DOI: 10.5061/dryad.zkh1893g3
title: Landscape genomics reveals genetic signals of environmental adaptation of African wild eggplant

description: Crop wild relatives possess desirable traits that confer resilience to
 various environmental stresses. We applied landscape genomics, that
 associates environment with genomic variation to understand the genetic
 basis of their adaptation.    In this study, we applied
 landscape genomics to examine the differences in allele frequency of
 15,416 Single Nucleotide Polymorphisms (SNPs) among 153 accessions of wild
 eggplant relatives from Africa, the principal hotspot of these wild
 relatives. Further, we explored the correlation between the genetic
 variations and the bio-climatic and soil conditions at their collection
 sites.   Our results showed that the environment has a greater
 impact on the genetic variation in the eggplant wild relative populations
 compared to the geographical distances between collection sites while
 controlling for population structure. These findings indicate the
 relevance of the environment in shaping genetic variation in eggplant
 relatives over time. We detected also candidate SNPs associated with ten
 environmental factors. Some of these SNPs signal genes involved in
 pathways that help with adaptation to environmental stresses such as
 drought, heat, cold, salinity, pests, and diseases. 
description: <strong>SNP
 dataset</strong> According to the
 manufacturer's instructions, we isolated the genomic DNA from fresh
 leaves of five seedlings per accession using the FavorPrep Plant Genomic
 DNA Extraction Mini Kit (FAVORGEN). We then constructed the sequencing
 library following the approach of Elshire <em>et al</em>.
 (2011). Genomic DNA was quantified by Qubit and normalized to 100ng in
 96-well plates. We digested the DNA samples using the restriction enzyme
 <em>Ape</em>KI and ligated them with two adapters for
 sequencing, followed by the polymerase chain reaction to amplify the
 target DNA fragments to complete the sequencing library preparation. A
 service provider did sequencing with the Illumina HiseqX platform in a
 pair-end 150bp run.For the SNP calling, we followed
 mainly the manual of Stacks software (Catchen et al., 2013). In short, we
 filtered the raw reads by quality and demultiplexed using the process
 radtags program. We then mapped the retained reads to the eggplant
 reference genome (Eggplant_V4.1.fa) (Barchi et al.,
 2021) using the Burrows-Wheeler Aligner (BWA)
 version 0.7.17 (Li & Durbin, 2009). We sorted and indexed the
 reads using Samtools version 1.15.1 (Li et al., 2009), after which we
 performed the variant calling using the gstacks and population programs in
 Stacks software. We further filtered the SNPs and the accessions with less
 than 20% missing data and a Minor Allele Frequency (MAF) &gt; 0.05,
 giving the final high-quality SNP dataset comprising 15,146
 SNPs.  <strong>Environmental variable
 dataset</strong> We downloaded the grids for 19
 bioclimatic variables, solar radiation, wind speed, and vapor pressure
 derived from WorlClim 2.1 (Fick & Hijmans, 2017) at a resolution
 of 2.5 minutes. The 19 bioclimatic variables were each downloaded as
 annual data averages between 1970 and 2000. We averaged the monthly solar
 radiation, wind, and vapor pressure rasters to obtain annual value rasters
 from this period.  We downloaded the soil data from the SoilGrids database
 released in 2016 (https://soilgrids.org/) through ISRIC—WDC Soils (Hengl et
 al., 2017) at 250-meter resolution and at a depth of 15-30 cm,
 approximately the depth at which the eggplant roots can grow.
 Soil
 variables included nitrogen, soil organic carbon, organic carbon density,
 organic carbon stock, cation exchange capacity, pH, clay sand, and silt
 content. The
 soil dataset resolution was  aggregated to match that of the
 climate data using the <em>resample
 </em>and<em> extent</em> functions of the
 <em>raster</em> package in R (Hijmans,
 2023),
 ensuring they are consistent in both resolution and
 extent. The
 environmental variables for each accession with the
 <em>extract</em> function of the R <em>raster</em>
 package (Hijman, 2023) using the GIS coordinates at sampling points to
 obtain a full data set of all the climate and soil variables. For our
 modeling, we selected the environmental variables based on Variance
 Inﬂation Factors (VIFs) selecting for variables with a VIF less than
 5.
description: # Landscape genomics reveals genetic signals of environmental adaptation
 of African wild eggplant ## Description of the data and file structure
 **File list:** * Wild African eggplant SNP dataset.vcf * Wild African
 eggplant sample details and environmental data.csv ## **Data specific
 information** 1\. Wild African eggplant SNP dataset\.vcf The data lines in
 the .vcf file include * CHROM - chromosome: An identifier from the
 reference genome * POS - position: The reference position, with the 1st
 base having position 1. Positions are sorted numerically, in increasing
 order, within each reference sequence CHROM * ID - identifier:
 Colon-separated list of unique identifiers for the SNPs * REF - reference
 base(s) * ALT - alternate base(s): A list of alternate non-reference
 alleles. * QUAL - quality: Phred-scaled quality score for the assertion
 made in ALT * FILTER - filter status: PASS if this position has passed all
 filters, i.e., a call is made at this position * INFO - additional
 information * The genotype names e.g. aethiopicum1 are the accessions used
 in the analysis. The names are derived from the species names followed by
 a number denoting the sequence of the genotype in the order of the
 species. The genotype, even within the same species, maybe from different
 populations. Further information about the genotypes is found in the
 following file. 2\. Wild African eggplant sample details and environmental
 data\.csv * Number of genotypes (rows): 153 * Genotype details (rows 1-7):
 Genotype: sequencing code; VINO: variety identification number at the
 WorldVeg genebank in Taiwan; Taxon: genotype name derived from the species
 name and a sequential number in the list (same as the genotype names on
 the vcf file); lon: longitude; lat: latitude; Country: Country ISO codes
 for the country of origin of the accessions. The country codes include:
 TZA- Tanzania, KEN- Kenya, UGA- Uganda, SDN- Sudan, NGA- Nigeria, and GHA-
 Ghana. * Number of environmental variables: 10 PDrM\_14: Precipitation of
 the driest month (mm); PWaQ\_18: Precipitation of the warmest quarter
 (mm); PCoQ\_19: Precipitation of the coldest quarter (mm); MTWeQ\_8: Mean
 temperature of the wettest quarter (°C); srad: solar radiation (kJ
 m<sup>-2</sup> day<sup>-1</sup>); phh2o: pH of the
 soil water (pH \* 10); nitrogen: soil nitrogen content (cg/kg); clay: soil
 clay content (g/kg); silt: soil silt content (g/kg); ocd: organic carbon
 deposit (hg/m<sup>3</sup>). *** ## Sharing/Access information
 This is a section for linking to other ways to access the data, and for
 linking to sources the data is derived from, if any. Data was derived from
 the following sources: * WorlClim 2.1 climate data for 1970-2000 (Fick
 & Hijmans, 2017) at a resolution of 2.5 minutes
 (https://www.worldclim.org/data/worldclim21.html) * SoilGrids database
 released in 2016 ([https://soilgrids.org/](https://soilgrids.org/))
 through ISRIC—WDC Soils (Hengl et al., 2017) at 250-meter resolution and
 at a depth of 15-30 cm ## Code/Software