Sequence-based machine learning reveals 3D genome differences between bonobos and chimpanzees

Machine learning reveals the diversity of human 3D chromatin contact patterns

Understanding variation in chromatin contact patterns across human populations is critical for interpreting non-coding variants and their ultimate effects on gene expression and phenotypes. However, experimental determination of chromatin contacts at a population-scale is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the diversity 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We then apply this approach to thousands of diverse modern humans and the inferred human-archaic hominin ancestral genome. While patterns of 3D contact divergence genome-wide are qualitatively similar to patterns of sequence divergence, we find that 3D divergence in local 1-megabase genomic windows does not follow sequence divergence. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, 26% of genomic windows have rare 3D contact variation observed in a small number of individuals. Using in silico mutagenesis we find that most sequence changes to do not result in changes to 3D chromatin contacts. However in windows with substantial 3D divergence, just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals diverse contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.