An approximate bayesian estimator suggests strong, recurrent selective sweeps in Drosophila.
The recurrent fixation of newly arising, beneficial mutations in a species reduces levels of linked neutral variability. Models positing frequent weakly beneficial substitutions or, alternatively, rare, strongly selected substitutions predict similar average effects on linked neutral variability, if the product of the rate and strength of selection is held constant. We propose an approximate Bayesian (ABC) polymorphism-based estimator that can be used to distinguish between these models, and apply it to multi-locus data from Drosophila melanogaster. We investigate the extent to which inference about the strength of selection is sensitive to assumptions about the underlying distributions of the rates of substitution and recombination, the strength of selection, heterogeneity in mutation rate, as well as the population's demographic history. We show that assuming fixed values of selection parameters in estimation leads to overestimates of the strength of selection and underestimates of the rate. We estimate parameters for an African population of D. melanogaster (ŝ approximately 2E-03, ) and compare these to previous estimates. Finally, we show that surveying larger genomic regions is expected to lend much more discriminatory power to the approach. It will thus be of great interest to apply this method to emerging whole-genome polymorphism data sets in many taxa.
Jensen
Jeffrey D
JD
Section of Ecology, Behavior and Evolution, University of California San Diego, La Jolla, California, United States of America. jjensen@ucsd.edu
Thornton
Kevin R
KR
Andolfatto
Peter
P
D000349:Africa; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D003710:Demography; D004331:Drosophila melanogaster / Q000235:genetics*; D019143:Evolution, Molecular; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D049750:Genome, Insect; D008957:Models, Genetic; D015233:Models, Statistical; D011110:Polymorphism, Genetic; D012641:Selection, Genetic