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Dr. Nilah Ioannidis, Departments of Biomedical Data Science and Genetics, Stanford University: Computational methods for interpreting genetic variation of unknown significance

Seminar | February 13 | 4-5 p.m. | Soda Hall, HP Auditorium 306

Center for Computational Biology, Electrical Engineering and Computer Sciences (EECS)

Understanding the clinical significance of personal genome variation is a major challenge for personalized medicine, with large numbers of variants of unknown significance discovered in next-generation sequencing studies. I will first discuss two machine learning tools that we recently developed to predict the clinical significance of individual genetic variants. REVEL is a random forest that predicts the pathogenicity of missense variants, with an emphasis on rare missense variants found in whole exome and whole genome sequencing studies. FIRE is a set of random forests that predicts whether single nucleotide variants, including both coding and noncoding variants, are likely to regulate the expression levels of nearby genes. I will also discuss the complementary approach of analyzing combinations of genetic variants to predict their collective effects on complex traits and intermediary phenotypes. By predicting personal gene expression levels from personal genetic variation using recently proposed linear regression models, we conducted a transcriptome-wide association study of cutaneous squamous cell carcinoma, a common form of skin cancer, to identify genes whose predicted expression levels are associated with disease risk. My ongoing work focuses on developing improved methods for predicting the effects of personal genome variation on gene expression patterns and linking them to downstream effects on diseases and other complex traits.

Dr. Ioannidis is a postdoctoral scholar in the Biomedical Data Science and Genetics departments at Stanford University, where she works on computational and statistical methods for interpreting personal genomes. During her PhD in Biophysics at Harvard University, she worked in the Biological Engineering department at MIT and developed methods to analyze the dynamics of intracellular particles using hidden Markov modeling and Bayesian inference. She previously served as Research Director at the Jain Foundation, studying the rare genetic disease dysferlinopathy, a form of limb-girdle muscular dystrophy. She also has an MPhil in Chemistry from the University of Cambridge and a BA in Biochemical Sciences from Harvard.