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Data from: Quantitative genetics of temperature performance curves of Neurospora crassa

Kronholm, Ilkka, University of Jyväskylä, https://orcid.org/0000-0002-4126-0250
Moghadam, Neda N., University of Jyväskylä
Sidhu, Karendeep, University of Jyväskylä
Summanen, Pauliina A. M., University of Jyväskylä
Ketola, Tarmo, University of Jyväskylä
ilkka.kronholm@jyu.fi, neda.n.nasiri-moghadam@jyu.fi, karendeepsidhu@googlemail.com, pauliina.a.m.summanen@student.jyu.fi, tarmo.t.ketola@jyu.fi
Published May 22, 2020 on Dryad. https://doi.org/10.5061/dryad.pk0p2ngk9

Cite this dataset

Kronholm, Ilkka et al. (2020). Data from: Quantitative genetics of temperature performance curves of Neurospora crassa [Dataset]. Dryad. https://doi.org/10.5061/dryad.pk0p2ngk9

Abstract

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.

Methods

For each growth assay, the data are the distances the mycelium has grown over time in a linear tube. From this data growth rates can be calculated using the R scripts provided with the dataset. For further details about the methods, see the manuscript in bioarxiv: doi: https://doi.org/10.1101/2020.01.16.909093

Usage notes

This is the data for the manuscript: "Quantitative genetics of temperature performance curves of Neurospora crassa. It is the growth of 428 different Neurospora crassa genotypes in six different temperatures. The goal was to estimate quantitative genetic parameters of the temperature performance curve. Please see the manuscript at bioarxiv for more details: doi: https://doi.org/10.1101/2020.01.16.909093

See the included file Readme.txt for details