed a systematic literature search in PubMed and EMBASE for EWASs to test the association between DNA methylation and type 2 diabetes and/or glycaemic traits and reviewed the search results. For replication purposes we selected 100 unique CpGs identified in peripheral blood, pancreas, adipose tissue and liver from 15 EWASs, using study-specific Bonferroni-corrected significance thresholds. Methylation data (Illumina 450K array) in whole blood from 100 type 2 diabetic individuals and 100 control individuals from the Lifelines study were available. Multivariate linear models were used to examine the associations of the specific CpGs with type 2 diabetes and glycaemic traits. RESULTS: From the 52 CpGs identified in blood and selected for replication, 15 CpGs showed nominally significant associations with type 2 diabetes in the Lifelines sample (p < 0.05). The results for five CpGs (in ABCG1, LOXL2, TXNIP, SLC1A5 and SREBF1) remained significant after a stringent multiple-testing correction (changes in methylation from -3% up to 3.6%, p < 0.0009). All associations were directionally consistent with the original EWAS results. None of the selected CpGs from the tissue-specific EWASs were replicated in our methylation data from whole blood. We were also unable to replicate any of the CpGs associated with HbA1c levels in the healthy control individuals of our sample, while two CpGs (in ABCG1 and CCDC57) for fasting glucose were replicated at a nominal significance level (p < 0.05). CONCLUSIONS/INTERPRETATION: A number of differentially methylated CpGs reported to be associated with type 2 diabetes in the EWAS literature were replicated in blood and show promise for clinical use as disease biomarkers. However, more prospective studies are needed to support the robustness of these findings.

The impact of epigenomic next-generation sequencing approaches on our understanding of neuropsychiatric disorders.

Patients suffering from psychiatric disorders have a life span burden, which represents an enormous human, family, social, and economical cost. Several concepts have revolutionized our way of appraising neuropsychiatric disorders (NPDs). They result from a combination of genetic factors and environmental insults, and their etiology finds roots in the neurodevelopmental period. As epigenetic mechanisms tightly control brain development, exposure to adverse conditions disturbing the epigenetic landscape of the fetal brain increases the risk of developing NPDs, due to the persistence of abnormal epigenetic signatures, at distance from the initial stimulus. Here, we review these concepts and discuss recent results based on next-generation sequencing (NGS) approaches that have shed light on the mechanisms that underlie the emergence of NPDs, highlighting the importance of epigenetic phenomena. Because epigenetic mechanisms are potentially reversible, unraveling the epigenetic contribution to the etiology of NPDs is key to the design of future therapeutic strategies. Early diagnosis of patients prone to NPDs for early intervention represents a challenge that waits for biomarkers of vulnerability, and could be decisive for improving the outcome and prognosis of "at-risk" patients.

Epigenetics as the mediator of fetal programming of adult onset disease: what is the evidence?

The Developmental Origins of Health and Disease hypothesis describes how early life environmental factors influence development in a way that impacts later health and disease risk. The hypothesis is supported by a large number of animal studies and a smaller number of observational studies in humans. Epigenetic variation induced in early life has emerged as a prime candidate to be the mediator of such effects, but little direct evidence of this relation exists in humans, primarily due to the inherent problems associated with unraveling the relative contributions of genetic and environmental variables to phenotypic diversity. There are several prerequisites for establishing a causal link that include demonstrating interindividual epigenetic variability in early life in response to specific environmental exposures. Further, compelling evidence linking epigenetic change to disease, prior to onset is required. Finally, the functional relevance of specific epigenetic change must be demonstrated. Evidence is emerging in all of these areas but, ultimately, only large longitudinal life-course studies, commencing prior to birth, can provide direct evidence in support of a role of epigenetic processes as a driver of Developmental Origins of Health and Disease in humans.

DNA methylation and type 2 diabetes: a systematic review.

OBJECTIVE: DNA methylation influences gene expression and function in the pathophysiology of type 2 diabetes mellitus (T2DM). Mapping of T2DM-associated DNA methylation could aid early detection and/or therapeutic treatment options for diabetics. DESIGN: A systematic literature search for associations between T2DM and DNA methylation was performed. Prospero registration ID: CRD42020140436. METHODS: PubMed and ScienceDirect databases were searched (till October 19, 2023). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and New Castle Ottawa scale were used for reporting the selection and quality of the studies, respectively. RESULT: Thirty-two articles were selected. Four of 130 differentially methylated genes in blood, adipose, liver or pancreatic islets (TXNIP, ABCG1, PPARGC1A, PTPRN2) were reported in > 1 study. TXNIP was hypomethylated in diabetic blood across ethnicities. Gene enrichment analysis of the differentially methylated genes highlighted relevant disease pathways (T2DM, type 1 diabetes and adipocytokine signaling). Three prospective studies reported association of methylation in IGFBP2, MSI2, FTO, TXNIP, SREBF1, PHOSPHO1, SOCS3 and ABCG1 in blood at baseline with incident T2DM/hyperglycemia. Sex-specific differential methylation was reported only for HOOK2 in visceral adipose tissue (female diabetics: hypermethylated, male diabetics: hypomethylated). Gene expression was inversely associated with methylation status in 8 studies, in genes including ABCG1 (blood), S100A4 (adipose tissue), PER2 (pancreatic islets), PDGFA (liver) and PPARGC1A (skeletal muscle). CONCLUSION: This review summarizes available evidence for using DNA methylation patterns to unravel T2DM pathophysiology. Further validation studies in diverse populations will set the stage for utilizing this knowledge for identifying early diagnostic markers and novel druggable pathways.

