378 research outputs found
Abstract 1319: Epigenome-wide association study reveals differential DNA methylation consistent with progression of multiple myeloma
No full textAbstract
Purpose: Multiple myeloma (MM) is the second most common hematological malignancy in the US. It is characterized by a clonal expansion of plasma cells in the bone marrow and extramedullary sites and is preceded by two precursor conditions including monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM). Strong evidence suggests a germline and environmental etiology. However, efforts to characterize heritable changes in gene activity, such as DNA methylation, have not been widely reported.
Methods: We examined epigenome-wide DNA methylation as markers of MGUS, SMM and MM in peripheral blood obtained from treatment-naïve European American cases with heavy-chain IgG or IgA MGUS (n=60), SMM (n=31) and MM (n=54) and age- and sex-matched controls (n=79) included from the University of Alabama at Birmingham, University of Chicago and the Mayo Clinic, Rochester [54.5% males; mean age, 64 years (range, 36 to 86)]. We quantified DNA methylation of over 450,000 CpG and non-CpG loci using the Infinium HumanMethylation450 array (Illumina). Differentially methylated positions were calculated using a general linear model framework adjusted for confounders and cellular heterogeneity.
Results: A total of 6 CpGs were differentially methylated in MM cases compared to controls at a level of genome-wide statistical significance. MM was associated with hypomethylation at differentially methylated positions inside SBNO2 (P=3.37x10-10), WIZ (P=1.12x10-8), CA6 (P=4.29x10-8) and ADORA1 (P=3.68x10-8) as well as intergenic positions proximal to TNFRSF8 (Chr 1p36.22; P=2.24x10-9) and ENDOV (Chr 17q25.3; P=2.74x10-8). Each of these loci, with the exception of CA6 and ADORA1, were hypomethylated in each of the 3 plasma cell dyscrasia phenotypes including MGUS and SMM and MM cases compared to controls (P<0.03), albeit not at a level of genome-wide statistical significance.
Conclusions: These preliminary findings suggest that differences in DNA methylation may contribute to altered risk of MM, as well as its precursor conditions, and may play a role in plasma cell dyscrasia progression as a consequence of heritable changes in gene activity due to past exposures. Replication in a large yet similarly well-characterized population is warranted.
Citation Format: Stephen D. Gragg, Devin Absher, Xiangqin Cui, Christina Pillion, Richard Myers, Shaji Kumar, Luciano Costa, Brian Chiu, Celine Vachon, Elizabeth Brown. Epigenome-wide association study reveals differential DNA methylation consistent with progression of multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1319. doi:10.1158/1538-7445.AM2017-1319</jats:p
Statistical quantification of methylation levels by next-generation sequencing.
No full textRecently, 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.In 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.We 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
Targeted sequencing of large genomic regions with CATCH-Seq.
No full textCurrent target enrichment systems for large-scale next-generation sequencing typically require synthetic oligonucleotides used as capture reagents to isolate sequences of interest. The majority of target enrichment reagents are focused on gene coding regions or promoters en masse. Here we introduce development of a customizable targeted capture system using biotinylated RNA probe baits transcribed from sheared bacterial artificial chromosome clone templates that enables capture of large, contiguous blocks of the genome for sequencing applications. This clone adapted template capture hybridization sequencing (CATCH-Seq) procedure can be used to capture both coding and non-coding regions of a gene, and resolve the boundaries of copy number variations within a genomic target site. Furthermore, libraries constructed with methylated adapters prior to solution hybridization also enable targeted bisulfite sequencing. We applied CATCH-Seq to diverse targets ranging in size from 125 kb to 3.5 Mb. Our approach provides a simple and cost effective alternative to other capture platforms because of template-based, enzymatic probe synthesis and the lack of oligonucleotide design costs. Given its similarity in procedure, CATCH-Seq can also be performed in parallel with commercial systems
The effect of repeat blocking with increased concentrations of Cot-1 DNA within the CATCH-Seq hybridization step of a chromosome 11 target.
No full text<p>Total numbers of on target and off target read yields in millions within non-repetitive sequences (A) or repetitive sequences (B). (C–H) On and off target read yields within repeat structures based on different thresholds of size (C,E,G) or divergence (D,F,H). Green and gray lines show on target and off target reads, respectively.</p
dCATCH-Seq: improved sequencing of large continuous genomic targets with double-hybridization
No full textRepeat structure description within a chromosome 11 target used for Cot-1 tests.
No full texta<p>total captured target size is 247.6 kb; target region shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111756#pone-0111756-g004" target="_blank">Figure 4</a>.</p>b<p>out of 50 million sampled reads at 20× Cot-1 concentration.</p>c<p>all repeat hg19 coordinates, sizes, and Smith-Waterman (SW) scores obtained from RepeatMasker within the UCSC Genome Browser. For descriptions of RepeatMasker, <a href="http://www.repeatmasker.org/" target="_blank">http://www.repeatmasker.org/</a>.</p><p>Repeat structure description within a chromosome 11 target used for Cot-1 tests.</p
Overview of the clone adapted template capture hybridization sequencing procedure.
No full text<p>BAC clone templates are selected to span genomic coordinates of interest, and pooled by percent mass of the composite target. BACs are sheared, ligated with T7 adapters to transcribe biotinylated RNA probes, and then solution hybridized with prepared libraries. Following capture, libraries are amplified by PCR, or bisulfite converted prior to amplification for analysis of DNA methylation. Target enriched libraries are pooled and sequenced.</p
Read depth plot of a chromosome 11 target for a sample showing median coverage among all samples used for capture.
No full text<p>Vertical bars indicate read depth with scale depicted on the left side of the panel. Red lines show percent GC content across non-overlapping 400 bp intervals spanning the target region with scale shown on the right side of the panel. Horizontal dotted line indicates 50% GC content. A repeat structure track (RepMask) is shown below the plot in gray derived from the UCSC genome browser for all repeats containing a Smith-Waterman score of at least 600, and larger than 200 bp in size. Genes are shown below the repeat track in dark blue and arrows depict gene orientation.</p
Read depth plot of a chromosome 11 target for a sample showing median coverage among all samples used for capture and bisulfite sequencing.
No full text<p>Vertical bars indicate read depth with scale depicted on the left side of the panel. Red lines show percent GC content across non-overlapping 400 bp intervals spanning the target region with scale shown on the right side of the panel. Horizontal dotted line indicates 50% GC content. A repeat structure track (RepMask) is shown below the plot in gray derived from the UCSC genome browser for all repeats containing a Smith-Waterman score of at least 600, and larger than 200 bp in size. Genes are shown below the repeat track in dark blue and arrows depict gene orientation.</p
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