Genomic selection in plant breeding: Key factors shaping two decades of progress

Molecular Plant, Journal Year: 2024, Volume and Issue: 17(4), P. 552 - 578

Published: March 12, 2024

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in past two decades, effectively accelerating genetic gains plant breeding.This article provides a holistic overview key factors that have influenced GP breeding during this period.We delved into pivotal roles training population size and diversity, their relationship with population, determining accuracy.Special emphasis was placed on optimizing size.We explored its benefits associated diminishing returns beyond an optimum size.This done while considering balance between resource allocation maximizing accuracy through current optimization algorithms.The density distribution single-nucleotide polymorphisms, level linkage disequilibrium, complexity, trait heritability, statistical machine-learning methods, non-additive effects are other vital factors.Using wheat, maize, potato as examples, we summarize effect these for various traits.The search high GP-theoretically reaching one when using Pearson's correlation metric-is active research area yet far from optimal traits.We hypothesize ultra-high sizes genotypic phenotypic datasets, effective methods support omics approaches (transcriptomics, metabolomics proteomics) coupled deep-learning algorithms could overcome boundaries limitations achieve highest possible accuracy, making selection tool breeding.

Language: Английский

---

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: Aug. 11, 2022

In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat (HS), salinity (SS), water-logging (WS), pre-harvest sprouting (PHS), and aluminium (AS) which predicted total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six out the 132 physically anchored were also verified genome-wide association studies. Around 43% had genetic physical confidence intervals less than 1 cM 5 Mb, respectively. Consequently, 539 genes in some selected providing 6 stresses. Comparative analysis underlying four RNA-seq based transcriptomic datasets unravelled 189 differentially expressed included 11 most promising candidate common among different datasets. The promoter showed promoters these include many responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, WUN-motif others. Further, overlapped 34 known genes. addition, numerous ortho-MQTLs maize, rice genomes discovered. These findings could help fine mapping gene cloning, well marker-assisted breeding for multiple tolerances wheat.

Language: Английский

---

Optimizing Plant Breeding Programs for Genomic Selection

Agronomy, Journal Year: 2022, Volume and Issue: 12(3), P. 714 - 714

Published: March 16, 2022

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with traits controlled by multiple minor effect alleles. Therefore, genomic (GS) was proposed estimate all markers simultaneously, thereby capturing their effects. However, breeding programs are still struggling identify best strategy implement it into programs. Traditional need be optimized GS effectively. This review explores optimization of variety release based on aspects breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing number lines evaluated, leveraging large amount phenotypic data collected across different growing seasons environments increase heritability estimates, intensity, accuracy. Breeding can leverage genotypic maximize genetic gain accuracy through methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, deep learning approaches. Overall, this describes various that plant utilize gains effectively breeding.

Language: Английский

---

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

Frontiers in Genetics, Journal Year: 2022, Volume and Issue: 12

Published: Jan. 21, 2022

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools plant breeding programs, especially major cereal crops. GS has demonstrated potential for selecting superior genotypes with high precision accelerating cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks explores new large-scale data acquisition methods. In this review, we discuss lesson learned from six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, groundnut, their implementation schemes are discussed after assessing impact programs. Here, status genomics provided those complete overview. GS’s progress until 2020 detail, relevant information links source codes implementing technology into most examples wheat Detailed about various strengthen field breeder coming years. Finally, highlight benefits merging selection, phenomics, machine deep learning that have resulted extraordinary results during recent years soybean. Hence, there adopting these technologies crops like groundnut. different programs will accelerate genetic gain would create food security, realizing need feed ever-growing population.

Language: Английский

---

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Frontiers in Genetics, Journal Year: 2022, Volume and Issue: 13

Published: Jan. 31, 2022

Soft white wheat is a class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due associated cost, time, amount of seed needed, phenotyping for the end-use trait delayed until later generations. Previously, we explored potential using genomic selection (GS) selecting superior genotypes earlier breeding program. Breeders typically measure multiple traits across locations, it opens up avenue exploring multi-trait–based GS models. This study’s main objective was explore multi-trait models predicting seven different cross-validation, independent prediction, across-location predictions The population consisted 666 soft planted 5 years at two locations Washington, United States. We optimized compared performances four uni-trait– models, namely, Bayes B, best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), random forests. accuracies were 5.5 7.9% uni-trait within-environment predictions. Multi-trait machine deep learning performed GBLUP B predictions, but their advantages diminished when genotype by environment component included model. highest improvement accuracy, that is, 35% obtained flour protein content with MLP study showed enhance accuracy information from previously phenotyped traits. It would assist speeding cycle time cost-friendly manner.

