Skip Navigation LinksHome > July 2011 - Volume 6 - Issue 4 > What did we learn on host's genetics by studying large cohor...
Current Opinion in HIV & AIDS:
doi: 10.1097/COH.0b013e3283478449
Cohort analysis of clinical and treatment outcomes: Edited by Carolyn Williams, Matthew Law and Francois Dabis

What did we learn on host's genetics by studying large cohorts of HIV-1-infected patients in the genome-wide association era?

Guergnon, Juliena; Theodorou, Ioannisa,b

Collapse Box

Abstract

Purpose of review: Genome-wide association studies (GWASs) performed in large cohorts of HIV-1-infected patients have shown that high throughput genomics can add valuable information in understanding disease progression. We report recent information gathered in the international field during the last few years and revisit the importance of well documented cohorts for genotype–phenotype association studies.

Recent findings: The majority of GWASs in the HIV-1 field found that viral loads and disease progression are under the control of variants located in the major histocompatibility complex (MHC) in untreated patients. Although these experiments brought a new and more objective vision of genotype–phenotype correlations in HIV-1 disease, they also pointed out that less than 15% of the observed phenotypic variability can be explained as common genetic variants. Most of the studies have included mainly white patients and the few studies performed in Africans are underpowered but suggest that MHC is probably not the only genetic determinant influencing disease progression in this population.

Summary: Although the first results of the GWASs in HIV disease look as a confirmation of previous findings, high throughput agnostic genomics entered the field of chronic infectious diseases and will probably unveil new genotype–phenotype associations in the future. Networks between existing cohorts leading to ‘virtual mega-cohorts’ will be necessary to increase the probability to discover new genetic pathways important for HIV disease. Finally, predictive models including genetic information for clinical usage is another challenge in HIV disease genetics.

© 2011 Lippincott Williams & Wilkins, Inc.

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.