The Plant Genome special section: Grain quality and nutritional genomics for breeding next‐generation crops DOI Creative Commons
Manish K. Pandey, Reyazul Rouf Mir, Nese Sreenivasulu

и другие.

The Plant Genome, Год журнала: 2023, Номер 16(4)

Опубликована: Дек. 1, 2023

By 2050, the world's population is expected to reach 9.8 billion according United Nations predictions (https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100). As a result, crop yields must roughly double in order feed an expanding global while still satisfying consumer demands for grain quality and nutrition. In addition enhancing nutritional value of food crops, making available affordable, nutrient-dense food, especially those who are economically disadvantaged, will be central pillar address security. The strategy improving traits breeding programs has been prioritized with recent advancements phenotyping seeds grains (metabolomics, mineral vitamins, assessing starch, proteins lipids, capturing preferred traits), sequencing technologies do high-throughput genotyping, functional genomics aided gene discovery, high-resolution trait mapping superior haplotype as well deploying genomic selection tools variety crops (Pandey et al., 2016; Varshney 2019). To improve dietary patterns, new generation foods ingredients improved intrinsic attributes needs generated through advanced methods. This help public health by increasing density optimizing complex carbohydrates, proteins, lipids. utilizing integrating both modern traditional techniques, it possible hasten production types yield, grain, quality. special issue highlights most significant findings, which cover developments genomics, including prediction related quality, enhancement nutritive cereals (rice, wheat, maize, oat) legume like groundnut. Overall, this includes collection studies deciphering genetic mechanisms micronutrients covering minerals such iron (Fe), zinc (Zn), vitamin enrichment (tocochromanols), pigmented bioactives, amino acids, fiber, fatty acid composition, safety, end user selected legumes. approach identifying regions controlling key nutrition successful contributed significantly marker discovery use (Cockram & Mackay, 2018). High concentrations essential lysine limiting high free asparagine prevent acrylamide during bread formation enhance wheat grain. article Oddy al. (2023) used understanding control composition UK soft major emphasis on lowering higher content. multivariate analysis showing these largely independent one another, largest effect acids being from environment. study also identified quantitative loci (QTLs) content, may prove useful applying appropriate strategies reduce programmes. Using same groundnut, Parmar co-localized main candidate genes content Zn reports identification six main-effect QTLs Fe five Interestingly, three that further facilitate fine diagnostic development pooled sequencing-based region biparental Gangurde (2022) QTL-Seq markers seed weight. successfully associated weight 182 SNPs genic intergenic regions. Although multiple important regions, Ulp proteases BIG SEED locus very because its detection other well. More importantly breed groundnut varieties bigger size, gene-specific Kompetitive allele-specific PCR were developed validated. It vital determine Meta-QTLs haplotypes target traits, numerous found several areas form influence Joshi addresses aspect paper biofortification rice performing meta-analysis 155 followed 57 MQTLs reduced confidence intervals. importantly, not only detected co-localization metal homeostasis but involvement network silico expression co-expression analyses. Furthermore, efficient biofortification. Another led Diers presented results architecture concentration protein, oil, meal protein using soybean nested association reported 107 marker-trait associations (MTAs) above-mentioned traits. few MTAs mapped within 5 cM intervals (94%) effects matched correlation between linked suggested would more effective large number small Derbyshire utilized pangenome based thousands lines identify alleles involved biosynthesis. instances missing wild soybean, FAD8 FAD2-2D oleic linoleic desaturation, respectively. frequency missense biosynthesis genes, could domestication. genome-wide (GWAS) alternate plant species rely variation various core collections, instead developing populations (Gangurde 2022; Sushmitha 2023). Panahabadi uses monosaccharides contents rice. Monosaccharides building blocks synthesis polymers or carbohydrates. 49 housed 17 located seven chromosomes whole all novel. Multiple promising potential validation breeding. next Mbanjo performs GWAS linking pigmentation seed. >280 SNPs, many than secondary metabolite accumulation pigmentation. Further, targeted 67 52 showed 24 Rc/bHLH17 OsIPT5 regulation wide range phenolic compounds color. information made exploited deployment rice-breeding program. Genomic emerged powerful prediction-based progenies crop-breeding even early generation, therefore, saving resources, time, precision added advantage. There plenty testing statistical models maize wheat. now, huge training size constitution, genotyping platforms, keeping mind genotype interaction environment soil, possibilities selection. case published emerging area Research Tibbs-Cortes provided exciting exotic-derived tocochromanols (vitamin E), human diet. expected, accuracies achieved when predicting each decreased performed diversity panel set. strength hypothesis optimal designing efficiently incorporate exotic germplasm into Tanaka worked sound data support prior QTL contributing causal conferring biological knowledge elevate E improves multi-trait model two panels inbred lines. Next, research Brzozowski was oat targeting methods 12 indicated variability accuracy family compared unrelated panel. families had half-sib set without it, suggesting approach. Meher tested eight Bayesian micro-nutrient Zn, Fe, β-carotenoid ridge regression reliable method revalidated reliability increases increase BLUE values response variables better Fradgeley summarized maintenance Bread Baking Quality trends over 50 years future application genomic-assisted no subsequent net loss gain due breeders’ selection, despite yield time. proposed reduction increased gluten combination changes industrial baking process enabled decades. Most diverse varied algorithm clarity best models, can realistic scenarios Gill implementing processing end-use hard winter (MTGP) outperformed single up twofold accuracy. suggests MTGP together flour sedimentation evaluated earlier generations predict generations. issue, review articles focusing nutrients plants Khan another allergens (2023). under changing environments role stressful biotechnological optimization nutrient acquisition, transport, distribution plants. provides bio-fortification optimize stress conditions emphasizes about concerns safety need protect negative food-born allergies. current updates predicts prospects allergen-depleted crops. discussed detail how advances molecular breeding, engineering, genome editing potentially health. Manish K. Pandey: Conceptualization; investigation; methodology; project administration; resources; supervision; writing—original draft; writing—review editing. Reyazul Rouf Mir: Nese Sreenivasulu:

