Sponsoring Section/Society: WNAR
Session Slot: 10:30-12:20 Monday
Estimated Audience Size: xx-xxx
AudioVisual Request: xxx
Session Title: Statistical Inference for Genetics
Theme Session: No
Applied Session: No
Session Organizer: Lazzeroni, Laura Stanford University
Address: Department of Statistics Stanford University Stanford, CA 94305
Phone: (415) 723-0947
Fax: (415) 725-8977
Email: laura@stat.stanford.edu
Session Timing: 110 minutes total (Sorry about format):
Opening Remarks by Chair - 5 or 0 minutes First Speaker - 30 minutes (or 25) Second Speaker - 30 minutes Third Speaker - 30 minutes Discussant - 10 minutes (or none) Floor Discusion - 10 minutes (or 5 or 15)
Session Chair: Guerra, Rudy Southern Methodist University
Address: Department of Statistical Science Southern Methodist University 3225 Daniel Avenue Dallas TX 73275-0332
Phone: (214) 768-2770
Fax:
Email: rguerra@mail.smu.edu
1. Evolutionary Graph Methods to Fine-map Disease Genes
Roeder, Kathryn, Carnegie Mellon University
Address: Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213
Phone: 412-268-2717
Fax: 412-268-7828
Email: roeder@stat.cmu.edu
Abstract: Recombinant mapping frequently places the location of a disease locus a few million base pairs. Various methods, all relying on population-level association between the disease locus and adjacent marker-loci, have been proposed to refine or fine-map disease genes. One of these methods uses haplotype data to infer the location of ancestral recombinant breaks. But there are no rigorous analytic techniques underlying this method, in large part because the processes generating the data are complex. We propose analytic methods that rely on graph theory and penalized likelihood to build evolutionary graphs that direct the search for the disease locus. We use evolutionary simulations to demonstrate the strengths and weaknesses of our methods and to draw distinctions between them and the `by-eye' methods currently used for haplotype fine-mapping.
2. Directed Stratgies for Sequencing the Genome
Lazzeroni, Laura, Stanford University
Address: Division of Biostatistics HRP Redwood Bldg., Stanford, CA 94305-4065
Phone: (415) 723-0947
Fax: (415) 725-8977
Email: laura@stat.stanford.edu
Abstract: It is impossible to sequence the entire genome or even a single chromosome using current laboratory techniques. In fact, the read length of a typical DNA sequence extends only a few hundred bases. Shotgun sequencing is one strategy used to cover longer regions. In that approach, the genome is randomly fragmented into shorter pieces that are sequenced and then reassembled into larger units. In contrast, directed sequencing uses various information to select fragments before sequencing them in their entirety. Directed sequencing can eliminate much redundant work and yield a more predictable process for completing a given region. This talk will address issues of probabilistic modeling, statistical estimation and optimization encountered in designing efficient and predictable strategies for directed sequencing.
3. Genetic Linkage Analysis and Change-point Problems
Siegmund, David, Stanford University
Address: Department of Statistics Sequoia Hall, Stanford, CA 94305-4065
Phone: 650-723-0598
Fax: 605-725-8977
Email: dos@stat.stanford.edu
Abstract: The goal of genetic linkage analysis is to locate genes related to particular traits, e.g., genes that may increase human susceptibility to particular diseases or genes that may increase productivity of agriculturally important species. The availability of increasing numbers of informative genetic markers at known locations makes it possible to study larger numbers of traits than was previously possible, and the design and analysis of the the appropriate experiments raise new statistical questions. I will review the genetic background of modern linkage analysis with special emphasis on three statistical problems: (i) multiple comparisons in the detection of linkage; (ii) the power to detect linkage as a function of the true genetic model, sample size, marker density and marker heterozygosity; and (iii) localization of genes by confidence regions. Some of these problems can be understood in terms of recent literature on change-point problems, to which they are closely related. Particular attention will be given to complex and quantitative traits, which one expects to involve multiple, possibly interacting, genes, and to the relative efficiency of different strategies.
Discussant: Guerra, Rudy Southern Methodist University
Address: Dept. of Statistics Southern Methodist University Dallas, TX 75275
Phone:
Fax:
Email:
List of speakers who are nonmembers: None