Computational and Statistical Genomics Branch (CSGB)

Joan E. Bailey Wilson, Ph.D.

Joan E. Bailey Wilson
Co-Chief & Senior Investigator
Computational and Statistical Genomics Branch

Head
Statistical Genetics Section


B.A. Western Maryland College, 1975
Ph.D. Indiana University, 1981

phone (443) 740-2921
fax (443) 740-2165
e-mail 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 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 to disentangle the roles that genes and environment play in disease causation.

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 has been particularly interested in lung cancer since the early 1980s - a time when very few scientists believed there might be a genetic link to the condition. 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. In a collaboration called the Genetic Epidemiology of Lung Cancer Consortium, Dr. Bailey-Wilson and others recently narrowed down the location of a potential lung-cancer gene to a region of chromosome 6. She and her collaborators are continuing the search for this and other lung cancer susceptibility loci.

Dr. Bailey-Wilson has used similar approaches to locate other cancer-related genes. For example, 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. Dr. Bailey-Wilson is focusing on additional susceptibility genes for these and other cancers in ongoing studies.

To keep pace with the analysis of the exponentially increasing number of genetic markers, Dr. Bailey-Wilson also develops and tests novel computational methods. Until relatively recently, fewer than 100 of these "signposts" along the genome had been identified. Now, there are millions of known markers and genome scientists identify more each day. She is also working to address the issue 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 kb apart display significantly greater linkage disequilibrium than markers that are between 100 to 5,000 kb apart.

Because standard linkage 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 is using association methods that take advantage of linkage disequilibrium data, HapMap data, and the sequence of the human genome to determine the location of genetic loci that increase risk for various diseases. She has used these and other analytical methods to determine, for example, whether alleles at specific marker loci are transmitted along with a disease through generations in families with several affected members. She has also 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 18, 2014