Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana

Abstract

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments.

Keywords: branching; fitness landscape; flowering time; mutation; natural selection.

Fig. 1.

Heat map of least-square means of accumulated photothermal units to bolting (BPTUs) in mutants relative to ecotype background on a log₂ scale, centered within plantings, visualizing two-step hierarchical cluster by a Euclidian distance, average-based algorithm for both genotype (rows) and planting (columns). All mutant pathways are represented, although not all genotypes, since some were not planted in all 8 sites and seasons. The first word of the row identifiers shows which pathway was manipulated in a mutant, as defined by FLOR-ID (51). Lowercase gene names indicate diminished function alleles, and uppercase, functional. Colons between pathways or genes indicate multiple genetic manipulations within a line, not gene fusions. Col and Ler indicate each line’s ecotype background, Col-0 and Ler-1, respectively. Genotypes with an induced mutation combined with a functional FRIGIDA (denoted by “FRI”) were relativized against FRI Col instead of Col-0.

Fig. 2.

Fitness landscapes from generalized additive models for accumulated photothermal units bolting and total branch number. Contour line labels show fitness in seed proxy units. BPTU refers to accumulated photothermal units to bolting. Points show line averages where lines are genotypes bulked under the same maternal conditions. “Col” refers to the Col-0 ecotype average; “Ler” to the Ler-1 ecotype average; “ColFRI” refers to the genotype with a functional version of FRI introgressed from the Sf-2 ecotype. Grey vector lines represent mutations induced in the Col and Ler ecotypes, and blue vector lines represent mutations induced in the FRI (Col) genotype.

Fig. 3.

Relationship between network connectedness and mutant phenotypic shift relative to ecotype background. BPTU is for accumulated photothermal units to bolting; branching is the total number of branches; and fitness is seed proxy number. Shaded areas show 95% confidence intervals; panels with asterisks denote significant linear regressions (P < 0.05) after Bonferroni correction.