Statistical Methods for Identity-by-Descent Maps

Stanford University, Stanford CA

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Abstract

[unreadable] DESCRIPTION (provided by applicant): Mapping genes associated with specific traits in human, plant and animal populations can be an important step in identifying the genes responsible for inherited diseases or in producing a better food supply. The aim of the proposed research is to develop and evaluate statistical methods to aid in the design and analysis of gene mapping studies in both human and experimental genetics, particularly when the whole genome or a portion is scanned to search for the relevant genes. The proposed statistical methodology is appropriate to the analysis of data obtained from any highly polymorphic, reasonably dense genetic map and can be applied to discrete, ordinal, or quantitative traits. Specific long term goals are the following: (1) Develop and evaluate general methodology for searching the genome to identify regions likely to contain genes affecting the trait or traits of interest. (2) Develop general, flexible models of multigenic traits, which allow gene x gene and gene x environment interactions; develop and compare statistical methods to study those models and for model selection. (3) Implement these methods in computer routines, concentrating on quantitative traits that are studied in moderately large, population based, multigenerational pedigrees. Results will be obtained by mathematical analysis and computer simulation, while analysis of experimental data will serve to validate and refine the models. The distinguishing features of the proposal are (i) systematic consideration of the effect of scanning markers distributed throughout the genome and (ii) exploitation of the relation between the statistics of gene map- ping problems and of "change-point" problems, which have been thoroughly studied in the recent statistical research. [unreadable] [unreadable] [unreadable]