Adaptive divergence in the monkey flower

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The yellow monkey flower, Mimulus guttatus. Photo from wikimedia.org

Theory suggests adaptive divergence can proceed in the face of gene flow when adaptive alleles occur in areas of the genome, such as chromosomal inversions, that are protected from recombination, which can break up beneficial allele pairings. In their recent Evolution paper, Twyford and Friedman determine phylogeographic structure and the role of an inversion in the adaptive divergence of life history strategies in the yellow monkey flower, Mimulus guttatus, across northwest North America.

Mimulus gattatus plants employ perennial or annual life history strategies. Perennial plants tend to occur in wetter sites, invest heavily in vegetative growth, and flower later in the season while annual plants occur in drier, drought prone areas and reproduce early in the season. The two ecotypes differ in flowering and senescence time, flower size, and potential to spread clonally, but have overlapping ranges and are fully interfertile. Previous studies found adaptive traits that differ between the perennial and adaptive ecotypes map to a chromosomal inversion that contains hundreds of genes.

Twyford and Friedman used a population genomics approach to 1) clarify the evolutionary relationships between annual and perennial populations of M. guttatus, 2) test whether the chromosomal inversion or some other genomic region(s) are associated with divergence between annual and perennial M. guttatus, and 3) determine the evolutionary history of the chromosomal inversion, specifically, which orientation is derived, how old is the inversion, and how did the inversion spread?

Figure 2. Contrasting patterns of genetic structure across 69 populations of Mimulus guttatus at genome-wide and inversion loci. Bayesian STRUCTURE plots for each population are shown for the optimal number of genetic clusters (K=2), with ancestry proportion (Q) on the Y-axis. a, Plot for 1400 genome-wide loci (100 per linkage group) grouped by life history, with the annual cluster in red and the perennial cluster in blue. b, Plot for genome-wide loci ordered by latitude, with the southern-most on the left and the most northerly on the right. c, Plot for 276 LD-filtered loci within the inversion, ordered by life history. d, Plot for inversion loci ordered by latitude. Coloured asterisks indicate those individuals known to be of annual (red) or perennial (blue) orientation from previous experimental crosses (Lowry and Willis, 2010).

Figure and caption from Twyford and Friedman 2015. Contrasting patterns of genetic structure across 69 populations of Mimulus guttatus at genome-wide and inversion loci. Bayesian STRUCTURE plots for each population are shown for the optimal number of genetic clusters (K=2), with ancestry proportion (Q) on the Y-axis. a, Plot for 1400 genome-wide loci (100 per linkage group) grouped by life history, with the annual cluster in red and the perennial cluster in blue. b, Plot for genome-wide loci ordered by latitude, with the southern-most on the left and the most northerly on the right. c, Plot for 276 LD-filtered loci within the inversion, ordered by life history. d, Plot for inversion loci ordered by latitude. Coloured asterisks indicate those individuals known to be of annual (red) or perennial (blue) orientation from previous experimental crosses (Lowry and Willis, 2010).

Clustering analyses showed that variation in SNPs across the genome was partitioned according to geography but not life history (panels B and A, respectively, in the figure above, taken from Twyford and Friedman 2015). However SNPs within the chromosomal inversion clearly cluster according to life history and less so with geography (panels C and D). In testing for candidate regions underlying divergence between ecotypes, the authors found that 40% of outlier loci mapped to the inversion, although it represents just 3.5% of genome-wide SNPs (figure below).

Figure and caption modified from Twyford adn Friedman 2015. Genomic patterns of divergence across annual and perennial ecotypes of Mimulus guttatus. a, Genome scan for allele frequency differences between ecotypes (FCT outlier scan). The x-axis represents physical position relative to the M. guttatus reference genome. Significant outliers with FCT greater than 0.2 are highlighted in red. The linkage group 8 inversion is shaded in blue.

Figure and caption modified from Twyford and Friedman 2015. Genome scan for allele frequency differences between ecotypes (FCT outlier scan). The x-axis represents physical position relative to the M. guttatus reference genome. Significant outliers with FCT greater than 0.2 are highlighted in red. The linkage group 8 inversion is shaded in blue.

In regards to the final aim of the paper, to determine the evolutionary history of the inversion, Twyford and Friedman conclude that it is likely ancient in origin given that sufficient time as passed for the development of a pattern of geographic subdivision within the inversion loci for both ecotypes and the presence of substantial nucleotide diversity within the inversion (i.e. the inversion is old enough to have acquired many mutations).

It seems very unlikely that the inversion is of recent origin, sweeping through existing populations. Instead it seems more likely that the inversion is old, potentially arising early in the origin of the ecotypes, and has subsequently experienced much the same demographic history as the collinear regions of the genome.

Reference:

Twyford, AD and Friedman, J (2015) Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion. Evolution. DOI: 10.1111/evo.12663

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About Melissa DeBiasse

I am a postdoctoral researcher at the University of Florida Whitney Laboratory for Marine Bioscience. As an evolutionary ecologist I am interested in the processes that generate biodiversity in marine ecosystems. My research uses experimental methods and genomic and phenotypic data to test how marine invertebrate species respond to biotic and abiotic stressors over ecological and evolutionary timescales.
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