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Computing That Serves

Colloquium: Harvard Medical School

Date: 

Thursday, March 27, 2014 - 11:00am

Speaker: 

Amy Williams

Host: 

Jay McCarthy

Title: Haplotype inference of large datasets and applications to gene conversion and disease studies

27 March 2014

1170 TMCB, 11:00 A.M.

The ongoing explosion of genetic data has enabled wide-ranging discoveries but created computational and analytic challenges. One such challenge is the inference of haplotypes from large scale genotype datasets. I will describe two methods I developed for inferring haplotypes from genotype datasets; One applies to unrelated individuals, one to families. I applied these methods to carry out two biological studies. First, I studied de novo gene conversions in genome-wide pedigree genotype data. This study examined the overall rate of gene conversions, the extent of GC bias, and the localization of such events in the genome. Second, I led a genome-wide association study of type 2 diabetes in 8,214 Mexicans and other Latin Americans. This study discovered a novel T2D susceptibility locus in which the strongest association signal includes four missense variants in the gene SLC16A11. Analysis showed that the haplotype underlying this association introgressed into modern humans from Neanderthals. Thus, the development and application of efficient computational methods enabled fundamental insights into the molecular processes of recombination and the genetic basis of common disease.

Biography: 

Amy Williams received PhD (2010) and SM (2005) degrees in Computer Science from Massachusetts Institute of Technology and two BS (2003) degrees in Computer Science and Mathematics from the University of Utah. From 2009-2013 she worked as a postdoctoral research fellow at Harvard Medical School, jointly advised by David Reich and David Altshuler. Currently she is a postdoctoral research associate at the Howard Hughes Medical Institute, advised by Molly Przeworski. Her research interests span the intersection of computer science and genetics, with a primary focus on using large data sets to study haplotype structure, evolution, and genealogy among individuals




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