on behalf of the UK HIV Drug Resistance Database

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Presentation transcript:

on behalf of the UK HIV Drug Resistance Database Population based study indicates viral genetic effect on HIV virulence is small but significant contact D-274 Emma Hodcroft Emma Hodcroft1, Esther Fearnhill2, Andrew Phillips3, David Dunn2, Deenan Pillay4, Jarrod Hadfield5 and Andrew J. Leigh Brown1 on behalf of the UK HIV Drug Resistance Database 1University of Edinburgh, UK; 2Medical Research Council Clinical Trials Unit, London, UK; 3Royal Free Hospital, London, UK; 4University College London, UK; 5University of Oxford, UK. emma.hodcroft@ed.ac.uk emmahodcroft.com m.emmahodcroft.com HIV DRD Steering Committee: tiny.cc/w2xwk Leigh Brown group: www.hivbio.org summary: Question: How much of the variation in viral load is due to the viral genetic effect? Previous studies have estimated the genetic effect or heritability (h2) of virulence in HIV at between 5-50%1-5 By analysing the viral phylogeny as a pedigree, h2 can be estimated using well-established quantitative genetic methods Of the 3 subtypes analysed (A, B, C), the B subtype produced a significant h2 estimate of 7.7% (CI 4.3-11.0%, p<0.0025) Collapsing poorly-supported nodes in the B-subtype tree did not change the estimate greatly (7.1%; CI 3.6-10.5%, p<0.0025) Sub-sampling the B-subtype tree failed to produce a significant h2 estimate The heritability of virulence in HIV is small but significant, needs a large sample size to be detected, and may be coming from the deeper tree structure introduction: results: Set-point viral load is an important predictor of virulence in HIV and varies greatly between individuals6 Previous studies have estimated the genetic effect (heritability) of virulence in HIV at between 5-50%1-5 However, these studies have had restricted inclusion and small sample sizes, and many may have been prone to the confounding effects of transmission pair studies Quantitative genetic techniques have been widely used for years to estimate heritability by connecting trait values to the degree of relatedness in pedigrees (Fig 1) By analysing phylogenies constructed from HIV sequences as pedigrees7 (Fig 2) and using these techniques, a large number of samples can be used and potential biases avoided Subtype Dataset Size Collapsed Mean Viral Genetic Effect (Conf Interval) Significance of Trees (p<0.0025) A 539 No 11.9% (-6.8 - 30.6%) C 1,821 9.6% (-2.9 - 21.0%) B 8,483 7.7% (4.3 - 11.0%) ** Yes 7.1% (3.6 - 10.5%) B (sub-sample) 2,120 3.5% (-2.4 - 9.4%) 4,241 5.5% (1.3 - 9.8%) 5/10 were significant† Pedigree Phylogeny †If only the significant trees are considered: h2 = 7.6% (2.8 - 12.3%) Only the B-subtype gave a significant h2 estimate, of 7.7% Collapsing poorly-supported nodes eliminated tip structure but did not affect the estimate Sub-sampling failed to consistently yield a significant estimate Fig 1 - Pedigree Fig 2 – Phylogeny methods: conclusions: Initial viral load was taken as an estimate of set-point, after excluding potential acute-stage or post-ART viral load measures Phylogenies for each of the three subtypes were reconstructed using FastTree8 Viral load data and phylogenies were linked to form a ‘pedigree’ which was analysed in ASReml9 Each analysis was compared to a model with no phylogeny to test for significance The heritability of virulence in HIV, at 7.7%, is small, but significant, matching the lower end of previous estimates The deeper tree structure seems to be the source of the heritability, rather than the tips A large sample size is needed to detect the heritability of virulence The sub-samples that were significant were perhaps those that retained the deep tree structure  Fig 3 - In the B-subtype tree, poorly-supported branches (bootstrap-equivalents < 0.9) were then collapsed to polytomies and re-analysed data: 55,556 sequences were available from the UK HIV Drug Resistance Database 11,096 of these had clinical data including viral load from UK CHIC 539 subtype A, 1,821 subtype C, and 8,483 subtype B sequences and viral loads were analysed 39 reference sequences from LANL were used as an outgroup  1. Tang J, et al. AIDS Res Hum Retroviruses 2004;20(1):19-25, 2. Hollingsworth TD, et al. PLoS Pathog 2010;6:e1000876. 3. Alizon S, et al. PLoS Pathog 2010;6(9). 4. Hecht FM, et al. AIDS 2010;24(7):941-5. 5. van der Kuyl AC, et al. AIDS 2010;24(10):1607-8. references Fig 4 - To investigate the effect of sample size, approximately one-quarter and one-half (N = 2120 & 4241, respectively) of the B-subtype data set was randomly sampled and re-analysed 6. Mellors JW, et al. Science 1996;272(5265):1167-70. 7. Hadfield JD, Nakagawa S. J Evo Biol 2010;23(3):494-508. 8. Gilmour AR, et al. www.vsni.co.uk 2009. 9. Price MN, et al. PLoS ONE 2010;5:e9490.