AIDS:
23 October 2010 - Volume 24 - Issue 16 - p 2596–2597
doi: 10.1097/QAD.0b013e32833e7a64
Correspondence
Interpretation of correlations in setpoint viral load in transmitting couples
Fraser, Christophe; Hollingsworth, T Déirdre
Author Information
Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Received 13 July, 2010
Accepted 20 July, 2010
Correspondence to Professor Christophe Fraser, PhD, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK. Tel: +44 20 7594 3397; e-mail: c.fraser@imperial.ac.uk
Two interesting reports studying correlations in HIV-1 setpoint viral loads (spVLs) and early viral loads within transmitting couples [1,2]were published recently, an observation which was foreshadowed in Zambia [3] and which we also recently showed in Uganda [4].
We wish to highlight that even weak correlation between spVL of individuals in transmitting couples can correspond to a large estimate for the role of viral genetic factors in determining spVL. Our difference in interpretation can be clarified by consideration of the sources of variance contributing to spVL (Fig. 1).
The spVL of index and secondary cases within transmitting couples may be determined by a combination of factors related to the broadly similar viral genotype (viral factors), and other nonviral factors including host factors, coinfections, specificity and nature of immunological response or chance effects (Fig. 1a).
Nonviral factors affect the spVL of both index and the secondary cases in couples, explaining the difference between correlation between patients (Fig. 1b) and estimates of the strength of viral factors (Fig. 1a).
Hecht et al.[1], van der Kuyl et al.[2] and Tang et al.[3] estimate the Pearson correlation coefficient, r, between spVL values of index cases and secondary cases in transmitting couples. We propose that if the aim is to infer the strength of viral factors on spVL, a better estimate can be obtained using analysis of variance (ANOVA) of within and between couple variance [4]. The coefficient of determination (R2) of the ANOVA is a lower-bound estimate for the proportion of variance in spVL explained by viral factors.
An additional benefit of ANOVA is that the analysis is easily adjusted for confounders such as sex of the host and viral subtype. A drawback is that the method is less transparent and requires extra adjustments for the parameters introduced into the model. In an idealized case wherein spVL values are normally distributed with identical variance, the estimates are related in a simple manner: the coefficient of determination (R2) of the ANOVA, as used in [4], is equal to the correlation coefficient (r), as used in [1–3]. Thus, the raw numbers are unchanged, but their interpretation in terms of the strength of viral effects in determining spVL is radically altered.
The unadjusted estimates for the proportion of variance in spVL explained by viral genotype in the four studies are 20% [3], 25% [2], 27% [4] and 55% [1]. These estimates are all higher than unadjusted estimates of 13% of variance in spVL explained by host genetic factors to date [5], although, of course, these latter estimates may increase as more host factors are discovered, and some host–virus interaction effects may be dually counted in individual analyses.
The additional observation that the relationship between spVL in transmitting pairs may depend on the stage of infection of the recipient[2] (based on a small sample size) is plausible and interesting, and may aid the detailed elucidation of the mechanisms linking viral factors to spVL.
The four published studies together represent 250 couples, and the link in spVL within couples seems consistent and robust. Attributing variation in viral load to viral, host and interaction effects will undoubtedly shed light on the mechanisms of HIV pathogenesis. These results also support the hypothesis that spVL has evolved at the population level [6].
References
1. Hecht FM, Hartogensis W, Bragg L, Bacchetti P, Atchison R, Grant R, et al. HIV RNA level in early infection is predicted by viral load in the transmission source. AIDS 2010; 24:941–945.
2. van der Kuyl AC, Jurriaans S, Pollakis G, Bakker M, Cornelissen M. HIV RNA levels in transmission sources only weakly predict plasma viral load in recipients. AIDS 2010; 24:1607–1608.
3. Tang J, Tang S, Lobashevsky E, Zulu I, Aldrovandi G, Allen S, Kaslow RA. HLA allele sharing and HIV type 1 viremia in seroconverting Zambians with known transmitting partners. AIDS Res Hum Retroviruses 2004; 20:19–25.
4. Hollingsworth TD, Laeyendecker O, Shirreff G, Donnelly CA, Serwadda D, Wawer MJ, et al. HIV-1 transmitting couples have similar viral load set-points in Rakai, Uganda. PLoS Pathog 2010; 6:e1000876.
5. Fellay J, Ge D, Shianna KV, Colombo S, Ledergerber B, Cirulli ET, et al. Common genetic variation and the control of HIV-1 in humans. PLoS Genet 2009; 5:e1000791.
6. Fraser C, Hollingsworth TD, Chapman R, de Wolf F, Hanage WP. Variation in HIV-1 set-point viral load: Epidemiological analysis and an evolutionary hypothesis. Proc Natl Acad Sci USA 2007; 104:17441–17446.
© 2010 Lippincott Williams & Wilkins, Inc.
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