FW: April 4th 2013 Broad Institute Distinguished Lecturer in Computational Biology

[Wang, Xuefeng]

Anyone going?

 

 

From: McDonald, Jill
Sent: Wednesday, April 03, 2013 9:53 AM
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Subject: FW: April 4th 2013 Broad Institute Distinguished Lecturer in Computational Biology

 

 

From: bist...@broadinstitute.org [mailto:bist...@broadinstitute.org]
Sent: Wednesday, April 03, 2013 9:37 AM
To: ta...@broadinstitute.org; cb...@broadinstitute.org; cbbo...@broadinstitute.org
Subject: 2013 Broad Institute Distinguished Lecturer in Computational Biology

 

April 4, 2013

4:00 PM - 5:30 PM

Auditorium, 7CC Lobby

3pm: Reception in Lobby 4pm: Lecture in Auditorium

2013 Broad Institute Distinguished Lecturer in Computational Biology

Speaker: Daphne Koller, PhD
Affiliation: Professor, Computer Science Department, Stanford University
Title: Genetics of complex traits: combining genomics and probabilistic models
Abstract:
Recent technological advances have allowed us to collect genomic data on an unprecedented scale, with the promise of revealing genetic variants, genes, and pathways disrupted in clinically relevant human phenotypes. However, identifying functional variants and ultimately unraveling the genetics of complex traits from such data have presented significant challenges. With millions of genetic factors to consider, spurious associations and lack of statistical power are major hurdles. Further, we cannot easily assess the functional role of trait-associated variants, particularly for those that lie outside of protein-coding regions of the genome.
In this talk, I will present two complementary approaches that offer improvements in the analysis of genetic variation in complex traits. First, I will discuss the direct identification of functional variants on a large scale through the use of gene expression as a high-resolution cellular phenotype. We have sequenced RNA from 922 genotyped individuals to provide a direct window into the distribution, properties, and consequences of thousands of regulatory variants affecting diverse gene expression traits including splicing and allelic expression. Second, I will discuss the use of structured probabilistic models to integrate diverse sources of data, including genomic annotations and gene network information, into models of genetic variation in both transcriptional and higher-level disease traits. By considering the biological and cellular processes underlying the genetics of complex traits, these models significantly improve our power to identify the full range of relevant variants and their interactions, especially those with smaller effect sizes. Together, these two approaches offer the potential to greatly improve our understanding of human genetic variation and its role in disease risk.