Category Archives: Wiley: The Plant Genome

Multi‐locus genome‐wide association study reveal genomic regions underlying root system architecture traits in Ethiopian sorghum germplasm

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Abstract

The identification of genomic regions underlying the root system architecture (RSA) is vital for improving crop abiotic stress tolerance. To improve sorghum (Sorghum bicolor L. Moench) for environmental stress tolerance, information on genetic variability and genomic regions linked to RSA traits is paramount. The aim of this study was, therefore, to investigate common quantitative trait nucleotides (QTNs) via multiple methodologies and identify genomic regions linked to RSA traits in a panel of 274 Ethiopian sorghum accessions. Multi-locus genome-wide association study was conducted using 265,944 high-quality single nucleotide polymorphism markers. Considering the QTN detected by at least three different methods, a total of 17 reliable QTNs were found to be significantly associated with root angle, number, length, and dry weight. Four QTNs were detected on chromosome SBI-05, followed by SBI-01 and SBI-02 with three QTNs each. Among the 17 QTNs, 11 are colocated with previously identified root traits quantitative trait loci and the remaining six are genome regions with novel genes. A total of 118 genes are colocated with these up- and down-streams of the QTNs. Moreover, five QTNs were found intragenic. These QTNs are S5_8994835 (number of nodal roots), S10_55702393 (number of nodal roots), S1_56872999 (nodal root angle), S9_1212069 (nodal root angle), and S5_5667192 (root dry weight) intragenic regions of Sobic.005G073101, Sobic.010G198000, Sobic.001G273000, Sobic.009G013600, and Sobic.005G054700, respectively. Particularly, Sobic.005G073101, Sobic.010G198000, and Sobic.009G013600 were found responsible for the plant growth hormone-induced RSA. These genes may regulate root development in the seedling stage. Further analysis on these genes might be important to explore the genetic structure of RSA of sorghum.

Genome‐wide identification, gene expression and haplotype analysis of the rhomboid‐like gene family in wheat (Triticum aestivum L.)

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Abstract

The rhomboid-like (RBL) gene encodes serine protease, which plays an important role in the response to cell development and diverse stresses. However, genome-wide identification, expression profiles, and haplotype analysis of the RBL family genes have not been performed in wheat (Triticum aestivum L.). This study investigated the phylogeny and diversity of the RBL family genes in the wheat genome through various approaches, including gene structure analysis, evolutionary relationship analysis, promoter cis-acting element analysis, expression pattern analysis, and haplotype analysis. The 41 TaRBL genes were identified and divided into five subfamilies in the wheat genome. RBL family genes were expanded through segmented duplication and purification selection. The cis-element analysis revealed their involvement in various stress responses and plant development. The results of RNA-seq and quantitative real-time-PCR showed that TaRBL genes displayed higher expression levels in developing spike/grain and were differentially regulated under polyethylene glycol, NaCl, and abscisic acid treatments, indicating their roles in grain development and abiotic stress response. A kompetitive allele-specific PCR molecular marker was developed to confirm the single nucleotide polymorphism of TaRBL14a gene in 263 wheat accessions. We found that the elite haplotype TaRBL14a-Hap2 showed a significantly higher 1000-grain weight than TaRBL14a-Hap11 in at least three environments, and the TaRBL14a-Hap2 was positively selected in wheat breeding. The findings will provide a good insight into the evolutionary and functional characteristics of the TaRBL genes family in wheat and lay the foundation for future exploration of the regulatory mechanisms of TaRBL genes in plant growth and development, as well as their response to abiotic stresses.

Comparative genomics points to tandem duplications of SAD gene clusters as drivers of increased α‐linolenic (ω‐3) content in S. hispanica seeds

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Abstract

Salvia hispanica L. (chia) is a source of abundant ω-3 polyunsaturated fatty acids (ω-3-PUFAs) that are highly beneficial to human health. The genomic basis for this accrued ω-3-PUFA content in this emerging crop was investigated through the assembly and comparative analysis of a chromosome-level reference genome for S. hispanica. The highly contiguous 321.5-Mbp genome assembly covering all six chromosomes enabled the identification of 32,922 protein-coding genes. Two whole-genome duplications (WGD) events were identified in the S. hispanica lineage. However, these WGD events could not be linked to the high α-linolenic acid (ALA, ω-3) accumulation in S. hispanica seeds based on phylogenomics. Instead, our analysis supports the hypothesis that evolutionary expansion through tandem duplications of specific lipid gene families, particularly the stearoyl-acyl carrier protein desaturase (ShSAD) gene family, is the main driver of the abundance of ω-3-PUFAs in S. hispanica seeds. The insights gained from the genomic analysis of S. hispanica will help establish a molecular breeding target that can be leveraged through genome editing techniques to increase ω-3 content in oil crops.

