DOI: 10.5061/dryad.n5tb2rbzx
title: Data from: Rapid, nonparallel genomic evolution of Brassica rapa (field mustard) under experimental drought

description: While we know that climate change can potentially cause rapid phenotypic
 evolution, our understanding of the genetic basis and degree of genetic
 parallelism of rapid evolutionary responses to climate change is limited.
 In this study, we combined the resurrection approach with an evolve and
 resequence design to examine genome-wide evolutionary changes following
 drought. We exposed genetically similar replicate populations of the
 annual plant Brassica rapa derived from a field population in southern
 California to four generations of experimental drought or watered
 conditions in a greenhouse. Genome-wide sequencing of ancestral and
 descendant population pools identified hundreds of SNPs that showed
 evidence of rapidly evolving in response to drought. Several of these were
 in stress response genes, and two were identified in a prior study of
 drought response in this species. However, almost all genetic changes were
 unique among experimental populations, indicating that the evolutionary
 changes were largely non-parallel, despite the fact that genetically
 similar replicates of the same founder population had experienced
 controlled and consistent selection regimes. This non-parallelism of
 evolution at the genetic level is potentially because of polygenetic
 adaptation allowing for multiple different genetic routes to similar
 phenotypic outcomes. Our findings help to elucidate the relationship
 between rapid phenotypic and genomic evolution and shed light on the
 degree of parallelism and predictability of genomic evolution to
 environmental change.
description: We combined experimental evolution with the resurrection approach
 to explore adaptation to experimental drought in <em>Brassica
 rapa</em> (field mustard). We founded 24
 experimental populations from lines which originated from a natural
 population and randomly assigned each experimental population to a
 watering regime. We assigned 8 populations for storage as ancestors, 8 to
 receive four generations of experimental drought, and 8 to receive four
 generations of experimental well-watered conditions. During these four
 generations of experimental evolution, drought regime populations received
 a draw-drown drought treatment aimed to mimic Mediterranean drought while
 well-watered regime populations were watered every other day. We collected
 data (flowering time, survival, and seed mass) on a subset of individuals
 each generation to explore trends in fitness and selection during
 experimental evolution. After experimental evolution,
 we grew all experimental populations (ancestors included) under unstressed
 (well-watered) conditions and extracted DNA from bud tissue from
 individuals for all 8 drought populations, all 8 watered populations, and
 2 ancestor replicates. Buds were pooled by population before DNA
 extraction and library prep with an Illumina DNA PCR-free library
 preparation kit. We then performed pooled sequencing on an Illumina
 NovaSeq 6000 to obtain our fastq files.
description: Our data submission includes Python and R scripts.
description: # README file for Johnson et al. 2023 manuscript published in Journal of
 Evolutionary Biology ## Article Citation: Johnson, S. E., Tittes, S.,
 & Franks, S. J. (2023). Rapid, nonparallel genomic evolution of
 Brassica rapa (field mustard) under experimental drought. Journal of
 Evolutionary Biology, 36(3), 550-562. ## Repository location: DOI:
 10.5061/dryad.n5tb2rbzx ## Note: Files are named as 2022 because that is
 when the final analyses and submission were prepared, but the manuscript
 was published in 2023. ## Data files: "Truseq3-PE-2.fa" contains
 adapters used to trim reads with trimmomatic software. "bam.txt"
 is a list of bam file names used in the fais_baypass.py script.
 "brassica.poolsize" is a list of the ploidy number of each
 population used when running the core and axillary models in BayPass.
 "brassica.covariates" is a file that encodes whether each
 population is ancestor or descendant for the first covariate (row 1) and,
 if descendant, drought or watered for the second covariate (row 2) when
 running the axillary model in BayPass.
 "Johnson_et_al_2022_Evolution_individuals_data.csv" contains
 phenotypic data and fitness collected from the test generation and
 previously published in Johnson et al. 2022 in Evolution.
 "Johnson_et_al_2022_Evolution_individuals_data.xlsx" contains
 the same data as Johnson_et_al_2022_Evolution_individuals_data.csv in the
 first tab and a description of variables in the second tab.
 "Atha__v__Brap.tsv" contains a list of B. rapa genes with A.
 thaliana annotations added with OrthoFinder and was obtained from Prof
 Michael Barker at the University of Arizona. * Atha__v__Brap.tsv
 variables: * Orthogroup: Unique nine-digit orthogroup ID between the TAIR
 Arabidopsis thaliana genome
 () and the
 V1.5 Brassica rapa genome from BRAD
 (). * Atha: AGI locus code
 (AGI) consisting of the prefix "At", a one-digit chromosome
 number, the character "G" for gene, a unique five-digit number,
 and a period followed by a one digit number describing the splice variant.
 * Brap: Unique identifier code for Brassica rapa genes consisting of the
 prefix "Bra" and then a unique six-digit number. ## Code files:
 "Johnson_et_al_2022_JEB_code.md" contains all command line code
 used to generate published results. "Fig1_code.R" contains all R
 code used to generate Figure 1, which plots snp frequencies of populations
 on the PC axis. "Fig2_code.R" contains all R code used to
 generate Figure 2, which displays Manhattan plots of differentiated SNPs
 identified 1) between ancestors and descendants with our Bayesian model in
 BayPass and 2) between drought replicates and ancestors with fisher exact
 tests. "tajimas_pi_code.R" contains all R code used to calculate
 mean and se nucleotide diversity (pi) from .pi files generated with
 PoPoolation software. "gene_enrichment_code.R" contains all R
 code used to run enrichment analysis in R from counts of significant and
 nonsignificant SNPs generated with bedtools intersect.
 "effective_pop_size_estimate_code.R" contains all R code used to
 estimate effective population size from reproduction data provided in
 Johnson_et_al_2022_Evolution_individuals_data.csv.
 "fais_baypass.py" produces a baypass format .txt file for
 downstream Baypass analysis using the reference genome and a .txt file
 with the names of the sorted bam files to use.
 "filter_baypass.R" is used to filter close variable loci (if
 within 100 bp, keeping the one with the most variation) prior to running
 models in BayPass. "build_genepairs.py" contains a python script
 to make a table of B. rapa and A. thaliana gene pairs from
 Atha__v__Brap.tsv. "build_genepairs_tests.py" contains a python
 script to run unit tests. "Flowering_genes.R" created
 flowering_genes.tsv list from 
 "Stress_genes.R" created stress_genes.tsv list from NCBI list of
 stress genes. "merge_stress_flowering.R" merges
 flowering_genes.tsv and stress_genes.tsv to create
 flowering_and_stress_genes.tsv.