Mutation rate variations in the human genome are encoded in DNA shape

Single nucleotide mutation rates have critical implications for human evolution and genetic diseases. Accurate modeling of these mutation rates has long remained an open problem since the rates vary substantially across the human genome. A recent model, however, explained much of the variation by considering higher order nucleotide interactions in the local (7-mer) sequence context around mutated nucleotides. Despite this models predictive value, we still lack a biophysically-grounded understanding of genome-wide mutation rate variations. DNA shape features are geometric measurements of DNA structural properties, such as helical twist and tilt, and are known to capture information on interactions between neighboring nucleotides within a local context. Motivated by this characteristic of DNA shape features, we used them to model mutation rates in the human genome. The DNA shape feature based models show up to 15% higher accuracy than the current nucleotide sequence-based models and pinpoint DNA structural properties predictive of mutation rates in the human genome. Further analyzing the mutation rates of individual positions of transcription factor (TF) binding sites in the human genome, we found a strong association between DNA shape and the position-specific mutation rates. The trend holds for hundreds of TFs and is even stronger in evolutionarily conserved regions. To our knowledge, this is the first attempt that demonstrates the structural underpinnings of nucleotide mutations in the human genome and lays the groundwork for future studies to incorporate DNA shape information in modeling genetic variations.