Inference of population genetic parameters from an irregular time series of seasonal influenza virus sequences
- 주제(키워드) serial sample , influenza virus , summary statistics , mismatch distribution
- 주제(기타) Genetics & Heredity
- 설명문(일반) [Croze, Myriam; Kim, Yuseob] Ewha Womans Univ, Div EcoSci, Seoul 03760, South Korea; [Kim, Yuseob] Ewha Womans Univ, Dept Life Sci, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 OXFORD UNIV PRESS INC
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000181516
- 본문언어 영어
- Published As http://dx.doi.org/10.1093/genetics/iyaa039
초록/요약
Basic summary statistics that quantify the population genetic structure of influenza virus are important for understanding and inferring the evolutionary and epidemiological processes. However, the sampling dates of global virus sequences in the last several decades are scattered nonuniformly throughout the calendar. Such temporal structure of samples and the small effective size of viral population hampers the use of conventional methods to calculate summary statistics. Here, we define statistics that overcome this problem by correcting for the sampling-time difference in quantifying a pairwise sequence difference. A simple linear regression method jointly estimates the mutation rate and the level of sequence polymorphism, thus providing an estimate of the effective population size. It also leads to the definition of Wright's FST for arbitrary time-series data. Furthermore, as an alternative to Tajima's D statistic or the site-frequency spectrum, a mismatch distribution corrected for sampling-time differences can be obtained and compared between actual and simulated data. Application of these methods to seasonal influenza A/H3N2 viruses sampled between 1980 and 2017 and sequences simulated under the model of recurrent positive selection with metapopulation dynamics allowed us to estimate the synonymous mutation rate and find parameter values for selection and demographic structure that fit the observation. We found that the mutation rates of HA and PB1 segments before 2007 were particularly high and that including recurrent positive selection in our model was essential for the genealogical structure of the HA segment. Methods developed here can be generally applied to population genetic inferences using serially sampled genetic data.
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