bioRxivBioinformaticsPreprint
Common germline polymorphisms and somatic cancer mutations exhibit non-random positional overlap across the human genome
Thais Silva TavaresiD, Daniela Souza Lima Barbosa, Renan Pedra de SouzaiD, R. da Silva, Thiago Peixoto LealiD, Maycon Douglas de OliveiraiD, Carolina Silva-CarvalhoiD, Luigi MarchionniiD, Mateus Henrique GouveiaiD, Francisco Pereira LoboiD
Abstract
Germline and somatic mutations have traditionally been studied independently because they arise in distinct biological contexts and are shaped by different selective pressures. Despite these differences, both originate from the same molecular processes of DNA damage, replication error, and DNA repair. Yet this separation has limited the opportunity of investigation of genomic loci recurrently mutated across both mutational landscapes. Identifying such mutational co-occurrences may provide unique insights into the principles governing recurrent mutation. Here, we show that common germline polymorphisms and cancer-associated somatic SNVs recur at identical genomic positions across the human genome, sharing the same nucleotide substitutions more frequently than expected by chance. This recurrence persists within coding regions, is only minimally explained by the canonical hotspot contexts evaluated here (CpG islands and microsatellites), and is associated with a mutational signature profile enriched for the ubiquitous clock-like SBS5 signature together with DNA repair-associated signatures. Importantly, this overlap pattern is not shared across other germline variation: rare (AF<1%) and clinically classified variants exhibit significantly less overlap than expected. Together, these findings support the existence of intrinsically vulnerable genomic loci and provide a framework for investigating the mechanisms underlying recurrent mutation.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: eQTL / molecular QTL mapping
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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