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Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation.

ABSTRACT: DNA methylation is an essential epigenetic modification that plays a key role associated with the regulation of gene expression during differentiation, but in disease states such as cancer, the DNA methylation landscape is often deregulated. There are now numerous technologies available to interrogate the DNA methylation status of CpG sites in a targeted or genome-wide fashion, but each method, due to intrinsic biases, potentially interrogates different fractions of the genome. In this study, we compare the affinity-purification of methylated DNA between two popular genome-wide techniques, methylated DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain-based capture (MBDCap), and show that each technique operates in a different domain of the CpG density landscape. We explored the effect of whole-genome amplification and illustrate that it can reduce sensitivity for detecting DNA methylation in GC-rich regions of the genome. By using MBDCap, we compare and contrast microarray- and sequencing-based readouts and highlight the impact that copy number variation (CNV) can make in differential comparisons of methylomes. These studies reveal that the analysis of DNA methylation data and genome coverage is highly dependent on the method employed, and consideration must be made in light of the GC content, the extent of DNA amplification, and the copy number.

SUBMITTER: Robinson MD

PROVIDER: S-EPMC2989998 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case-Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD. RESULTS:Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls. CONCLUSIONS:CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association.

Dataset Information

Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation.

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