Sorghum pan-genome explores the functional utility to accelerate the genetic gain

Abstract

Sorghum (Sorghum bicolor L.) is one of the most important food crops in the arid and rainfed production ecologies. It is a part of resilient farming and is projected as a smart crop to overcome the food and nutritional challenges in the developing world. The development and characterisation of the sorghum pan-genome will provide insight into genome diversity and functionality, supporting sorghum improvement. We built a sorghum pan-genome using reference genomes as well as 354 genetically diverse sorghum accessions belonging to different races. We explored the structural and functional characteristics of the pan-genome and explain its utility in supporting genetic gain. The newly-developed pan-genome has a total of 35,719 genes, a core genome of 16,821 genes and an average of 32,795 genes in each cultivar. The variable genes are enriched with environment responsive genes and classify the sorghum accessions according to their race. We show that 53% of genes display presence-absence variation, and some of these variable genes are predicted to be functionally associated with drought traits. Using more than two million SNPs from the pan-genome, association analysis identified 398 SNPs significantly associated with important agronomic traits, of which, 92 were in genes. Drought gene expression analysis identified 1,788 genes that are functionally linked to different conditions, of which 79 were absent from the reference genome assembly. This study provides comprehensive genomic diversity resources in sorghum which can be used in genome assisted crop improvement.

Unlocking the Genetic Landscape: Enhanced Insights into Sweet Sorghum Genomes through Comprehensive superTranscriptomic Analysis

“…superTranscriptome identified a total of 45864 genes, which is higher than the previously reported in sorghum (McCormick et al, 2018), sweet sorghum (Cooper et al, 2019), sorghum pan-genome (Ruperao et al, 2021;Tao et al, 2021;Wang et al, 2021), rice pangenome (Zhao et al, 2018;Qin et al, 2021), barley (Jayakodi et al, 2020), and much lesser than maize pan-genome (Hufford et al, 2021). The results validate that the superTranscriptome approach identified comparatively more expressed genes and suggests that a single reference genome cannot report all genes expressed in sweet sorghum.…”

“…Identification of core, dispensable, and private genes in the sweet sorghum. The Pangenome analysis classified the gene families into the core, dispensable, and cloud categories for many plants, such as sorghum (Ruperao et al, 2021;Tao et al, 2021;Wang et al, 2021), rice (Sun et al, 2017;Zhao et al, 2018;Qin et al, 2021), maize (Hirsch et al, 2014), and pea (Yang et al, 2022). The superTranscripts annotated 15 diverse sorghum genomes, (53.69%) superTranscripts were reported with remarkable presence/absence variation on 15 sorghum genomes (excluding single genome superTranscripts and core genes), suggesting that these genes are contributing to variability in sorghum (Supplemental Data S5, Sheet 2).…”

“…[http://dataverse.icrisat.org] (Ruperao et al, 2021). The 223 sweet sorghum RNA-seq accessions for seven diverse sweet sorghum genotypes namely Rio (Cooper et al, 2019;Y.…”

Pangenomics in crop improvement—from coding structural variations to finding regulatory variants with pangenome graphs

“…A recently constructed sorghum [ Sorghum bicolor (L.) Moench] pangenome was used to identify drought‐responsive genes in transcriptomic data generated from both resistant and susceptible genotypes (Abdel‐Ghany et al., 2020; Ruperao et al., 2021; Varoquaux et al., 2019). A total of 79 genes absent from the reference genome were confirmed as differentially expressed during drought stress, including two resistance specific genes, Sobic.005G069800 and Sobic.006G127800 , which were found to co‐map with plant height and leaf pigment traits.…”

“…Recently a VG‐based pantranscriptome pipeline became available, which allows construction of spliced pangenome graphs, mapping of RNA sequencing data, and haplotype‐aware expression quantification (Sibbesen et al., 2021). The pangenome graph also provides a convenient framework for the genotyping of SVs across a large number of individuals, for example, for use in SV GWAS studies (Liu et al., 2020; Ruperao et al., 2021; Song et al., 2020; Zhao et al., 2020). The availability of comprehensive, well‐annotated pangenome graphs including both genes and regulatory elements will become a key stepping‐stone for the next generation of genomic analyses.…”