Title: Evaluating the roles of drift and selection in trait loss along an elevational gradient
{"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."]} more »« less
Buysse, Sophia F; Pérez, Samuel G; Puzey, Joshua R; Garrison, Ava; Bradburd, Gideon S; Oakley, Christopher G; Tonsor, Stephen J; Picó, F Xavier; Josephs, Emily B; Conner, Jeffrey K
(, Evolution)
Shaw, Ruth; Connallon, Tim
(Ed.)
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 conserved across evolutionary time often provide compelling\n examples of key adaptations for a given taxonomic group. Tetradynamy is\n the presence of four long stamens plus two short stamens within a flower\n and is conserved across most of the roughly 4000 species in the mustard\n family, Brassicaceae. While this differentiation in stamens is\n hypothesized to play a role in pollination efficiency, very little is\n known about the potential function of the two stamen types. The present\n study sheds new light on this mystery using wild radish (Raphanus\n raphanistrum), a widespread and well-studied tetradynamous plant. We used\n data collected from slow-motion videos of pollinators visiting wild radish\n flowers to test three non-mutually exclusive adaptive hypotheses: 1) short\n and long stamens are specialized for either feeding or pollinating, 2)\n short and long stamens are specialized for different pollinator taxa, and\n 3) the presence of short and long stamens increases pollinator movement\n and thus effectiveness. We find evidence consistent with hypothesis three,\n but no evidence for hypotheses one or two. Thus, tetradynamy may be an\n adaptation for generalized pollination, enabling effective visits by the\n variety of pollinators visiting most species of Brassicaceae."],"TechnicalInfo":["# Data from: Testing adaptive hypotheses for an evolutionarily conserved\n trait through slow-motion videos of pollinators The data contained in\n these files was generated from close observation of slow-motion video\n footage by the same experimenter for each variable. ## Description of\n Files ### MainData.csv Data related to slow-motion video analysis,\n including plant information, anther and stigma contact, and number of\n movements Missing data are indicated by "NA" #### Basic Video\n Info in Columns A:F * VideoID: unique individual video identifier *\n PlantID: unique individual plant identifier with the following format -\n "PopulationCode FamilyCode-Replicate" * PopulationCode: BINY =\n natural population, Sep = separation-selected, Exsertion =\n exsertion-selected * FamilyCode: unique 3-5 character code for a given\n maternal seed family * Replicate: individual plant number between 0 and 9,\n where replicate 0 is indicated by the lack of a hyphen and number * Date:\n date of observations * Year: year of observations * Pollinator: taxa of\n visiting pollinator * VideoLength: total length of visit in 1/8 real-time\n seconds #### Feeding Info in Columns G:N * G:K are binary columns in which\n 1 indicates the visit included foraging in the given category, 0 indicates\n lack of foraging, and ? indicates uncertainty ("Short" = short\n stamen anthers, "Long" = long stamen anthers) * L:N summarize\n the info from G:K in different ways * Foraging: whether the visit included\n foraging on nectar, pollen, or both * Feed_All: for visits including\n pollen-foraging, whether foraging was on short stamen anthers, long stamen\n anthers, or both * Feed_Bin: same as Feed_All but groups "Long"\n and "Short" into "One" #### Contact Info in Columns\n O:AM Columns have the following format:\n "ResponseVariable_BodySection_FlowerPart" * ResponseVariable is\n what kind of contact is being recorded and can take three values: * sec:\n duration of contact in 1/8 real-time seconds * bin: binary contact, 1 =\n contacted and 0 = not contacted * n: count of body sections contacted\n (sums binary contact with Legs, Ventral, Side) * BodySection is the part\n of the pollinator body contacted and can take four values: Ventral, Side,\n Legs, or Total (sum of prior 3) * FlowerPart is the part of the flower\n contacted by the pollinator and can take 4 values: S (short stamen\n anthers), L (long stamen anthers), Stigma, or Anthers (both short and long\n stamen anthers) #### Movement Info in Columns AN:AR * Between_Moves: # of\n movements from feeding on one stamen to another * Within_Moves: # of\n movements within stamen types, combining movements from long to long\n stamen ("Long.Long_Moves") and movements from short to short\n stamen ("Short.Short_Moves") * Total_Moves: total # of movements\n from one stamen to another ### DyeSwab.csv Data from small preliminary\n test in which 3 bees were swabbed with gelatin cubes after visiting\n flowers with short and long stamens marked with different colors of\n fluorescent dye. * ID: unique individual bee identifier * BodySection: the\n body section swabbed * NParticles: count of dye particles contained in\n gelatin swab * StamenType: type of stamen matching the color of counted\n particles ### Final_Analysis_Dryad.R R script of all analyses used in the\n paper. * Details provided as comments within script. * The script was run\n in RStudio running R v. 4.4.2."]}
Abstract Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eXpedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.
