Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza

Abstract

Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.

Inferring the Allelic Series at QTL in Multiparental Populations

“…We demonstrated this for the MCV QTL, which has a known causal gene (Kelada et al 2012). We anticipate our tree-informed approach will be useful in haploid systems, as in Azim Ansari and Didelot (2016) and Cybis et al (2018), because most haploids do not recombine, and thus have a single phylogenetic history for their entire genome.…”

“…When J is large, it may be preferable to include the branch mutations b in the posterior sampling procedure, as in Azim Ansari and Didelot (2016), rather than integrating over them to precompute the prior distribution. We outline this approach in Appendix C. This will require mixing over the larger space of branch mutations b, though, rather than the smaller space of allelic series M. Another alternative is to disregard the explicit tree structure, and instead use patristic distances between haplotypes as input for a distance dependent CRP (Blei and Frazier 2009), as in Cybis et al (2018). It would be interesting to compare results from a distance dependent CRP with the tree-informed CRP that we have defined here.…”

“…This defines a prior distribution over the allelic series that is informed by a tree, p(M|T). In this way, our approach is similar to other models that include phylogenetic information; for example, by modeling distributional "changepoints" on a tree (Azim Ansari and Didelot 2016), or by using phylogenetic distance as an input for a distance-dependent CRP (Cybis et al 2018), among others (Zhang et al 2012;Thompson and Kubatko 2013). In particular, Azim Ansari and Didelot (2016) specify a prior distribution over the allelic series by defin-ing the prior probability that each branch of a tree is functionally mutated with respect to a phenotype (in their case, a categorical trait).…”

Inferring the Allelic Series at QTL in Multiparental Populations

“…We demonstrated this for the MCV QTL, which has a known causal gene ( Kelada et al 2012 ). We anticipate our tree-informed approach will be useful in haploid systems, as in Ansari and Didelot (2016) and Cybis et al (2018) , because most haploids do not recombine, and thus have a single phylogenetic history for their entire genome.…”

“…Ewens’s sampling formula provides an intuitive mechanism for introducing prior information about haplotype relatedness: assuming that the phylogenetic tree of the haplotypes is known rather than random. This defines a prior distribution over the allelic series that is informed by a tree, In this way, our approach is similar to other models that include phylogenetic information; for example, by modeling distributional “changepoints” on a tree ( Ansari and Didelot 2016 ), or by using phylogenetic distance as an input for a distance-dependent CRP ( Cybis et al 2018 ), among others ( Zhang et al 2012 ; Thompson and Kubatko 2013 ; Behr et al 2020 ; Selle et al 2020 ). In particular, Ansari and Didelot (2016) specify a prior distribution over the allelic series by defining the prior probability that each branch of a tree is functionally mutated with respect to a phenotype (in their case, a categorical trait).…”