Estimating fitness effects of mutations in the presence of genetic linkage

Probabilistic prognosis of the evolution of population requires the knowledge of the fitness effects of mutations at different genomic sites. However, the signature of natural selection is eclipsed by strong noise in data, because the common ancestors of different sites render their evolution inter-dependent. Together with mutation and recombination, genetic linkage also makes evolution stochastic and requires averaging over many independent populations. Here we develop a method designed to work in the presence of strong linkage effects. For testing, we apply it to simulated genomic sequences generated by a Monte Carlo algorithm with known selection coefficients. Results demonstrate the good accuracy of the estimates of relative selection coefficients, if more than ten independent populations are used for averaging. Infrequent recombination partly compensates linkage effects and improves the accuracy. The method is then used to estimate the selection coefficients of 10,000 genomic sites of E. coli . These findings enable the inference of adaptive landscape under the conditions of strong multi-site linkage.