A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting.

Abstract

BACKGROUND: Humans vary in their susceptibility to acquiring Staphylococcus aureus infection, and research suggests that there is a genetic basis for this variability. Several recent genome-wide association studies (GWAS) have identified variants that may affect susceptibility to infectious diseases, demonstrating the potential value of GWAS in this arena. METHODS: We conducted a GWAS to identify common variants associated with acquisition of S. aureus bacteremia (SAB) resulting from healthcare contact. We performed a logistic regression analysis to compare patients with healthcare contact who developed SAB (361 cases) to patients with healthcare contact in the same hospital who did not develop SAB (699 controls), testing 542,410 SNPs and adjusting for age (by decade), sex, and 6 significant principal components from our EIGENSTRAT analysis. Additionally, we evaluated the joint effect of the host and pathogen genomes in association with severity of SAB infection via logistic regression, including an interaction of host SNP with bacterial genotype, and adjusting for age (by decade), sex, the 6 significant principal components, and dialysis status. Bonferroni corrections were applied in both analyses to control for multiple comparisons. RESULTS: Ours is the first study that has attempted to evaluate the entire human genome for variants potentially involved in the acquisition or severity of SAB. Although this study identified no common variant of large effect size to have genome-wide significance for association with either the risk of acquiring SAB or severity of SAB, the variant (rs2043436) most significantly associated with severity of infection is located in a biologically plausible candidate gene (CDON, a member of the immunoglobulin family) and may warrant further study. CONCLUSIONS: The genetic architecture underlying SAB is likely to be complex. Future investigations using larger samples, narrowed phenotypes, and advances in both genotyping and analytical methodologies will be important tools for identifying causative variants for this common and serious cause of healthcare-associated infection.

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Citation

Published Version (Please cite this version)

10.1186/1471-2334-14-83

Publication Info

Nelson, Charlotte L, Kimberly Pelak, Mihai V Podgoreanu, Sun Hee Ahn, William K Scott, Andrew S Allen, Lindsay G Cowell, Thomas H Rude, et al. (2014). A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting. BMC Infect Dis, 14. p. 83. 10.1186/1471-2334-14-83 Retrieved from https://hdl.handle.net/10161/13316.

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Scholars@Duke

Podgoreanu

Mihai V. Podgoreanu

Associate Professor of Anesthesiology

Basic-Translational:
1. Systems biology approaches to modeling perioperative cardiovascular injury and adaptation.
2. Mechanisms of perioperative myocardial injury; functional genomics applied to perioperative myocardial injury.
3. Metabolic consequences of perioperative myocardial ischemia-reperfusion injury.
4. Animal models and comparative genomic approaches to study perioperative myocardial ischemia-reperfusion injury.
5. Functional genomics of vein graft disease.
6. Animal models of vein graft disease.
7. Genetic association studies in perioperative medicine.
8. Clinico-genomic risk prediction models for perioperative and long-term adverse cardiovascular outcomes following cardiac surgery.

Clinical:
9. Intraoperative quantification of tissue perfusion by contrast echocardiography.
10. Use of myocardial tissue deformation indices to characterize perioperative ventricular dysfunction/stunning
11. 3-D echocardiographic evaluation of the right ventricle

Allen

Andrew Scott Allen

Professor of Biostatistics & Bioinformatics

My research focuses on developing new statistical methods for identifying susceptibility loci involved in complex human disease.  It involves a mix of genetics, statistics, and computer science and is motivated by the complexities of real data encountered in collaborative disease-gene mapping projects.

Ruffin

Felicia Ruffin

Research Program Leader, Tier 1

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