Population and medical genetic inference using Biobank-scale statistical methods

Seminar Series

Tuesday, March 30, 2021 - 01:00
Zoom Conference

Abstract: The quest to understand the interplay between evolution, genes and traits has been revolutionized by the collection of rich phenotypic and genetic data across hundreds of thousands of individuals in diverse populations. However analyses of these Biobank-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 complex traits and diseases.

Speaker: Sriram Sankararaman is an assistant 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 clinical 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 postdoctoral 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.

 

Zoom: https://duke.zoom.us/j/96601032370?pwd=WGVhS0wzVnNidTlQSUcwaEw0WEhmUT09

Passcode: 649659