HomeResearchIntramural ResearchResearch Branches at NHGRIInherited Disease Research Branch Bailey-Wilson Lab

Joan E. Bailey-Wilson, Ph.D. Co-Chief & Senior Investigator Inherited Disease Research Branch Head Statistical Genetics Section B.A. Western Maryland College, 1975 Ph.D. Indiana University, 1981 (410) 550-7509 (410) 550-7513 jebw@nhgri.nih.gov Johns Hopkins University 333 Cassell Dr, Suite 2000 Baltimore, MD 21224 Selected Publications Center for Inherited Disease Research [cidr.jhmi.edu]

Dr. Bailey-Wilson develops new statistical methods and software and performs analyses that guide other genome scientists in their hunt for disease-associated genes. Trained in statistical genetics, she is interested in understanding the genetics of complex diseases and developing novel methodologies that can be used to disentangle the roles that genes and environment play in causing these diseases.

Collaborating with other researchers, Dr. Bailey-Wilson studies a range of diseases, including lung cancer, prostate cancer, breast cancer, myopia and other eye diseases, and cleft lip and palate. She is particularly interested in lung cancer research, a research focus for her since the early 1980s - a time when very few scientists believed there might be a genetic link to lung cancer. Today, significantly more data support the idea that there are susceptibility alleles for one or more unknown genes that dramatically increase certain smokers' risk of developing lung cancer. Dr. Bailey-Wilson, working recently with both NIH and non-NIH scientists in a collaboration called the Genetic Epidemiology of Lung Cancer Consortium, narrowed down the location of a potential lung-cancer gene to a region of chromosome 6.

Dr. Bailey-Wilson and others have used similar approaches to locate other cancer-related genes. For example, in the mid-1990s, she and her collaborators published evidence that genes involved in prostate cancer reside on specific regions of chromosomes 1, 8, and X. These findings have been replicated, and two candidate genes have been cloned: HPC1, which encodes ribonuclease L, and MSRI, which encodes the macrophage scavenger receptor 1. In ongoing studies, Dr. Bailey-Wilson and her collaborators are looking for additional susceptibility genes for these and other cancers.

To keep pace with the analysis of the exponentially increasing number of genetic markers, Dr. Bailey-Wilson also develops novel computational methods. Just a few years ago, less than 100 of these "signposts" along the genome had been identified. Now, there are hundreds of thousands of known markers, and genome scientists identify more each day. Current computer programs cannot handle such large numbers of markers, so Dr. Bailey-Wilson is working with her colleagues to come up with tractable ways of addressing this problem. She also is working to address the problem of linkage disequilibrium, or the nonrandom association of closely spaced loci. Linkage disequilibrium can be caused by a low frequency of recombinations between two loci when they are very close together on a chromosome. The closer two loci are, the more likely they are to exhibit linkage disequilibrium. Thus, markers that are only 100 kilobases apart display significantly greater linkage disequilibrium than markers that are between 100 to 5,000 kilobases apart.

Because standard statistical analysis methods typically assume no linkage disequilibrium exists between loci, Dr. Bailey-Wilson is adapting these methods to study sets of dense genetic markers. She has used these and other analytic methods to determine, for example, whether alleles at specific marker loci are transmitted along with a disease through the generations in families with several affected members. She and her coworkers also have used statistical methods to determine the marker alleles that people with a specific disease carry more frequently — and disease-free people carry less frequently — than can be explained by chance. This work has helped to greatly reduce the number of target regions through which investigators need to search for potential disease-related genes.

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Last Reviewed: May 27, 2008