Category Archives: Wiley: The Plant Genome

Genome‐wide dissection and haplotype analysis identified candidate loci for nitrogen use efficiency under drought conditions in winter wheat

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Abstract

Climate change causes extreme conditions like prolonged drought, which results in yield reductions due to its effects on nutrient balances such as nitrogen uptake and utilization by plants. Nitrogen (N) is a crucial nutrient element for plant growth and productivity. Understanding the mechanistic basis of nitrogen use efficiency (NUE) under drought conditions is essential to improve wheat (Triticum aestivum L.) yield. Here, we evaluated the genetic variation of NUE-related traits and photosynthesis response in a diversity panel of 200 wheat genotypes under drought and nitrogen stress conditions to uncover the inherent genetic variation and identify quantitative trait loci (QTLs) underlying these traits. The results revealed significant genetic variations among the genotypes in response to drought stress and nitrogen deprivation. Drought impacted plant performance more than N deprivation due to its effect on water and nutrient uptake. GWAS identified a total of 27 QTLs with a significant main effect on the drought-related traits, while 10 QTLs were strongly associated with the NUE traits. Haplotype analysis revealed two different haplotype blocks within the associated region on chromosomes 1B and 5A. The two haplotypes showed contrasting effects on N uptake and use efficiency traits. The in silico and transcript analyses implicated candidate gene coding for cold shock protein. This gene was the most highly expressed gene under several stress conditions, including drought stress. Upon validation, these QTLs on 1B and 5A could be used as a diagnostic marker for NUE and drought tolerance screening in wheat.

Identification of robust yield quantitative trait loci derived from cultivated emmer for durum wheat improvement

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Abstract

Durum wheat (Triticum turgidum ssp. durum L.) is an important world food crop used to make pasta products. Compared to bread wheat (Triticum aestivum L.), fewer studies have been conducted to identify genetic loci governing yield-component traits in durum wheat. A potential source of diversity for durum is its immediate progenitor, cultivated emmer (T. turgidum ssp. dicoccum). We evaluated two biparental populations of recombinant inbred lines (RILs) derived from crosses between the durum lines Ben and Rusty and the cultivated emmer wheat accessions PI 41025 and PI 193883, referred to as the Ben × PI 41025 (BP025) and Rusty × PI 193883 (RP883) RIL populations, respectively. Both populations were evaluated under field conditions in three seasons with an aim to identify quantitative trait loci (QTLs) associated with yield components and seed morphology that were expressed in multiple environments. A total of 44 and 34 multi-environment QTLs were identified in the BP025 and RP883 populations, respectively. As expected, genetic loci known to govern domestication and development were associated with some of the QTLs, but novel QTLs derived from the cultivated emmer parents and associated with yield components including spikelet number, grain weight, and grain size were identified. These QTLs offer new target loci for durum wheat improvement, and toward that goal, we identified five RILs with increased grain weight and size compared to the durum parents. These materials along with the knowledge of stable QTLs and associated markers can help to expedite the development of superior durum varieties.

Understanding role of roots in plant response to drought: Way forward to climate‐resilient crops

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Abstract

Drought stress leads to a significant amount of agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. Roots play a crucial role in regulating plant development under drought stress. In this review, we have summarized the studies on the role of roots and root-mediated plant responses. We have also discussed the importance of root system architecture (RSA) and the various structural and anatomical changes that it undergoes to increase survival and productivity under drought. Various genes, transcription factors, and quantitative trait loci involved in regulating root growth and development are also discussed. A summarization of various instruments and software that can be used for high-throughput phenotyping in the field is also provided in this review. More comprehensive studies are required to help build a detailed understanding of RSA and associated traits for breeding drought-resilient cultivars.

Analyzing genomic variation in cultivated pumpkins and identification of candidate genes controlling seed traits

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Abstract

Pumpkins are important vegetable crops widely grown worldwide, and seeds are considered a popular nutraceutical food and an excellent source of protein, oil, and vitamins. Seed size is one of the most important targets for commercial breeding in Cucurbita species; studies have shown that pumpkin seed size variation has a similar trend with fruit size, shape, and seed yield. However, few studies have been conducted to identify genetic loci controlling seed-related traits in cultivated pumpkins. This study analyzed the genomic characteristics of pumpkin breeding materials of 321 Cucurbita accessions collected worldwide, including Cucurbita moschata, Cucurbita maxima, and Cucurbita pepo, using extensive single nucleotide polymorphisms obtained from the genotyping-by-sequencing method, significant genetic variations were identified within and between Cucurbita species. Four major cultivar fruit types were further revealed in C. moschata species, and significant differentiation patterns were detected in several chromosomal regions. A total of 15 significant loci associated with pumpkin seed traits were mapped through a genome-wide association approach; 32 genes previously reported to be associated with seed size regulation in Arabidopsis and Oryza sativa were located in the intervals defined by linkage disequilibrium. Through this study, we gained a deep understanding of the genomic variation distribution across Cucurbita species. The available genetic resources and the associated genetic contents could be used in commercial pumpkin breeding and will facilitate molecular marker-assisted selection in pumpkin seed trait improvement.

