Mingyao Li, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania.
Peter Mueller, PhD, Department of Mathematics, Department of Statistics and Data Sciences, University of Texas at Austin
Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison
Snehalata Huzurbazar, PhD, Department of Biostatistics, West Virginia University
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
This week's weekly Biostatistics Seminar will feature Dr. Daniel J. Schaid, Professor of Biostatistics, Health Sciences Research and Medical Genetics, Mayo Clinic, speaking on Genetic analysis of multiple correlated traits (genetic pleiotropy).
Statistical Methods for Genetic Pleiotropy: Sequential Multivariate Tests to DetermineWhich Traits are Associated
The statistical association of a single trait with genetic data has revolutionized human genetics, with many genome-wide association studies providing guidance on genetic factors influencing human health. Pleiotropy – the association of more than one trait with a genetic marker – is believed to be common, yet current multivariate methods do not formally test pleiotropy. Current multivariate methods, such as multivariate regression of multiple traits on a genetic marker, or reverse regression of a genetic marker on multiple traits, test the null hypothesis that no traits are associated with a genetic marker; a statistically significant finding could result from only one trait driving the association. We developed a new formal test of pleiotropy, so that so that rejection of the null hypothesis implies at least two traits are associated with the marker. We further refined our approach to sequentially test the number of associated traits, in order to identify which traits are statistically associated, while accounting for the correlation among the traits. The new methods, with simulations illustrating it properties, will be presented, as well as application to a study of the genetics of response to small pox vaccination. The proposed sequential testing is not limited to genetic data – it can be used for any setting that attempts to evaluate which traits are associated with a predictor variable in a regression setting.
Last Updated On Tuesday, August 30, 2016 by Valenti, Renee Nerozzi
Created On Thursday, July 14, 2016