Submitted to: American Society for Virology Meeting
Publication Type: Abstract Only
Publication Acceptance Date: May 1, 2006
Publication Date: July 15, 2006
Citation: French, R.C., Stenger, D.C. 2006. Estimating of effective population sizes of laboratory and field samples of wheat streak mosaic virus. American Society for Virology Meeting. Technical Abstract: We previously have used two different approaches, each essentially measuring the rate of genetic drift, to arrive at estimates effective population size (Ne) of wheat streak mosaic virus (WSMV) that are remarkably low. Fixation rates in plants infected with two strains of WSMV yielded Ne = 4 for systemic infection of wheat tillers, and changes in single nucleotide polymorphism (SNP) frequencies during passages from plant to plant suggested an Ne of about 14 - 20. These results may seem paradoxical, given that cells of infected plants each contain more than 100,000 virions, but can be understood by considering the huge variance in reproduction among WSMV genomes. Only a tiny fraction of viral genomes produced in a cell, or present in ground sap inoculum from infected plants, spread to adjacent cells or initiate new infections. Here we used a “sample size n marbles in Ne boxes” simulation approach (Wakeley and Takahashi, 2003, Mol. Biol. Evol. 20:208-213) to estimate Ne from the SNP patterns found during the serial passage experiment, and in a set of 49 field isolates of WSMV. The method provides a joint estimate of population size and theta, the population mutation rate parameter. For the WSMV passage data, Ne = 6.5 and theta = 5.5; for the field population, Ne = 4 and theta = 24. While these estimates arise from the patterns of polymorphic sites in sequence data, they are similar to those based on genetic drift rates. Both laboratory and field samples of WSMV exhibit large negative values of Tajima’s D parameter and an excess of singleton substitutions that can be seen to be a major consequence of low Ne. The higher value of theta for the field population suggests that other factors, such as natural selection, population subdivision, or population growth, also may be involved in shaping the observed pattern of sequence polymorphism.