Mehrdad Bakhtiari

Ph.D. Student, University of California San Diego

Fine-mapping the Favored Mutation in a Positive Selective Sweep

Location

Halls department, Hall 6

Date and Time

Thursday, 28 December 2017
17:00 - 18:00

Abstract

Methods to identify signatures of selective sweeps in population genomics data have been actively developed, but most do not identify the specific mutation favored by the selective sweep. We present a method, iSAFE, that uses a statistic derived solely from population genetics signals to pinpoint the favored mutation even when the signature of selection extends to 5Mbp. iSAFE was tested extensively on simulated data and in human populations from the 1000 Genomes Project, at 22 loci with previously characterized selective sweeps. For 14 of the 22 loci, iSAFE ranked the previously characterized candidate mutation among the 13 highest scoring (out of ~21,000 variants). Three loci did not show a strong signal. For the 5 remaining loci, iSAFE identified previously unreported mutations as being favored. In these regions, all of which involve pigmentation-related genes, iSAFE identified identical selected mutations in multiple non-African populations suggesting an out-of-Africa onset of selection.

Bio

Mehrdad is a Ph.D. student in computer science at University of California San Diego, under the supervision of Prof. Vineet Bafna. His general research interest is in human genomics, with a focus on finding complex structural variations to improve understanding of human biology and genetics of disease. Mehrdad currently works on developing efficient algorithms to study Variable Number of Tandem Repeats in the human genome, which are known to cause several Mendelian disorders including neuropsychiatric disorders. Using these methods on the large-scale whole genome and exome sequencing datasets reveal disease-causing variants and their likely molecular roles. He has also done work in cancer genomics and selection. Mehrdad received his B.S in Computer Engineering from the University of Tehran (2016), where he worked on RNA structure prediction algorithms as an undergraduate student.