Язык: Английский

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

Swas Kaushal,

Harsimardeep S. Gill, Mohammad Maruf Billah

и другие.

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Май 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

Язык: Английский

Процитировано

8
A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality DOI Creative Commons

Zhaoheng Zhang,

Dan Liu,

Binyong Li

и другие.

Molecular Plant, Год журнала: 2024, Номер 17(7), С. 1038 - 1053

Опубликована: Май 24, 2024

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds baked goods. Superior end-use quality major breeding target; however, improving it especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families are repetitive, gaps commonplace in several genome assemblies. To overcome these barriers efficiently identify superior SSP alleles, we developed "PanSK" (Pan-SSP k-mer) genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each gene at pangenomic level reveal untapped diversity across landraces modern cultivars. Genome-wide association studies k-mers identified 23 associated novel targets improvement. We evaluated effect rye secalin found removal ω-secalins from 1BL/1RS translocation lines enhanced quality. Finally, using machine-learning-based inspired by PanSK, predicted phenotypes high accuracy genotypes alone. This study provides effective approach design genes, enabling varieties processing capabilities improved

Язык: Английский

Процитировано

3
Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat DOI Creative Commons
Subash Thapa, Harsimardeep S. Gill,

Jyotirmoy Halder

и другие.

The Plant Genome, Год журнала: 2024, Номер unknown

Опубликована: Июнь 9, 2024

Fusarium head blight (FHB) remains one of the most destructive diseases wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping FHB resistance traits, Fusarium-damaged kernels (FDK), deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering progress breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way select these inaccurate phenotyping a hurdle exploiting this approach. Here, we used artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability correlation with DON. Further, GS using AI-based (FDK_QVIS/FDK_QNIR) showed two-fold increase predictive ability (PA) compared traditionally estimated (FDK_V). Next, was evaluated along other traits multi-trait (MT) models predict The inclusion FDK_QNIR FDK_QVIS days heading as covariates improved PA DON by 58% over baseline single-trait model. We next hyperspectral imaging FHB-infected novel avenue improve MT selected wavebands derived from surpassed model around 40%. Finally, phenomic prediction integrating deep learning directly observed accuracy (R

Язык: Английский

Процитировано

3
Low density marker‐based effectiveness and efficiency of early‐generation genomic selection relative to phenotype‐based selection in dolichos bean (Lablab purpureus L. Sweet) DOI Creative Commons
Mugali Pundalik Kalpana, S. Ramesh, Chindi Basavaraj Siddu

и другие.

