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. 2020 May;101(5-1):052409.
doi: 10.1103/PhysRevE.101.052409.

Inferring genetic fitness from genomic data

Hong-Li Zeng  1   2 , Erik Aurell  3   4
Affiliations

Inferring genetic fitness from genomic data

Hong-Li Zeng et al. Phys Rev E. 2020 May.

Abstract

The genetic composition of a naturally developing population is considered as due to mutation, selection, genetic drift, and recombination. Selection is modeled as single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness). The problem is posed to infer epistatic fitness from population-wide whole-genome data from a time series of a developing population. We generate such data in silico and show that in the quasilinkage equilibrium phase of Kimura, Neher, and Shraiman, which pertains at high enough recombination rates and low enough mutation rates, epistatic fitness can be quantitatively correctly inferred using inverse Ising-Potts methods.

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