Rapid technological advances mean that the data used in human gene-mapping is changing rapidly. Technologies that allow 100,000s to 1,000,000s of SNPs to be characterized in each individual are now widely used, and whole genome re-sequencing technologies are emerging. Extracting the full benefits of these sequencing technologies will require new analytical models and tools, both because (a) approaches and implementations designed to handle more modest amounts of data can not always handle high-throughput data or provide only cumbersome ways for doing so and because (b) the nature of the data generated by re-sequencing studies will often by quite different from that generated by more conventional genotyping technologies. Here, we propose to develop statistical methods and software tools that can handle high-throughput re- sequencing data, including results from shotgun re-sequencing of whole genomes or candidate regions. Our proposed methods and tools will aid the analysis both of case-control samples and of mixed samples of pedigrees and unrelated individuals. We also propose to conduct simulation experiments to develop guidelines on how these new sequencing technologies can be deployed effectively in gene-mapping studies. We hope they will allow investigators to extract the benefits of new high-throughput data-sources in the genetic dissection of complex traits. Rapid technological advances mean the data used in human gene mapping is changing rapidly. New technologies for analyzing genetic variation on a large scale are being deployed in the dissection of complex multi-factorial traits such as asthma, diabetes, and obesity. Here, we propose to develop efficient methods for analyzing high-throughput sequence data and to distribute software tools that allow others to extract maximum benefit from our methods.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH084698-02
Application #
7691827
Study Section
Special Emphasis Panel (ZMH1-ERB-C (06))
Program Officer
Yao, Yin Y
Project Start
2008-09-25
Project End
2011-06-30
Budget Start
2009-07-01
Budget End
2010-06-30
Support Year
2
Fiscal Year
2009
Total Cost
$371,585
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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