Dataset Information

High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction.

ABSTRACT: Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs).Using stringent false discovery rate control, 15 % of methylation sites show genetic influence. Most meQTLs are local, where the associated SNP and methylation site are in close genomic proximity. Distant meQTLs and those spanning different chromosomes are less common. Most local meQTLs encompass common SNPs that alter CpG sites (CpG-SNPs). Local meQTLs encompassing CpG-SNPs are enriched in regions of inactive chromatin in blood cells. In contrast, local meQTLs lacking CpG-SNPs are enriched in regions of active chromatin and transcription factor binding sites. Of 393 local meQTLs that overlap disease-associated regions from genome-wide studies, a high percentage encompass common CpG-SNPs. These meQTLs overlap active enhancers, differentiating them from CpG-SNP meQTLs in inactive chromatin.Genetic influence on the human blood methylome is common, involves several heterogeneous processes and is predominantly dependent on local sequence context at the meQTL site. Most meQTLs involve CpG-SNPs, while sequence-dependent effects on chromatin binding are also important in regions of active chromatin. An abundance of local meQTLs resulting from methylation of CpG-SNPs in inactive chromatin suggests that many meQTLs lack functional consequence. Integrating meQTL and Roadmap Epigenomics data could assist fine-mapping efforts.

SUBMITTER: McClay JL

PROVIDER: S-EPMC4699364 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction.

McClay Joseph L JL Shabalin Andrey A AA Dozmorov Mikhail G MG Adkins Daniel E DE Kumar Gaurav G Nerella Srilaxmi S Clark Shaunna L SL Bergen Sarah E SE Hultman Christina M CM Magnusson Patrik K E PK Sullivan Patrick F PF Aberg Karolina A KA van den Oord Edwin J C G EJ

Genome biology 20151223

<h4>Background</h4>Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTL ...[more]

PMID: 26699738

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Project description:Background/aimsRecently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method.MethodsIn this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process.ResultsWe compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the bayesian method. In both simulation and real data analysis of Methyl-Seq data, the bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40-300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays.

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Project description:Background: Neuroendocrine neoplasms (NENs) are a heterogeneous group of neoplasms that span from well-differentiated neuroendocrine tumors (NETs) to highly aggressive neoplasms classified as neuroendocrine carcinomas (NECs). The genomic landscape of NENs has not been well studied. The aim of this study is to confirm the feasibility of next generation sequencing (NGS) testing circulating tumor DNA (ctDNA) in patients with NENs and characterize common alterations in the genomic landscape. Results: Of the 320 NEN patients, 182 (57%) were male with a median age of 63 years (range: 8-93) years. Tumor type included pancreatic NET (N = 165, 52%), gastrointestinal NEC (N = 52, 16%), large cell lung NEC (N = 21, 7%), nasopharyngeal NEC (N = 16, 5%) and NEC/NET not otherwise specified (N = 64, 20%). ctDNA NGS testing was performed on 338 plasma samples; 14 patients had testing performed twice and 2 patients had testing performed three times. Genomic alterations were defined in 280 (87.5%) samples with a total of 1,012 alterations identified after excluding variants of uncertain significance (VUSs) and synonymous mutations. Of the 280 samples with alterations, TP53 associated genes were most commonly altered (N = 145, 52%), followed by KRAS (N = 61, 22%), EGFR (N = 33, 12%), PIK3CA (N = 30, 11%), BRAF (N = 28, 10%), MYC (N = 28, 10%), CCNE1 (N = 28, 10%), CDK6 (N = 22, 8%), RB1 (N = 19, 7%), NF1 (N = 19, 7%), MET (N = 19, 7%), FGFR1 (N = 19, 7%), APC (N = 19, 7%), ERBB2 (N = 16, 6%) and PTEN (N = 14, 5%). Conclusions: Evaluation of ctDNA was feasible among individuals with NEN. Liquid biopsies are non-invasive methods that can provide personalized options for targeted therapies in NEN patients. Patients and Methods: Molecular alterations in 338 plasma samples from 320 patients with NEN were evaluated using clinical-grade NGS of ctDNA (Guardant360®) across multiple institutions. The test detects single nucleotide variants in 54-73 genes, copy number amplifications, fusions, and indels in selected genes.

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Dataset Information

High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction.

Publications

High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction.

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