Abstract
Copy number variants (CNVs) are a dynamic feature of the human genome that play important roles in human adaptation and susceptibility to both common and rare disease [1]. The distribution of CNVs in mammalian genomes is nonrandom, and several sequence features have been associated with CNV breakpoints and regions of high structural mutability [2]–[8]. Based on an analysis of DNA methylation patterns in human sperm, Li et al. recently reported a significant relationship between CNVs and hypomethylation in the male germline [9], leading to the suggestion that DNA hypomethylation plays a causative role in the generation of structural variation. Given the potentially profound implications of this report for the study of human disease, we read the findings of Li et al. with great interest. However, after systematically reanalyzing the relationship between CNVs and DNA methylation patterns in sperm, we have identified several cryptic confounders in the data that we believe seriously undermine the conclusions of Li et al. We outline and discuss each of these in detail below.
Highlights
In their analysis, Li et al first divided the genome into 100 kb windows, with each window being scored for the presence of Copy number variants (CNVs) ascertained from studies of both normal controls and individuals with a variety of disease states
As these ‘‘methylation deserts’’ are enriched .2fold for CpG islands (CGIs) compared to the genome average [9], we measured the distribution of mapped reads across the genome in relation to CGIs and observed a strong tendency for preferential sampling of sites located within CGIs and their flanks in these ‘‘methylation deserts’’ (Figure 1c)
Preferential sampling of CGIs, regions that tend to be inherently unmethylated in sperm [10,17], likely underlies a significant fraction of the regions labeled as ‘‘methylation deserts.’’ Crucially, after excluding CpGs lying within 2 kb of CGIs, the mean methylation level for the remainder of these windows is greater than that in the rest of the genome (79.1% versus 77.2%)
Summary
Li et al first divided the genome into 100 kb windows, with each window being scored for the presence of CNVs ascertained from studies of both normal controls and individuals with a variety of disease states. Li et al defined windows that showed the lowest mean methylation levels by bisulfite sequencing (either the 1st or 5th percentiles) or had a MI = 0 as ‘‘methylation deserts,’’ and observed an increased prevalence of CNVs in these regions.
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