Ashley Sobel Leonard1, Daniel B Weissman2, Benjamin Greenbaum3, Elodie Ghedin4, Katia Koelle5. 1. Department of Biology, Duke University, Durham, North Carolina, USA. 2. Department of Physics, Emory University, Atlanta, Georgia, USA. 3. Tisch Cancer Institute, Departments of Medicine, Oncological Sciences, and Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 4. Center for Genomics and Systems Biology, Department of Biology, and College of Global Public Health, New York University, New York, New York, USA. 5. Department of Biology, Duke University, Durham, North Carolina, USA katia.koelle@emory.edu.
Volume 91, no. 14, e00171-17, 2017, https://doi.org/10.1128/JVI.00171-17. As an application of the transmission bottleneck size estimation method developed in this paper, we used a previously published influenza A data set first presented by L. L. M. Poon, T. Song, R. Rosenfeld, X. Lin, et al. [Nat Genet 48(2):195-200, 2016, https://doi.org/10.1038/ng.3479]. Recently, K. S. Xue and J. D. Bloom (Nat Genet, 25 February 2019, https://doi.org/10.1038/s41588-019-0349-3) have shown that the Poon et al. data set is “technically contaminated” with read pairs split between unrelated samples, which had the effect of inflating the similarities in allele frequencies between samples. As a result, when we applied our betabinomial approach to the Poon et al. data set, it yielded transmission bottleneck size estimates that are incongruous with, and larger than, other transmission bottleneck size estimates for seasonal influenza A virus. The validity of the betabinomial estimation method presented in our paper is itself unaffected. While we therefore continue to encourage the use of our developed estimation method on other data sets, we would like to caution the reader against citing our paper as providing evidence for a loose transmission bottleneck size for influenza A virus.Computer code for the betabinomial transmission bottleneck size estimation method is available on GitHub at https://github.com/koellelab/betabinomial_bottleneck.
Authors: Katrina A Lythgoe; Matthew Hall; Luca Ferretti; Mariateresa de Cesare; George MacIntyre-Cockett; Amy Trebes; Monique Andersson; Newton Otecko; Emma L Wise; Nathan Moore; Jessica Lynch; Stephen Kidd; Nicholas Cortes; Matilde Mori; Rebecca Williams; Gabrielle Vernet; Anita Justice; Angie Green; Samuel M Nicholls; M Azim Ansari; Lucie Abeler-Dörner; Catrin E Moore; Timothy E A Peto; David W Eyre; Robert Shaw; Peter Simmonds; David Buck; John A Todd; Thomas R Connor; Shirin Ashraf; Ana da Silva Filipe; James Shepherd; Emma C Thomson; David Bonsall; Christophe Fraser; Tanya Golubchik Journal: Science Date: 2021-03-09 Impact factor: 47.728