Negative association between higher maternal pre-pregnancy body mass index and breastfeeding outcomes is not mediated by DNA methylation.

The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated with low breast feeding is maternal body mass index (BMI), which is used as a measure of obesity. The negative relationship between maternal obesity and breastfeeding is likely caused by a variety of social, psychological, and physiological factors. Maternal obesity may also have a direct biological association with breastfeeding through changes in maternal DNA methylation. Here, we investigate this potential biological association using data from a UK-based cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). We find that pre-pregnancy body mass index (BMI) is associated with lower initiation to breastfeed and shorter breastfeeding duration. We conduct epigenome-wide association studies (EWAS) of pre-pregnancy BMI and breastfeeding outcomes, and run candidate-gene analysis of methylation sites associated with BMI identified via previous meta-EWAS. We find that DNA methylation at cg11453712, annotated to PHTP1, is associated with pre-pregnancy BMI. From our results, neither this association nor those at candidate-gene sites are likely to mediate the link between pre-pregnancy BMI and breastfeeding.

Data science using the human epigenome for predicting multifactorial diseases and symptoms.

Tweetable abstract This article reviews machine learning models that leverages epigenomic data for predicting multifactorial diseases and symptoms as well as how such models can be utilized to explore new research questions.

Genetics and epigenetics of human aggression.

There is substantial variation between humans in aggressive behavior, with its biological etiology and molecular genetic basis mostly unknown. This review chapter offers an overview of genomic and omics studies revealing the genetic contribution to aggression and first insights into associations with epigenetic and other omics (e.g., metabolomics) profiles. We allowed for a broad phenotype definition including studies on "aggression," "aggressive behavior," or "aggression-related traits," "antisocial behavior," "conduct disorder," and "oppositional defiant disorder." Heritability estimates based on family and twin studies in children and adults of this broadly defined phenotype of aggression are around 50%, with relatively small fluctuations around this estimate. Next, we review the genome-wide association studies (GWAS) which search for associations with alleles and also allow for gene-based tests and epigenome-wide association studies (EWAS) which seek to identify associations with differently methylated regions across the genome. Both GWAS and EWAS allow for construction of Polygenic and DNA methylation scores at an individual level. Currently, these predict a small percentage of variance in aggression. We expect that increases in sample size will lead to additional discoveries in GWAS and EWAS, and that multiomics approaches will lead to a more comprehensive understanding of the molecular underpinnings of aggression.

Dietary polyphenols and their relationship to the modulation of non-communicable chronic diseases and epigenetic mechanisms: A mini-review.

Chronic Non-Communicable Diseases (NCDs) have been considered a global health problem, characterized as diseases of multiple factors, which are developed throughout life, and regardless of genetics as a risk factor of important relevance, the increase in mortality attributed to the disease to environmental factors and the lifestyle one leads. Although the reactive species (ROS/RNS) are necessary for several physiological processes, their overproduction is directly related to the pathogenesis and aggravation of NCDs. In contrast, dietary polyphenols have been widely associated with minimizing oxidative stress and inflammation. In addition to their antioxidant power, polyphenols have also drawn attention for being able to modulate both gene expression and modify epigenetic alterations, suggesting an essential involvement in the prevention and/or development of some pathologies. Therefore, this review briefly explained the mechanisms in the development of some NCDs, followed by a summary of some evidence related to the interaction of polyphenols in oxidative stress, as well as the modulation of epigenetic mechanisms involved in the management of NCDs.

Data Analysis of DNA Methylation Epigenome-Wide Association Studies (EWAS): A Guide to the Principles of Best Practice.

Array-based EWAS have become an increasingly popular technique to identify population epigenetic effects, particularly in humans. With the arrival of nonhuman species arrays, such as the mouse, this is likely to become an even more widely used technology. This chapter provides the less experienced researcher a guide to the analysis of data from the most widely used platform, the Illumina Infinium Methylation assay. This includes an overview of quality filtering, data normalization, analysis options, and techniques to improve the interpretation of results.

Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting.

For large-scale hypothesis testing such as epigenome-wide association testing, adaptively focusing power on the more promising hypotheses can lead to a much more powerful multiple testing procedure. In this chapter, we introduce a multiple testing procedure that weights each hypothesis based on the intraclass correlation coefficient (ICC), a measure of "noisiness" of CpG methylation measurement, to increase the power of epigenome-wide association testing. Compared to the traditional multiple testing procedure on a filtered CpG set, the proposed procedure circumvents the difficulty to determine the optimal ICC cutoff value and is overall more powerful. We illustrate the procedure and compare the power to classical multiple testing procedures using an example data.