Earth's temperature is increasing due to anthropogenic CO2 emissions; and organisms need either to adapt to higher temperatures, migrate into colder areas, or face extinction. Temperature affects nearly all aspects of an organism's physiology via its influence on metabolic rate and protein structure, therefore genetic adaptation to increased temperature may be much harder to achieve compared to other abiotic stresses. There is still much to be learned about the evolutionary potential for adaptation to higher temperatures, therefore we studied the quantitative genetics of growth rates in different temperatures that make up the thermal performance curve of the fungal model system Neurospora crassa. We studied the amount of genetic variation for thermal performance curves and examined possible genetic constraints by estimating the G‐matrix. We observed a substantial amount of genetic variation for growth in different temperatures, and most genetic variation was for performance curve elevation. Contrary to common theoretical assumptions, we did not find strong evidence for genetic trade‐offs for growth between hotter and colder temperatures. We also simulated short‐term evolution of thermal performance curves of N. crassa, and suggest that they can have versatile responses to selection.

While mutation rates have been extensively studied, variation in mutation rates throughout the genome is poorly understood. To understand patterns of genetic variation, it is important to understand how mutation rates vary. Chromatin modifications may be an important factor in determining variation in mutation rates in eukaryotic genomes. To study variation in mutation rates, we performed a mutation accumulation experiment in the filamentous fungusNeurospora crassa, and sequenced the genomes of the 40 MA lines that had been propagated asexually for approximately 1015 [1003, 1026] mitoses. We detected 1322 mutations in total, and observed that the mutation rate was higher in regions of low GC, in domains of H3K9 trimethylation, in centromeric regions, and in domains of H3K27 trimethylation. The rate of single nucleotide mutations in euchromatin was 2.46 [2.19, 2.77] × 10-10. In contrast, the mutation rate in H3K9me3 domains was tenfold higher: 2.43 [2.25, 2.62] × 10-9. We also observed that the spectrum of single nucleotide mutations was different between H3K9me3 and euchromatic domains. Our statistical model of mutation rate variation predicted a moderate amount of extant genetic variation, suggesting that the mutation rate is an important factor in determining levels of natural genetic variation. Furthermore, we characterized mutation rates of structural variants, complex mutations, and the effect of local sequence context on the mutation rate. Our study highlights that chromatin modifications are associated with mutation rates, and accurate evolutionary inferences should take variation in mutation rates across the genome into account.

Mutation rate have been extensively studies in eukaryotes. However, much less is known about the variation of mutation rate across the genome. Chromatin modifications may be an important factor in determining mutation rate variation in eukaryotic genomes. We performed a mutation accumulation experiment in the filamentous fungus Neurospora crassa and detected mutations in the MA-lines by genome sequencing. We detected over 1300 mutations, which happened during asexual propagation. This leads to markedly different mutation rate and spectrum than during sexual reproduction as previously investigated. We observed that GC-content, H3K9 methylation, and centromeric regions have large influence on mutation rate, with higher mutation rate in H3K9 and centromeric regions. H3K27 trimethylation had no effect on mutation rate. We also observe that the spectrum of single nucleotide mutations is different in H3K9 and euchromatic domains. We validate our mutation model by comparing the predictions to natural genetic variation and observed that a moderate amount of extant genetic variation can be predicted by our model. Furthermore, we characterize structural variants and complex mutations that happen in Neurospora crassa. Our study highlights that chromatin modifications influence mutation rate and accurate evolutionary inferences must take this variation in mutation rates into account.

1Earth's temperature is increasing due to anthropogenic CO 2 emissions; and organ-2 isms need either to adapt to higher temperatures, migrate into colder areas, or face 3 extinction. Temperature affects nearly all aspects of organism's physiology via its in-4 fluence on metabolic rate and protein structure. Compared to other abiotic stresses, 5 genetic adaptation to increased temperature may be much harder to achieve due to sys-6 temic effects of temperature. As the evolutionary potential for adaptation to higher 7 temperatures is relatively unknown, we studied the quantitative genetics of thermal 8 performance curves of the fungal model system Neurospora crassa. We asked whether 9 there is genetic variation for thermal performance curves and examined possible ge-10 netic of evolution constraints by estimating the G-matrix. We observed substantial 11 amount of genetic variation for growth in different temperatures, and most genetic 12 variation was for performance curve elevation. Contrary to common theoretical as-13 sumptions we did not find strong evidence for genetic trade-offs for growth between 14 hotter and colder temperatures. We also simulated short term evolution of thermal per-15 formance curves of N. crassa, and suggest that they can have versatile responses to 16 selection. 17 Earth's temperature is rising due to anthropogenic activities (IPCC, 2013). The challenge most 19 organisms will face in a warming world is that they have to either adapt to warmer conditions or 20 migrate into colder areas to avoid extinction (Deutsch et al., 2008; Dillon et al., 2010; Araújo et al., 21 2013; Merilä and Hendry, 2014). Temperature is a unique abiotic stress, because the kinetics of 22 all biochemical reactions and protein stability are affected by temperature. As such, temperature 23 influences nearly all aspects of an ectothermic organism's physiology (Schulte, 2015; Arcus et al., 24 2016). Therefore, adapting to a higher temperature may be much more difficult than adapting to 25 a more specific environmental stress. For some anthropogenic stresses, such as antibiotics or her-26 bicides, decades of research have revealed strong evolutionary adaptation to these stresses (Davies 27 and Davies, 2010; Powles and Yu, 2010). However, genetic basis of adaptation to temperature is 28 likely to much more complex (Hochachka and Somero, 2002). 29According to quantitative genetic theory, evolution is possible if variation in a trait is heritable 30 and selection acts on this variation. However, the evolution of multivariate traits can be complicated 31 by genetic correlations, allowing evolution to proceed only in few directions or possibly preventing 32 it altogether (Walsh and Blows, 2009). The more entangled traits are with each other, the more 33 difficult the evolution of the underlying genetic network and the phenotype can be. 34The ability of an organism to tolerate different temperatures is often described by thermal per-35 formance curve (Huey and Kingsolver, 1989, 1993), which describes the fitn...

Thirty seed lots from four spring wheat varieties produced in Finland in 2019 were RE-tested according to ISTA rules, counting the number of seeds with a 2 mm-long radicle after 48 hours in a germination test at 15°C in darkness. There were significant differences among the varieties in RE counts (p < 0.001). There were also differences within varieties in RE counts that could indicate vigour differences between the seed lots. The thousand seed weight (tsw) varied between varieties. However, tsw did not explain differences in RE counts (p = 0.10). The variety effect complicates the interpretation of RE results and our results suggest that RE results should only be used to compare seed lots within the same variety. Varieties should preferably have established RE result baselines. It would not be easy to assess new varieties until the necessary data has been generated.