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

[https://doi.org/10.5061/dryad.08kprr5c7](https://doi.org/10.5061/dryad.08kprr5c7)

## Description of the data and file structure

1\. Environmental variables data

It consists of 324 eggplant samples 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.or...