Phylogenetic Modeling of Regulatory Element Turnover Based on Epigenomic Data

Dukler, N., Huang, Y-F., Siepel, A. (March 2020) Phylogenetic Modeling of Regulatory Element Turnover Based on Epigenomic Data. Mol Biol Evol, 37 (7). pp. 2137-2152. ISSN 0737-4038

URL: https://pubmed.ncbi.nlm.nih.gov/32176292/
DOI: 10.1093/molbev/msaa073

Abstract

Evolutionary changes in gene expression are often driven by gains and losses of cis-regulatory elements (CREs). The dynamics of CRE evolution can be examined using multi-species epigenomic data, but so far such analyses have generally been descriptive and model-free. Here, we introduce a probabilistic modeling framework for the evolution of CREs that operates directly on raw chromatin immunoprecipitation and sequencing (ChIP-seq) data and fully considers the phylogenetic relationships among species. Our framework includes a phylogenetic hidden Markov model, called epiPhyloHMM, for identifying the locations of multiply aligned CREs, and a combined phylogenetic and generalized linear model, called phyloGLM, for accounting for the influence of a rich set of genomic features in describing their evolutionary dynamics. We apply these methods to previously published ChIP-seq data for the H3K4me3 and H3K27ac histone modifications in liver tissue from nine mammals. We find that enhancers are gained and lost during mammalian evolution at about twice the rate of promoters, and that turnover rates are negatively correlated with DNA sequence conservation, expression level, and tissue breadth, and positively correlated with distance from the transcription start site, consistent with previous findings. In addition, we find that the predicted dosage sensitivity of target genes positively correlates with DNA sequence constraint in CREs but not with turnover rates, perhaps owing to differences in the effect sizes of the relevant mutations. Altogether, our probabilistic modeling framework enables a variety of powerful new analyses.

Item Type: Paper
Subjects: bioinformatics
bioinformatics > computational biology
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
CSHL Authors:
Communities: CSHL labs > Siepel lab
Cold Spring Harbor Laboratory of Quantitative Biology
Depositing User: Adrian Gomez
Date: 16 March 2020
Date Deposited: 02 Apr 2020 15:23
Last Modified: 14 Sep 2020 21:54
PMCID: PMC7306682
Related URLs:
URI: https://repository.cshl.edu/id/eprint/39215

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