Identifying Genomic Regions Targeted During Eggplant Domestication Using Transcriptome Data

Abstract

Identifying genes and traits that have diverged during domestication provides key information of importance for maintaining and even increasing yield and nutrients in existing crops. A "bottom-up" population genetics approach was used to identify signatures of selection across the eggplant genome, to better understand the process of domestication. RNA-seq data were obtained for 4 wild eggplants (Solanum insanum L.) and 16 domesticated eggplants (S. melongena L.) and mapped to the eggplant genome. Single-nucleotide polymorphism (SNPs) exhibiting signatures of selection in domesticates were identified as those exhibiting high FST between the 2 populations (evidence of significant divergence) and low π for the domesticated population (indicative of a selective sweep). Some of these regions appear to overlap with previously identified quantitative trait loci for domestication traits. Genes in regions of linkage disequilibrium surrounding these SNPs were searched against the Arabidopsis thaliana and tomato genomes to find orthologs. Subsequent gene ontology (GO) enrichment analysis identified over-representation of GO terms related to photosynthesis and response to the environment. This work reveals genomic changes involved in eggplant domestication and improvement, and how this compares to observed changes in the tomato genome, revealing shared chromosomal regions involved in the domestication of both species.

Keywords: Solanum melongena; domestication; eggplant; selection; transcriptomics.

Figure 1.

Cumulative percentage graph of haplotype block size. The point at which the rate of increase of cumulative percentage declines is marked on the x and y axes.

Figure 2.

Chromosomal distributions of SNPs and outliers represented through Manhattan plots of −log₁₀(P) for F_ST and π. Significant loci after Holm–Bonferroni correction are beyond the dotted line. Centre ideogram chromosomes show gene density. Plotted using KaryoploteR in R (Gel and Serra, 2017).