Lane Center for Computational Biology Seminar
- Gates&Hillman Centers
- KATHRYN ROEDER
- Department of Statistics
- Carnegie Mellon University
Statistics and Genetics Open a Window into Autism
Rare variants identified from DNA sequence, especially de novo loss of function (LoF) mutations, have identified genes involved in risk for autism spectrum disorders (ASD). Multiple de novo LoF mutations in the same gene demonstrate that gene affects risk. De novo mutations occur twofold more often in ASD probands than their siblings, implying that half of the genes hit are risk genes. He et al. (2013) extract more information by using a statistical model, called TADA for Transmission And De novo Association, that integrates data from family and case-control studies to infer the likelihood a gene affects risk. Still, given limited sequence data, can we garner yet more information? Progress has been made as part of a collaborative effort to develop systems biological approaches to understanding ASD pathophysiology. Using ASD risk genes as foci, we hypothesize that genes expressed at the same developmental period and brain region, and with highly correlated co-expression, are functionally interrelated and more likely to affect risk. To find these genes we model two kinds of data: gene co-expression in specific brain regions and periods of development; and the TADA results from published sequencing studies. We model the ensemble data as a Hidden Markov Random Field, in which the graph structure is determined by gene co-expression and the model combines these interrelationships with node-specific observations: gene identity; expression; genetic data; and whether it affects risk, which will be estimated. This analysis identifies ~100 genes that plausibly affect risk, many novel and others implicated despite relatively weak genetic evidence. We will describe how these results can be used to expand our understanding of the genetics of ASD (e.g., nominating genes for targeted sequencing in new samples) and ASD neurobiology.
Kathryn Roeder is Professor of Statistics and Computational Biology. Currently her work focuses on statistical genetics and the genetic basis of complex disease. Her group has published extensively on methods for gene mapping and the genetics of autism. Roeder’s career began in the biological sciences, during which time she spent a year living in the wilderness regions of the Pacific Northwest as a research assistant for the Department of Wildlife Resources. In 1988 she received her Ph.D. in Statistics from Pennsylvania State University. Next she spent 6 years on the Statistics faculty at Yale University where she played a pivotal role developing the foundations of DNA forensic inference. In 1994 Roeder joined the Department of Statistics at Carnegie Mellon University. She has developed statistical methods in a wide spectrum of areas, including high dimensional inference, mixture models and nonparametric statistics. She has served as an associate editor of JASA, Biometrics and American Journal of Human Genetics. She is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics. In 1997 she received the COPSS Presidents award and the Snedecor Award for outstanding work in statistical applications.