Plant Genome最新文献

Ronald Phillips, a maize geneticist, developed his career exploiting maize and the genetics of other species to help bring plant science into the era of molecular genetics. He was driven by belief in the value of service for the common good and in the value and importance of science for its own sake and for agriculture and food security, in particular. His career was a journey along the frontiers of plant science-from early DNA isolation to whole genome sequence revelations and into agricultural biotechnology. He represented the progress along the way in the maize genetics community, in national and international science and at the highest levels of influence. He was a caring, celebrated scientist who made a difference for people and institutions and left plant science so much further advanced than when he joined it in the mid-1960s.

An ensemble of multiple genomic prediction models has grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyze the predictive behavior at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualize how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the teosinte nested association mapping study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across subpopulations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs.

Over 50% of arable land available for cereal production in sub-Saharan Africa is severely infested with Striga hermonthica (Del.) Benth, posing a significant challenge to agricultural productivity in the region. In this study, we performed association mapping of plant height, panicle height, number of leaves per plant, field fresh grain weight, dry grain weight, and chlorophyll with 6,094,317 single nucleotide polymorphism (SNP) markers for Striga resistance genes in diverse sorghum [Sorghum bicolor (L.) Moench] breeding lines and varieties released for resistance breeding. Chromosomes containing significant SNPs in FASTmrMLM and FarmCPU models were identified and computed. Chromosomes 1, 2, 3, 4, and 6 harbored SNPs significant for Striga tolerance in sorghum for agronomic-related traits. Agronomic traits measured revealed significant SNP counts as follows: plant height (4), panicle height (3), leaves per plant (2), foliar fresh grain weight (8), dry grain weight (2), and chlorophyll content (3). After successful validation, the 22 newly identified SNP markers linked to Striga resistance can be used for trait introgression and marker-assisted selection to increase Striga resistance in sorghum. We detected 12 SNPs using the FASTmrMLM model without adjusting the threshold level. However, no significant SNPs were detected with FarmCPU before the threshold was adjusted. Also, we identified 95 significant SNPs upon lowering the Bonferroni threshold value to p < 0.001. The parent materials for the intraspecific cross that produced the currently accessible molecular map were selected from the gene pool of cultivated sorghum. This map is invaluable for real-world breeding applications. Subsequent crosses among cultivated sorghum genotypes of interest to breeders will likely produce polymorphic segregating Diversity Array Technology (DArTSeq) markers within the cultivated gene pool.

The global population, set to exceed 10 billion by 2050, presents enormous challenges to food, health, nutrition, energy, and environmental security. Plant breeding methods have continuously evolved to develop improved crop varieties to meet these demands. Among the recent developments, genetically modified crops (GMCs) have emerged as a viable option to enhance crop yields, nutritional value, biofuel potential, and climatic adaptability. However, extensive application of GMCs is a very controversial subject due to biosafety issues, environmental impacts, economic viability, and legal considerations. This review presents a critical evaluation of the merits and limitations of GMCs, along with a discussion of available alternative approaches, with particular reference to the Indian context. While GMCs have been developed with increased yields, improved shelf life, reduced pesticide and herbicide use, and improved stress tolerance, potential risks such as health hazards and socioeconomic impacts on smallholding farmers in the developing world cannot be disregarded. Besides, regulatory policies and public perception have a significant influence on the acceptability and commercialization of GMCs, especially in countries like India. The discussion therefore encompasses other sustainable alternatives, including marker-assisted selection, genomics-aided breeding, cisgenesis, intragenesis, and stringently regulated gene editing, that embody environment-friendly approaches to agricultural enhancement. A collective assessment of these techniques is presented in order to examine their prospects for delivering long-term biosecurity without compromising environmental and human health. By integrating scientific advances, policy environments, and social perceptions, this review aims to present a balanced perspective of GMCs and their role in the future of global agriculture, particularly in the Global South.

To identify chromosomal regions and candidate genes controlling important wheat (Triticum aestivum L.) traits under heat stress, a doubled haploid population was developed from a cross between KSG0057, a selection out of PBW343 and KSG1190, a heat stress tolerant line. The population was evaluated for grain yield, 100-grain weight, grain weight of main spike, number of grains per main spike, number of spikelets per main spike, spike length, and plant height during 2016-2017 and 2017-2018 at two locations in India. Heat stress was applied by conducting trials under normal, late, and very late sown conditions. The mapping population was genotyped using sequencing-based genotyping to target the genic fraction of the genome. A 3875.3 cM linkage map was constructed via 674 high-quality single nucleotide polymorphisms. Composite interval mapping was done for individual environments and for reduction percentage due to late and very late planting. With 38 regions common between the two types of analysis, 66 genomic regions containing 155 quantitative trait loci (QTLs) for individual environments and 100 containing 152 QTLs for the reduction percentage analysis were identified. Of the 155 QTLs, 82 were found only under very late sown conditions. Of the 152 QTLs, 40 were for heat stress and 45 for severe heat stress. The QTL-containing regions ranged from 1.5 kb to 684.9 Mb in size, with 35 being <1 Mb and 30 being <5 Mb. Number of genes in these 35 regions ranged from 1 to 19. Candidate genes for 16 of the regions were identified as the underlying region of 1.5-287.5 kb contained only one gene each.

