A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control
View/ Open
Author
Bartha, István
Carlson, Jonathan M
Brumme, Chanson J
McLaren, Paul J
Brumme, Zabrina L
John, Mina
Haas, David W
Martinez-Picado, Javier
Dalmau, Judith
López-Galíndez, Cecilio
Casado, Concepción
Rauch, Andri
Günthard, Huldrych F
Bernasconi, Enos
Vernazza, Pietro
Klimkait, Thomas
Yerly, Sabine
O’Brien, Stephen J
Listgarten, Jennifer
Pfeifer, Nico
Lippert, Christoph
Fusi, Nicolo
Kutalik, Zoltán
Müller, Viktor
Harrigan, P Richard
Heckerman, David
Telenti, Amalio
Fellay, Jacques
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.7554/eLife.01123Metadata
Show full item recordCitation
Bartha, I., J. M. Carlson, C. J. Brumme, P. J. McLaren, Z. L. Brumme, M. John, D. W. Haas, et al. 2013. “A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control.” eLife 2 (1): e01123. doi:10.7554/eLife.01123. http://dx.doi.org/10.7554/eLife.01123.Abstract
HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10−12). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the ‘intermediate phenotype’ nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host–pathogen interaction. DOI: http://dx.doi.org/10.7554/eLife.01123.001Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3807812/pdf/Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:11878911
Collections
- HMS Scholarly Articles [17917]
Contact administrator regarding this item (to report mistakes or request changes)