IMS
Session Slot: 8:30-10:20 Wednesday
Estimated Audience Size: 125-175
AudioVisual Request: None
Session Title: Statistics and Genetics
Theme Session: No
Applied Session: Yes
Session Organizer: McPeek, Mary Sara Department of Statistics, University of Chicago
Address: Department of Statistics, 5734 S. University Ave., Chicago, IL 60637
Phone: 773-702-7554
Fax: 773-702-9810
Email: mcpeek@galton.uchicago.edu
Session Timing: 110 minutes total (Sorry about format):
Opening Remarks by Chair - 0 minutes First Speaker - 35 minutes Second Speaker - 35 minutes Third Speaker - 35 minutes Discussant - none Floor Discusion - 5 minutes
Session Chair: McPeek, Mary Sara Department of Statistics, University of Chicago
Address: Department of Statistics, 5734 S. University Ave., Chicago, IL 60637
Phone: 773-702-7554
Fax: 773-702-9810
Email: mcpeek@galton.uchicago.edu
1. Mapping disease genes using distantly related individuals
Feingold, Eleanor, Department of Human Genetics, University of Pittsburgh
Address: Department of Human Genetics, 130 DeSoto Street, Crabtree Hall A310, Pittsburgh, PA 15260
Phone: 412-383-8599
Fax: 412-624-3020
Email: efein@helix.hgen.pitt.edu
Durham, L. Kathryn, Imperial Cancer Research Fund, U.K.
Abstract: One standard method for mapping human disease genes is to collect families in which two or more members are affected with the disease, and search for regions where the affected relatives share genetic material (identity by descent). More recently, success has been seen with "linkage disequlibrium" mapping, which uses putatively unrelated individuals from a population. Both of these methods have been studied from a statistical point of view, in differing degrees of depth. We consider the statistical problems that arise in an intermediate situation: a study that searches for shared genome segments among distant relatives, whose exact relationship may be known or unknown.
2. Bayesian haplotype analysis for linkage disequilibrium
Liu, Jun, Department of Statistics, Stanford University
Address: Department of Statistics, Stanford University, Stanford, CA 94305
Phone: 650-723-2623
Fax: 650-725-8977
Email: jliu@stat.stanford.edu
Teng, Jun, Stanford University
Risch, Neil, Stanford University
Abstract: Haplotype analysis of disease chromosomes can help identify probable historical recombination events and consequently localize the disease gene. Available analyses only use marginal and pairwise allele frequency information. In this talk we propose a Bayesian framework that helps in utilizing full haplotype information overcoming various complications such as missing data, multiple ancestral clusters, and possible data contaminations.
3. Assessing linkage disequilibrium by the decay-of-haplotype-sharing method
McPeek, Mary Sara, Department of Statistics, University of Chicago
Address: Department of Statistics, 5734 S. University Ave., Chicago, IL 60637
Phone: 773-702-7554
Fax: 773-702-9810
Email: mcpeek@galton.uchicago.edu
Strahs, Andrew, University of Chicago
DiRienzo, Anna, University of Chicago
Hall, Diana, University of Chicago
Abstract: The extent of linkage disequilibrium in a genomic region for a given population reflects such factors as isolation of the population, historical changes in population size, and natural selection at the locus. Populations which tend to show a high degree of linkage disequilibrium are expected to be particularly useful for genetic mapping. Most previous measures of linkage disequilbrium have been based on pairwise locus data. We have developed a method for assessing linkage disequilibrium based on analysis of multilocus haplotypes. The method models the rate of decrease of haplotype sharing with increasing distance. In addition to measuring linkage disequilibrium, it also has the potential to detect genes by population association. We have applied the method to compare the extent of linkage disequilibrium in two genomic regions across several populations, including Italians, Ashkenazim, Hutterites, African Americans, Gambians, and Bamileke. The results are directly applicable to development of strategies for mapping complex traits in various human populations.
List of speakers who are nonmembers: None