Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity

Genomic resources, opportunities, and prospects for accelerated improvement of millets

KEY MESSAGE

Genomic resources, alongside the tools and expertise required to leverage them, are essential for the effective improvement of globally significant millet crop species. Millets are essential for global food security and nutrition, particularly in sub-Saharan Africa and South Asia. They are crucial in promoting nutrition, climate resilience, economic development, and cultural heritage. Despite their critical role, millets have historically received less investment in developing genomic resources than major cereals like wheat, maize, and rice. However, recent advancements in genomics, particularly next-generation sequencing technologies, offer unprecedented opportunities for rapid improvement in millet crops. This review paper provides an overview of the status of genomic resources in millets and in harnessing the recent opportunities in artificial intelligence to address challenges in millet crop improvement to boost productivity, nutrition, and end quality. It emphasizes the significance of genomics in tackling global food security issues and underscores the necessity for innovative breeding strategies to translate genomics and AI into effective breeding strategies for millets.

A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1

The parasitic weed Striga (Striga hermonthica) limits productivity of sorghum (Sorghum bicolor) and other cereals in sub-Saharan Africa and elsewhere. Improved host plant genetics is an effective control method but verified loci contributing to Striga resistance are limited. LOW GERMINATION STIMULANT 1 remains the only known sorghum locus affecting resistance to Striga. Functional loss (lgs1) alleles at this locus result in low Striga germination stimulant activity. We developed a robust polymerase chain reaction (PCR)-based LGS1 marker that detects all known natural lgs1 alleles. We have successfully used this marker to improve Striga resistance in our sorghum breeding program. To check its utility among diverse sets of germplasm, we genotyped 406 lines of the sorghum association panel (SAP) with the marker and phenotyped them for Striga germination stimulant activity. The SAP contains 23 lines (6%) with lgs1 mutations that involve a complete loss of this gene. Three previously described deletion alleles (lgs1-1, lgs1-2, and lgs1-3) ranging from 28.5 to 34 kbp are present among SAP members with a new one, lgs1-6, missing nearly 50 kbp relative to the reference genome. All 23 members of the SAP carrying lgs1 alleles had low Striga germination stimulant activity. The smaller previously described intragenic deletion mutations lgs1-4 and lgs1-5 are not present in the SAP. The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.

Trait association and prediction through integrative k-mer analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Genomic co-localization of variation affecting agronomic and human gut microbiome traits in a meta-analysis of diverse sorghum

Substantial functional metabolic diversity exists within species of cultivated grain crops that directly or indirectly provide more than half of all calories consumed by humans around the globe. While such diversity is the molecular currency used for improving agronomic traits, diversity is poorly characterized for its effects on human nutrition and utilization by gut microbes. Moreover, we know little about agronomic traits' potential tradeoffs and pleiotropic effects on human nutritional traits. Here, we applied a quantitative genetics approach using a meta-analysis and parallel genome-wide association studies of Sorghum bicolor traits describing changes in the composition and function of human gut microbe communities, and any of 200 sorghum seed and agronomic traits across a diverse sorghum population to identify associated genetic variants. A total of 15 multiple-effect loci (MEL) were initially found where different alleles in the sorghum genome produced changes in seed that affected the abundance of multiple bacterial taxa across 2 human microbiomes in automated in vitro fermentations. Next, parallel genome-wide studies conducted for seed, biochemical, and agronomic traits in the same population identified significant associations within the boundaries of 13/15 MEL for microbiome traits. In several instances, the colocalization of variation affecting gut microbiome and agronomic traits provided hypotheses for causal mechanisms through which variation could affect both agronomic traits and human gut microbes. This work demonstrates that genetic factors affecting agronomic traits in sorghum seed can also drive significant effects on human gut microbes, particularly bacterial taxa considered beneficial. Understanding these pleiotropic relationships will inform future strategies for crop improvement toward yield, sustainability, and human health.

