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Saturday, June 1
Computational Statistics
Computational Statistics E-Posters
Sat, Jun 1, 9:30 AM - 10:30 AM
Grand Ballroom Foyer

Approximate Bayesian Computational Statistical Methods to Estimate the Strength of Divergent Selection in Yeast (306345)

Ousseini Issaka Salia, University of Idaho 
*Martyna Lukaszewicz, University of Idaho 

Keywords: approximate Bayesian computation, biostatistics, computational evolutionary biology

Genomic data provides the possibility to learn how the environmental conditions structure genetic make-up of organisms. However, analytical tools have not kept up with the wealth of genomic data, so that we are still limited in our ability to make detailed inferences about how natural selection and other evolutionary processes affect genomic variation. Markers with elevated genetic differentiation between populations have traditionally been used to detect loci under divergent selection. But the regions with most differences in genome sequence may expand over large, physically linked regions on chromosomes, a phenomenon known as genomic islands of divergent selection. Genomic islands are not simply predictable result of divergent selection but rather the region size of genomic islands depends on strength of selection on genes, migration between populations and recombination rate of parental genomes, making it difficult to identify by comparative genomics alone. We aim to test on Saccharomyces cerevisiae whether under low selection and migration the genomic islands of divergent selection lead to frequency of alleles being different than if the loci were independent, and if weaker selection and higher migration and recombination rates breakdown linkage disequilibrium around new mutations. Two heterothallic and haploid Saccharomyces cerevisiae strains of opposite mating were crossed to create in admixed ancestral population which was used to test theses hypothesis. The recombination and migration rates of this population were controlled in laboratory environment. We developed a simulator to simulate genomic data under different parameter values for divergent selection. We determine which population genetics statistics are the most informative under the model of divergent selection with migration and recombination. The simulator is used to develop approximate Bayesian computational methods to make inferences on population genetic parameters.