Abstract

Genetic dissection of forage yield traits is critical to the development of sorghum as a forage crop. In the present study, association mapping was performed with 85,585 SNP markers on four forage yield traits, namely plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW) among 245 sorghum accessions evaluated in four environments. A total of 338 SNPs or quantitative trait nucleotides (QTNs) were associated with the four traits, and 21 of these QTNs were detected in at least two environments, including four QTNs for PH, ten for TN, six for SD, and one for FW. To identify candidate genes, dynamic transcriptome expression profiling was performed at four stages of sorghum development. One hundred and six differentially expressed genes (DEGs) that were enriched in hormone signal transduction pathways were found in all stages. Weighted gene correlation network analysis for PH and SD indicated that eight modules were significantly correlated with PH and that three modules were significantly correlated with SD. The blue module had the highest positive correlation with PH and SD, and the turquoise module had the highest negative correlation with PH and SD. Eight candidate genes were identified through the integration of genome-wide association studies (GWAS) and RNA sequencing. Sobic.004G143900, an indole-3-glycerol phosphate synthase gene that is involved in indoleacetic acid biosynthesis, was down-regulated as sorghum plants grew in height and was identified in the blue module, and Sobic.003G375100, an SD candidate gene, encoded a DNA repair RAD52-like protein 1 that plays a critical role in DNA repair-linked cell cycle progression. These findings demonstrate that the integrative analysis of omics data is a promising approach to identify candidate genes for complex traits.

Highlights

  • Sorghum is an important grain and forage crop

  • Since forage yield traits are usually controlled by many genes, genome-wide association studies (GWAS), which are useful for dissecting complex traits, have been used extensively to map forage yield-related traits in sorghum

  • Extensive variation in plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW) was observed in all four environments in the 245 accessions (Table 1)

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Summary

Introduction

Sorghum is an important grain and forage crop. It is widely cultivated worldwide because of its broad adaptability and tolerance to drought, waterlogging, and salinity (Rooney et al, 2007). Since forage yield traits are usually controlled by many genes, genome-wide association studies (GWAS), which are useful for dissecting complex traits, have been used extensively to map forage yield-related traits in sorghum. The majority of these studies have been cataloged in the Sorghum QTL Atlas (Mace et al, 2019).. Spindel et al (2018) reported another 213 genomic regions that are associated with sorghum biomass and/or drought tolerance, and Habyarimana et al (2020) reported 42 single-nucleotide polymorphisms (SNPs) associated with plant height, eight with dry mass fraction of fresh material, and 17 with dry biomass yield in sorghum. Spindel et al (2018) reported another 213 genomic regions that are associated with sorghum biomass and/or drought tolerance, and Habyarimana et al (2020) reported 42 single-nucleotide polymorphisms (SNPs) associated with plant height, eight with dry mass fraction of fresh material, and 17 with dry biomass yield in sorghum. Kong et al (2020) reported six QTLs that were related to basal stem diameter, six to middle stem diameter, and five to rachis diameter explained 28.9, 26.0, and 20.0% of phenotypic variation for the corresponding traits, respectively. Dos Santos et al (2020) used 100,435 SNP markers to identify associations between sorghum plant height and dry forage yield and reported that early season plant height could be used to select for dry forage yield. Chen et al (2020) identified a biomass yield 1 (by1) mutant that affected sorghum biomass and grain yield through primary and secondary metabolism regulation via the shikimate pathway

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