The BCM Human Genome Sequencing Center (HGSC) has developed a large scale, flexible DNA sequencing resource to solve major problems in human genetics and support clinical care. Technical excellence and innovation has allowed the use of multiple sequence platforms and the adaptation of the best sample resources and appropriate data models to match project requirements. In the first 18 months of the proposed timeline at least 1 petabase of DNA sequence will be generated, and 90% of capacity will be dedicated to cancer and human genetic studies. The remainder will be applied to comparative and metagenomics. All of this is made possible because of the HGSC's integrated high throughput data production pipeline that spans sample procurement and processing, sequence production, informatics, data analysis, and dissemination. The modular nature of this pipeline allows us to meet, or even exceed production expectations while at the same time provides flexibility to serve the diverse (and growing) needs of the genomics and biomedical research community. Overall one-half of the capacity will be dedicated to ongoing and emerging NHGRI programs with the remainder to a series of 11 innovative Center Initiated Projects (CIPs). The CIPs include solving the basis of selected common chronic human diseases, piloting a newborn screen and a national population-based health care model, deeply sampling human genetic diversity, identifying critical somatic mutations in rare and familiar cancers and the role of epigenomics in cancer therapy. For understanding common disease, we will develop future CIPs relevant to emerging priorities in human health such as Alzheimer?s disease. We will also provide a complete genome analysis of all the laboratory rhesus macaques in the USA, as well as study their microbiome. The virome in health and disease will be characterized and an educational program, based upon personal genome sequences will be delivered. The overall objectives, specific aims, and CIPs embodied in this proposal will transform biology through genomics and lead to a new phase of clinical applications.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54HG003273-11
Application #
8584300
Study Section
Special Emphasis Panel (ZHG1-HGR-P (O2))
Program Officer
Wang, Lu
Project Start
2003-11-10
Project End
2015-10-31
Budget Start
2013-11-01
Budget End
2014-10-31
Support Year
11
Fiscal Year
2014
Total Cost
$4,785,338
Indirect Cost
$255,459
Name
Baylor College of Medicine
Department
Genetics
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Karaca, Ender; Posey, Jennifer E; Coban Akdemir, Zeynep et al. (2018) Phenotypic expansion illuminates multilocus pathogenic variation. Genet Med :
Ricketts, Christopher J; De Cubas, Aguirre A; Fan, Huihui et al. (2018) The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep 23:313-326.e5
Raghavan, Neha S; Brickman, Adam M; Andrews, Howard et al. (2018) Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease. Ann Clin Transl Neurol 5:832-842
Knijnenburg, Theo A; Wang, Linghua; Zimmermann, Michael T et al. (2018) Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep 23:239-254.e6
Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3
Blue, E E; Yu, C-E; Thornton, T A et al. (2018) Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease. Genes Brain Behav 17:e12429
Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7
Li, Chang; Grove, Megan L; Yu, Bing et al. (2018) Genetic variants in microRNA genes and targets associated with cardiovascular disease risk factors in the African-American population. Hum Genet 137:85-94
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Poynton, Helen C; Hasenbein, Simone; Benoit, Joshua B et al. (2018) The Toxicogenome of Hyalella azteca: A Model for Sediment Ecotoxicology and Evolutionary Toxicology. Environ Sci Technol 52:6009-6022

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