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Genetics. 2019 Aug;212(4):1321-1336. doi: 10.1534/genetics.119.302074. Epub 2019 May 30.

Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models.

Author information

1
Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Wien, Austria.
2
Department of Biological Physics, MTA-ELTE "Lendulet" Evolutionary Genomics Research Group, Eötvös University, Pázmány P. stny. 1A, Budapest 1117, Hungary.
3
Institut für Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Wien, Austria ck202@st-andrews.ac.uk.
4
Centre for Biological Diversity, School of Biology, University of St Andrews, Fife KY16 9TH, UK.

Abstract

As multi-individual population-scale data become available, more complex modeling strategies are needed to quantify genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We apply the new framework to a great ape dataset, where we found patterns of allelic selection that match those of genome-wide GC-biased gene conversion (gBGC). In particular, we show that great apes have patterns of allelic selection that vary in intensity-a feature that we correlated with great apes' distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, promoting more polymorphisms in the base composition of great ape genomes. We further assess the impact of GC-bias in molecular analysis, and find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted for. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics.

KEYWORDS:

GC-bias; Moran model; allelic selection; boundary mutations; gBGC; great apes

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