Statistical models for large-scale genomic data
Speaker: Sriram Sankararaman
Date: June 24, 2022, noon
The quest to understand the interplay between evolution, genes and traits has been revolutionized by the collection of rich phenotypic and genetic data across millions of individuals in diverse populations. However analyses of these large-scale datasets present substantial statistical and computational challenges.
I will describe how we bring together statistical and computational insights to design accurate and highly scalable algorithms for a suite of inference problems ranging from estimating fine-scale population structure to dissecting the genetic architecture of complex traits. By applying these methods to about half a million individuals from the UK Biobank, we obtain novel insights into genetic loci under recent positive selection, how genetic effects are distributed across the genome, and the relative contributions of additive, dominance and gene-environment interaction effects to trait variation.
Sriram Sankararaman is an associate professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics and biology. His lab develops machine learning algorithms to analyze genomic data and biomedical data with the broad goal of understanding the interplay between evolution, genomes and traits. He received his undergraduate degree in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow at Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award and fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation and the Simons Institute.