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. 2025 Jan 17;25(1):69.
doi: 10.1186/s12870-025-06077-w.

Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage

Muluken Enyew  1   2   3 , Mulatu Geleta  4 , Kassahun Tesfaye  5   6 , Amare Seyoum  7   8 , Tileye Feyissa  5 , Admas Alemu  4 , Cecilia Hammenhag  4 , Anders S Carlsson  4
Affiliations

Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage

Muluken Enyew et al. BMC Plant Biol. .

Abstract

Root system architecture (RSA) plays an important role in plant adaptation to drought stress. However, the genetic basis of RSA in sorghum has not been adequately elucidated. This study aimed to investigate the genetic bases of RSA traits through genome-wide association studies (GWAS) and determine genomic prediction (GP) accuracy in sorghum landraces at the seedling stage. Phenotypic data for nodal root angle (NRA), number of nodal roots (NNR), nodal root length (NRL), fresh shoot weight (FSW), dry shoot weight (DSW), and leaf area (LA) were collected from 160 sorghum accessions grown in soil-based rhizotrons. The sorghum panel was genotyped with 5,000 single nucleotide polymorphism (SNP) markers for use in the current GWAS and GP studies. A multi-locus model, Fixed and random model Circulating Probability Unification (FarmCPU), was applied for GWAS analysis. For GP, ridge-regression best linear unbiased prediction (RR-BLUP) and five different Bayesian models were applied. A total of 17 SNP loci significantly associated with the studied traits were identified, of which nine are novel loci. Among the traits, the highest number of significant marker-trait associations (MTAs) was identified for nodal root angle on chromosomes 1, 3, 6, and 7. The SNP loci that explain the highest proportion of phenotypic variance (PVE) include sbi32853830 (PVE = 18.2%), sbi29954292 (PVE = 18.1%), sbi24668980 (PVE = 10.8%), sbi3022983 (PVE = 7%), sbi29897704 (PVE = 6.4%) and sbi29897694 (PVE = 5.3%) for the traits NNR, LA, SDW, NRA, NRL and SFW, respectively. The genomic prediction accuracy estimated for the studied traits using five Bayesian models ranged from 0.30 to 0.63 while it ranged from 0.35 to 0.60 when the RR-BLUP model was used. The observed moderate to high prediction accuracy for each trait suggests that genomic selection could be a feasible approach to sorghum RSA-targeted selection and breeding. Overall, the present study provides insights into the genetic bases of RSA and offers an opportunity to speed up breeding for drought-tolerant sorghum varieties.

Keywords: GWAS; Genomic prediction; Quantitative trait locus; Root system architecture; Sorghum.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable.

Figures

Fig. 1
Fig. 1
Histogram of the frequency distributions of Best Linear Unbiased Prediction (BLUP) values of six RSA traits targeted in this study. NRA = nodal root angle, NNR = number of nodal roots, NRL = nodal root length, FSW = fresh shoot weight, DSW = dry shoot weight, and LA = leaf area
Fig. 2
Fig. 2
A kinship matrix presented as a heatmap, with red representing the highest correlation between genotype pairs and yellow indicating the lowest correlation. A hierarchical tree of individuals is shown based on their kinship relationships
Fig. 3
Fig. 3
The Manhattan and Quantile–quantile (QQ) plots showing the significant SNPs across the 10 sorghum chromosomes identified by the current GWAS analysis for (A) the nodal root angle (NRA), (B) number of nodal roots (NNR), (C) nodal root length (NRL), (D) fresh shoot weight (FSW), (E) dry shoot weight (DSW) and (F) leaf area (LA) at seedling stage
Fig. 4
Fig. 4
Boxplots of the most significant SNPs sbi20340807, sbi29954292, sbi29897704, sbi29897694, sbi3632542 and sbi32853830 with their allelic effects on nodal root angle (NRA), number of nodal roots (NNR), nodal root length (NRL), fresh shoot weight (FSW), dry shoot weight (DSW), and leaf area (LA), respectively. Statistical significance for differences between allele effects was determined using Tukey’s HSD (honestly significant difference) test. Different letters in the same box indicate significant phenotypic differences among plants with corresponding genotypes at that locus (P < 0.05)
Fig. 5
Fig. 5
The genomic prediction (GP) accuracy of five Bayesian models and the Ridge-regression best linear unbiased prediction (RR-BLUP) model for the nodal root angle (NRA), number of nodal roots (NNR), nodal root length (NRL), fresh shoot weight (FSW), dry shoot weight (DSW), and leaf area (LA) in sorghum at seedling stage
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