The Plant Genome, Год журнала: 2025, Номер 18(2)

Опубликована: Май 26, 2025

Abstract Genomic prediction has been demonstrated to be an efficient approach for the selection of candidates based on marker information in many crops. However, efforts understand efficiency genomic over phenotype‐based understudied crops such as dolichos bean ( Lablab purpureus L. Sweet) are limited. Our objectives were (i) explore effective density achieving high accuracy and (ii) assess effectiveness seed yield at early segregating generations bean. In this study, training population, which consisted F 5:6 recombinant inbreds, had a shared common parent with breeding 2 generation population. The populations genotyped newly synthesized simple sequence repeat‐based markers. was assessed by using varying number markers predictions 11 different models. Furthermore, comparing genetic gains progenies between genotypes selected predicted phenotypically genotypes. results indicate that low‐density evenly distributed throughout genome sufficient integration programs. proved two times more than phenotypic early‐generation beans. have significant impact adopting regular programs Dolichos beans low cost.

Язык: Английский

Процитировано

0
Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials DOI Creative Commons
Jordan McBreen, Md Ali Babar, Diego Jarquín

и другие.

Agronomy, Год журнала: 2025, Номер 15(6), С. 1315 - 1315

Опубликована: Май 28, 2025

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes later-stage big (BP) trials. Genomic (G) were combined with hyperspectral (H) multispectral + thermal (M) imaging across the 2022 2023 growing seasons at Plant Science Research Education Unit, Citra, Florida. A panel of 312 genotypes was analyzed using GBLUP-based models, integrating G H M SP BP yield. models demonstrated promising predictive ability, achieving moderate within-year (0.43 0.51) across-year (0.43) accuracies, while reached 0.53 0.58 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy 0.47 when SP, G, used 2023. Additionally, top 25% specificity (coincide index) evaluated, showing up 47–51% within a year 43–45% between years overlap highest predicted-yielding lines trials, further emphasizing potential for early selection. These findings suggest that provide meaningful predictions yields, enabling earlier faster

Язык: Английский

Процитировано

0
Association mapping and genomic prediction for processing and end‐use quality traits in wheat (Triticum aestivum L.) DOI Creative Commons
Harsimardeep S. Gill, Emily Conley, Charlotte Brault

и другие.

The Plant Genome, Год журнала: 2024, Номер 18(1)

Опубликована: Ноя. 13, 2024

Abstract End‐use and processing traits in wheat ( Triticum aestivum L.) are crucial for varietal development but often evaluated only the advanced stages of breeding program due to amount grain needed labor‐intensive phenotyping assays. Advances genomic resources have provided new tools address selection these complex earlier process. We used association mapping identify key variants underlying various end‐use quality evaluate usefulness prediction hard red spring from Northern United States. A panel 383 lines cultivars representing diversity University Minnesota was genotyped using Illumina 90K single nucleotide polymorphism array multilocation trials standard assessments quality. Sixty‐three associations or flour characteristics, mixograph, farinograph, baking were identified. The majority mapped vicinity glutenin/gliadin other known loci. In addition, a putative novel multi‐trait identified on chromosome 6AL, candidate gene analysis revealed eight genes interest. Further, had high predictive ability (PA) mixograph farinograph traits, with PA up 0.62 0.50 cross‐validation forward prediction, respectively. deployment 46 markers GWAS predict dough‐rheology yielded low moderate traits. results this study suggest that early generations can be effective assays not

Язык: Английский

Процитировано

1
Advancing water absorption capacity in hard winter wheat using a multivariate genomic prediction approach DOI Creative Commons
Meseret A. Wondifraw, Zachary J. Winn, Scott D. Haley

и другие.

Crop Science, Год журнала: 2024, Номер 64(6), С. 3086 - 3098

Опубликована: Авг. 24, 2024

Abstract The water absorption capacity (WAC) of hard wheat ( Triticum aestivum L.) flour affects end‐use quality characteristics, including loaf volume, bread yield, and shelf life. However, improving WAC through phenotypic selection is challenging. Phenotyping for time consuming and, as such, often limited to evaluation in the latter stages breeding process, resulting retention suboptimal lines longer than desired. This study investigates potential univariate multivariate genomic predictions an alternative WAC. A total 497 winter genotypes were evaluated multi‐environment advanced yield elite trials over 8 years (2014–2021). was done via solvent (SRC) using a (SRC‐W). Traits that exhibited significant correlation r ≥ 0.3) with SRC‐W earlier included prediction models. Kernel hardness diameter obtained single kernel characterization system (SKCS), break (T‐Flour) included. Cross‐validation showed mean accuracy SRC be = 0.69 ± 0.005, while bivariate models improved 0.82 0.003. Forward validation up 0.81 model + All traits (SRC‐W, Diameter, SKCS diameter, F‐Flour, T‐Flour). These results suggest incorporating correlated into can improve early‐generation accuracy.

