Population structure and demographic history
To investigate the structure of the dataset, we used a model-based
clustering approach as implemented in Admixture (Alexanderet al. 2009). For the Admixture analyses the number of
populations was inferred by analyzing different number of populations
(K) and the cross-validation (CV) error for each K. The CV error is used
to find which K has the best predictive accuracy, but does not try to
determine the absolute K. The full data set including all isolates and
SNPs were analyzed, in addition to reduced data sets of only European
and only Japanese isolates (SNP data set reduced by minor allele count ≥
1 in VCFtools (Danecek et al. 2011)). All datasets were
transformed from vcf format to plink format in VCFtools.
Furthermore, the variation and genetic distance between and within
populations were visualized by a PCA plot analyzed in Eigensoft
(Price et al. 2006). The PCA analyses were run on the full SNP
data set, and also on the split data sets (European and the Japanese
isolates) as in the Admixture analyses. Three PC axes were
produced for each of the three PCA analyses.
Coalescent simulations were used to infer the demographic history ofS. lacrymans in Europe and Japan using the model-based approach
implemented in Fastsimcoal2 (Excoffier & Foll 2011; Excoffieret al. 2013). In Fastsimcoal2 the likelihood of
predefined evolutionary models can be compared. In addition, demographic
parameters, such as the effective population size, population growth
rate, as well as timing of evolutionary events, can be estimated for the
different evolutionary models. To test the divergence of S.
lacrymans , we defined three realistic evolutionary models, supported by
what is known from the literature. The first model represents a
scenario, where S. lacrymans moved to an indoor environment in
Japan, before migrating to the built environment in Europe. The second
model represents a scenario where S. lacrymans has moved into the
built environment, independently in Japan and Europe, from two natural
populations that diverged prior to the colonization into houses. In
addition to the divergence between Europe and Japan, the change in
population size is important for understanding current patterns of
genetic variation. The European population has been shown to be highly
reduced in genetic diversity, likely resulting from a founder event when
the population was established. To account for that, we included a
population growth rate in Europe for both models. In the third model, we
also implemented a growth rate for the Japanese population.
The likelihood of each model was inferred from the simulated site
frequency spectrum (SFS) fitted to the observed minor allele frequency
spectrum with the composite likelihood calculated in
Fastsimcoal2. For each model 50 independent
Fastsimcoal2 runs of 1000000 coalescence simulations and 40
cycles were analyzed. Confidence intervals for the point estimates were
calculated using the parametric bootstrap approach used in Excoffieret al . (2013). We analyzed the data with both
10-7 and 10-8 as the mutation rate
per site per year. The number of generations rather than years was
calculated, as commonly used for such analyses. The generation time forS. lacrymans is probably highly context-dependent. For instance,
under optimal growth conditions, the fungus can colonize, grow and
expand extremely quickly and fruit after one year, and probably fruit
successively for several years. Alternatively, under sub-optimal
conditions the fruiting frequency will vary extensively. It is highly
plausible that fruiting in the human-made habitat will lead to a
reaction from the home owner and often the death of the fungus. Compared
to other taxa, all somatic mutations in a fungal individual have a
chance to contribute to the next generation, explaining the different
scales of mutation rates across organisms. There are also few available
estimates of mutation rates for wood decay basidiomycetes. Recently, a
mutation rate of 10-10 was estimated for a single
diploid individual of the fungal pathogen and wood decay fungusArmillaria gallica (Anderson et al. 2018). This relatively
slow mutation rate is probably not representative of a sexually
reproductive and flexible population. Regarding other fungal phyla,
higher mutations rates have been estimated, e.g. 7.29 ×
10-7 for the chytrid B. dendrobatidis (O’Hanlonet al. 2018), 2.4 × 10−6 to 2.6 ×
10−6 for ascomycete yeast Saccharomyces
cerevisiae (Gallone et al. 2016) and on average 1.98 ×
10-8 in Magnaporthe oryzae (Ascomycota) using
tip dating of temporally separated samples (Gladieux et al.2018). Thus, using both mutation rates of 10-7 and
10-8 in our demographic analyses allows us to explore
the effect of mutation rates on the analyses.