Learning natural selection from the site frequency spectrum

Roy Ronen, Nitin Udpa, Eran Halperin, Vineet Bafna*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective pressures. Over the last two decades, many tests have been proposed to identify genomic signatures of natural selection. However, the power of these tests changes unpredictably from one dataset to another, with no single dominant method. We build upon recent work that connects many of these tests in a common framework, by describing how positive selection strongly impacts the observed site frequency spectrum (SFS). Many of the proposed tests quantify the skew in SFS to predict selection. Here, we show that the skew depends on many parameters, including the selection coefficient, and time since selection. Moreover, for each of the different regimes of positive selection, informative features of the scaled SFS can be learned from simulated data and applied to population-scale variation data. Using support vector machines, we develop a test that is effective over all selection regimes. On simulated datasets, our test outperforms existing ones over the entire parameter space. We apply our test to variation data from Drosophila melanogaster populations adapted to hypoxia, and identify new loci that were missed by previous approaches, but strengthen the role of the Notch pathway in hypoxia tolerance.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 17th Annual International Conference, RECOMB 2013, Proceedings
Number of pages4
StatePublished - 2013
Event17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013 - Beijing, China
Duration: 7 Apr 201310 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7821 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013


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