Viral genetic variation accounts for a third of variability in HIV-1 set-point viral load in Europe

Abstract

HIV-1 set-point viral load-the approximately stable value of viraemia in the first years of chronic infection-is a strong predictor of clinical outcome and is highly variable across infected individuals. To better understand HIV-1 pathogenesis and the evolution of the viral population, we must quantify the heritability of set-point viral load, which is the fraction of variation in this phenotype attributable to viral genetic variation. However, current estimates of heritability vary widely, from 6% to 59%. Here we used a dataset of 2,028 seroconverters infected between 1985 and 2013 from 5 European countries (Belgium, Switzerland, France, the Netherlands and the United Kingdom) and estimated the heritability of set-point viral load at 31% (CI 15%-43%). Specifically, heritability was measured using models of character evolution describing how viral load evolves on the phylogeny of whole-genome viral sequences. In contrast to previous studies, (i) we measured viral loads using standardized assays on a sample collected in a strict time window of 6 to 24 months after infection, from which the viral genome was also sequenced; (ii) we compared 2 models of character evolution, the classical "Brownian motion" model and another model ("Ornstein-Uhlenbeck") that includes stabilising selection on viral load; (iii) we controlled for covariates, including age and sex, which may inflate estimates of heritability; and (iv) we developed a goodness of fit test based on the correlation of viral loads in cherries of the phylogenetic tree, showing that both models of character evolution fit the data well. An overall heritability of 31% (CI 15%-43%) is consistent with other studies based on regression of viral load in donor-recipient pairs. Thus, about a third of variation in HIV-1 virulence is attributable to viral genetic variation.

Fig 1. Predictions from the Brownian motion (BM) and Ornstein-Uhlenbeck (OU) models of evolution.

(A) Illustration of the models of character evolution on a phylogeny (top panel), showing unconstrained neutral evolution leading to increasing genetic variance under the BM model (middle panel) versus stabilizing selection around an optimum θ under the OU model, which results in stable variance over time (bottom panel). Edges of the phylogeny were arbitrarily colored for illustrative purposes. (B) The distribution of gold standard viral load (GSVL) over evolutionary time (as quantified by root-to-tip distance [i.e., distance from the common ancestor as assessed by the phylogeny]). Points are the data; boxplots show the median, lower, and upper quartiles, and the whiskers are the lower and upper quartile minus or plus 1.5 times the interquartile range for 8 bins of equal size. (C) The correlation coefficient of GSVL across 511 phylogenetic cherries in the subtype B phylogeny as a function of the patristic distance between cherries. Phylogenetic cherries were grouped by patristic distance in 10 bins of equal size. Points are the data, the dashed line is a decreasing exponential fit on the data, and thick lines show predictions from the maximum likelihood (ML) BM and OU models. The large points at patristic distance 0 show the population-level heritability estimated under the BM (blue) and OU (red) model. The data used in the figure are provided as S1 Data.

Fig 2. Maximum likelihood estimates of heritability (points) and bootstrap confidence intervals (segments) for subtype B sequence.

This is shown for the BM and OU model and the GSVL and SPVL measures of viral load for the whole-genome phylogeny; for the OU model and GSVL on the phylogeny inferred only from gag, pol, and env genes (light grey box); and for several stratifications of the data, only when the size of the subset was greater than 400 (dark grey box). CH: Switzerland, NL: the Netherlands; male sex; MSM. The data used in the figure are provided as S1 Data.

Fig 3. Maximum likelihood estimates of heritability across the genome.

Heritability was inferred for overlapping windows of 1,000 bp separated by 500 bp for the Ornstein–Uhlenbeck (OU) model (black bullets) and the Brownian motion (BM) model (grey bullets). The horizontal dashed lines are the whole-genome heritability estimates. The 3 colored segments show heritability for gag, pol, and env genes in blue, green, red (for OU only). Confidence intervals (grey and colored regions) reflect phylogenetic uncertainty. The largest heritability is in the region where gag and pol overlap. We also show entropy—a measure of genetic diversity—along the genome (dashed curve and right axis). Entropy at a position was calculated as −Σ_{i ∈ {A,C,G,T}}p_i log(p_i), and we show the average entropy over 200-bp windows. The data used in the figure are provided as S1 Data.