Root rot caused by Aphanomyces euteiches is a major concern in pea (Pisum sativum L.). The lack of other effective control strategies makes crucial the development of resistant varieties. Although partial resistance has been reported, its quantitative inheritance, the association of resistance-linked genomic regions with unfavorable agronomic traits, and the limited understanding of soil pathogen populations hinder its progress in breeding programs. To search for alternative genomic regions associated with this partial resistance, a genome-wide association study (GWAS) was performed on a pea collection not yet explored for A. euteiches resistance in genetic studies. The 323 accessions of the collection were inoculated with RB84 isolate, and foliar and root symptoms were assessed 20 days after inoculation. The performed GWAS revealed 27 significantly associated markers among 26,045 SilicoDArT and 7033 single-nucleotide polymorphism marker datasets. Detected markers were distributed along the seven pea chromosomes, with 12 within previously described quantitative trait loci (QTLs). Chromosomes 2 and 5 harbored a significant number of associated markers, identified here for the first time, highlighting promising regions for future investigation. Twenty-one candidate resistance genes were identified. This study uncovers new genomic regions linked with A. euteiches resistance and provides molecular markers and candidate genes to support precision breeding. Newly identified QTL may be more effective against specific isolates than known QTL, enabling improved QTL rotation in the field.

The age of molecular cytogenetic analysis of crop plants dawned in the late 1960s and early 1970s with new advances in the identification of somatic chromosomes by C-banding and fluorescence in situ hybridization concurrent with advances in DNA cloning, sequencing, and mapping. In this perspective article dedicated to Ronald Phillips, I review the contributions of molecular cytogenetic research to chromosome biology and crop improvement. I argue that molecular cytogenetics and wide hybridization (intergeneric and interspecific hybridization followed by introgressive breeding) will continue to play a key role in developing climate-resilient crop germplasm. However, this will happen only if the lack of investment and retrenchment of faculty engaged in molecular cytogenetics is reversed across US land-grant universities.

The Big Breeding Innovation Team (Big BIT) maize (Zea mays L.) experiment was one of the largest genomic data-informed predictive breeding validation studies ever conducted. The experiment was a multi-location, multi-year, multi-tester, multi-population study involving F1 maize hybrids created by crossing individual doubled haploids to inbred testers. The purpose of the study, performed by DuPont Pioneer/Corteva Agriscience in 2017, 2018, and 2019, was to build comprehensive datasets to help answer a wide range of practical questions focused on optimizing predictive breeding strategies in maize. The purpose of our study is to (1) describe the design and unique features of our study and (2) discuss learnings with practical implications for plant breeders. Since the same F1 maize hybrids were grown across three distinct years, we use basic descriptive summary statistics to discuss our learnings. We provide a technical justification for the use of basic statistics and discuss  the expected theoretical prediction accuracy of genomic estimated breeding values (GEBVs) of Big BIT individuals and families, and predictive abilities obtained by performing large-scale cross-validations. Our study provides multi-year field data-based evidence that, for inbred/variety development focused plant improvement efforts, early-stage genetic evaluation should be based on GEBVs generated from wide-area testing training datasets. This holds true for candidates for selection with or without own phenotypic records.

We conducted simulations of common bean (Phaseolus vulgaris L.) breeding programs to better understand the interplay between different choices a breeder must make when launching a genomic selection (GS) pipeline. We complement preceding studies on optimizing model parameters and training set makeup by exploring the practical implementation of GS in a common bean breeding program aimed at increasing seed yield. We simulated 24 GS implementation pathways on (1) what generation to train a new prediction model, (2) what generation to select parents for the next cycle, (3) which generation to collect training data, and (4) whether to use a parametric (ridge regression best linear unbiased predictor) or a nonparametric model (artificial neural network) for estimating breeding values. We found that early generation parent selections (also called rapid-cycle GS) generally resulted in higher gain over three breeding cycles compared to late-generation parent selections. When implementing a new parametric genomic prediction model, training data should be as diverse as possible, while also matching testing data in terms of genetic makeup and allele frequency. Parametric models showed more consistent genomic estimated breeding value prediction accuracy, while nonparametric models fluctuated, showing both the highest and the lowest prediction accuracy across all pathways. Despite the trade-off between gains and genetic variance, nonparametric models showed greater balance of allelic diversity and gains. We observed that the key to sustained gains over time is the renewal of genetic variance. Our results indicate a potential for the use of nonparametric models, but more investigation will be required to stabilize their performance.