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Abstract PremiseStudies into the evolution and development of leaf shape have connected variation in plant form, function, and fitness. For species with consistent leaf margin features, patterns in leaf architecture are related to both biotic and abiotic factors. However, for species with inconsistent leaf shapes, quantifying variation in leaf shape and the effects of environmental factors on leaf shape has proven challenging. MethodsTo investigate leaf shape variation in a species with inconsistently shaped leaves, we used geometric morphometric modeling and deterministic techniques to analyze approximately 500 digitized specimens ofCapsella bursa‐pastoriscollected throughout the continental United States over 100 years. We generated a morphospace of the leaf shapes and modeled leaf shape as a function of environment and time. ResultsLeaf shape variation ofC. bursa‐pastoriswas strongly associated with temperature over its growing season, with lobing decreasing as temperature increased. While we expected to see changes in variation over time, our results show that the level of leaf shape variation was consistent over the 100 years. ConclusionsOur findings showed that species with inconsistent leaf shape variation can be quantified using geometric morphometric modeling techniques and that temperature is the main environmental factor influencing leaf shape variation. 

Abstract Traits that have lost function sometimes persist through evolutionary time. Persistence may occur if there is not enough standing genetic variation for the trait to allow a response to selection, if selection against the trait is weak relative to drift, or if the trait has a residual function. To determine the evolutionary processes shaping whether nonfunctional traits are retained or lost, we investigated short stamens in 16 populations of Arabidopsis thaliana along an elevational cline in northeast Spain. A. thaliana is highly self-pollinating and prior work suggests short stamens do not contribute to self-pollination. We found a cline in short stamen number from retention of short stamens in high-elevation populations to incomplete loss in low-elevation populations. We did not find evidence that limited genetic variation constrains short stamen loss at high elevations, nor evidence for divergent selection on short stamens between high and low elevations. Finally, we identified loci associated with short stamens in northeast Spain that are different from loci associated with variation in short stamens across latitudes from a previous study. Overall, we did not identify the evolutionary mechanisms contributing to an elevational cline in short stamen number so further research is clearly warranted. 

