An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences

Area of Science:

• Genomics
• Evolutionary Biology
• Epigenetics

Background:

• Understanding human genomic function is crucial.
• Epigenomic data offers potential insights into this function.
• Previous studies have explored the relationship between epigenomics and genomics.

Purpose of the Study:

• To quantify the information epigenomic datasets provide about human genomic function.
• To investigate how multiple epigenomic features collectively inform about genomic function.
• To understand the evolutionary forces shaping human genetic variation.

Main Methods:

• Analysis of nine epigenomic features across 115 human cell types.
• Application of a probabilistic evolutionary model fitted to human and nonhuman primate genomes.
• Measurement of information as entropy reduction to assess functional constraints.

Main Results:

• Several epigenomic features provide more information about genomic function when analyzed in combination.
• Human genetic variation entropy is primarily driven by mutation and neutral drift.
• Cell-type-specific FitCons scores reveal inter-cell type relationships.
• Approximately 8% of nucleotide sites appear to be constrained by natural selection.

Conclusions:

• Epigenomic datasets contain significant information about human genomic function.
• The combination of epigenomic features enhances the understanding of genomic constraints.
• Natural selection plays a role in constraining a subset of nucleotide sites in the human genome.