High performance computation lies at the heart of modern human statistical genomics. In the Department of Genetics at the Southwest Foundation for Biomedical Research (SFBR), we specialize in the genetic dissection of human complex diseases that typically involve the actions of many genes and environmental factors. For a given genetic project on complex diseases, we typically generate vast amounts of sequence- based genetic, transcriptomic, and disease-related phenotypic information. Because family-based gene localization and identification studies are substantially more powerful than those undertaken in unrelated individuals, we also focus on studies involving very large extended pedigrees. Statistical genetic theory can be invoked to show that such a design is optimal when rare genetic variants are likely to play a role in disease risk. Accumulating results from genome-wide association studies, which are capable of only finding common disease-related genetic variants, now strongly suggest that rare variants are of substantial importance in human complex disease. However, the utilization of extended pedigree information incurs a strong computational price, since family-based data are non-independent and therefore must be analyzed simultaneously. These issues all lead to the need for ever more powerful computational technologies. In this application, we request funds to purchase a large Opteron-based high performance parallel computing cluster (an M&A 5000 Core Computational Cluster) to specifically aid ongoing research projects with more than $81 million dollars of NIH funding.

Public Health Relevance

The Department of Genetics at the Southwest Foundation for Biomedical Research focuses on understanding the genetic factors involved in human common complex diseases, including cardiovascular disease, diabetes, obesity, aging, pre-eclampsia, psychiatric disorders and infectious diseases. Investigators employ state-of-the science statistical/computational methods to process vast amounts of sequence-based genetic, transcriptomic, and disease-related phenotypic data to localize and identify novel genes influencing disease risk. Acquisition of a high performance parallel computing cluster (the M&A 5000 Core Computational Cluster) will speed the pace of gene discovery in such disorders. The proposed equipment will also significantly enhance both job creation and job retention at the Southwest Foundation for Biomedical Research and M&A Technology, Inc.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biomedical Research Support Shared Instrumentation Grants (S10)
Project #
1S10RR029392-01
Application #
7840009
Study Section
Special Emphasis Panel (ZRG1-BST-M (30))
Program Officer
Levy, Abraham
Project Start
2010-05-06
Project End
2011-05-05
Budget Start
2010-05-06
Budget End
2011-05-05
Support Year
1
Fiscal Year
2010
Total Cost
$2,068,328
Indirect Cost
Name
Texas Biomedical Research Institute
Department
Type
DUNS #
007936834
City
San Antonio
State
TX
Country
United States
Zip Code
78245
Mamtani, Manju; Kulkarni, Hemant; Wong, Gerard et al. (2016) Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts. Lipids Health Dis 15:67
Cheeseman, Ian H; Miller, Becky; Tan, John C et al. (2016) Population Structure Shapes Copy Number Variation in Malaria Parasites. Mol Biol Evol 33:603-20
Chittoor, Geetha; Kent Jr, Jack W; Almeida, Marcio et al. (2016) GWAS and transcriptional analysis prioritize ITPR1 and CNTN4 for a serum uric acid 3p26 QTL in Mexican Americans. BMC Genomics 17:276
Chevalier, Frédéric D; Le Clec'h, Winka; Eng, Nina et al. (2016) Independent origins of loss-of-function mutations conferring oxamniquine resistance in a Brazilian schistosome population. Int J Parasitol 46:417-24
Glahn, David C; Williams, Jeff T; McKay, D Reese et al. (2015) Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry 77:75-83
Dager, Alecia D; McKay, D Reese; Kent Jr, Jack W et al. (2015) Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacology 40:412-20
Knowles, Emma E M; McKay, D Reese; Kent Jr, Jack W et al. (2015) Pleiotropic locus for emotion recognition and amygdala volume identified using univariate and bivariate linkage. Am J Psychiatry 172:190-9
Sprooten, Emma; Gupta, Cota Navin; Knowles, Emma E M et al. (2015) Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24. Am J Med Genet B Neuropsychiatr Genet 168:678-86
Rubicz, Rohina; Yolken, Robert; Drigalenko, Eugene et al. (2015) Genome-wide genetic investigation of serological measures of common infections. Eur J Hum Genet 23:1544-8
Vaughan, Ashley M; Pinapati, Richard S; Cheeseman, Ian H et al. (2015) Plasmodium falciparum genetic crosses in a humanized mouse model. Nat Methods 12:631-3

Showing the most recent 10 out of 71 publications