Statistical Challenges in the Analyses of Genome-Wide Genetic Data

Speaker: Prof. Sriram Sankararaman
Affiliation: University of California Los Angeles

Abstract:  Advances in DNA sequencing are generating large quantities of genomic data that could allow us to answer fundamental questions in biology and medicine. One class of genomic studies attempt to correlate variation in traits (such as diseases) with variation in genes across large numbers of individuals. These studies hold the promise of discovering genetic variants associated with diseases as well as predicting disease risk based on individual genomes. The high-dimensionality and the heterogeneous nature of the data, however, leads to a number of statistical as well as computational challenges. I will describe two important challenges. The first arises from the hidden genetic structure of human populations that can lead to the inference of spurious associations between genetic variants and disease. I will describe latent variable models that can infer this hidden structure and show how these inferences lead to novel insights into the genetics of diseases. A second challenge is the problem of preserving the privacy of individuals that participate in these studies. I will talk about a technique for breaching privacy (by detecting the participation of individuals from genome-wide summary statistics) as well as our attempts to characterize the statistical limits of such attacks that allow us to determine how such data can be safely released.

Biography: Sriram Sankararaman is an Assistant Professor in the Departments of Computer Science and Human Genetics at UCLA. His research interests lie at the interface of computer science, statistics and biology. He is interested in developing statistical machine learning algorithms to understand evolutionary processes and the genetics of complex phenotypes. He received a Ph.D. in Computer Science from U C Berkeley (2010) supervised by Michael Jordan and Kimmen Sjolander and was a post-doctoral fellow in the Department of Genetics, Harvard Medical School before joining UCLA. He is the recipient of a NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), a Harvard Science of the Human Past fellowship (2012) and a Berkeley fellowship (2004).

For more information contact Professors Suhas Diggavi & Mani Srivastava


Date(s) - Mar 13, 2017
12:30 pm - 1:30 pm

EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095