Single-probe analyses in epigenome-wide association studies (EWAS) have identified associations between DNA methylation and many phenotypes, but do not take into account information from neighboring ...probes. Methods to detect differentially methylated regions (DMRs) (clusters of neighboring probes associated with a phenotype) may provide more power to detect associations between DNA methylation and diseases or phenotypes of interest.
We proposed a novel approach, GlobalP, and perform comparisons with 3 methods-DMRcate, Bumphunter, and comb-p-to identify DMRs associated with log triglycerides (TGs) in real GAW20 data before and after fenofibrate treatment. We applied these methods to the summary statistics from an EWAS performed on the methylation data. Comb-p, DMRcate, and GlobalP detected very similar DMRs near the gene CPT1A on chromosome 11 in both the pre- and posttreatment data. In addition, GlobalP detected 2 DMRs before fenofibrate treatment in the genes ETV6 and ABCG1. Bumphunter identified several DMRs on chromosomes 1 and 20, which did not overlap with DMRs detected by other methods.
Our novel method detected the same DMR identified by two existing methods and detected two additional DMRs not identified by any of the existing methods we compared.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide ...examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach.
Subjects (
= 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included. Following the quality control steps of removing certain cytosine-phosphate-guanine (CpG) probes, the post-/premethylation changes (post/pre) were log transformed and the association was performed on 208,449 CpG sites. An additive linear mixed-effects model was used to test the association between each CpG probe and single nucleotide polymorphisms (SNPs) around ±1 Mb region, with age, sex, smoke, batch effect, and principal components included as covariates. Bonferroni correction was applied to define the significance threshold (
< 5.6 × 10
, given a total of 89,217,303 tests). Finally, we integrated our response-meQTL (re-meQTL) findings with the published genome-wide association study (GWAS) catalog of human diseases/traits.
We identified 1087 SNPs as
re-meQTLs associated with 610 CpG probes/sites located in 351 unique gene loci. Among these 1087
re-meQTL SNPs, 229 were unique and 6 were co-localized at 8 unique disease/trait loci reported in the GWAS catalog (enrichment
= 1.51 × 10
). Specifically, a lipid SNP, rs10903129, located in intron regions of gene
, was a re-meQTL (
= 3.12 × 10
) associated with the CpG probe cg09222892, which is in the upstream region of the gene
indicating a new target gene for rs10903129. In addition, we found that SNP rs12710728 has a suggestive association with cg17097782 (
= 1.77 × 10
), and that this SNP is in high linkage disequilibrium (LD) (R
> 0.8) with rs7443270, which was previously reported to be associated with fenofibrate response (
= 5.00 × 10
).
By using a novel analytic approach, we efficiently identified thousands of
re-meQTLs that provide a unique resource for further characterizing functional roles and gene targets of the SNPs that are most responsive to fenofibrate treatment. Our efficient analytic approach can be extended to large response quantitative trait locus studies with large sample sizes and multiple time points data.
Genome-wide association studies performed on triglycerides (TGs) have not accounted for epigenetic mechanisms that may partially explain trait heritability.
Parent-of-origin (POO) effect association ...analyses using an agnostic approach or a candidate approach were performed for pretreatment TG levels, posttreatment TG levels, and pre- and posttreatment TG-level differences in the real GAW20 family data set. We detected 22 genetic variants with suggestive POO effects with at least 1 phenotype (P ≤ 10
). We evaluated the association of these 22 significant genetic variants showing POO effects with close DNA methylation probes associated with TGs. A total of 18 DNA methylation probes located in the vicinity of the 22 SNPs were associated with at least 1 phenotype and 6 SNP-probe pairs were associated with DNA methylation probes at the nominal level of P < 0.05, among which 1 pair presented evidence of POO effect. Our analyses identified a paternal effect of SNP rs301621 on the difference between pre- and posttreatment TG levels (P = 1.2 × 10
). This same SNP showed evidence for a maternal effect on methylation levels of a nearby probe (cg10206250; P = 0.01). Using a causal inference test we established that the observed POO effect of rs301621 was not mediated by DNA methylation at cg10206250.
We performed POO effect association analyses of SNPs with TGs, as well as association analyses of SNPs with DNA methylation probes. These analyses, which were followed by a causal inference test, established that the paternal effect at the SNP rs301621 is induced by treatment and is not mediated by methylation level at cg10206250.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Maternal gestational diabetes mellitus (GDM) has been associated with adverse outcomes in the offspring. Growing evidence suggests that the epigenome may play a role, but most previous studies have ...been small and adjusted for few covariates. The current study meta-analyzed the association between maternal GDM and cord blood DNA methylation in the Pregnancy and Childhood Epigenetics (PACE) consortium.
Seven pregnancy cohorts (3,677 mother-newborn pairs 317 with GDM) contributed results from epigenome-wide association studies, using DNA methylation data acquired by the Infinium HumanMethylation450 BeadChip array. Associations between GDM and DNA methylation were examined using robust linear regression, with adjustment for potential confounders. Fixed-effects meta-analyses were performed using METAL. Differentially methylated regions (DMRs) were identified by taking the intersection of results obtained using two regional approaches: comb-p and DMRcate.
Two DMRs were identified by both comb-p and DMRcate. Both regions were hypomethylated in newborns exposed to GDM in utero compared with control subjects. One DMR (chr 1: 248100345-248100614) was located in the
promoter, and the other (chr 10: 135341870-135342620) was located in the gene body of
. Individual CpG analyses did not reveal any differentially methylated loci based on a false discovery rate-adjusted
value threshold of 0.05.
