Computer Science

Colloquium Speaker Dr. Vineet Bafna from UC San Diego


Dr. Bafna is a Professor at the Department of Computer Science and Engineering at UC San Diego. For more information on Dr. Bafna, visit his website at: http://proteomics.ucsd.edu/vbafna

Title: Predicting Carriers of Ongoing Selective Sweeps without knowledge of the favored allele

Host: Prof. Ilias Tagkopoulos

When: Thursday, February 26th at 3:10pm

Where: 1127 Kemper Hall

Abstract:
Methods for detecting the genomic signatures of natural selection are heavily studied, and have been successful in identifying many selective sweeps. For the vast majority of these sweeps, the adaptive allele remains unknown, making it difficult to distinguish carriers of the sweep from non-carriers. Because carriers of ongoing selective sweeps are likely to contain a future most recent common ancestor, identifying them may prove useful in predicting the evolutionary trajectory — for example, in contexts involving drug-resistant pathogen strains or cancer sub-clones. The main contribution of this paper is the development and analysis of a new statistic, the Haplotype Allele Frequency (HAF) score, assigned to individual haplotypes in a sample. The HAF score naturally captures many of the properties shared by haplotypes carrying an adaptive allele. We provide a theoretical model for the behavior of the HAF score under different evolutionary scenarios, including neutral Wright-Fischer evolution, exponential growth, and the trajectory of HAF-scores during a selective sweep.

We validate the theoretical analysis using extensive simulations, and demonstrate how the HAF-scores change dynamically with the progression of selective sweep, and are different for carriers and non-carriers of a favorable allele. We use this observation to design an algorithm, PreCIOSS (Predicting Carriers of Ongoing Selective Sweeps) to identify carriers of the adaptive allele in selective sweeps, and we demonstrate its power on simulations of both hard and soft selective sweeps, as well as on data from well-known sweeps in human populations.

Location
1127 Kemper Hall

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