Direct Coupling Analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the Quasi-Linkage Equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of Clonal Competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3,000 genomes of the human pathogen Streptococcus pneumoniae.
@article{arxiv.1808.03478,
title = {DCA for genome-wide epistasis analysis: the statistical genetics perspective},
author = {Chen-Yi Gao and Fabio Cecconi and Angelo Vulpiani and Hai-Jun Zhou and Erik Aurell},
journal= {arXiv preprint arXiv:1808.03478},
year = {2018}
}