Landscape genomics analysis: A comprehensive guide to enhance the conservation and use of plant genetic resources

Applying landscape genomics will significantly advance our understanding of biodiversity, informing effective genetic rescue and conservation strategies and crop development programs. Continued research will expand and refine these methods, broadening the range of taxa for comparison. This data has been used to develop a landscape genomics manual that offers insights into landscape genomic studies using several commonly applied methods. It also includes a collection of R scripts for achieving specific outcomes and creating simplified graphical displays of the results using a case study based on Indian eggplant accessions.

Methods
Landscape genomics manual data

1.       Environmental variables data

Consists of 324  from the Indian subcontinent (Meta_envdata.csv ).

The bioclimatic variables are related to temperature precipitation, solar radiation, wind speed, and vapor pressure from WorldClim 2.0 (Fick and Hijmans, 2017) with a 2.5 min (~5km) resolution. The data represent a 30-year average from 1970 to 2000. We averaged the monthly solar radiation, wind, and vapor pressure rasters to obtain annual value rasters from this period.

The Soil variables included nitrogen, soil organic carbon, organic carbon density, organic carbon stock, cation exchange capacity, pH, clay sand, and silt content. 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 a depth of 15-30 cm.

2.       Genomic data

Contains a set of 4,308 SNPs dataset (IndBng_SPET.vcf).

SPET library construction

DNA was extracted using the Qiagen plant mini-prep, the LGC Sbeadex kit, the SILEX protocol (Vilanova et al., 2020), or a modified CTAB method. A total of 33 DNA samples of the reference S. melongena ‘67/3’ line (nearly one per plate), obtained from a unique seed batch (Barchi et al., 2021; Barchi et al., 2019), were included as controls. The final set of 5082 (5K) probes previously identified was used, and libraries were prepared as previously reported (Barchi et al., 2019) to genotype the whole set of accessions at IGATech (Udine, Italy). Sequencing was performed on an Illumina NextSeq 500 platform (Illumina, Inc., San Diego, CA, USA) using 150SE chemistry. The raw sequencing data are available at NCBI SRA (BioProject ID PRJNA808188 and PRJNA542231). Accessions having an average read depth of <10 were discarded from the subsequent analyses.

Read alignment and variant calling.

Base calling and demultiplexing were carried out using the standard Illumina pipeline. The read quality check and adapter trimming were done using ERNE (Del Fabbro et al., 2013) and Cutadapt (Martin, 2011) software. After alignment to the reference eggplant genome (Barchi et al., 2021), using BWA-MEM (Li, 2013) with default parameters. SNP calling was obtained with GATK 4.1.9 (DePristo et al., 2011), following the software best practices in June 2021 for germline short variant discovery and as previously described in Barchi et al. (2019). To extract high-confidence SNPs, Vcftools (Danecek et al., 2011) was applied using the following parameters: min-meanDP 15 and no more than 5% of missing data.