Balancing selection is frequently invoked as a mechanism that maintains variation within and across populations. However, there are few examples of balancing selection operating on loci underpinning complex traits, which frequently display high levels of variation. We investigated mechanisms that may maintain variation in a focal polymorphism—leaf chemical profiles of a perennial wildflower (Boechera stricta, Brassicaceae)—explicitly interrogating multiple ecological and genetic processes including spatial variation in selection, antagonistic pleiotropy and frequency-dependent selection. A suite of common garden and greenhouse experiments showed that the alleles underlying variation in chemical profile have contrasting fitness effects across environments, implicating two ecological drivers of selection on chemical profile: herbivory and drought. Phenotype–environment associations and molecular genetic analyses revealed additional evidence of past selection by these drivers. Together, these data are consistent with balancing selection on chemical profile, probably caused by pleiotropic effects of secondary chemical biosynthesis genes on herbivore defence and drought response.
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The new reference genome assemblies and raw Nanopore reads for the SAD12 and LTM genotypes have been submitted to NCBI (BioProject number PRJNA609209). The short reads of the GBS data for the CFR-NIL families have been submitted to NCBI (BioProject number PRJNA659863). Previously published genomic data are archived with ref. 55. All other data reported in this manuscript are archived in the Dryad digital data repository (https://doi.org/10.5061/dryad.7h44j0zsr)56. All biological materials are available from the Arabidopsis Biological Resource Center (ABRC) or from the authors.
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We thank the Duke University greenhouse staff, E. Hornstein, S. Mahanes, L. Schumm, R. Bingham, N. Niezgoda, C. Ried, A. Simha, W. de Vries, E. Cousins and K. Stinson for assistance with fieldwork in Colorado; E. Raskin, C. Strock, B. Guyton, W. Mitchell, J. Lessing, M. Olszack, A. Zemenick, M. McMunn, K. Stiff, S. Clemens, T. Park, S. Shriber-Olds and R. Colautti for assistance with fieldwork in Idaho; and J. Reithel, S. Sprott and C. Heald for logistical support that facilitated fieldwork. We thank the Rocky Mountain Biological Laboratory, the Crested Butte Land Trust, the US Forest Service, D. Finlayson, R. Capps, A. Mears and P. Lehr for permission to conduct field experiments. We thank V. Grant and A. Zhao for assistance with laboratory experiments, E. Iversen at the Duke University Statistical Consulting Center for guidance regarding some analyses, and K. Donohue, M. Rausher and W. Morris for feedback that greatly improved this manuscript. We thank the Computer and Information Networking Center at National Taiwan University for high-performance computing facilities and the Technology Commons in the College of Life Science at National Taiwan University for molecular biology equipment. This work was supported by the Ministry of Science and Technology of Taiwan (grant no. 108-2636-B-002-004 to C.-R.L.), the Guangdong Natural Science Funds for Distinguished Young Scholar (grant no. 2018B030306040 to B.W.), the National Institutes of Health (grant no. R01 GM086496 to T.M.-O.) and the Rocky Mountain Biological Laboratory (Snyder Endowment graduate fellowship to L.N.C.).
The authors declare no competing interests.
Peer review information Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Genotype frequency does not alter the effect of BCMA1/3 on herbivore resistance or survival.
In experimental arrays in which we manipulated the starting genotype frequency of the BCMA1/3 homozygotes, there was no effect of genotype frequency on herbivore damage or survival. In each panel, points represent least-squares means estimates of the response variable for each genotype in each BCMA1/3 frequency treatment, and error bars represent ± one standard error.
BCMA1/3 alleles in the CFR-NIL background confer contrasting response to drought by altering morphological traits such as leaf size and number as well as physiological traits such as growth. Both genotypes reduce leaf water content under drought, but genetic differences in this response were only marginally significant. In each panel, points represent least-squares means estimates of the response variable for each genotype in each BCMA1/3 frequency treatment, and error bars represent ± one standard error.
Extended Data Fig. 3 Drought-related climate variables are correlated with multivariate climatic predictors of BC-ratio.
a BC-ratio varies across climate space, with PC1 the strongest predictor (Supplementary Table 9). b Linear models and permutation tests reveal that low BC-ratio phenotypes are significantly correlated with drier environments of origin. Points in all panels represent phenotypic (LS mean BC-ratio) and environmental variation (WorldClim data from the location of origin) of a broad panel of accessions. Colors represent BC-ratio, ranging from 0% (blue) to 100% (red). Shapes denote genetic groups as described in Wang et al. (2019). COL: circles; NOR: triangles; UTA: squares; WES: crosses. Black lines represent lines of best fit estimated using linear models using discrete groups to control for population structure (‘approach A’) as described in Supplementary Methods.
Panes correspond to linear models presented in Supplementary Table 11. In each pane, gray bars show the frequency distribution of the test statistic relating each climate variable to BC-ratio from 10,000 permutations shuffling BC-ratio values without replacement (Supplementary Methods), red arrows show the observed F-statistic from each true model (Supplementary Table 11), and dashed lines mark the location of the extreme 95% tail in the empirical cumulative distribution function of permuted F-statistics, using three different methods to control for population structure (columns A-C; Supplementary Methods).
Extended Data Fig. 5 Functional and copy number variation in BCMA evolved recently within B. stricta.
Maximum likelihood phylogenetic reconstruction of BCMA copy sequences (excluding severely truncated copies) elucidates the evolutionary history of BCMA duplications in Boechera. Colored boxes behind (pseudo)gene names categorize features as follows: blue boxes contain nonfunctional BCMA pseudogenes on chromosome 7, light yellow boxes contain functional copies of BCMA2 on chromosome 2, and dark yellow boxes contain functional copies of BCMA3 and BCMA1 on chromosome 7. Shaded boxes indicate paralogs in B. retrofracta, and green box indicates the A. thaliana ortholog (CYP79F1). Scale bar shows genetic distance in nucleotide differences per base pair.
Chromosomal pedigree showing how closest flanking recombinant near-isogenic lines (CFR-NILs) were generated for use in laboratory and field experiments. See Methods and Supplementary Information for details. Within each step, diploid homologous pairs of Chromosome 7 are shown.
Extended Data Fig. 7 Long-read assemblies of the LTM and SAD12 parents reveal substantial variation in tandem repeats and BCMA copy number in a 200 kb region on chromosome 7.
Functional BCMA gene copies are indicated in yellow; red circles show severely truncated, non-functional BCMA copies; blue ellipses indicate close-to-full-length copies of BCMA containing frameshift deletions or transposon insertions. Blue and yellow elements match those shown in Extended Data Figures 5 and Supplementary Figure 2.
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Carley, L.N., Mojica, J.P., Wang, B. et al. Ecological factors influence balancing selection on leaf chemical profiles of a wildflower. Nat Ecol Evol 5, 1135–1144 (2021). https://doi.org/10.1038/s41559-021-01486-0
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