In this preprint -- a collaboration with Colin Russell and Boris Shraiman -- we show that it is possible to predict which individual from a population is most closely related to future populations. To this end, we have developed a method that uses the branching pattern of genealogical trees to estimate which part of the tree contains the "fittest" sequences, where fit means rapidly multiplying. Those that multiply rapidly, are most likely to take over the population. We demonstrate the power of our method by predicting the evolution of seasonal influenza viruses.

**How does it work?**
Individuals adapt to a changing environment by accumulating beneficial
mutations, while avoiding deleterious mutations. We model this process
assuming that there are many such mutations which change fitness in
small increments. Using this model, we calculate the probability that an
individual that lived in the past at time *t* leaves *n* descendants in
the present. This distributions depends critically on the fitness of the
ancestral individual. We then extend this calculation to the probability
of observing a certain branch in a genealogical tree reconstructed from
a sample of sequences. A branch in a tree connects an individual A that
lived at time *t*~A~ and had fitness *x*~A~ and with an individual B
that lived at a later time *t*~B~ with fitness *x*~B~ as illustrated in
the figure. B has descendants in the sample, otherwise the branch would
not be part of the tree. Furthermore, all sampled descendants of A are
also descendants of B, otherwise the connection between A and B would
have branched between $t_A$ and $t_B$. We call the mathematical object
describing fitness evolution between A and B "branch propagator" and denote it by
$g(x_B, ,t_B |x_A,t_A)$.

The joint probability distribution of fitness values of all nodes of the tree is given by a product of branch propagators. We then calculate the expected fitness of each node and use it to rank the sampled sequences. The top ranked sequence is our prediction for the sequence of the progenitor of the future population.

**Why do we care?**
Being able to
predict evolution could have immediate applications. The best example is
the seasonal influenza vaccine, that needs to be updated frequently to
keep up with the evolving virus. Vaccine strains are chosen among
sampled virus strains, and the more closely this strain matches the
future influenza virus population, the better the vaccine is going to
be. Hence by predicting a likely progenitor of the future, our method
could help to improve influenza vaccines. One of our predictions is
shown in the figure, with the top ranked sequence marked by a black
arrow.

Influenza is not the only possible application. Since the algorithm only requires a reconstructed tree as input, it can be applied to other rapidly evolving pathogens or cancer cell populations. In addition, to being useful, the ability to predict also implies that the model captures an essential aspect of evolutionary dynamics: influenza evolution is to a substantial degree -- enough to enable prediction -- dependent on the accumulation of small effect mutations.

**Comparison to other approaches**
Given the importance of good influenza vaccines, there has been a number
of previous efforts to anticipate influenza virus evolution, typically
based on using patterns of molecular evolution from historical data.
Along these lines, Luksza and
Lässighave
recently presented an explicit fitness model for influenza virus
evolution that rewards mutations at positions known to convey antigenic
novelty and penalizes likely deleterious mutations (+a few other
things). By using molecular influenza specific signatures, this model is
complementary to ours that uses only the tree reconstructed from
nucleotide sequences. Interestingly, the two models do more or less
equally well and combining different methods of prediction should result
in more reliable results.