Inferring genetic fitness from genomic data
arXiv:2001.02173 · doi:10.1103/PhysRevE.101.052409
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 Quasi-Linkage Equilibrium (QLE) phase of Kimura, Neher and Shraiman, that pertains at high enough recombination rates and low enough mutation rates, epistatic fitness can be quantitatively correctly inferred using inverse Ising/Potts methods.
References in corpus (16)
- Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
- Identification of direct residue contacts in protein-protein interaction by message passing
- Quantification of the effect of mutations using a global probability model of natural sequence variation
- Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
- Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1
- Inverse statistical problems: from the inverse Ising problem to data science
- Inverse Statistical Physics of Protein Sequences: A Key Issues Review
- Stochastic Models of Evolution in Genetics, Ecology and Linguistics
- Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
- Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
- Competition between recombination and epistasis can cause a transition from allele to genotype selection
- Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction
- Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by Direct Coupling Analysis
- Statistical Genetics and Evolution of Quantitative Traits
- Biophysical inference of epistasis and the effects of mutations on protein stability and function
- Correlation-Compressed Direct Coupling Analysis
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- Two fitness inference schemes compared using allele frequencies from 1,068,391 sequences sampled in the UK during the COVID-19 pandemic
- Classification of SARS-CoV-2 Variants through The Epistatical Circos Plots with Convolutional Neural Networks
- Inverse Ising techniques to infer underlying mechanisms from data
- Network reconstruction from asynchronously updated evolutionary game