New Methods for Genomic Studies in African American Women
| Institution: | University of Southern California | ||
| Investigator(s): |
Daniel Stram , Ph.D. -
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| Award Cycle: | 2009 (Cycle 15) | Grant #: 15UB-8402 | Award: $442,631 |
| Award Type: | SRI Request for Proposal (RFP) | ||
| Research Priorities | |||
| Prevention & Risk Reduction>The intersection of environment and disparities | |||
Initial Award Abstract (2009)
Introduction: Genome-wide association scan (GWAS) studies identify which genes are associated with disease by looking at hundreds of thousands genetic markers (polymorphisms) in large numbers of people with and without disease. GWAS studies of breast cancer have already yielded important information regarding a woman’s inherited risk of breast cancer; however, much about disease susceptibility and about ethnic or racial disparities in the burden of disease is still to be learned. Now for the first time a GWAS study is producing data for African American women with breast cancer: the African American Breast Cancer (AABC) study. This US-wide consortium of studies of African American breast cancer cases and controls represents a large investment of money and other resources. Over 1 million genetic markers are being analyzed for these study participants in order to find genes associated with increased risk of breast cancer in African American women.
Detailed studies of the genetics of breast cancer among African American women are necessary as a part of the effort to fully understand breast cancer pathways and biology as well as to understand racial/ethnic disparities in this disease. Further development of statistical methods to address the complex data coming from the AABC study is necessary to obtain maximum scientific value from the important investment that this study represents.
Hypotheses: New statistical methods and techniques can be applied to the AABC data to better understand the relationship between disease risk and over 1 million measurements of genetic variation for each study participant.
General methodology: We will develop statistical methods, and apply them along with other new and established methods to analyze the AABC study data. We will conduct computer-based realistic simulation studies (based on evolutionary simulation techniques) to determine which of the statistical techniques can be expected to give the best results with real data. The analysis of the AABC data will also attempt to find new disease associations that help understand differences in individual, genetic susceptibility to breast cancer.
Innovative elements: GWAS studies have engendered much novel statistical research into how best to analyze the resulting data to detect disease associations. We will develop and evaluate novel methods and compare these to other methods, including some that are newly proposed but not yet fully explored.
Symposium Abstract (2010)
A great deal of research has focused on how genetics affect disease susceptibility, including breast cancer. Many studies collect genetic data to compare people with a disease (cases) and those free of that disease (controls). After such a study is completed, a great deal of data has been generated which raises the question: can we reuse genetic data from participants genotyped as controls from other studies, or does each new study needs its own controls?
Given the huge investments made recently in large scale genotyping of cases and controls for various diseases, the ability to leverage existing data would represent an enormous savings of money and time. We are studying whether studies where cases and controls are sampled differently will give correct answers and are as powerful statistically as when new control data is also genotyped. This question is especially important in understanding the genetic causes of disease in as yet relatively understudied population groups, such as African Americans, in order to speed up progress as much as possible.
We give theoretical results about the power of studies that reuse existing control genotypes based on statistical considerations. We also provide analysis of real data from a major study of the genetic causes of breast cancer in African American women in order to shed practical light upon this issue.
