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Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees
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DOI：10.1093/gbe/evae210.png)
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Abstract
En 中文
The 3D structure of the genome is an important mediator of gene expression. As phenotypic divergence is largely driven by gene regulatory variation, comparing genome 3D contacts across species can further understanding of the molecular basis of species differences. However, while experimental data on genome 3D contacts in humans are increasingly abundant, only a handful of 3D genome contact maps exist for other species. Here, we demonstrate that human experimental data can be used to close this data gap. We apply a machine learning model that predicts 3D genome contacts from DNA sequence to the genomes from 56 bonobos and chimpanzees and identify species-specific patterns of genome folding. We estimated 3D divergence between individuals from the resulting contact maps in 4,420 1 Mb genomic windows, of which similar to 17% were substantially divergent in predicted genome contacts. Bonobos and chimpanzees diverged at 89 windows, overlapping genes associated with multiple traits implicated in Pan phenotypic divergence. We discovered 51 bonobo-specific variants that individually produce the observed bonobo contact pattern in bonobo-chimpanzee divergent windows. Our results demonstrate that machine learning methods can leverage human data to fill in data gaps across species, offering the first look at population-level 3D genome variation in nonhuman primates. We also identify loci where changes in 3D folding may contribute to phenotypic differences in our closest living relatives.
Keywords:
bonobo
chimpanzee
gene regulation
3D genome folding
machine learning
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