Quantitative trait locus mapping reveals an independent genetic basis for joint divergence in leaf function, life-history, and floral traits between scarlet monkeyflower (Mimulus cardinalis) populations
Fishman, Lila et al. (2021), Quantitative trait locus mapping reveals an independent genetic basis for joint divergence in leaf function, life-history, and floral traits between scarlet monkeyflower (Mimulus cardinalis) populations, Dryad, Dataset, https://doi.org/10.5061/dryad.m0cfxpp2q
Across taxa, vegetative and floral traits that vary along a fast-slow life-history axis are often correlated with leaf functional traits arrayed along the leaf economics spectrum, suggesting a constrained set of adaptive trait combinations. Such broad-scale convergence may arise from genetic constraints imposed by pleiotropy (or tight linkage) within species, or from natural selection alone. Understanding the genetic basis of trait syndromes and their components is key to distinguishing these alternatives and predicting evolution in novel environments.
We used a line-cross approach and quantitative trait locus (QTL) mapping to characterize the genetic basis of twenty leaf functional/physiological, life history, and floral traits in hybrids between annualized and perennial populations of scarlet monkeyflower (Mimulus cardinalis).
We mapped both single and multi-trait QTLs for life history, leaf function and reproductive traits, but found no evidence of genetic co-ordination across categories. A major QTL for three leaf functional traits (thickness, photosynthetic rate, and stomatal resistance) suggests that a simple shift in leaf anatomy may be key to adaptation to seasonally dry habitats.
Our results suggest that the co-ordination of resource-acquisitive leaf physiological traits with a fast life history and more selfing mating system results from environmental selection rather than functional or genetic constraint. Independent assortment of distinct trait modules, as well as a simple genetic basis to leaf physiological traits associated with drought escape, may facilitate adaptation to changing climates.
The spreadsheet contains four sheets.
1. phenotypic data for all individuals in the experiment (including parents and F1 hybrids and all f2s hybrid) used for calculation of quantiative genetic summaries (heritability, genetic covariances). Both raw values and standardized values (see methods) are included.
2. phenotype matrix for the subset of F2 hybrids used for genetic mapping.
3. genotype matrix for the subset of F2 hybrids used for genetic mapping, coded as CC for CE10 homozygote, CW for heterozygote and WW for WFM homozygote.
4. genetic map of markers used for QTL mapping (marker numbers are not meaningful but match those in the genotype matrix)
Both phenotypes and genotypes are not raw data. Phenotypic values such as relative growth rate, assimilation rate, or leaf thickness were calculated, as described in the Methods text, from underlying measurements. The called genotypes are derived from gene-capture sequence data available on the Sequence Read Archive, using the protocols described in the text. The authors are willing to share the raw phenotypic data (e.g. leaf area) by request. We can privately provide spreadsheets that match markers to physical map positions.
There are missing genotypes and phenotypes for some individuals (indicated by NA or - in the cell). Genetic correlations were calculated using individuals without missing data for any trait.
The three F2 files are in rQTL2 input format.
National Science Foundation, Award: DEB-1407333
National Science Foundation, Award: OIA-1736249
National Science Foundation, Award: DEB-1457763