Язык: Английский

Процитировано

0
Integrating multi‐trait genomic selection with simulation strategies to improve grain yield and parental line selection in rice DOI
C. Anilkumar, Rameswar Prasad Sah, T. P. Muhammed Azharudheen

и другие.

Annals of Applied Biology, Год журнала: 2024, Номер unknown

Опубликована: Дек. 4, 2024

Abstract Inclusion of correlated secondary traits in the prediction primary trait multi‐trait genomic selection (GS) models can improve predictive ability. Our objectives present investigations were to (i) evaluate effectiveness and single‐trait GS for higher ability (ii) compare breeding potential parental lines selected based on phenotype grain yield rice. We used data five as evaluated predict yield, a trait. Yield related functional markers prediction. Breeding populations simulated using best parents through selection. Results suggest that model resulted abilities (0.82 yield) than (0.76 have produce superior progenies. conclude use approach is advantageous over models, also help selecting developing improved populations. The results study scope improving quantitative

Язык: Английский

Процитировано

0
Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program DOI Creative Commons

Dipendra Shahi,

James Todd,

K. A. Gravois

и другие.

The Plant Genome, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 30, 2024

Genomic selection can enhance the rate of genetic gain cane and sucrose yield in sugarcane (Saccharum L.), an important industrial crop worldwide. We assessed predictive ability (PA) for six traits, such as theoretical recoverable sugar (TRS), number stalks (NS), stalk weight (SW), (CY), (SY), fiber content (Fiber) using 20,451 single nucleotide polymorphisms (SNPs) with 22 statistical models based on genomic estimated breeding values 567 genotypes within across five stages Louisiana program. TRS SW high heritability showed higher PA compared to other while NS had lowest. Machine learning (ML) methods, random forest support vector machine (SVM), outperformed others predicting traits low heritability. ML methods predicted SY highest accuracy cross-stage predictions, Bayesian CY accuracy. Extended best linear unbiased prediction accounting dominance epistasis effects a slight improvement few traits. When both TRS, which be available early stage 2, were considered multi-trait model, 5 could increase up 0.66 0.30 single-trait model. Marker density assessment suggested 9091 SNPs sufficient optimal all The study demonstrated potential historical data devise strategies clonal programs.

Язык: Английский

Процитировано

0
The Plant Genome special section: Grain quality and nutritional genomics for breeding next‐generation crops DOI Creative Commons
Manish K. Pandey, Reyazul Rouf Mir, Nese Sreenivasulu

и другие.

The Plant Genome, Год журнала: 2023, Номер 16(4)

