  {\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.
