Genomic variants concurrently listed in a somatic and a germline mutation database have implications for disease-variant discovery and genomic privacy

Abstract

AbstractBackgroundMutations arise in the human genome in two major settings: the germline and soma. These settings involve different inheritance patterns, chromatin structures, and environmental exposures, all of which might be predicted to differentially affect the distribution of substitutions found in these settings. Nonetheless, recent studies have found that somatic and germline mutation rates are similarly affected by endogenous mutational processes and epigenetic factors.ResultsHere, we quantified the number of single nucleotide variants that co-occur between somatic and germline call-sets (cSNVs), compared this quantity with expectations, and explained noted departures. We found that three times as many variants are shared between the soma and germline than is expected by independence. We developed a new, general-purpose statistical framework to explain the observed excess of cSNVs in terms of the varying mutation rates of different kinds substitution types and of genomic regions. Using this metric, we find that more than 90% of this excess can be explained by our observation that the basic substitution types (such as N[C->T]G, C->A, etc.) have correlated mutation rates in the germline and soma. Matched-normal read depth analysis suggests that an appreciable fraction of this excess may also derive from germline contamination of somatic samples.ConclusionOverall, our results highlight the commonalities in substitution patterns between the germline and soma. The universality of some aspects of human mutation rates offers insight into the potential molecular mechanisms of human mutation. The highlighted similarities between somatic and germline mutation rates also lay the groundwork for future studies that distinguish disease-causing variants from a genomic background informed by both somatic and germline variant data. Moreover, our results also indicate that the depth of matched normal sequencing necessary to ensure genomic privacy of donors of somatic samples may be higher than previously appreciated. Furthermore, the fact that we were able to explain such a high portion of recurrent variants using known determinants of mutation rates is evidence that the genomics community has already discovered the most important predictors of mutation rates for single nucleotide variants.