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14 December 2018

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Heart + Lung Health FEST 2016

Alan Bernstein Distinguished Lecture Recipient

About Dr. John Quackenbush


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Dr. John Quackenbush received his PhD in theoretical physics from UCLA in 1990. Following a physics postdoc, he received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project, spending two years at the Salk Institute and two years at Stanford University working in genomics and computational biology. In 1997 he moved to The Institute for Genomic Research (TIGR), pioneering expression analysis. He joined the Dana-Farber Cancer Institute and the Harvard School of Public Health in 2005. In 2012, he and Mick Correll founded Genospace, a precision medicine software company.


Lecture Abstract: Using Networks to Re-examine the Genome-Phenome Connection


Dana-Farber Cancer Institute and Harvard TH Chan School of Public Health The problem with genome-wide association studies (GWAS) is dramatically illustrated in two recent publications. The first analyzed data from 253,288 individuals and found that 697 single nucleotide polymorphisms (SNPs) could explain about 20% of human height variability, but approximately 9,500 SNPs were needed to raise that to 29% [1]. The second surveyed 339,224 individuals and identified 97 loci that can account for 2.7% of body mass index (BMI) variation [2]. These and other similar results leave little hope that using standard GWAS studies surveying millions of genetic variants across ever larger populations, will lead us to identify the genetic factors driving complex traits. As an alternative, we have developed a revolutionary new way of exploring and exploiting the structure of expression Quantitative Trait Loci (eQTL) networks to explain how weak effect SNPs can combine to drive biological processes and to identify those SNPs most likely to perturb cellular function. As a way of bridging the bat between SNPs and phenotype, we will also explore modeling of gene regulatory networks and methods that can help us model regulation in individuals as well as transitions between phenotypic