We, as a society, have made tremendous investments in developing the scientific infrastructure to enable breakthrough discoveries on the primary biological risk factors for common disorders, such as diabetes, asthma, and cardiovascular disease (and related quantitative traits), that account for a disproportionate and growing share of public health care expenditures. While it is clear that the relationships between genetic variation and common disease phenotypes are complex, there is good preliminary evidence that identifying genetic risk factors for common disorders will improve understanding of their etiology and pathogenesis, leading to more specific and effective therapies. Moreover, since virtually all common disease arises as a consequence of the actions and interactions of both genetic and non-genetic risk factors, the identification of genetic risk factors should allow design of epidemiological studies to identify more specific non-genetic risk factors that might be cost-effective targets for prevention of disease. The recent development of high throughput platforms for very large-scale SNP genotyping makes genome-wide association mapping a viable additional tool in the effort to identify genetic risk factors for common disease. Effective use of this tool, however, will require considerable methodological development. Thus, we propose to develop (including software tools and web interfaces) and apply a variety of approaches for facilitating genome-wide association studies, including 1) improved allele/genotype calling algorithms for high-throughput genotyping platforms (to reduce non-random patterns of missing data), and 2) measures of information content for linkage disequilibrium mapping, analogous to those previously developed for linkage mapping. We will utilize these tools, as well as novel analytic approaches that we are developing, to conduct genome-wide association mapping studies on 3) more than 100,000 SNPs typed genome-wide in 350 Mexican Americans with type 2 diabetes and a random sample of 350 Mexican Americans from Starr County, TX; and 4) more than 500,000 SNPs typed in 700 Hutterites with data on a variety of phenotypes related to metabolic, pulmonary and cardiovascular disease. Our proposal builds on long-standing collaborations in methods development and data analysis among members of our research team, which includes human, population, and statistical geneticists and statisticians. ? ? ? ?

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZHG1-HGR-P (J1))
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Gan, Weiniu
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University of Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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