Abstract
Detecting combinations of alleles that diverged between subpopulations via selection signature statistics can contribute to decipher the phenomenon of epistasis. This research focused on the simulation of genomic data from subpopulations under divergent epistatic selection (ES). We used D’IS2 and FST statistics in pairs of loci to scan the whole-genome. The results showed the ability to identify loci under additive-by-additive ES (ESaa) by reporting large statistical departures between subpopulations with a high level of divergence, while it did not show the same advantage in the other types of ES. Despite this, limitations such as the difficulty to distinguish between the quasi-complete fixation of one locus by ESaa from other events were observed. However, D’IS2 can detect loci under ESaa by defining a minimum boundary for the minor allele frequency on a multiple subpopulation analysis where ES only takes place in one subset. Even so, the major limitation was distinguishing between ES and single-locus selection (SS); therefore, we can conclude that divergent locus can be also a result of ES. The test conditions with D-statistics of both Ohta (1982a, 1982b) and Black and Krafsur (1985) did not provide evidence to differentiate ES in our simulation framework of isolated subpopulations.
Highlights
Detecting combinations of alleles that diverged between subpopulations via selection signature statistics can contribute to decipher the phenomenon of epistasis
The expected values of D’IS2 and FST are zero under null selection. Those statistics rose by the level of divergence between subpopulations
Different combinations of alleles are favoured within the same subpopulation by ES15 were considered: additive-by-additive (ESaa) (e.g., A1_B1 and A2_B2), but just one combination tended to spread within a subpopulation by chance (96% of times)
Summary
Detecting combinations of alleles that diverged between subpopulations via selection signature statistics can contribute to decipher the phenomenon of epistasis. The results showed the ability to identify loci under additive-by-additive ES (ESaa) by reporting large statistical departures between subpopulations with a high level of divergence, while it did not show the same advantage in the other types of ES Limitations such as the difficulty to distinguish between the quasi-complete fixation of one locus by ESaa from other events were observed. The main objective of this research is to evaluate selection signature statistics to detect ES on simulated data sets in order to find a useful methodology to identify interactions between genes at the genome level
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