- Title: Assistant Professor, Department of Human Genetics and Center for Computational Medicine and Biology, University of Michigan
- Education: PhD, California Institute of Technology, 1998
- Recommended by: Margit Burmeister
Jun Li’s role in a number of genome-wide association studies in the last few years can be linked directly to a moment of serendipity some years earlier. After completing his undergraduate degree in physics at Beijing University, Li headed to Caltech to earn his PhD in the fields of electrophysiology and biophysics. It was while he was knee-deep in analyzing ion channel data that he realized that a very similar kind of analysis was being conducted in traditional linkage analysis studies in genetics.
That realization altered Li’s course. For his postdoc, he joined the genome center at Stanford to make the transition to human genetics. His years studying with center director Rick Myers — first through his postdoc position and more recently as a research-track scientist — taught him the value of large-scale association studies. Li credits Myers with guiding his scientific interests and providing “the best environment possible” for cutting his teeth in the field.
Now, Li is just starting the first year of a faculty appointment. As an assistant professor at the University of Michigan, he’s building on a longtime collaboration between his new school and his old: Stanford and Michigan have been partners for quite some time in a study on bipolar disorder, Li says. He’ll be continuing that work, as well as pursuing opportunities to join projects for cardiovascular disease and cancer.
Li says he is setting up his lab at Michigan to be half experimental and half computational. “I’ve always been working on the quantitative aspect of data analysis,” he says, “so my skill set is more like applied statistics than a wet lab scientist.” Also on the to-do list: Li will be helping set up the infrastructure for a core facility that will support genotyping, gene expression, and sequencing.
Publications of note
Li was first author on a paper published this September in BMC Genomics (“Sample matching by inferred agonal stress in gene expression analyses of the brain”). The publication, with senior author Rick Myers, describes a new stress rating system to evaluate gene expression patterns in brain samples.
The rating system is meant to help scientists normalize for stress levels so they can more effectively analyze neurological gene expression.
As a scientist involved with a number of genome-wide association studies, Li is no stranger to genotyping technology. It’s still the state of the art for disease association studies, he says. But he believes that will have to change for the field to really advance to a position where it could contribute valuable information to a clinical setting. To that end, he says that next-generation sequencing “is the next frontier.” Ultra-fast and ultra-cheap sequencing would open the doors for massive scans of not just sequence data, he says, but also copy number variation and chromatin immunoprecipitation information to help assess epigenetic states.
And the Nobel goes to...
While Li doesn’t have the Nobel on the brain, he says that his goal is no different than that of thousands of researchers out there: to get a handle on complex diseases. While he predicts that some of these will turn out to “be relatively easy to solve with the current paradigm,” he says that there will also be “difficult diseases … such as psychiatric disorders and hypertension. These are quite dynamic and might be multiple diseases folded into one. One of the holy grails, of course, for many of us is to solve the genetic basis for these particularly complex diseases — and furthermore to make [that] useful in a clinical setting.”