Family-mapping studies are often able to locate a disease gene within an area of 5 cM, but such areas may contain 50+ genes. Methods based on linkage disequilibrium (LD) in population data have the potential to pinpoint disease-associated genes. This proposal begins with an existing LD mapping algorithm which is computationally limited to areas of 0.5 cM or less, and develops three approaches to making it usable for human disease-mapping studies. (1) Simplify the model of recombination, tracking fewer recombinations by disregarding fine recombinational structure between adjacent SNPs. (2) Construct the map in overlapping windows along the chromosome, rather than attempting to analyze the entire region simultaneously. (3) Pre-compute the ancient genealogical relationships for a region of the genome (one of the ENCODE regions will be used as a proof of concept). Essentially all modern samples will share portions of their deep genealogy;pre-computation will greatly reduce the redundant work done by different groups seeking disease loci in the same chromosomal region. This proposal will transform a powerful but computationally expensive mapping algorithm into one of practical use. Finding the specific genes which contribute to development of a disease is important in diagnosis, understanding, and treatment. Diagnostic tests built on a rough idea of a disease gene's location often work only in the ethnicity for which they were developed;tests informed by the actual causative gene or genes can work in all populations. Knowledge of the causative genes can also illuminate the mechanisms of disease and provide targets for treatment design. Public Health Relevance: Finding the precise gene or genes contributing to a human disease is important for diagnosis, understanding, and treatment. Family-based studies often identify a large chromosomal region containing 50+ genes;population-based studies are needed to narrow the location further. This proposal will extend a fine-scale gene-location algorithm based on population data so that it can be used in larger studies and across wider areas of uncertainty.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG004839-01
Application #
7561283
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Brooks, Lisa
Project Start
2009-07-20
Project End
2012-04-30
Budget Start
2009-07-20
Budget End
2010-04-30
Support Year
1
Fiscal Year
2009
Total Cost
$425,000
Indirect Cost
Name
University of Washington
Department
Genetics
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
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