Partial correlation analysis indicates causal relationships between GC-content, exon density and recombination rate in the human genome

TL;DR

This study uses partial correlation analysis to identify causal relationships among GC-content, exon density, and recombination rate in the human genome, revealing that recombination rate and exon density independently influence GC-content.

Contribution

It introduces a causal inference approach using partial correlations and graphical models to distinguish direct from indirect associations in genome features.

Findings

Recombination rate and exon density are uncorrelated unconditionally.

Conditioning on GC-content reveals an inverse correlation between recombination rate and exon density.

Recombination rate and exon density are independent causes of GC-content variation.

Abstract

{\bf Background}: Several features are known to correlate with the GC-content in the human genome, including recombination rate, gene density and distance to telomere. However, by testing for pairwise correlation only, it is impossible to distinguish direct associations from indirect ones and to distinguish between causes and effects. {\bf Results}: We use partial correlations to construct partially directed graphs for the following four variables: GC-content, recombination rate, exon density and distance-to-telomere. Recombination rate and exon density are unconditionally uncorrelated, but become inversely correlated by conditioning on GC-content. This pattern indicates a model where recombination rate and exon density are two independent causes of GC-content variation. {\bf Conclusions}: Causal inference and graphical models are useful methods to understand genome evolution and the mechanisms of isochore evolution in the human genome.