Language: Английский

---

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities

Frontiers in Genetics, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 5, 2023

Wheat is the most important source of food, feed, and nutrition for humans livestock around world. The expanding population has increasing demands various wheat products with different quality attributes requiring development cultivars that fulfills specific end-users including millers bakers in international market. Therefore, breeding programs continually strive to meet these standards by screening their improved lines every year. However, direct measurement end-use traits such as milling baking qualities requires a large quantity grain, traits-specific expensive instruments, time, an expert workforce which limits process. With advancement sequencing technologies, study entire plant genome possible, genetic mapping techniques quantitative trait locus genome-wide association studies have enabled researchers identify loci/genes associated wheat. Modern marker-assisted selection genomic allow utilization resources prediction high accuracy efficiency speeds up crop improvement cultivar endeavors. In addition, candidate gene approach through functional well comparative genomics facilitated translation information from several species wild relatives This review discusses wheat, control mechanisms, use genetics approaches improvement, future challenges opportunities breeding.

Language: Английский

---

GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat

Molecular Breeding, Journal Year: 2023, Volume and Issue: 43(3)

Published: Feb. 20, 2023

Language: Английский

---

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: May 30, 2024

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding climate-resilient wheat cultivars. In this study, we explored applicability Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for or multi-trait (MT) prediction grain yield (GY), test weight (TW), protein content (GPC) in winter wheat. Significant correlations were observed between agronomic HTP-based across different growth stages Using a neural network (DNN) model, predictions showed robust accuracies GY, TW, GPC single location R

Language: Английский

---

Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat

The Crop Journal, Journal Year: 2022, Volume and Issue: 10(6), P. 1695 - 1704

Published: April 26, 2022

Fusarium head blight (FHB), also known as scab, is a devastating fungal disease of wheat that causes significant losses in grain yield and quality. Quantitative inheritance cumbersome phenotyping make FHB resistance challenging trait for direct selection breeding. Genomic to predict traits has shown promise several studies. Here, we used univariate multivariate genomic prediction models evaluate the accuracy (PA) different using 476 elite advanced breeding lines developed by South Dakota State University hard winter program. These were assessed index (DIS), percentage damaged kernels (FDK) three nurseries 2018, 2019, 2020 (TP18, TP19, TP20) evaluated training populations (TP) (GP) traits. We observed moderate PA DIS (0.39 0.35) FDK (0.35 0.37) TP19 TP20, respectively, while slightly higher was (0.41 0.38 FDK) when TP20 (TP19 + 20) combined leverage advantage large population. Although GP with approach including plant height days heading covariates did not significantly improve over models, DON increased 20% DIS, FDK, DTH multi-trait model 2020. Finally, 20 forward calculate genomic-estimated values (GEBVs) preliminary at an early stage up 0.59 0.54 demonstrating earlier stages lines. Our results suggest expensive like can facilitate rejection highly susceptible materials

Language: Английский

---

Ascertaining yield and grain protein content stability in wheat genotypes having the Gpc-B1 gene using univariate, multivariate, and correlation analysis

Frontiers in Genetics, Journal Year: 2022, Volume and Issue: 13

Published: Sept. 7, 2022

The high performance and stability of wheat genotypes for yield, grain protein content (GPC), other desirable traits are critical varietal development food nutritional security. Likewise, the genotype by environment (G × E) interaction (GEI) should be thoroughly investigated favorably utilized whenever selection decisions made. present study was planned with following two major objectives: 1) determination GEI some advanced across four locations (Ludhiana, Ballowal, Patiala, Bathinda) Punjab, India; 2) best GPC yield in various environments. Different univariate [Eberhart Ruessll's models; Perkins Jinks' Wrike's Ecovalence; Francis Kannenberg's models], multivariate (AMMI GGE biplot), correlation analyses were used to interpret data from multi-environmental trial (MET). Consequently, both provided almost similar results regarding top-performing stable genotypes. analysis variance revealed that variation due environment, genotype, highly significant at 0.01 0.001 levels significance all studied traits. days flowering, plant height, spikelets per spike, maturity, 1000-grain weight specifically affected whereas mainly GEI. Genotypes, on hand, had a greater impact than environmental conditions. As result, investigation necessary identify because very evaluated Yield, weight, spikelet maturity observed have positive correlations, implying feasibility their simultaneous enhancement. However, negative yield. Patiala found most discriminating also effective representative GPC, Ludhiana Eventually, NILs (BWL7508, BWL7511) selected as top environments GPC.

Language: Английский

---