The Bioinformatics and Computational Biology Graduate Student Organization (BCB GSO) is holding a symposium for faculty and graduate students on April 19th, 2019 from 8:30 am to 4:30 pm at Reiman Gardens. Please REGISTER HERE to attend this event.
The BCB GSO is excited to welcome four speakers to this year’s symposium, including:
Dr. Jennifer Clarke (University of Nebraska-Lincoln)
Dr. Arun Sethuraman (California State University San Marcos)
Dr. Carol Huang (New York University)
Dr. Justin Walley (Iowa State University)
In addition, the symposium will include three talks by graduate students from the BCB program, and a poster session where all graduate students are invited to share their research. This event will be an excellent opportunity to learn about current research in bioinformatics being carried out both here at Iowa State University and elsewhere, and to network with faculty and graduate students with a diverse range of expertise in the field.
8:30 am - 9:00 am Breakfast
9:00 am - 9:15 am Welcome and Opening Remarks (Basil Khuder)
9:15 am - 10:05 am Dr. Jennifer Clarke (Introduced by Paul Villanueva)
10:10 am - 11:00 am Dr. Arun Sethuraman (Introduced by Natalia Acevedo-Luna)
11:00 am - 12:00 pm Poster Session (Posters located in the main hallway)
12:00 pm - 1:00 pm Lunch
1:00 pm - 1:50 pm Dr. Carol Huang (Introduced by Zach Lozier)
1:55 pm - 2:45 pm Dr. Justin Walley (Introduced by Valeria Velasquez Zapata)
2:45 pm - 3:00 pm Break
3:00 pm - 4:00 pm Student Speakers (Weijia Su, Vishnu Ramasubramanian, Xiyu Peng)
4:00 pm - 4:15 pm Closing Remarks (BCB DOGE - Dr. Carolyn Lawrence-Dill)
Speaker: Dr. Arun Sethuraman, PhD, Assistant Professor of Population Genetics, Department of Biological Sciences, California State University San Marcos, San Marcos, CA
PhD in Bioinformatics and Computational Biology (BCB), Minor in Interdepartmental Genetics (IG), 2013
PhD Advisors: Dr. Fredric Janzen (EEOB), Dr. Karin Dorman (Statistics, GDCB)
Title: Genomic Islands and Castaways - Model-based assessments of differential introgression during divergence and speciation
Demographic changes such as fluctuating population sizes, differential migration (or gene flow) can confound natural selection, and affect rates of genome evolution, local adaptation, reproductive isolation, and eventual speciation. Besides identifying differentially migrating genes (and genomic regions) that are ``labeled'' to be retroactively causal to adaptive evolution and speciation, there is significant impetus to understand, and perhaps estimate the underlying demography that affects current genomic diversity. Studies of both targeted genomic sequences, and whole genomes, often assess divergence as measured by either the absolute number of mutations accumulated between diverged populations, or as the relative difference in allele frequencies (or heterozygosities) between populations (eg. Fst). However, none of these methods help disentangle the effects of demography from migration and/or selection. Using model-based likelihood methods to directly estimate, and decouple the effects of differential migration and demography across genomic loci offers an ideal solution to detect differential introgression, and population demography and build hypotheses around its underlying evolutionary processes. I describe a computationally efficient parallelized implementation of mixture-model based isolation with migration (IM) analysis to assign loci to classes based on their shared coalescent histories (population sizes, or migration rates). I apply this method to several genomic data sets (great apes - chimpanzees and bonobos, Anopheles mosquitoes, threespine sticklebacks, Mullerian mimics of Heliconius butterflies, mice, European rabbits, and fruitflies), that have been previously characterized (perhaps erroneously) using genome-wide scans of differentiation. I then apply this method to identify and classify regions of the human genome from Europe and Asia that have differentially introgressed from Neanderthals. By comparison with two commonly used methods (Fst, Dxy), I show conclusively that this method is informative of both identifying genomic regions of differential demography, and of building hypotheses on the forms, and effects of natural selection acting on loci in question.