Near‐gapless genome assemblies of Williams 82 and Lee cultivars for accelerating global soybean research

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Abstract

Complete, gapless telomere-to-telomere chromosome assemblies are a prerequisite for comprehensively investigating the architecture of complex regions, like centromeres or telomeres and removing uncertainties in the order, spacing, and orientation of genes. Using complementary genomics technologies and assembly algorithms, we developed highly contiguous, nearly gapless, genome assemblies for two economically important soybean [Glycine max (L.) Merr] cultivars (Williams 82 and Lee). The centromeres were distinctly annotated on all the chromosomes of both assemblies. We further found that the canonical telomeric repeats were present at the telomeres of all chromosomes of both Williams 82 and Lee genomes. A total of 10 chromosomes in Williams 82 and eight in Lee were entirely reconstructed in single contigs without any gap. Using the combination of ab initio prediction, protein homology, and transcriptome evidence, we identified 58,287 and 56,725 protein-coding genes in Williams 82 and Lee, respectively. The genome assemblies and annotations will serve as a valuable resource for studying soybean genomics and genetics and accelerating soybean improvement.

Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context

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Abstract

Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross-validation, inter-environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross-validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross-environment predictions were statistically lower than cross-validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high-performance lines in inter-environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

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Abstract

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

Genetic erosion within the Fabada dry bean market class revealed by high‐throughput genotyping

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The Fabada market class within the dry beans has a well-differentiated seed phenotype with very large white seeds. This work investigated the genetic diversity maintained in the seed collections within this market class and possible genetic erosion over the last 30 years. A panel with 100 accessions was maintained in seed collections for 30 years, 57 accessions collected from farmers in 2021, six cultivars developed in SERIDA, and 16 reference cultivars were gathered and genotyped with 108,585 SNPs using the genotyping-by-sequencing method. Filtering based on genotypic and phenotypic data was carried out in a staggered way to investigate the genetic diversity among populations. The dendrogram generated from genotyping revealed 90 lines forming 16 groups with identical SNP profiles (redundant lines) from 159 lines classified as market-class Fabada according to their passport data. Seed phenotyping indicated that 19 lines were mistakenly classified as Fabada (homonymies), which was confirmed in the dendrogram built without redundant lines. Moreover, this study provides evidence of genetic erosion between the population preserved for 30 years and the currently cultivated population. The conserved population contains 54.6% segregation sites and 41 different SNP profiles, whereas the cultivated population has 19.6% segregation sites and 26 SNP profiles. The loss of genetic variability cannot be attributed to the diffusion of modern cultivars, which increase genetic diversity (six new SNP profiles). The results allow for the more efficient preservation of plant genetic resources in genebanks, minimizing redundant accessions and incorporating new variations based on genotypic and phenotypic data.

Genome‐wide association study of soluble solids content, flesh color, and fruit shape in citron watermelon

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Fruit quality traits are crucial determinants of consumers’ willingness to purchase watermelon produce, making them major goals for breeding programs. There is limited information on the genetic underpinnings of fruit quality traits in watermelon. A total of 125 citron watermelon (Citrullus amarus) accessions were genotyped using single nucleotide polymorphisms (SNPs) molecular markers generated via whole-genome resequencing. A total of 2,126,759 genome-wide SNP markers were used to uncover marker-trait associations using single and multi-locus GWAS models. High broad-sense heritability for fruit quality traits was detected. Correlation analysis among traits revealed positive relationships, with the exception of fruit diameter and fruit shape index (ratio of fruit length to fruit diameter), which was negative. A total of 37 significant SNP markers associated with soluble solids content, flesh color, fruit length, fruit diameter, and fruit shape index traits were uncovered. These peak SNPs accounted for 2.1%–23.4% of the phenotypic variation explained showing the quantitative inheritance nature of the evaluated traits. Candidate genes relevant to fruit quality traits were uncovered on chromosomes Ca01, Ca03, Ca06, and Ca07. These significant molecular markers and candidate genes will be useful in marker-assisted breeding of fruit quality traits in watermelon.

Gene expression profiling of soaked dry beans (Phaseolus vulgaris L.) reveals cell wall modification plays a role in cooking time

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Dry beans (Phaseolus vulgaris L.) are a nutritious food, but their lengthy cooking requirements are barriers to consumption. Presoaking is one strategy to reduce cooking time. Soaking allows hydration to occur prior to cooking, and enzymatic changes to pectic polysaccharides also occur during soaking that shorten the cooking time of beans. Little is known about how gene expression during soaking influences cooking times. The objectives of this study were to (1) identify gene expression patterns that are altered by soaking and (2) compare gene expression in fast-cooking and slow-cooking bean genotypes. RNA was extracted from four bean genotypes at five soaking time points (0, 3, 6, 12, and 18 h) and expression abundances were detected using Quant-seq. Differential gene expression analysis and weighted gene coexpression network analysis were used to identify candidate genes within quantitative trait loci for water uptake and cooking time. Genes related to cell wall growth and development as well as hypoxic stress were differentially expressed between the fast- and slow-cooking beans due to soaking. Candidate genes identified in the slow-cooking beans included enzymes that increase intracellular calcium concentrations and cell wall modification enzymes. The expression of cell wall-strengthening enzymes in the slow-cooking beans may increase their cooking time and ability to resist osmotic stress by preventing cell separation and water uptake in the cotyledon.