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AIDS. 2011 Oct 23;25(16):F13-9. doi: 10.1097/QAD.0b013e328349f089.

Designing a genome-based HIV incidence assay with high sensitivity and specificity.

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Department of Biostatistics and Computational Biology, University of Rochester, New York 14642-0001, USA.



Considerable inaccuracy in estimates of HIV incidence has been a serious obstacle to the development of efficient HIV/AIDS prevention and interventions. Accurately distinguishing recent or incident infections from chronic infections enables one to monitor epidemics and evaluate the impact of HIV prevention/intervention trials. However, serological testing has not been able to realize these promises due to a number of critical limitations. Our study is to design a novel scheme of identifying incident infections in a highly accurate manner, based on the characteristics of HIV gene diversification within an infected individual.


We perform a comprehensive meta-analysis on 5596 full envelope HIV genes generated by single genome amplification-direct sequencing from 182 incident and 43 chronic cases. We devise a binary classification test based on the tail characteristics of the Hamming distance distribution of sequences.


We identify a clear signature of incident infections, the presence of closely related strains in the sampled HIV envelope gene sequences in each HIV-infected patient, in both single-variant and multivariant transmissions. The sequence similarity used as a biomarker is found to have high specificity and sensitivity, greater than 95%, and is robust to viral and host-specific factors such as the clade of the viral strain, viral load, and the length and location of sequences in the HIV envelope gene.


Because of rapid and continuing improvements in sequencing technology and cost, sequence-based incidence assays hold great promise as a means of quantifying HIV incidence from a single blood test.

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