Genomic evolution and insights into agronomic trait innovations of Sesamum species
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ABSTRACT:
SUBMITTER: Miao H
PROVIDER: S-EPMC10811377 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
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Project description:Sesame (Sesamum indicum L.) is one of the oldest oilseed crops. In order to investigate the evolutionary characters according to the Sesame Genome Project, apart from sequencing its nuclear genome, we sequenced the complete chloroplast genome of S. indicum cv. Yuzhi 11 (white seeded) using Illumina and 454 sequencing. Comparisons of chloroplast genomes between S. indicum and the 18 other higher plants were then analyzed. The chloroplast genome of cv. Yuzhi 11 contains 153,338 bp and a total of 114 unique genes (KC569603). The number of chloroplast genes in sesame is the same as that in Nicotiana tabacum, Vitis vinifera and Platanus occidentalis. The variation in the length of the large single-copy (LSC) regions and inverted repeats (IR) in sesame compared to 18 other higher plant species was the main contributor to size variation in the cp genome in these species. The 77 functional chloroplast genes, except for ycf1 and ycf2, were highly conserved. The deletion of the cp ycf1 gene sequence in cp genomes may be due either to its transfer to the nuclear genome, as has occurred in sesame, or direct deletion, as has occurred in Panax ginseng and Cucumis sativus. The sesame ycf2 gene is only 5,721 bp in length and has lost about 1,179 bp. Nucleotides 1-585 of ycf2 when queried in BLAST had hits in the sesame draft genome. Five repeats (R10, R12, R13, R14 and R17) were unique to the sesame chloroplast genome. We also found that IR contraction/expansion in the cp genome alters its rate of evolution. Chloroplast genes and repeats display the signature of convergent evolution in sesame and other species. These findings provide a foundation for further investigation of cp genome evolution in Sesamum and other higher plants.
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Project description:Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.
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Project description:Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for > 70 years in the United States and Canada, consisting of 20-50 entries each year at 10-20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.
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Project description:IntroductionLegume crops are an important source of protein and oil for human health and in fixing atmospheric N2 for soil enrichment. With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement.ObjectivesPresent study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution.MethodsWe employed in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies.ResultsWe have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of "QTL-hotspot" identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, 'Legumepedia'.ConclusionThis study reports chromosome-length genome assemblies of six legume species and demonstrates the utility of these assemblies in crop improvement. The genomic resources developed here will have significant role in accelerating genetic improvement applications of legume crops.
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Project description:BackgroundHelicoverpa armigera and Helicoverpa zea are major caterpillar pests of Old and New World agriculture, respectively. Both, particularly H. armigera, are extremely polyphagous, and H. armigera has developed resistance to many insecticides. Here we use comparative genomics, transcriptomics and resequencing to elucidate the genetic basis for their properties as pests.ResultsWe find that, prior to their divergence about 1.5 Mya, the H. armigera/H. zea lineage had accumulated up to more than 100 more members of specific detoxification and digestion gene families and more than 100 extra gustatory receptor genes, compared to other lepidopterans with narrower host ranges. The two genomes remain very similar in gene content and order, but H. armigera is more polymorphic overall, and H. zea has lost several detoxification genes, as well as about 50 gustatory receptor genes. It also lacks certain genes and alleles conferring insecticide resistance found in H. armigera. Non-synonymous sites in the expanded gene families above are rapidly diverging, both between paralogues and between orthologues in the two species. Whole genome transcriptomic analyses of H. armigera larvae show widely divergent responses to different host plants, including responses among many of the duplicated detoxification and digestion genes.ConclusionsThe extreme polyphagy of the two heliothines is associated with extensive amplification and neofunctionalisation of genes involved in host finding and use, coupled with versatile transcriptional responses on different hosts. H. armigera's invasion of the Americas in recent years means that hybridisation could generate populations that are both locally adapted and insecticide resistant.
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Project description:Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.
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