{"Abstract":["Traits that have lost function sometimes persist through evolutionary\n time. Persistence may occur if there is not enough standing genetic\n variation for the trait to allow a response to selection, if selection\n against the trait is weak relative to drift, or if the trait has a\n residual function. To determine the evolutionary processes shaping whether\n nonfunctional traits are retained or lost, we investigated short stamens\n in 16 populations of Arabidopsis thaliana along an elevational cline in\n northeast Spain. A. thaliana is highly self-pollinating and prior work\n suggests short stamens do not contribute to self-pollination. We found a\n cline in short stamen number from retention of short stamens in high\n elevation populations to incomplete loss in low elevation populations. We\n did not find evidence that limited genetic variation constrains short\n stamen loss at high elevations, nor evidence for divergent selection on\n short stamens between high and low elevations. Finally, we identified loci\n associated with short stamens in northeast Spain that are different from\n loci associated with variation in short stamens across latitudes from a\n previous study. Overall, we did not identify the evolutionary mechanisms\n contributing to an elevational cline in short stamen number so further\n research is clearly warranted. This dryad dataset includes the GWAS output\n results. See the github for phenotypic data and SRA for genotypic data."],"TechnicalInfo":["# Evaluating the roles of drift and selection in trait loss along an\n elevational gradient Dataset DOI:\n [10.5061/dryad.8sf7m0d0z](10.5061/dryad.8sf7m0d0z) ## Description of the\n data and file structure These files are the relatedness matrices and GWAS\n output files for a GWAS on short stamen number in *A.\n thaliana* from an elevation gradient across the Pyrenees. The\n associated paper is "Evaluating the Roles of Drift and Selection in\n Trait Loss along an Elevational Gradient" by Buysse et al. The code\n used to generate the files can be found on\n github: [https://github.com/sfbuysse/A_thaliana_StamenLoss_2025](https://github.com/sfbuysse/A_thaliana_StamenLoss_2025).  The input data is SNP information for 61 genotypes from 16 native populations of *A. thaliana*. ### Files and variables #### File: RelatednessMatrices.zip **Description:** **RelatednessMatrices.zip** contains centered Relatedness Matrices made with GEMMA v0.98.4. Relatedness matrices are *.cXX.txt and *.log.txt show the code and run log information. allSNPs.PlinkFiltering_Asin, allSNPs.PlinkFiltering_Binary, allSNPs.PlinkFiltering_raw : identical relatedness matrices made using all SNPs in the dataset after filtering with Plink. Names were changed to match the phenotype files to run the GWAS.  allSNPs.PlinkFiltering*_*raw_subset : centered relatedness matrix made with all SNPs after plink filtering but only the individuals with some short stamen loss (mean short stamen number < 2). NoCent.PlinkFiltering_Asin, NoCent.PlinkFiltering_Binary, NoCent.PlinkFiltering_raw  : identical relatedness matrices made after excluding the centromere region and filtering with Plink. Names were changed to match the phenotype files to run the GWAS.  NoCent.PlinkFiltering_raw_subset. : centered relatedness matrix made after excluding the centromere and plink filtering but only the individuals with some short stamen loss (mean short stamen number < 2). #### File: GWAS.zip **Description:** **GWAS.zip** contains GWAS output files. The GWAS output files are  *.assoc.txt and the code information is  *.log.txt. GWAS were run in GEMMA v0.98.4. Within each .assoc.txt file the columns are as follows: * chr = chromosome * rs = snp id (chromosome:base pair position) * ps = base pair position * n_miss = number of genotypes missing genetic information at that SNP * allele1 = minor allele * allele2 = major allele * af = minor allele frequency * beta = affect size * se = standard error for beta * log_lH1 = log liklihood of alternative hypothesis that beta does not equal 0 (H0 is that beta =0) * l_remle = restricted maximum liklihood estimates for lambda * l_mle = maximum liklihood estimates for lambda * p_wald = p value from the Wald test * p_lrt = p value from liiklihood ratio test * p_score = p value from score test allSNPs.PlinkFiltering_Asin.c : include allSNPs after filtering with plink. phenotypes were arcsine transformed before GWAS. Centered relatedness matrix used. allSNPs.PlinkFiltering_Binary.c : include allSNPs after filtering with plink. phenotypes were transformed to a binary trait before GWAS - no short stamen loss = 0, any short stamen loss = 1. Centered relatedness matrix used. allSNPs.PlinkFiltering_raw.c : include allSNPs after filtering with plink. phenotypes were not transformed before GWAS. Centered relatedness matrix used. allSNPs.PlinkFiltering*_*raw_subset.c : include allSNPs after filtering with plink. phenotypes were not transformed before GWAS but the individuals used were subset down to only those that had some short stamen loss (mean short stamen number < 2). Centered relatedness matrix used. NoCent.PlinkFiltering_Asin.c : Centromere excluded. Plink Filtering as before. Arcsine transformed phenotypes. Centered relatedness matrix. NoCent.PlinkFiltering_Binary.c : Centromere excluded. Plink Filtering as before. Phenotypes converted to a binary trait. Centered relatedness matrix. NoCent.PlinkFiltering_raw.c : Centromere excluded. Plink Filtering as before. Phenotypes not transformed. Centered relatedness matrix. NoCent.PlinkFiltering_raw_subset.c : Centromere excluded. Plink Filtering as before. Individuals subset to only those that had some short stamen loss. Centered relatedness matrix. ## Code/software We used GEMMA v0.98.4 to create the files. ## Access information Other publicly accessible locations of the data: * [https://github.com/sfbuysse/A_thaliana_StamenLoss_2025](https://github.com/sfbuysse/A_thaliana_StamenLoss_2025) : scripts and information for creation of input files and use of output files after generation. * Genotypic data used is submitted to NCBI SRA as accession PRJNA1246133."]} 

Divergent selection across the landscape can favor the evolution of local adaptation in populations experiencing contrasting conditions. Local adaptation is widely observed in a diversity of taxa, yet we have a surprisingly limited understanding of the mechanisms that give rise to it. For instance, few have experimentally confirmed the biotic and abiotic variables that promote local adaptation, and fewer yet have identified the phenotypic targets of selection that mediate local adaptation. Here, we highlight critical gaps in our understanding of the process of local adaptation and discuss insights emerging from in-depth investigations of the agents of selection that drive local adaptation, the phenotypes they target, and the genetic basis of these phenotypes. We review historical and contemporary methods for assessing local adaptation, explore whether local adaptation manifests differently across life history, and evaluate constraints on local adaptation. 

Abstract Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.