Waterman, Robin; Song, Sally; Bhandari, Nicholas; Conner, Jeffrey K
(, Royal Society Open Science)
Traits conserved across evolutionary time often provide compelling examples of key adaptations for a given taxonomic group. Tetradynamy is the presence of four long stamens plus two short stamens within a flower and is conserved across most of the roughly 4000 species in the mustard family, Brassicaceae. While this differentiation in stamens is hypothesized to play a role in pollination efficiency, very little is known about the potential function of the two stamen types. The present study sheds new light on this mystery using wild radish (Raphanus raphanistrum), a widespread and well-studied tetradynamous plant. We used data collected from slow-motion videos of pollinators visiting wild radish flowers to test three adaptive hypotheses (not mutually exclusive): (H1) short and long stamens are specialized for either feeding or pollinating; (H2) short and long stamens are specialized for different pollinator taxa; and (H3) the presence of short and long stamens increases pollinator movement and thus effectiveness. We find evidence consistent with hypothesis H3, but no evidence for hypotheses H1 or H2. Thus, tetradynamy may be an adaptation for generalized pollination, enabling effective visits by the variety of pollinators visiting most species of Brassicaceae.
Abstract Genome-wide association studies (GWAS) are integral for studying genotype-phenotype relationships and gaining a deeper understanding of the genetic architecture underlying trait variation. A plethora of genetic associations between distinct loci and various traits have been successfully discovered and published for the model plant Arabidopsis thaliana. This success and the free availability of full genomes and phenotypic data for more than 1,000 different natural inbred lines led to the development of several data repositories. AraPheno (https://arapheno.1001genomes.org) serves as a central repository of population-scale phenotypes in A. thaliana, while the AraGWAS Catalog (https://aragwas.1001genomes.org) provides a publicly available, manually curated and standardized collection of marker-trait associations for all available phenotypes from AraPheno. In this major update, we introduce the next generation of both platforms, including new data, features and tools. We included novel results on associations between knockout-mutations and all AraPheno traits. Furthermore, AraPheno has been extended to display RNA-Seq data for hundreds of accessions, providing expression information for over 28 000 genes for these accessions. All data, including the imputed genotype matrix used for GWAS, are easily downloadable via the respective databases.
Buysse, Sophia, Pérez, Samuel G, Puzey, Joshua R, Garrison, Ava, Bradburd, Gideon, Oakley, Christopher G, Tonsor, Stephen J, Pico, F Xavier, Josephs, Emily B, and Conner, Jeffrey K. Evaluating the roles of drift and selection in trait loss along an elevational gradient. Web. doi:10.5061/dryad.8sf7m0d0z.
Buysse, Sophia, Pérez, Samuel G, Puzey, Joshua R, Garrison, Ava, Bradburd, Gideon, Oakley, Christopher G, Tonsor, Stephen J, Pico, F Xavier, Josephs, Emily B, & Conner, Jeffrey K. Evaluating the roles of drift and selection in trait loss along an elevational gradient. https://doi.org/10.5061/dryad.8sf7m0d0z
Buysse, Sophia, Pérez, Samuel G, Puzey, Joshua R, Garrison, Ava, Bradburd, Gideon, Oakley, Christopher G, Tonsor, Stephen J, Pico, F Xavier, Josephs, Emily B, and Conner, Jeffrey K.
"Evaluating the roles of drift and selection in trait loss along an elevational gradient". Country unknown/Code not available: Dryad. https://doi.org/10.5061/dryad.8sf7m0d0z.https://par.nsf.gov/biblio/10641418.
@article{osti_10641418,
place = {Country unknown/Code not available},
title = {Evaluating the roles of drift and selection in trait loss along an elevational gradient},
url = {https://par.nsf.gov/biblio/10641418},
DOI = {10.5061/dryad.8sf7m0d0z},
abstractNote = {{"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."]}},
journal = {},
publisher = {Dryad},
author = {Buysse, Sophia and Pérez, Samuel G and Puzey, Joshua R and Garrison, Ava and Bradburd, Gideon and Oakley, Christopher G and Tonsor, Stephen J and Pico, F Xavier and Josephs, Emily B and Conner, Jeffrey K},
}
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