Project Description

Prof. John Quackenbush

Dana-Farber Cancer Institute and
Harvard T.H. Chan School of Public Health, USA

Using networks to link genotype to phenotype

Systems and synthetic biology

Friday 7 July 2017

John Quackenbush received his PhD in theoretical physics from UCLA in 1990. Following a postdoctoral fellowship in physics, he received an NIH Special Emphasis Research Career Award to work on the Human Genome Project. He spent 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 microarray expression technologies and analytical methods. John joined the Dana-Farber Cancer Institute and the Harvard TH Chan School of Public Health in 2005, where he uses computational and systems biology to better understand the complexities of human disease. In 2011 he co-founded Genospace, a precision medicine software company, which was acquired by Hospital Corporation of America in 2017. John has numerous awards, including recognition as a 2014 White House Open Science Champion of Change for working to make genomic data available, useful, and accessible.


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 states.

  1. Wood A.R., et al. (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genetics 46(11):1173-1186.
  2. Locke A.E., et al. (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538):197-206.