Author: Palmer, Duncan S.; Turner, Isaac; Fidler, Sarah; Frater, John; Goedhals, Dominique; Goulder, Philip; Huang, Kuan-Hsiang Gary; Oxenius, Annette; Phillips, Rodney; Shapiro, Roger; Vuuren, Cloete van; McLean, Angela R.; McVean, Gil
Title: Mapping the drivers of within-host pathogen evolution using massive data sets Document date: 2019_7_9
ID: 100r7w2n_66
Snippet: where P is the probability of observing an escape mutation, X i are binary variables taking the value 0 or 1 (for presence/absence of HLA i) depending on the HLA status of the host associated to a given leaf sequence. T is a further binary variable, which takes the value -1 if the state at the hidden transmitted state is wild-type, and 1 if it is escape. Given that these transmitted states are unknown, they are summed over using tree peeling. a i.....
Document: where P is the probability of observing an escape mutation, X i are binary variables taking the value 0 or 1 (for presence/absence of HLA i) depending on the HLA status of the host associated to a given leaf sequence. T is a further binary variable, which takes the value -1 if the state at the hidden transmitted state is wild-type, and 1 if it is escape. Given that these transmitted states are unknown, they are summed over using tree peeling. a i s and c are fitted using maximum likelihood optimisation schemes as for PhyloD. Following Carlson et al. we again use forward selection and likelihood ratio tests and add parameters to the model if p < 0.05.
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