The era of panomics-driven gene discovery in plants

Panomics is an approach to integrate multiple 'omics' datasets, generated using different individuals or natural variations. Considering their diverse phenotypic spectrum, the phenome is inherently associated with panomics-based science, which is further combined with genomics, transcriptomics, metabolomics, and other omics techniques, either independently or collectively. Panomics has been accelerated through recent technological advancements in the field of genomics that enable the detection of population-wide structural variations (SVs) and hence offer unprecedented insights into the genetic variations contributing to important agronomic traits. The present review provides the recent trends of panomics-driven gene discovery toward various traits related to plant development, stress tolerance, accumulation of specialized metabolites, and domestication/dedomestication. In addition, the success stories are highlighted in the broader context of enhancing crop productivity.

Nonphotochemical quenching kinetics GWAS in sorghum identifies genes that may play conserved roles in maize and Arabidopsis thaliana photoprotection

Photosynthetic organisms must cope with rapid fluctuations in light intensity. Nonphotochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. We quantified variation in NPQ kinetics across a large sorghum (Sorghum bicolor) association panel in four environments, uncovering significant genetic control for NPQ. A genome-wide association study (GWAS) confidently identified three unique regions in the sorghum genome associated with NPQ and suggestive associations in an additional 61 regions. We detected strong signals from the sorghum ortholog of Arabidopsis thaliana Suppressor Of Variegation 3 (SVR3) involved in plastid-nucleus signaling. By integrating GWAS results for NPQ across maize (Zea mays) and sorghum-association panels, we identified a second gene, Non-yellowing 1 (NYE1), originally studied by Gregor Mendel in pea (Pisum sativum) and involved in the degradation of photosynthetic pigments in light-harvesting complexes. Analysis of nye1 insertion alleles in A. thaliana confirmed the effect of this gene on NPQ kinetics in eudicots. We extended our comparative genomics GWAS framework across the entire maize and sorghum genomes, identifying four additional loci involved in NPQ kinetics. These results provide a baseline for increasing the accuracy and speed of candidate gene identification for GWAS in species with high linkage disequilibrium.

Revealing the Genetic Diversity and Population Structure of Garlic Resource Cultivars and Screening of Core Cultivars Based on Specific Length Amplified Fragment Sequencing (SLAF-Seq)

Garlic is an important vegetable and condiment that has good medical and health care effects. At present, the origin of Chinese garlic and its association with other types of quality are limited to the molecular marker level, and there are few reports at the genome level. Therefore, this study is based on the specific length amplified fragment sequencing (SLAF-seq) of 102 copies of garlic germplasm resources, the group structure, and further screening of the core germplasm. SLAF-seq of 102 garlic cultivars yielded 1949.85 Mb of clean data and 526,432,275 SNPs. Through principal component analysis, evolutionary tree, population structure, and genetic relationship analysis, all garlic cultivars were divided into 3 groups. Among them, Group 1 contains 45 Chinese cultivars and 1 Egyptian cultivar, which are distributed mainly in the coastal and central areas of China. Group 2 contains 36 Chinese cultivars and 1 U.S. cultivar, which are distributed mainly in Northwest China. Group 3 contains 19 Chinese cultivars, which are distributed mainly in Xinjiang, China. The genetic diversity results indicate that the fixation index (Fst) values of Group 1 and Group 2 are lower than those of Group 1 and Group 3 and that the diversity of nucleotides (π) of Group 3 is greater than those of Group 2 and Group 1. Finally, the 30 parts of the cultivars were used as the core germplasms, and there was no difference between the two cultivars in terms of core quality. In summary, this study provides tags for the determination of garlic molecular markers and genotypes and provides a theoretical basis for subsequent resource protection and utilization, genetic positioning of important agronomic traits, and molecular marking agglomeration breeding.

Unravelling sorghum functional genomics and molecular breeding: past achievements and future prospects

Sorghum, renowned for its substantial biomass production and remarkable tolerance to various stresses, possesses extensive gene resources and phenotypic variations. A comprehensive understanding of the genetic basis underlying complex agronomic traits is essential for unlocking the potential of sorghum in addressing food and feed security and utilizing marginal lands. In this context, we provide an overview of the major trends in genomic resource studies focusing on key agronomic traits over the past decade, accompanied by a summary of functional genomic platforms. We also delve into the molecular functions and regulatory networks of impactful genes for important agricultural traits. Lastly, we discuss and synthesize the current challenges and prospects for advancing molecular design breeding by gene-editing and polymerization of the excellent alleles, with the aim of accelerating the development of desired sorghum varieties.

