Fitness Inference and Its Application to SARS-CoV-2 Genomic Sequences

by Hong-Li Zeng

Thursday 13 Aug 2020, 13:00 → 14:00 Europe/Stockholm

AlbaNova Main Building

The natural driving forces of an evolutionary population are selection (high fitness indicates the probability of having more offspring is higher), mutation, recombination, and random drift. Thus, the fitness inference is crucial to understand the evolutionary process of a population from the statistical genetics point of view.

Kimura showed that the genotype distribution of a population has a
Gibbs-Boltzmann form when the population is in the quasi-linkage-equilibrium (QLE) state, which can be achieved through weak mutations and high recombination rates.  Neiher and Shraiman extended the statement and pointed out that the epistatic contribution of the fitness is proportional to the direct couplings (which can be inferred by kinds of direct couplings analysis (DCA) approache).

As an application of KNS theory, we considered all complete SARS-CoV-2genomes deposited in the GISAID repository until three different cut-off dates.  We reconstruct the epistatic contributions to the fitness from polymorphic loci. Several pronounced epistasic interactions between viral genes are found. These interactions could serve as hypothesis for the biological experiments. They could also give potential targets for combinatorial treatments of COVID-19.

This talk is based on a paper under review in a journal, shortly to appear on arXiv.