Maternal GDM was associated with lower cord blood methylation levels within two regions, including the promoter of
, a gene associated with autism spectrum disorder, and the gene body of
, which is upregulated in type 1 and type 2 diabetes. Future studies are needed to understand whether these associations are causal and possible health consequences.
Type 2 diabetes (T2D) is caused by interactions between genetic predisposition and environmental exposure. DNA methylation, a key epigenetic mark, is one measure of the molecular impact of ...environmental factors on DNA. We tested for associations between glycemic traits and peripheral blood cell DNA methylation in 473,669 CpG sites across the genome in individuals from multiple ancestries, and tested for causal relationships at these loci.
We conducted an epigenome-wide fixed effects meta-analysis for 3 glycemic traits-fasting glucose (FG), log fasting insulin (FI), and HbA1c—in 13,543 nondiabetic individuals of African (N=3,746), European (N=9,254), and Hispanic (N=543) ancestry across 16 cohorts. All analyses were adjusted for age, sex, smoking status, cell types, BMI, and technical covariates. We used Mendelian randomization (MR) analyses to determine whether glycemic traits causally influence methylation at the CpG sites identified in the meta-analysis. We performed MR analyses in Framingham Heart Study individuals (N=6,472), using separate genetic risk scores (GRS) as instruments for each of the 3 glycemic traits.
In the epigenome-wide meta-analysis of all ancestries, there were 43 CpG sites associated with FG, 89 with FI, and 54 with HbA1c, using a Bonferroni threshold of P<1.1×10-7. In addition to detecting many novel candidate loci for each trait, we found associations with all 3 glycemic traits at previously reported T2D methylated loci SREBF1, LINC00649, and ABCG1. In MR analyses, we showed that a higher GRS for FI was associated with greater methylation at cg17058475 in CPT1A (P=4×10-4) and higher GRS for HbA1c with greater methylation at cg05779219 near THBD (P=4×10-4).
We identified many novel candidate methylation loci associated with glycemic traits and 2 CpG sites where MR analysis indicated that methylation is causally influenced by glycemic traits. These findings support the hypothesis that variation in glycemia influences peripheral blood DNA methylation.
Disclosure
D.A. DiCorpo: None. S. Lent: None. W. Guan: None. M. Hivert: None. J.S. Pankow: None.
The rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of ...8 research groups focused on gene searching methods.
Although the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.
A causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Over 100 associated genetic loci have been robustly associated with schizophrenia. Gene prioritization and pathway analysis have focused on a priori hypotheses and thus may have been unduly ...influenced by prior assumptions and missed important causal genes and pathways. Using a data-driven approach, we show that genes in associated loci: (1) are highly expressed in cortical brain areas; (2) are enriched for ion channel pathways (false discovery rates <0.05); and (3) contain 62 genes that are functionally related to each other and hence represent promising candidates for experimental follow up. We validate the relevance of the prioritized genes by showing that they are enriched for rare disruptive variants and de novo variants from schizophrenia sequencing studies (odds ratio 1.67, P = 0.039), and are enriched for genes encoding members of mouse and human postsynaptic density proteomes (odds ratio 4.56, P = 5.00 × 10(-4); odds ratio 2.60, P = 0.049).The authors wish it to be known that, in their opinion, the first 2 authors should be regarded as joint First Author.
Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and ...other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group.
Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms.
The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.
DNA methylation is an epigenetic modification that plays an important role in gene regulation. DNA methylation varies between individuals and between tissues in the same individual. Many cohorts have ...measured DNA methylation in one or more tissues at hundreds of thousands of sites across the genome using methylation microarrays, and a standard analysis approach is to model the relationship between DNA methylation and a phenotype at each site and in each tissue separately. In this thesis, we explore methods for jointly analyzing multiple sites and/or multiple tissues. First, we propose a novel approach to identify differentially methylated regions (DMRs), neighboring sites in a single tissue associated with a phenotype, and compare our approach to two existing approaches to detect DMRs. We show that our method is useful when there are multiple sites in a region with weak or moderate associations with a phenotype. Then, we return to single-site analysis but evaluate methods for analyzing data from multiple tissues, accounting for correlation between two tissue samples from the same individual. We consider methods to model both the mean and variance of methylation sites as well as methods to model mean methylation only. In addition to evaluating existing models, we propose a novel random-effects meta-analysis, which is appropriate for meta-analyzing multiple parameters from correlated studies (or tissues). We show that we have inflated type I error with all meta-analysis methods and methods which model the variance of methylation. Finally, we evaluate methods to incorporate information from multiple sites and multiple tissues in association tests. We examine a gene set analysis method, MAGENTA, which was developed for genetic association studies, and propose an extension that is appropriate for DNA methylation data.
We evaluated five methods for detecting differentially methylated regions (DMRs): DMRcate, comb-p, seqlm, GlobalP and dmrff.
We used a simulation study and real data analysis to evaluate performance. ...Additionally, we evaluated the use of an ancestry-matched reference cohort to estimate correlations between CpG sites in cord blood.
Several methods had inflated Type I error, which increased at more stringent significant levels. In power simulations with 1-2 causal CpG sites with the same direction of effect, dmrff was consistently among the most powerful methods.
This study illustrates the need for more thorough simulation studies when evaluating novel methods. More work must be done to develop methods with well-controlled Type I error that do not require individual-level data.
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