  The evolutionary dynamics of HIV during the chronic phase of infection is
driven by the host immune response and by selective pressures exerted through
drug treatment. To understand and model the evolution of HIV quantitatively,
the parameters governing genetic diversification and the strength of selection
need to be known. While mutation rates can be measured in single replication
cycles, the relevant effective recombination rate depends on the probability of
coinfection of a cell with more than one virus and can only be inferred from
population data. However, most population genetic estimators for recombination
rates assume absence of selection and are hence of limited applicability to
HIV, since positive and purifying selection are important in HIV evolution.
Here, we estimate the rate of recombination and the distribution of selection
coefficients from time-resolved sequence data tracking the evolution of HIV
within single patients. By examining temporal changes in the genetic
composition of the population, we estimate the effective recombination to be
r=1.4e-5 recombinations per site and generation. Furthermore, we provide
evidence that selection coefficients of at least 15% of the observed
non-synonymous polymorphisms exceed 0.8% per generation. These results provide
a basis for a more detailed understanding of the evolution of HIV. A
particularly interesting case is evolution in response to drug treatment, where
recombination can facilitate the rapid acquisition of multiple resistance
mutations. With the methods developed here, more precise and more detailed
studies will be possible, as soon as data with higher time resolution and
greater sample sizes is available.
