Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea L.) germplasm DOI Creative Commons
Richard Oteng‐Frimpong, Benjamin Karikari, Emmanuel Kofi Sie

et al.

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 5, 2023

Early leaf spot (ELS) and late (LLS) diseases are the two most destructive groundnut in Ghana resulting ≤ 70% yield losses which is controlled largely by chemical method. To develop resistant varieties, present study was undertaken to identify single nucleotide polymorphism (SNP) markers putative candidate genes underlying both ELS LLS. In this study, six multi-locus models of genome-wide association were conducted with best linear unbiased predictor obtained from 294 African germplasm screened for LLS as well image-based indices severity 2020 2021 8,772 high-quality SNPs a 48 K SNP array Axiom platform. Ninety-seven associated ELS, five across chromosomes 2 sub-genomes. From these, twenty-nine unique detected at least one or more traits 16 explained phenotypic variation ranging 0.01 - 62.76%, exception chromosome (Chr) 08 (Chr08), Chr10, Chr11, Chr19. Seventeen potential predicted ± 300 kbp stable/prominent positions (12 5, down- upstream, respectively). The results provide basis understanding genetic architecture germplasm, would be valuable breeding varieties upon further validation.

Language: Английский

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations DOI Creative Commons
Alper Adak, Seth C. Murray, Jacob D. Washburn

et al.

New Phytologist, Journal Year: 2024, Volume and Issue: 242(1), P. 121 - 136

Published: Feb. 13, 2024

Summary Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed patterns two different maize ( Zea mays L.) populations using high‐throughput phenotyping with a population consisting 515 recombinant inbred lines (RILs) grown Texas and hybrid containing 1090 hybrids Missouri. Two models, Gaussian peak functional principal component analysis (FPCA), were employed to study Normalized Green–Red Difference Index (NGRDI) scores. The model showed strong correlations c. 0.94 RILs 0.97 hybrids) between modeled non‐modeled trajectories. Functional differentiated NGRDI trajectories under conditions, capturing substantial variability (75%, 20%, 5% RILs; 88% 12% hybrids). By comparing these models conventional BLUP values, common quantitative trait loci (QTLs) identified, candidate genes brd1 , pin11 zcn8 rap2 . harmony loci's additive effects growing degree days, as well differentiation RIL haplotypes across stages, underscores significant interplay driving development. These findings contribute advancing plant–environment interactions have implications crop improvement strategies.

Language: Английский

Citations

7
A New Approach to Identifying Sorghum Hybrids Using UAV Imagery Using Multispectral Signature and Machine Learning DOI Creative Commons
Dthenifer Cordeiro Santana, Gustavo de Faria Theodoro, Ricardo Gava

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(1), P. 23 - 23

Published: Jan. 5, 2024

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the collection of morphological and physiological information from several crops. This approach, also known as high-throughput phenotyping, combined with data processing by machine learning (ML) algorithms, provide fast, accurate, large-scale discrimination genotypes field, which is crucial for improving efficiency breeding programs. Despite their importance, studies aimed at accurately classifying sorghum hybrids using spectral variables input sets ML models are still scarce literature. Against this backdrop, study aimed: (I) discriminate based on canopy reflectance different bands (SB) vegetation indices (VIs); (II) evaluate performance algorithms hybrids; (III) best dataset algorithms. A field experiment was carried out 2022 crop season a randomized block design three replications six hybrids. At 60 days after emergence, flight over experimental area Sensefly eBee real time kinematic. The acquired sensor were: blue (475 nm, B_475), green (550 G_550), red (660 R_660), Rededge (735 RE_735) e NIR (790 NIR_790). From SB acquired, (VIs) were calculated. Data submitted classification analysis, settings (using only SB, VIs, + VIs) tested: artificial neural networks (ANN), support vector (SVM), J48 decision trees (J48), random forest (RF), REPTree (DT) logistic regression (LR, conventional technique used control). There differences signature each hybrid, made it possible differentiate them SBs VIs. ANN algorithm performed accuracy metrics tested, regardless used. In case, use feasible due speed practicality analyzing data, does not require calculations perform RF showed better when VIs an input. provided all did good except RF. provides accurate identification hybrids, ANNs inputs stand (above 55 CC, above 0.4 kappa around 0.6 F-score). makes wavelengths indices. Processing techniques emphasis inputs.

Language: Английский

Citations

6
Integrating in-field Vis-NIR leaf spectroscopy and deep learning feature extraction for growth-stage dependent and independent genotyping of wheat plants DOI

Bakhtiyar Salehi,

Seyed Ahmad Mireei, Mehrnoosh Jafari

et al.

Biosystems Engineering, Journal Year: 2024, Volume and Issue: 238, P. 188 - 199

Published: Feb. 1, 2024

Language: Английский

Citations

6
Recent developments in multi-omics and breeding strategies for abiotic stress tolerance in maize (Zea mays L.) DOI Creative Commons
Muhammad Qudrat Ullah Farooqi, Ghazala Nawaz, Shabir Hussain Wani

et al.

Frontiers in Plant Science, Journal Year: 2022, Volume and Issue: 13

Published: Sept. 23, 2022

High-throughput sequencing technologies (HSTs) have revolutionized crop breeding. The advent of these has enabled the identification beneficial quantitative trait loci (QTL), genes, and alleles for improvement. Climate change made a significant effect on global maize yield. To date, well-known omic approaches such as genomics, transcriptomics, proteomics, metabolomics are being incorporated in breeding studies. These identified novel biological markers that utilized improvement against various abiotic stresses. This review discusses current information morpho-physiological molecular mechanism stress tolerance maize. utilization omics to improve is highlighted. As compared single approach, integration multi-omics offers great potential addressing challenges stresses productivity.

Language: Английский

Citations

28
Multi-locus genome-wide association studies reveal genomic regions and putative candidate genes associated with leaf spot diseases in African groundnut (Arachis hypogaea L.) germplasm DOI Creative Commons
Richard Oteng‐Frimpong, Benjamin Karikari, Emmanuel Kofi Sie

et al.

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 5, 2023

Early leaf spot (ELS) and late (LLS) diseases are the two most destructive groundnut in Ghana resulting ≤ 70% yield losses which is controlled largely by chemical method. To develop resistant varieties, present study was undertaken to identify single nucleotide polymorphism (SNP) markers putative candidate genes underlying both ELS LLS. In this study, six multi-locus models of genome-wide association were conducted with best linear unbiased predictor obtained from 294 African germplasm screened for LLS as well image-based indices severity 2020 2021 8,772 high-quality SNPs a 48 K SNP array Axiom platform. Ninety-seven associated ELS, five across chromosomes 2 sub-genomes. From these, twenty-nine unique detected at least one or more traits 16 explained phenotypic variation ranging 0.01 - 62.76%, exception chromosome (Chr) 08 (Chr08), Chr10, Chr11, Chr19. Seventeen potential predicted ± 300 kbp stable/prominent positions (12 5, down- upstream, respectively). The results provide basis understanding genetic architecture germplasm, would be valuable breeding varieties upon further validation.

Language: Английский

Citations

14