The overall goal of our research is to understand how genetic variation contributes to the etiology of complex diseases. Current projects include:
Psychiatric genetics
We have been involved for several years in gene expression studies of bipolar disorder, major depression, and schizophrenia (see below) and have worked on genetic association studies of candidate genes for autism. Currently we are part of a collaborative effort to perform a genomewide association study of bipolar disorder, in which we analyzed 1200 cases and 800 controls across 550K SNP loci. Beyond the first-pass analysis of single-marker association we are actively working on several related aspects: replication in other cohorts, control of population stratification, and analysis of copy number variation. This project involves Dr. Richard Myers group at Stanford and a University of Michigan team led by Drs. Mike Boehnke, Huda Akil, Stan Watson, and Margit Burmeister.
Solexa/454 sequencing
Massively parallel sequencing methods (also known as Next-Generation Sequencing) can produce much larger amounts of sequencing data at a fraction of cost when compared to the traditional capillary sequencing methods. We are interested in applying the Solexa and 454 technologies to a variety of genetic studies, including resequencing disease cohort samples and population samples at targeted genomic regions, sequence-based gene expression comparisons, paired-end sequencing for studying copy number variation, and analyses of chromatin immunoprecipitation products (CHiP-SEQ). This effort relies on the support of the DNA sequencing Core at the Human Genetics Department (http://seqcore.brcf.med.umich.edu/), which has recently acquired the capability to perform Illumina genotyping, Solexa sequencing and 454 sequencing.
Gene expression studies of psychiatric disorders
We are members of the Pritzker Neuropsychiatric Disorders Research Consortium (http://www.pritzkerneuropsych.org/people/researchers.htm) and have been involved for several years in an effort to identify gene expression "signatures" in brain tissues of those who suffered from bipolar disorder, major depression, and schizophrenia. We use Affymetrix and Illumina microarrays, serial analysis of gene expression, and quantitative RT-PCR to analyze multiple regions from each brain, and have currently analyzed >1000 RNA samples. The Li lab plays a key role in the ongoing analysis of these data.
Cancer
The Cancer Genome Atlas project is an NIH/NCI-funded, coordinated effort to characterize molecular abnormalities in tumor samples. Its goal is to create high-quality genomewide datasets across multiple levels of biological regulation, from DNA, RNA, microRNA, DNA methylation, to clinical phenotypes in order to accelerate our understanding of the molecular basis of cancer genesis and progression, and to provide useful targets for large-scale genome sequencing. Dr. Li was a member of the Stanford Cancer Genome Characterization Center of the TCGA, and will continue this collaboration from the University of Michigan. We are currently involved in (1) using Illumina genotyping Beadchips to examine copy number variation (CNV) in tumor and normal samples, (2) analyzing CNV data across different platforms, summarizing across samples, and perform integrated analysis with genotype, mRNA, methylation, and microRNA data.
Population genetics
We are interested in using patterns of genetic variation in different parts of the world to help understand the history and evolution of different populations. For example, by using genotyping data we can study signatures of mutation, recombination, migration, demography, selection, as well as random drift, and evaluate the relative impact of these different forces in shaping the present state of genetic diversity in humans. Equally important is to establish a population perspective to genetic studies of human traits and common diseases. To this goal we are interesting in correlating population differences in genotype, phenotype and environment in order to understand population stratification, local selection, recent admixture, and population-specific environmental factors. Recently we have examined the Human Genome Diversity Project samples by using the latest genotyping technologies, and have released to the public genotypes for 938 unrelated individuals representing >50 populations at 650,000 SNP loci. We are in the process of deeply analyzing this genomewide dataset, and preparing for additional sequenced-based experiments that allow us to study fully-ascertained panels of genetic markers.
Copy number variation
Structural changes in DNA, especially gains and losses of specific chromosomal segments, are an important class of genetic variation, and may underlie a broad spectrum of human diseases. These variants can now be studied in a genomewide fashion with the use of microarrays. We are interested in using genotyping raw intensity data for >550K SNPs to estimate copy number changes in several large studies, including the Cancer Genome Atlas Project, the genomewide association study of bipolar disorder, and the population genetic studies. In particular, we are developing analytical tools for cross-platform and cross-sample integration, while addressing issues regarding data normalization and integration with RNA and epigenetics data.
