Models of RNA virus evolution and their roles in vaccine design

Ojosnegros, Samuel; Beerenwinkel, Niko

The evolutionary dynamics of influenza drive its immune escape and give rise to a new dominant strain every season. Therefore, vaccine design is not only supported by immunoinformatics methods for epitope prediction [63] [64] [65] , but also by statistical genetics and phylogenetic methods for analyzing genetic diversity and predicting evolutionary changes. To predict the evolution of the influenza HA gene, phylogenetic trees were constructed based on DNA sequences derived from viruses during the years 1983 through 1997 [22] . Eighteen codons were identified to be under positive selective pressure and the genetic diversity at these loci was significantly higher than at the other loci of the HA gene [59] . The rationale for predicting the next dominant virus is that extant strains with additional mutations at the 18 loci will be better adapted to evade the host immune response and thus have a selective advantage in the coming season. Phylogenetic analysis confirmed that the viral lineages with the greatest number of mutations in the positively selected codons were the ancestors of future H3 lineages in 9 out of 11 influenza seasons [22] . This approach to predicting the evolution of influenza relies on solving two classical evolutionary biology problems: the detection of genetic loci under selective pressure and the reconstruction of the evolutionary history of a set of individuals. Quantifying the relative contributions of selection versus random genetic drift is a longstanding task rooted in Kimura's theory of neutral evolution which predicts that most mutations are selectively neutral [66, 67] . Selection is typically identified by testing for departure from neutrality, although such deviations can also have different causes. The statistical tests are either based on the allelic distribution or on comparing variability in different classes of mutations [68] . Tajima's D is the prototype test of the first kind [69] . It detects differences in two distinct estimates of genetic diversity. The null distribution of the test statistic D is obtained from sampling genealogies according to the coalescent, a stochastic process describing the sampling variation [70, 71] . Similarly, the Ewens-Watterson test compares the observed to the expected homozygosity based on the Ewens sampling formula for the infinite-alleles model [72, 73] . In the second category of tests fall the McDonald-Kreitman test [74] and likelihood ratio tests based on the allelic distribution in nonsynonymous versus synonymous sites [58, 75] . Codon usage in influenza sequences has also been analyzed based on codon volatility, which measures the degree to which a random nucleotide mutation is expected to change the corresponding amino acid [76] .