Alexander Wilson, Ph.D.
Computational and Statistical Genomics Branch
B.A. Western Maryland College, 1975
Ph.D. Indiana University, 1980
333 CASSELL DR, Suite 2000
BALTIMORE, MD 21224
Dr. Wilson's research group (the Genometrics Section) uses approaches from genetics, bioinformatics, statistics and computer science to develop new methodologies for performing statistical genetic analysis. They apply these methods to data from collaborative studies. By analyzing the patterns of millions of genetic sequence variants, the Genometrics Section identifies chromosomal regions, genes and variants that are responsible for variation at these traits and measures their effects. Dr. Wilson's research covers a wide range of disorders, from scoliosis (extreme curvature of the spine) and craniosynostosis to obesity and cardiovascular disease.
Dr. Wilson developed the Genometric Analysis Simulation Program (GASP), which enabled scientists to create artificial populations or families with different mixtures of known genetic and environmental influenced diseases. Statistical geneticists can use GASP to try out new analytical approaches. Investigators at more than 70 institutions in at least 14 countries have used GASP to test new methodologies and as a teaching tool. Dr. Wilson also helps to develop new methodologies to bolster statistical geneticists' toolkits. He combined a traditional test of heritability with a linear regression method in a way that simplifies and significantly reduces the cost of testing for the heritability of quantitative traits (ROMP). Recent theoretical efforts have focused on the development of linear regression methods that use multiple and/or stepwise regression in regions that are bounded by recombination hotspots (areas of increased recombination) over the entire genome. This tiled regression approach (TRAP) is being used to test for trait-marker associations in genome-wide association studies and in next-generation sequence data. This approach can be used to identify the set of variants that best influence the trait in a genome-wide context, considering all the variants in the genome simultaneously.
Dr. Alexander F. Wilson graduated from McDaniel College, magna cum laude, with a B.A. in biology in 1975, and received the H.P. Studivant Award as the Outstanding Biology Major. He received his Ph.D. in medical genetics from Indiana University under the direction of P. Michael Conneally, Ph.D., (1980) and did his postdoctoral training in statistical genetics with Robert C. Elston, Ph.D., in the Department of Biometry, Louisiana State University Medical Center (1980-1982). He remained at Louisiana State University, rising to the rank of tenured Full Professor in 1993. He was recruited to the National Human Genome Research Institute in 1995.
He is a senior investigator and the head of the Genometrics Section, and co-chief of the Computational and Statistical Genomics Branch, at the National Human Genome Research Institute, NIH, and an adjunct professor, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health. Dr. Wilson is an active member of the American Society of Human Genetics (ASHG) and the International Genetic Epidemiology Society (IGES). He has served on the ASHG Program Committee (2010-2013), on the IGES Board of Directors, and is currently the President of IGES.
He has been a member of or has directed the dissertation committees of 16 students and has trained over a dozen post-doctoral students and visiting faculty. He has received numerous awards, including the Indiana University School of Medicine Department of Medical Genetics Distinguished Alumnus Award, the Western Maryland College Trustee Alumni Award, the NIH Director's Award and induction into Phi Beta Kappa as an alumni member.
His research interests focus on the identification of genetic effects that may be responsible for phenotypic variation in quantitative traits (e.g., traits related to cardiovascular disease and scoliosis), the coding and non-coding elements that may be responsible for their expression, and the investigation of the statistical properties of newly developed methods of genetic analysis for quantitative traits.
Last Updated: January 6, 2015