The rate that HIV-infected individuals progress to AIDS and death varies greatly. Viral load taken during the asymptomatic phase of the disease is one of the best-known predictors of HIV progression rate and transmission risk, and is known to be in uenced by both host and environmental factors. However, the role that the virus itself plays in determining the viral load is less clear. Previous studies have attempted to quantify the amount the viral genome in uences viral load, or the heritability of viral load, using transmission pairs and phylogenetic signal in small sample sizes, but have produced highly disparate estimates. E cient and accurate methods to estimate heritability have been utilised by quantitative geneticists for years, but are rarely applied to non-pedigree data. Here, I present a novel application of a population-scale method based in quantitative genetics to estimate the heritability of viral load in HIV using a viral phylogeny. This new phylogenetic method allows the inclusion of more samples than ever previously used, and avoids confounding e ects associated with transmission pair studies. This new method was applied to the two largest HIV subtypes found in the UK, subtypes B and C, using sequences and clinical data from UK-wide HIV databases. For subtype B (n=8,483) and C (n=1,821), I estimated that 5.7% (CI 2.8{8.6%) and 29.7% (CI 14.8{44.7%) of the variance in viral load is determined by the viral genome, respectively. These estimates suggest that viral in uence on viral load varies greatly between subtypes, with subtype C having much larger viral control over viral load than subtype B. I expanded the phylogenetic method to test whether the component of the viral load determined by the virus has changed over time. In subtype B, I foundevidence of a small but signi cant decrease in the viral component of viral load of -0.05 log10 copies/mL/yr. I built a stochastic, individual-based model capable of simulating a realistic HIV epidemic, with heritable viral loads that in uence transmission and disease progression, capable of generating data sets to assess the accuracy of phylogenetic methods. This was successfully used to generate epidemics approximating those in a small African village and a Western `men who have sex with men' community under a variety of conditions. To test the accuracy of the new phylogenetic heritability estimation method, simulated datasets were generated with the heritability of viral load set at values of 30%, 50%, 70%, and 90%. Unfortunately, complications in the heritability equation used prevented full assessment of the new phylogenetic method on the simulated data. Future development of the model will enable simulation of realistic viral loads under varying heritability values, enabling simulation of data sets that can be used to test this and other heritability estimation methods. This new phylogenetic method allows accurate estimation of heritability in large datasets, and has provided valuable insight into the viral in uence on viral load in HIV.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:685764 |
| Date | January 2015 |
| Creators | Hodcroft, Emma B. |
| Contributors | Leigh Brown, Andrew ; Rambaut, Andrew |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/15814 |
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