Module dates/times: Monday, July 16; 8:30 a.m. -5 p.m.; Tuesday, July 17, 8:30 a.m.-5 p.m., and Wednesday, July 18, 8:30 a.m.-Noon
This module considers the analyses now possible for whole-genome sequence data collected on large numbers of individuals. Specific topics include characterization of de novo mutations and the comparison of growth rates among populations. Also covered, how sequence data allow detailed examination of the signatures of natural selection and methods to compare selective constraints across populations and to seek evidence for recent, population-specific adaptation. The analysis of identity-by-descent segment sharing and random projection for IBD detection (RaPID) to infer demographic history will be covered, as will methods to reconstruct the genetic architecture of major human diseases.
Ryan Hernandez is Associate Professor of Bioengineering and Therapeutic Sciences at the University of California, San Francisco. His research focuses on computational genomics: characterizing patterns of genetic variation within and between populations using large-scale genome resequencing data; developing novel population genetic simulation techniques; and exploiting population genetic models of demographic history and natural selection to interrogate the genetic basis of disease. He recently published “Prominent features of the amino acid mutation landscape in cancer.” PLoS One 12:Article e0183273.
Tim O’Connor is Assistant Professor at the University of Maryland School of Medicine. His research explores the effects of evolution and population structure on the genomic architecture of disease and other phenotypes. He has a track record of developing new algorithms and statistics to interdisciplinary biological problems as well as the use of large multifaceted data sets, particularly the output of next-generation sequenc- ing. He is especially interested in the recent evolution of New World populations such as Hispanic Americans, African Americans, and the Old Order Amish. He recently published “Accurate and equitable medical genomic analysis requires an understanding of demography and its influence on sample size and ratio.” Genome Biology 18:Article 42.
Hernandez and O’Connor recently jointly published “Using genotype array data to compare multi- and single-sample variant calls and improve variant call sets from deep coverage whole-genome sequencing data.” Bioinformatics 33:1147-1153.
Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)