Genome‐wide analysis of PvMADS in common bean and functional characterization of PvMADS31 in Arabidopsis thaliana as a player in abiotic stress responses

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Changing climatic conditions with rising temperatures and altered precipitation patterns pose significant challenges to agricultural productivity, particularly for common bean crops. Transcription factors (TFs) are crucial regulators that can mitigate the impact of biotic and abiotic stresses on crop production. The MADS-box TFs family has been implicated in various plant physiological processes, including stress-responsive mechanisms. However, their role in common bean and their response to stressful conditions remain poorly understood. Here, we identified 35 MADS-box gene family members in common bean, with conserved MADS-box domains and other functional domains. Gene duplication events were observed, suggesting the significance of duplication in the evolutionary development of gene families. The analysis of promoter regions revealed diverse elements, including stress-responsive elements, indicating their potential involvement in stress responses. Notably, PvMADS31, a member of the PvMADS-box gene family, demonstrated rapid upregulation under various abiotic stress conditions, including NaCl, polyethylene glycol, drought, and abscisic acid (ABA) treatments. Transgenic plants overexpressing PvMADS31 displayed enhanced lateral root development, root elongation, and seed germination under stress conditions. Furthermore, PvMADS31 overexpression in Arabidopsis resulted in improved drought tolerance, likely attributed to the enhanced scavenging of ROS and increased proline accumulation. These findings suggest that PvMADS31 might play a crucial role in modulating seed germination, root development, and stress responses, potentially through its involvement in auxin and ABA signaling pathways. Overall, this study provides valuable insights into the potential roles of PvMADS-box genes in abiotic stress responses in common bean, offering prospects for crop improvement strategies to enhance resilience under changing environmental conditions.

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating

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The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection

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Effects of individual single-nucleotide polymorphism (SNP) markers and the size of “training” and “test” populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of “training” to “test” population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of “training” to “test” population size increased. However, the prediction accuracy of the six GS methods reduced to a range of −0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for “training” sets, but rrBLUP tended to outperform Bayesian methods in independent “test” sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

Common signatures of selection reveal target loci for breeding across soybean populations

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Understanding the underlying genetic bases of yield-related selection and distinguishing these changes from genetic drift are critical for both improved understanding and future success of plant breeding. Soybean [Glycine max (L.) Merr.] is a key species for world food security, yet knowledge of the mechanism of selective breeding in soybean, such as the century-long program of artificial selection in U.S. soybean germplasm, is currently limited to certain genes and loci. Here, we identify genome-wide signatures of selection in separate populations of soybean subjected to artificial selection for increased yield by multiple breeding programs in the United States. We compared the alternative soybean breeding population (AGP) created by USDA-ARS to the conventional public soybean lines (CGP) developed at three different stages of breeding (ancestral, intermediate, and elite) to identify shared signatures of selection and differentiate these from drift. The results showed a strong selection for specific haplotypes identified by single site frequency and haplotype homozygosity methods. A set of common selection signatures was identified in both AGP and CGP that supports the hypothesis that separate breeding programs within similar environments coalesce on the fixation of the same key haplotypes. Signatures unique to each breeding program were observed. These results raise the possibility that selection analysis can allow the identification of favorable alleles to enhance directed breeding approaches.

Long‐ and short‐read sequencing methods discover distinct circular RNA pools in Lotus japonicus

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Circular RNAs (circRNAs) are covalently closed single-stranded RNAs, generated through a back-splicing process that links a downstream 5′ site to an upstream 3′ end. The only distinction in the sequence between circRNA and their linear cognate RNA is the back splice junction. Their low abundance and sequence similarity with their linear origin RNA have made the discovery and identification of circRNA challenging. We have identified almost 6000 novel circRNAs from Lotus japonicus leaf tissue using different enrichment, amplification, and sequencing methods as well as alternative bioinformatics pipelines. The different methodologies identified different pools of circRNA with little overlap. We validated circRNA identified by the different methods using reverse transcription polymerase chain reaction and characterized sequence variations using nanopore sequencing. We compared validated circRNA identified in L. japonicus to other plant species and showed conservation of high-confidence circRNA-expressing genes. This is the first identification of L. japonicus circRNA and provides a resource for further characterization of their function in gene regulation. CircRNAs identified in this study originated from genes involved in all biological functions of eukaryotic cells. The comparison of methodologies and technologies to sequence, identify, analyze, and validate circRNA from plant tissues will enable further research to characterize the function and biogenesis of circRNA in L. japonicus.

Genome‐wide SNP discovery and genotyping delineates potential QTLs underlying major yield‐attributing traits in buckwheat

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Buckwheat (Fagopyrum spp.) is an important nutritional and nutraceutical-rich pseudo-cereal crop. Despite its obvious potential as a functional food, buckwheat has not been fully harnessed due to its low yield, self-incompatibility, increased seed cracking, limited seed set, lodging, and frost susceptibility. The inadequate availability of genomics resources in buckwheat is one of the major reasons for this. In the present study, genome-wide association mapping (GWAS) was conducted to identify loci associated with various morphological and yield-related traits in buckwheat. High throughput genotyping by sequencing led to the identification of 34,978 single nucleotide polymorphisms that were distributed across eight chromosomes. Population structure analysis grouped the genotypes into three sub-populations. The genotypes were also characterized for various qualitative and quantitative traits at two diverse locations, the analysis of which revealed a significant difference in the mean values. The association analysis revealed a total of 71 significant marker–trait associations across eight chromosomes. The candidate genes were identified near 100 Kb of quantitative trait loci (QTLs), providing insights into several metabolic and biosynthetic pathways. The integration of phenology and GWAS in the present study is useful to uncover the consistent genomic regions, related markers associated with various yield-related traits, and potential candidate genes having implications for being utilized in molecular breeding for the improvement of economically important traits in buckwheat. Moreover, the identified QTLs will assist in tracking the desirable alleles of target genes within the buckwheat breeding populations/germplasm.