Globally deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but is bolstered by RMES2

Durable host plant resistance (HPR) to insect pests is critical for sustainable agriculture. Natural variation exists for aphid HPR in sorghum (Sorghum bicolor), but the genetic architecture and phenotype have not been clarified and characterized for most sources. In order to assess the current threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and additional HPR architecture in globally admixed populations selected under severe sorghum aphid infestation in Haiti. We found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR response typical of gene-for-gene interaction. A second resistant gene, Resistance to Melanaphis sorghi 2 (RMES2), was more frequent than RMES1 resistant alleles in landraces and historic breeding lines. RMES2 contributes early and mid-season aphid resistance in a segregating F2 population; however, RMES1 was only significant with mid-season fitness. In a fixed population with high sorghum aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of severe antagonistic pleiotropy. Associations with resistance colocated with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, an HPR source vulnerable to biotype shift via selection pressure (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2), which may be staving off a biotype shift and is critical for sustainable sorghum production.

Major impacts of widespread structural variation on sorghum

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

A large sequenced mutant library - valuable reverse genetic resource that covers 98% of sorghum genes

Mutant populations are crucial for functional genomics and discovering novel traits for crop breeding. Sorghum, a drought and heat-tolerant C4 species, requires a vast, large-scale, annotated, and sequenced mutant resource to enhance crop improvement through functional genomics research. Here, we report a sorghum large-scale sequenced mutant population with 9.5 million ethyl methane sulfonate (EMS)-induced mutations that covered 98% of sorghum's annotated genes using inbred line BTx623. Remarkably, a total of 610 320 mutations within the promoter and enhancer regions of 18 000 and 11 790 genes, respectively, can be leveraged for novel research of cis-regulatory elements. A comparison of the distribution of mutations in the large-scale mutant library and sorghum association panel (SAP) provides insights into the influence of selection. EMS-induced mutations appeared to be random across different regions of the genome without significant enrichment in different sections of a gene, including the 5' UTR, gene body, and 3'-UTR. In contrast, there were low variation density in the coding and UTR regions in the SAP. Based on the Ka /Ks value, the mutant library (~1) experienced little selection, unlike the SAP (0.40), which has been strongly selected through breeding. All mutation data are publicly searchable through SorbMutDB (https://www.depts.ttu.edu/igcast/sorbmutdb.php) and SorghumBase (https://sorghumbase.org/). This current large-scale sequence-indexed sorghum mutant population is a crucial resource that enriched the sorghum gene pool with novel diversity and a highly valuable tool for the Poaceae family, that will advance plant biology research and crop breeding.

A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset

BACKGROUND

Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable.

RESULTS

Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq).

CONCLUSIONS

This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

Deleterious mutations predicted in the sorghum (Sorghum bicolor) Maturity (Ma) and Dwarf (Dw) genes from whole-genome resequencing

In sorghum [Sorghum bicolor (L.) Moench] the Maturity (Ma1, Ma2, Ma3, Ma4, Ma5, Ma6) and Dwarf (Dw1, Dw2, Dw3, Dw4) loci, encode genes controlling flowering time and plant height, respectively, which are critical for designing sorghum ideotypes for a maturity timeframe and a harvest method. Publicly available whole-genome resequencing data from 860 sorghum accessions was analyzed in silico to identify genomic variants at 8 of these loci (Ma1, Ma2, Ma3, Ma5, Ma6, Dw1, Dw2, Dw3) to identify novel loss of function alleles and previously characterized ones in sorghum germplasm. From ~ 33 million SNPs and ~ 4.4 million InDels, 1445 gene variants were identified within these 8 genes then evaluated for predicted effect on the corresponding encoded proteins, which included newly identified mutations (4 nonsense, 15 frameshift, 28 missense). Likewise, most accessions analyzed contained predicted loss of function alleles (425 ma1, 22 ma2, 40 ma3, 74 ma5, 414 ma6, 289 dw1, 268 dw2 and 45 dw3) at multiple loci, but 146 and 463 accessions had no predicted ma or dw mutant alleles, respectively. The ma and dw alleles within these sorghum accessions represent a valuable source for manipulating flowering time and plant height to develop the full range of sorghum types: grain, sweet and forage/biomass.

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.