Опубликована: Дек. 1, 2023

By 2050, the world's population is expected to reach 9.8 billion according United Nations predictions (https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100). As a result, crop yields must roughly double in order feed an expanding global while still satisfying consumer demands for grain quality and nutrition. In addition enhancing nutritional value of food crops, making available affordable, nutrient-dense food, especially those who are economically disadvantaged, will be central pillar address security. The strategy improving traits breeding programs has been prioritized with recent advancements phenotyping seeds grains (metabolomics, mineral vitamins, assessing starch, proteins lipids, capturing preferred traits), sequencing technologies do high-throughput genotyping, functional genomics aided gene discovery, high-resolution trait mapping superior haplotype as well deploying genomic selection tools variety crops (Pandey et al., 2016; Varshney 2019). To improve dietary patterns, new generation foods ingredients improved intrinsic attributes needs generated through advanced methods. This help public health by increasing density optimizing complex carbohydrates, proteins, lipids. utilizing integrating both modern traditional techniques, it possible hasten production types yield, grain, quality. special issue highlights most significant findings, which cover developments genomics, including prediction related quality, enhancement nutritive cereals (rice, wheat, maize, oat) legume like groundnut. Overall, this includes collection studies deciphering genetic mechanisms micronutrients covering minerals such iron (Fe), zinc (Zn), vitamin enrichment (tocochromanols), pigmented bioactives, amino acids, fiber, fatty acid composition, safety, end user selected legumes. approach identifying regions controlling key nutrition successful contributed significantly marker discovery use (Cockram & Mackay, 2018). High concentrations essential lysine limiting high free asparagine prevent acrylamide during bread formation enhance wheat grain. article Oddy al. (2023) used understanding control composition UK soft major emphasis on lowering higher content. multivariate analysis showing these largely independent one another, largest effect acids being from environment. study also identified quantitative loci (QTLs) content, may prove useful applying appropriate strategies reduce programmes. Using same groundnut, Parmar co-localized main candidate genes content Zn reports identification six main-effect QTLs Fe five Interestingly, three that further facilitate fine diagnostic development pooled sequencing-based region biparental Gangurde (2022) QTL-Seq markers seed weight. successfully associated weight 182 SNPs genic intergenic regions. Although multiple important regions, Ulp proteases BIG SEED locus very because its detection other well. More importantly breed groundnut varieties bigger size, gene-specific Kompetitive allele-specific PCR were developed validated. It vital determine Meta-QTLs haplotypes target traits, numerous found several areas form influence Joshi addresses aspect paper biofortification rice performing meta-analysis 155 followed 57 MQTLs reduced confidence intervals. importantly, not only detected co-localization metal homeostasis but involvement network silico expression co-expression analyses. Furthermore, efficient biofortification. Another led Diers presented results architecture concentration protein, oil, meal protein using soybean nested association reported 107 marker-trait associations (MTAs) above-mentioned traits. few MTAs mapped within 5 cM intervals (94%) effects matched correlation between linked suggested would more effective large number small Derbyshire utilized pangenome based thousands lines identify alleles involved biosynthesis. instances missing wild soybean, FAD8 FAD2-2D oleic linoleic desaturation, respectively. frequency missense biosynthesis genes, could domestication. genome-wide (GWAS) alternate plant species rely variation various core collections, instead developing populations (Gangurde 2022; Sushmitha 2023). Panahabadi uses monosaccharides contents rice. Monosaccharides building blocks synthesis polymers or carbohydrates. 49 housed 17 located seven chromosomes whole all novel. Multiple promising potential validation breeding. next Mbanjo performs GWAS linking pigmentation seed. >280 SNPs, many than secondary metabolite accumulation pigmentation. Further, targeted 67 52 showed 24 Rc/bHLH17 OsIPT5 regulation wide range phenolic compounds color. information made exploited deployment rice-breeding program. Genomic emerged powerful prediction-based progenies crop-breeding even early generation, therefore, saving resources, time, precision added advantage. There plenty testing statistical models maize wheat. now, huge training size constitution, genotyping platforms, keeping mind genotype interaction environment soil, possibilities selection. case published emerging area Research Tibbs-Cortes provided exciting exotic-derived tocochromanols (vitamin E), human diet. expected, accuracies achieved when predicting each decreased performed diversity panel set. strength hypothesis optimal designing efficiently incorporate exotic germplasm into Tanaka worked sound data support prior QTL contributing causal conferring biological knowledge elevate E improves multi-trait model two panels inbred lines. Next, research Brzozowski was oat targeting methods 12 indicated variability accuracy family compared unrelated panel. families had half-sib set without it, suggesting approach. Meher tested eight Bayesian micro-nutrient Zn, Fe, β-carotenoid ridge regression reliable method revalidated reliability increases increase BLUE values response variables better Fradgeley summarized maintenance Bread Baking Quality trends over 50 years future application genomic-assisted no subsequent net loss gain due breeders’ selection, despite yield time. proposed reduction increased gluten combination changes industrial baking process enabled decades. Most diverse varied algorithm clarity best models, can realistic scenarios Gill implementing processing end-use hard winter (MTGP) outperformed single up twofold accuracy. suggests MTGP together flour sedimentation evaluated earlier generations predict generations. issue, review articles focusing nutrients plants Khan another allergens (2023). under changing environments role stressful biotechnological optimization nutrient acquisition, transport, distribution plants. provides bio-fortification optimize stress conditions emphasizes about concerns safety need protect negative food-born allergies. current updates predicts prospects allergen-depleted crops. discussed detail how advances molecular breeding, engineering, genome editing potentially health. Manish K. Pandey: Conceptualization; investigation; methodology; project administration; resources; supervision; writing—original draft; writing—review editing. Reyazul Rouf Mir: Nese Sreenivasulu:

Язык: Английский

Процитировано

0