Fuli:Research

Research interests My research interest lies in utilizing large-scale genomics technologies, and computational and statistical tools to systematically study medical and population genetics/genomics. In medical genetics, I am particularly interested in understanding the genetic etiology of common complex human diseases. In population genetics, I am interested in studying the human evolutionary history indicated by genetic signatures in the human genome. Development of 1000Genomes data processing tools Fuli Yu et al. in collaboration with Jeff Reid, Cristi Coarfa and Rui Chen Reliable identification of genetic variants in re-sequencing data is the essential goal of the 1000 Genomes Project and is particular crucial in the exon-region sequencing endeavor. In the full-scale 1,000 Genomes Project, it is planned that the exonic regions will be sequenced at a much higher coverage than the rest of the genome. This presents a unique opportunity and challenge for bioinformatics pipeline development, as there is yet no software designed specifically for processing high coverage targeted sequencing data. Building on our extensive experience in analyzing the high coverage data from the 1000 Genomes Pilot 3 project, we aim to develop an integrated data processing pipeline and to develop a set of metrics in order to identify genomic variations for downstream analysis. Medical genetics of common complex diseases Common complex diseases such as cardiovascular disease, cerebrovascular disease, cancer, and diabetes account for most of the mortalities and morbidities in modern societies. Studies suggested strong influence of genetic variants in disease susceptibilities. Recent advances by large-scale association studies have uncovered many underlying predisposed regions for most of the common diseases. These efforts were paving the way to precisely pinpoint the causal genetic variants and understand the pathogenesis of complex diseases. Population genomics—Signature of recent positive selection in the human genome in collaboration with Yunxin Fu at UTH-SPH In modern terms, natural selection operates on genetic variations, which provide both evidences to support the mechanism of natural selection and the materials for it to act upon. The selection pressure interacts with individual phenotypes, but ultimately the objects of selection exist within the DNA variations. Natural selection has played an enormous role in all aspects of biology. The interplay between the environment, and the phenotypes and genotypes of organisms has increased the complexity of biology. More intriguingly, the loci under natural selection are functionally important and relevant to disease studies. Differences in selective pressures that challenged human populations left different signatures in the functionally important loci of the human genome. Therefore, a new approach to localize disease genes is to explore these evolutionarily selected loci and the underlying alleles in normal populations. Press Consortium Reports on HapMap 3 Findings 2010-09-02 GenomeWeb NEW YORK (GenomeWeb News) – Members of the International HapMap 3 Consortium reported in Nature today that they have used a combination of genotyping and targeted re-sequencing to catalog common and rare genetic variants in 11 populations from around the world. "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 2010-09-02 BCM ..."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... 1000 Genomes project releases pilot data 2010-06-21 BCM 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. ... Atlas-SNP2 2009-12-23 GenomeWeb 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. ...

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