Data analysis and statistical analysis
The distribution map of common vole samples used in this study (Fig. 1A)
was generated using the following R packages: ‘rworldmap’(South, 2011),
‘rworldxtra’(South, 2012), ‘RcolorBrewer’(Neuwirth, 2014),
‘maptools’(Bivand & Lewin-Koh, 2019), and ‘classInt’(Bivand, 2019).
Ellipse-like annual relationships between temperature and photoperiod
(Fig. 1B, C) were built using ~10-year (between
2000-2019) average monthly ambient temperatures obtained from local
weather stations (within 110km of sample location) at
http://www.wunderground.com. Photoperiod, based on civil twilight
times at dawn and dusk at different locations were retrieved from
https://www.timeanddate.com/. Grass growth in spring is used as aproxy for the onset of the favorable reproductive season. Grass
growth is initiated at 5-10°C air temperature(Cooper, 1964; Peacock,
1975, 1976). To include all locations in our analysis, a temperature
threshold at 6.6°C was used to deduce for further analysis the
corresponding predicted critical photoperiod (pCPP) that would initiate
optimal timing of reproduction.
The ‘Phyre2’ web portal for protein modeling was used to predict the
TSHR protein 3D structure (Fig. 2D)(Kelley, Mezulis, Yates, Wass, &
Sternberg, 2015). SNPs were detected by sequence alignments using ‘CLC
Sequence Viewer’ (version 8.0) (QIAGEN, Aarhus, Denmark). Chromatograms
were checked for sequencing quality and heterozygosity of SNPs in the
Mac OS software ‘4-peaks’ (Nucleobytes, Aalsmeer, the Netherlands).
Variation in DNA sequences were classified as SNPs if >3 of
the specimens contained the mutation. Putative transcription factor
bindings sites were predicted using AliBaba2(Grabe, 2002). To
statistically test gene-environment associations, we used a
population-based approach, in which an environmental variable was
modelled as a linear function of population allele frequency(Rellstab,
Gugerli, Eckert, Hancock, & Holderegger, 2015). Pearson’s correlation
tests were carried out: pairwise distances of allele frequencies were
correlated to pairwise geographical distance, pairwise latitudinal
difference, pairwise longitudinal difference, pairwise altitudinal
difference, and pairwise critical photoperiod difference.P -values were adjusted according to the Benjamini-Hochberg
procedure(Benjamini & Hochberg, 1995; Yekutieli & Benjamini, 1999),
which is one of the strongly recommended method to use in environmental
association analysis(Rellstab et al., 2015). Pairwise linkage
disequilibrium heatmaps (Fig. S3) were generated using the R-package
‘LDheatmap’(Shin, Blay, McNeney, & Jinko Graham, 2006). The constructed
phylogenetic tree (Fig. S2) from SNP frequency data by using the
neighbor-joining method(Saitou & Nei, 1987) was generated using
‘POPTREEW’(Takezaki, Nei, & Tamura, 2014). All other analyses were
performed using ‘RStudio’ (version 1.2.1335) and figures were generated
using the R-package ‘ggplot2’(Wickham, 2016).