A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive; and models show poor transferability among different datasets. This study had three specific objectives: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (average R 2 0.688), with Partial Least Squares Regression outperforming Deep Neural Network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (average R 2 0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.

Functional genomic effects of indels using Bayesian genome-phenome wide association studies in sorghum

High-throughput genomic and phenomic data have enhanced the ability to detect genotype-to-phenotype associations that can resolve broad pleiotropic effects of mutations on plant phenotypes. As the scale of genotyping and phenotyping has advanced, rigorous methodologies have been developed to accommodate larger datasets and maintain statistical precision. However, determining the functional effects of associated genes/loci is expensive and limited due to the complexity associated with cloning and subsequent characterization. Here, we utilized phenomic imputation of a multi-year, multi-environment dataset using PHENIX which imputes missing data using kinship and correlated traits, and we screened insertions and deletions (InDels) from the recently whole-genome sequenced Sorghum Association Panel for putative loss-of-function effects. Candidate loci from genome-wide association results were screened for potential loss of function using a Bayesian Genome-Phenome Wide Association Study (BGPWAS) model across both functionally characterized and uncharacterized loci. Our approach is designed to facilitate in silico validation of associations beyond traditional candidate gene and literature-search approaches and to facilitate the identification of putative variants for functional analysis and reduce the incidence of false-positive candidates in current functional validation methods. Using this Bayesian GPWAS model, we identified associations for previously characterized genes with known loss-of-function alleles, specific genes falling within known quantitative trait loci, and genes without any previous genome-wide associations while additionally detecting putative pleiotropic effects. In particular, we were able to identify the major tannin haplotypes at the Tan1 locus and effects of InDels on the protein folding. Depending on the haplotype present, heterodimer formation with Tan2 was significantly affected. We also identified major effect InDels in Dw2 and Ma1, where proteins were truncated due to frameshift mutations that resulted in early stop codons. These truncated proteins also lost most of their functional domains, suggesting that these indels likely result in loss of function. Here, we show that the Bayesian GPWAS model is able to identify loss-of-function alleles that can have significant effects upon protein structure and folding as well as multimer formation. Our approach to characterize loss-of-function mutations and their functional repercussions will facilitate precision genomics and breeding by identifying key targets for gene editing and trait integration.

Cross-species predictive modeling reveals conserved drought responses between maize and sorghum

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought-responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water-deficit responses. Differential gene expression identified few overlapping drought-associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress-responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought-responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals.

Genetic architecture and molecular regulation of sorghum domestication

Over time, wild crops have been domesticated by humans, and the knowledge gained from parallel selection and convergent domestication-related studies in cereals has contributed to current techniques used in molecular plant breeding. Sorghum (Sorghum bicolor (L.) Moench) is the world's fifth-most popular cereal crop and was one of the first crops cultivated by ancient farmers. In recent years, genetic and genomic studies have provided a better understanding of sorghum domestication and improvements. Here, we discuss the origin, diversification, and domestication processes of sorghum based on archeological discoveries and genomic analyses. This review also comprehensively summarized the genetic basis of key genes related to sorghum domestication and outlined their molecular mechanisms. It highlights that the absence of a domestication bottleneck in sorghum is the result of both evolution and human selection. Additionally, understanding beneficial alleles and their molecular interactions will allow us to quickly design new varieties by further de novo domestication.

Genetic control of morphological traits useful for improving sorghum

Global climate change and global warming, coupled with the growing population, have raised concerns about sustainable food supply and bioenergy demand. Sorghum [Sorghum bicolor (L.) Moench] ranks fifth among cereals produced worldwide; it is a C₄ crop with a higher stress tolerance than other major cereals and has a wide range of uses, such as grains, forage, and biomass. Therefore, sorghum has attracted attention as a promising crop for achieving sustainable development goals (SDGs). In addition, sorghum is a suitable genetic model for C₄ grasses because of its high morphological diversity and relatively small genome size compared to other C₄ grasses. Although sorghum breeding and genetic studies have lagged compared to other crops such as rice and maize, recent advances in research have identified several genes and many quantitative trait loci (QTLs) that control important agronomic traits in sorghum. This review outlines traits and genetic information with a focus on morphogenetic aspects that may be useful in sorghum breeding for grain and biomass utilization.