Evidence of reduced recombination rate in human regulatory domains
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13059_2017_Article_1308.pdf
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Author(s)
Ernst, Jason
Liu, Yaping
Sarkar, Abhishek Kulshreshtha
Kheradpour, Pouya
Kellis, Manolis
Date Issued
October 2017
Journal
Genome Biology
Publisher
BioMed Central
Citation
Liu, Yaping et al. "Evidence of reduced recombination rate in human regulatory domains" Genome Biology 2017, 18 (October 2017):193 © 2017 The Author(s)
Version
Final published version
Abstract
Background
Recombination rate is non-uniformly distributed across the human genome. The variation of recombination rate at both fine and large scales cannot be fully explained by DNA sequences alone. Epigenetic factors, particularly DNA methylation, have recently been proposed to influence the variation in recombination rate.
Results
We study the relationship between recombination rate and gene regulatory domains, defined by a gene and its linked control elements. We define these links using expression quantitative trait loci (eQTLs), methylation quantitative trait loci (meQTLs), chromatin conformation from publicly available datasets (Hi-C and ChIA-PET), and correlated activity links that we infer across cell types. Each link type shows a “recombination rate valley” of significantly reduced recombination rate compared to matched control regions. This recombination rate valley is most pronounced for gene regulatory domains of early embryonic development genes, housekeeping genes, and constitutive regulatory elements, which are known to show increased evolutionary constraint across species. Recombination rate valleys show increased DNA methylation, reduced doublestranded break initiation, and increased repair efficiency, specifically in the lineage leading to the germ line. Moreover, by using only the overlap of functional links and DNA methylation in germ cells, we are able to predict the recombination rate with high accuracy.
Conclusions
Our results suggest the existence of a recombination rate valley at regulatory domains and provide a potential molecular mechanism to interpret the interplay between genetic and epigenetic variations.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1186/s13059-017-1308-x
