November 27, 2012 — What does genome-wide analysis reveal about complex human traits? What’s the best way to design and analyze gene-sequencing studies?
These were some of the questions addressed at the sixth annual PQG (Program in Quantitative Genomics) Conference, held November 15-16, 2012. The event, sponsored jointly by the Harvard School of Public Health (HSPH) Department of Biostatistics and the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute, drew roughly 130 participants from around the world.
Attendees at the conference, held at the Joseph B. Martin Conference Center at Harvard Medical School and titled “Sequencing and Complex Traits: Beyond 1000 Genomes,” were an interdisciplinary group of scientists including statistical geneticists, genetic epidemiologists, and clinicians.
The two-day conference featured 18 speakers, including Magnus Nordborg, director of the Gregor Mendel Institute in Vienna, Austria, and Jian Yang from the University of Queensland in Brisbane, Australia, who was recently awarded the Lawrence Creative Prize by the Centenary Institute in Sydney.
The conference addressed a host of issues, including:
• How new genome sequencing technologies offer the potential to deliver increasingly precise insights into the biological causes of disease, which could in turn lead to possibly preventing diseases or even reversing their root causes
• A new method for DNA sequencing that costs substantially less than current costs and would make it much more practical to study thousands of samples
• A new, very fast method for conducting genome-wide association studies
• Better, more finely-turned sequencing methods for identifying the particular genes that put people at risk for ailments such as schizophrenia, type 2 diabetes, and heart attack
• Novel statistical methods to estimate an individual’s ancestry—knowledge that is important for human genetic research
• Analysis that sheds light on how the genomes of Neanderthals have shaped the genetic structure of modern human populations