High-quality genome assembly and pan-genome studies facilitate genetic discovery in mung bean and its improvement

Liu, Changyou; Wang, Yan; Peng, Jianxiang; Fan, Bin; Xu, Dongxu; Wu, Jing; Cao, Zhi-Hao; Gao, Yang; Wang, Xueqing; Li, Shutong; Su, Qiuzhu; Zhang, Zhixiao; Wang, Shen; Wu, Xiling; Qibing, Shang; Shi, Huiying; Shen, Yingchao; Wang, Bingbing; Tian, Jichun

doi:10.1016/j.xplc.2022.100352

Cited by 38 publications

(43 citation statements)

References 75 publications

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“…Recent studies suggest that genes involved in the photosynthesis pathway are essential for environmental adaptation to different light regimes and coping with climate change by regulating the circadian clock and light perception ( Kreps and Kay, 1997 ; Quint et al., 2016 ). For example, a pan-genome study of mung beans indicates that the presence/absence variation (PAV) of genes regulating the photosynthesis pathway enables mung beans to adapt to different environments ( Liu et al., 2022 ). Gene expansion through tandem duplication is important for stress response ( Hanada et al., 2008 ).…”

Section: Discussionmentioning

confidence: 99%

“…The genome sequences and protein sequences of two published yellowhorn (WF18 and Xsv2) were downloaded from Liang and Liu study ( Liang et al., 2019 , Liu et al., 2021a ). Orthologous genes among the yellowhorn genomes were identified by Orthovenn2 ( Xu et al., 2019 ), a web tool used to identify orthologous and paralogous genes, with a pairwise sequence similarity cut-off of 10- 5 and inflation of 1.5 to define orthologous cluster structure.…”

Section: Methodsmentioning

confidence: 99%

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High-quality genome assembly and comparative genomic profiling of yellowhorn (Xanthoceras sorbifolia) revealed environmental adaptation footprints and seed oil contents variations

Wang

Liang

et al. 2023

Front. Plant Sci.

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Yellowhorn (Xanthoceras sorbifolia) is a species of deciduous tree that is native to Northern and Central China, including Loess Plateau. The yellowhorn tree is a hardy plant, tolerating a wide range of growing conditions, and is often grown for ornamental purposes in parks, gardens, and other landscaped areas. The seeds of yellowhorn are edible and contain rich oil and fatty acid contents, making it an ideal plant for oil production. However, the mechanism of its ability to adapt to extreme environments and the genetic basis of oil synthesis remains to be elucidated. In this study, we reported a high-quality and near gap-less yellowhorn genome assembly, containing the highest genome continuity with a contig N50 of 32.5 Mb. Comparative genomics analysis showed that 1,237 and 231 gene families under expansion and the yellowhorn-specific gene family NB-ARC were enriched in photosynthesis and root cap development, which may contribute to the environmental adaption and abiotic stress resistance of yellowhorn. A 3-ketoacyl-CoA thiolase (KAT) gene (Xso_LG02_00600) was identified under positive selection, which may be associated with variations of seed oil content among different yellowhorn cultivars. This study provided insights into environmental adaptation and seed oil content variations of yellowhorn to accelerate its genetic improvement.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High-quality genome assembly and comparative genomic profiling of yellowhorn (Xanthoceras sorbifolia) revealed environmental adaptation footprints and seed oil contents variations

Wang

Liang

et al. 2023

Front. Plant Sci.

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“…Likewise, a pangenomic approach can describe the full complement of genes in the ‘core genome’ and ‘accessory genome’ to capture structural variation (not available in ‘single reference genome assembly’) at the species level [ 232 ]. Pangenome assemblies have been reported in chickpea [ 233 ], pigeon pea [ 234 ], soybean [ 235 ] and mungbean [ 236 ]. Thus, future construction and annotation of pangenomes for different grain legumes could reveal missing information on SPC structural variations in the available reference genome assemblies, expediting the development of grain legumes with enriched protein.…”

Section: Whole Genome Resequencing and Pangenome Sequencing For Eluci...mentioning

confidence: 99%

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and ‘Omics’ Tools

Jha

Nayyar

Parida

et al. 2022

IJMS

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Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based ‘dietary protein’ biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

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“…Initial analysis with structural variants identified from 787 yeast genomes revealed that genomic prediction with pan-genomic open reading frames (ORFs) was, on average, two times more accurate than SNPs across 35 different traits (Li and Simianer 2020). Advances in sequencing technologies and bioinformatics tools are allowing a more accurate identification of these variants (Ho et al 2019), which are relatively common in crop species (Montenegro et al 2017; Gao et al 2019; Liu et al 2020, 2022; Hufford et al 2021; Varshney et al 2021; Rijzaani et al 2022; Shang et al 2022). In addition, many studies have shown that SVs are associated with tolerance to biotic (Cook et al 2012; Zuo et al 2015) and abiotic stresses (Sutton et al 2007; Knox et al 2010; Maron et al 2013), changes in flowering time (Nitcher et al 2013; Würschum et al 2015), and plant architecture (Zhou et al 2009; Studer et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Importance of genetic architecture in marker selection decisions for genomic prediction

Coletta

Fernandes

Monnahan

et al. 2023

Preprint

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Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program.Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

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High-quality genome assembly and pan-genome studies facilitate genetic discovery in mung bean and its improvement

Cited by 38 publications

References 75 publications

High-quality genome assembly and comparative genomic profiling of yellowhorn (Xanthoceras sorbifolia) revealed environmental adaptation footprints and seed oil contents variations

High-quality genome assembly and comparative genomic profiling of yellowhorn (Xanthoceras sorbifolia) revealed environmental adaptation footprints and seed oil contents variations

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and ‘Omics’ Tools

Importance of genetic architecture in marker selection decisions for genomic prediction

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