Tianyuan Wang

GATA2 Is Associated with Familial Early-Onset Coronary Artery Disease

The transcription factor GATA2 plays an essential role in the establishment and maintenance of adult hematopoiesis. It is expressed in hematopoietic stem cells, as well as the cells that make up the aortic vasculature, namely aortic endothelial cells and smooth muscle cells. We have shown that GATA2 expression is predictive of location within the thoracic aorta; location is suggested to be a surrogate for disease susceptibility. The GATA2 gene maps beneath the Chromosome 3q linkage peak from our family-based sample set (GENECARD) study of early-onset coronary artery disease. Given these observations, we investigated the relationship of several known and novel polymorphisms within GATA2 to coronary artery disease. We identified five single nucleotide polymorphisms that were significantly associated with early-onset coronary artery disease in GENECARD. These results were validated by identifying significant association of two of these single nucleotide polymorphisms in an independent case-control sample set that was phenotypically similar to the GENECARD families. These observations identify GATA2 as a novel susceptibility gene for coronary artery disease and suggest that the study of this transcription factor and its downstream targets may uncover a regulatory network important for coronary artery disease inheritance.

Genomic convergence to identify candidate genes for Parkinson disease: SAGE analysis of the substantia nigra

Genomic convergence is a multistep approach that combines gene expression with genomic linkage to identify and prioritize susceptibility genes for complex disease. As a first step, we previously performed linkage analysis on 174 multiplex Parkinsons disease (PD) families, identifying five peaks for PD risk and two for genes affecting age at onset (AAO) in PD [Hauser et al., Hum Mol Genet 2003;12:671–677]. We report here the next step: serial analysis of gene expression [SAGE; Scott et al., JAMA 2001;286:2239–2242] to analyze substantia nigra tissue from three PD patients and two age‐matched controls. We find 933 differentially expressed genes (P < 0.05) between PD and controls, but of these, only 50 genes represented by unique SAGE tags map within our previously described PD linkage regions. Furthermore, genes encoded by mitochondrial DNA are expressed 1.5‐fold higher in PD patients versus controls, without an increase in the corresponding nuclear‐encoded mitochondrial components, suggesting an increase in mtDNA genomes in PD or a disjunction with nuclear expression. The next step in the genomic convergence process will be to screen these 50 high‐quality candidate genes for association with PD risk susceptibility and genetic effects on AAO.

Aging-related atherosclerosis is exacerbated by arterial expression of tumor necrosis factor receptor-1: evidence from mouse models and human association studies

Aging is believed to be among the most important contributors to atherosclerosis, through mechanisms that remain largely obscure. Serum levels of tumor necrosis factor (TNF) rise with aging and have been correlated with the incidence of myocardial infarction. We therefore sought to determine whether genetic variation in the TNF receptor-1 gene (TNFR1) contributes to aging-related atherosclerosis in humans and whether Tnfr1 expression aggravates aging-related atherosclerosis in mice. With 1330 subjects from a coronary angiography database, we performed a case-control association study of coronary artery disease (CAD) with 16 TNFR1 single-nucleotide polymorphisms (SNPs). Two TNFR1 SNPs significantly associated with CAD in subjects >55 years old, and this association was supported by analysis of a set of 759 independent CAD cases. In multiple linear regression analysis, accounting for TNFR1 SNP rs4149573 significantly altered the relationship between aging and CAD index among 1811 subjects from the coronary angiography database. To confirm that TNFR1 contributes to aging-dependent atherosclerosis, we grafted carotid arteries from 18- and 2-month-old wild-type (WT) and Tnfr1(-/-) mice into congenic apolipoprotein E-deficient (Apoe(-/-)) mice and harvested grafts from 1 to 7 weeks post-operatively. Aged WT arteries developed accelerated atherosclerosis associated with enhanced TNFR1 expression, enhanced macrophage recruitment, reduced smooth muscle cell proliferation and collagen content, augmented apoptosis and plaque hemorrhage. In contrast, aged Tnfr1(-/-) arteries developed atherosclerosis that was indistinguishable from that in young Tnfr1(-/-) arteries and significantly less than that observed in aged WT arteries. We conclude that TNFR1 polymorphisms associate with aging-related CAD in humans, and TNFR1 contributes to aging-dependent atherosclerosis in mice.

A general integrative genomic feature transcription factor binding site prediction method applied to analysis of USF1 binding in cardiovascular disease

Transcription factors are key mediators of human complex disease processes. Identifying the target genes of transcription factors will increase our understanding of the biological network leading to disease risk. The prediction of transcription factor binding sites (TFBSs) is one method to identify these target genes; however, current prediction methods need improvement. We chose the transcription factor upstream stimulatory factor l (USF1) to evaluate the performance of our novel TFBS prediction method because of its known genetic association with coronary artery disease (CAD) and the recent availability of USF1 chromatin immunoprecipitation microarray (ChIP-chip) results. The specific goals of our study were to develop a novel and accurate genome-scale method for predicting USF1 binding sites and associated target genes to aid in the study of CAD. Previously published USF1 ChIP-chip data for 1 per cent of the genome were used to develop and evaluate several kernel logistic regression prediction models. A combination of genomic features (phylogenetic conservation, regulatory potential, presence of a CpG island and DNaseI hypersensitivity), as well as position weight matrix (PWM) scores, were used as variables for these models. Our most accurate predictor achieved an area under the receiver operator characteristic curve of 0.827 during cross-validation experiments, significantly outperforming standard PWM-based prediction methods. When applied to the whole human genome, we predicted 24,010 USF1 binding sites within 5 kilobases upstream of the transcription start site of 9,721 genes. These predictions included 16 of 20 genes with strong evidence of USF1 regulation. Finally, in the spirit of genomic convergence, we integrated independent experimental CAD data with these USF1 binding site prediction results to develop a prioritised set of candidate genes for future CAD studies. We have shown that our novel prediction method, which employs genomic features related to the presence of regulatory elements, enables more accurate and efficient prediction of USF1 binding sites. This method can be extended to other transcription factors identified in human disease studies to help further our understanding of the biology of complex disease.

Analysis of Complex Disease Association and Linkage Studies Using the University of California Santa Cruz Genome Browser

The sequencing of the human genome, the identification of common single-nucleotide polymorphisms (SNPs) and haplotype blocks, and advances in microarray technology have enabled the study of complex diseases at a level of detail not previously imaginable. These have aided in the design and analyses of association and linkage studies of many complex diseases including cardiovascular disease. Recent technological advances have enabled the undertaking of large-scale genome-wide association studies (GWAS) that can assay hundreds of thousands of polymorphic sites on hundreds to thousands of individuals to find genomic regions associated with disease. Although results from these experiments enable the identification of smaller regions of association compared with previous studies, as with all linkage and association studies, there is the need for the further investigation of regions of interest for the causal genes or variants. The purpose of this review is to present a detailed demonstration as to how publicly available resources can be used to easily guide more detailed research into genomic regions of interest identified in linkage and association study data. Large-scale projects, such as the Human Genome Sequencing project,1,2 have generated large volumes and varieties of annotated genomic data necessitating the development of Internet-based tools to organize and make practically available these public data. One important tool in human disease research is the web-based graphical genome browsers that use the human genome sequence as the framework on which to organize genomic annotations, providing various ways for researchers to view and extract important information. Currently, there are 3 human genome browsers that have been developed for public use: (1) the National Center for Biotechnology Information (NCBI) Map Viewer3; (2) the University of California Santa Cruz (UCSC) Genome Browser4; and (3) the European Bioinformatics Institute’s Ensembl system.5 Although these genome browsers share common features and …