My research interest lies in utilizing large-scale genomics technologies, and computational and statistical tools to systematically study medical and population genetics/genomics.
1. We have developed widely used software tools (Atlas2 and SNPTools) for variant discovery in next-generation sequencing (NGS) data, and genotype/haplotype inference in large cohorts. We have also showcased the utilities of marrying the Cloud computing as infrastructure with the computational genomics to effectively address the data deluge challenge in the genomics field. We are very actively pursuing innovative algorithm development to address the NGS variant analysis issues.
2. My lab is driving the variant analysis in the CHARGE large-cohort consortium. CHARGE is aimed at the discovery of causal mutations for common complex traits. More than tens of thousands of study participants have been sequenced. This opens many new opportunities to reveal the genetic etiology of common complex human diseases. It also provides a perfect example for other projects for (a) tackling the informatics 'heavy-lifting'; (b) method development for associations; (c) variant prioritization. Meanwhile, the data produced from CHARGE can be informative for medical genetics and clinical diagnostics.
3. My lab is deeply interested in research topics in population genomics and applying popgen principles into disease genetics studies. We have a number of ongoing project concerning population genomics in both humans and other species.
4. We have also recently become intrigued by the Big Data development across the board. We are witnessing a rapid growth in both the underlying know-how and seemingly unlimited applications.
-BCM From the Labs covered the HapMap3
-Consortium Reports on HapMap 3 Findings
"In this paper, we really have an integrative analysis," co-author Fuli Yu, a researcher with Baylor College of Medicine's Human Genome Sequencing Center, told GenomeWeb Daily News. "We have both common and rare (very low frequency) genetic variation in this study." ... The HapMap 3 effort was led by Richard Gibbs, director of the BCM Human Genome Sequencing Center, David Altshuler, a geneticist at Harvard University and director of the Broad Institute's Program in Medical and Population Genetics, and the late Leena Peltonen, who was head of human genetics at the Wellcome Trust Sanger Institute.
"Despite the remarkable achievements following from the Human Genome Project, our knowledge of human genetic variation remains limited," Gibbs said in a statement. "Here we have studied more populations and were able to include [copy number polymorphisms] in genome-wide studies."
-HapMap 3 global in description of human variation
..."This map provides an important tool for future genome-wide association studies of diseases that allows scientists to look for both common and rare variations that may be associated with disease or response to drugs," said Dr. Fuli Yu, assistant professor in the Baylor College of Medicine Human Genome Sequencing Center. The Center played a major role in the sequencing studies that are cornerstones of the report that appears in the current issue of the journal Nature...
HOUSTON -- (June 21, 2010) -- The completion of three pilot projects designed to determine how best to build an extremely detailed map of human genetic variation begins a new chapter in the international project called 1,000 Genomes, said the director of the Baylor College of Medicine Human Genome Sequencing Center, a major contributor to the effort.
"Mapping all the shared normal variation in human populations is a critical step to interpreting medically actionable genetic changes," said Dr. Richard Gibbs, also a professor in the department of molecular and human genetics at BCM.
... "We also developed new methods to target variation in genes, and showed that this approach gave maximum information about this important class of human variation", said Dr. Fuli Yu, an assistant professor in the BCM Human Genome Sequencing Center and coordinator of the study. ...
This Week in Genome Research December 23, 2009
... Meanwhile, a group of researchers from the Baylor College of Medicine, Rice University, and Washington University report that they have come up with a way to sift through large amounts of high-throughput re-sequencing data and pick out genetic variants without getting duped by sequencing errors. Their computational tool — called Atlas-SNP2 — takes into account sequence context in training datasets to help distinguish between errors and authentic SNPs with a less than 10 percent false-positive error rate and a false-negative error rate of five percent or so. ...