Title: Algorithms for Population Genomics
Speaker: Serafim Batzoglou(associate professor),Stanford University
Time: Mar.23rd 10:00-11:00
Place: Classroom 2208B
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Abstract:
Population genomics, or the study of (large) groups of genomes, is a scientific discipline that is truly enabled by the increasing availability of inexpensive whole genome sequencing. Population genomics encompasses questions that directly relate to our health, population background, family history, and many other key aspects of our lives, so its study will be key in applying genomics technologies to the benefit of society. Algorithmic and statistical advances will be key in enabling this goal, because by its nature population genomics involves handling enormous datasets with complex statistical properties.
In this talk, I will cover our algorithmic work in three different population genomics topics: relationship inference, population ancestry inference, and functional SNP identification. First, I will talk about a new machine learning-based method that we developed for inferring the genealogical relationship between two (or more) individuals. CARROT, a system that we developed based on our method, can identify relationships up to fourth degree. Second, I will highlight some of our recent results on ancestry inference, namely the construction of an accurate and efficient ancestry inference framework, and its application to genotype data sampled from human populations worldwide. Finally, I will shift to genome wide association studies (GWAS); such studies have been successful in identifying SNPs associated with disease. However, associated SNPs are usually p;art of a larger region of linkage disequilibrium (LD), making it hard to identify the SNPs that have a biological link with the disease. I will talk about our methodology towards using ENCODE data to narrow down the lists of SNPs associated with the disease, to a significantly shorter list of "functional SNPs" that are more likely to have a functional effect.
