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

Oteng‐Frimpong, Richard; Karikari, Benjamin; Sie, Emmanuel Kofi; Kassim, Yussif Baba; Puozaa, Doris K.; Rasheed, Masawudu Abdul; Foncéka, Daniel; Okello, David Kallule; Balota, Maria; Burow, Mark D.; Ozias‐Akins, Peggy

doi:10.3389/fpls.2022.1076744

Cited by 12 publications

(4 citation statements)

References 104 publications

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“…Similar with findings by our group and others, H 2 was, in general, higher for the image-derived vegetation indices than for the yield and the manually measured, groundbased morpho-physiological traits [80,81]. This is probably because direct measurements are usually taken from small areas with few leaves from a few plants per plot, whereas aerial images include all the plants and all the visible leaves within each plot.…”

Section: Heritability and Model Developmentsupporting

confidence: 87%

Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data

Balota,

Sarkar,

Bennett

et al. 2024

Agriculture

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Peanut (Arachis hypogaea L.) plants respond to drought stress through changes in morpho-physiological and agronomic characteristics that breeders can use to improve the drought tolerance of this crop. Although agronomic traits, such as plant height, lateral growth, and yield, are easily measured, they may have low heritability due to environmental dependencies, including the soil type and rainfall distribution. Morpho-physiological characteristics, which may have high heritability, allow for optimal genetic gain. However, they are challenging to measure accurately at the field scale, hindering the confident selection of drought-tolerant genotypes. To this end, aerial imagery collected from unmanned aerial vehicles (UAVs) may provide confident phenotyping of drought tolerance. We selected a subset of 28 accessions from the U.S. peanut mini-core germplasm collection for in-depth evaluation under well-watered (rainfed) and water-restricted conditions in 2018 and 2019. We measured morpho-physiological and agronomic characteristics manually and estimated them from aerially collected vegetation indices. The peanut genotype and water regime significantly (p < 0.05) affected all the plant characteristics (RCC, SLA, yield, etc.). Manual and aerial measurements correlated with r values ranging from 0.02 to 0.94 (p < 0.05), but aerially estimated traits had a higher broad sense heritability (H2) than manual measurements. In particular, CO2 assimilation, stomatal conductance, and transpiration rates were efficiently estimated (R2 ranging from 0.76 to 0.86) from the vegetation indices, indicating that UAVs can be used to phenotype drought tolerance for genetic gains in peanut plants.

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Section: Heritability and Model Developmentsupporting

confidence: 87%

Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data

Balota,

Sarkar,

Bennett

et al. 2024

Agriculture

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“…The indices identified by RF to be highly important included NDVI, CSI, hue, GI, b*, and v*, which have also been identified as critical in earlier studies for LLS detection [36,73]. LLS severity is associated with leaf defoliation, which reduces the leaf area index of the canopy and the NDVI as well [74].…”

Section: Discussionmentioning

confidence: 90%

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Chapu,

Chandel,

Sie

et al. 2024

Agronomy

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Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

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“…The generation of phenotypic data for crops is a critical step in categorizing crops as sensitive or tolerant to the specific stress under investigation (Rahaman et al, 2015;Song et al, 2021;Oteng-Frimpong et al, 2023;Yoosefzadeh Najafabadi et al, 2023). The labor-intensive, time-consuming, potentially destructive, subjective, costly, inefficient, and lack of inter-or intra-rate repeatability of manual phenotyping (Araus et al, 2018) has prompted the need for efficient, automatic, and accurate technologies capable of capturing phenotypic data across all growth stages and correlating it with genomics information (Song et al, 2021;Xu et al, 2022c;Li et al, 2022c).…”

Section: Contribution Of Machine Learning To Fast-track Breeding Effortsmentioning

confidence: 99%

Temperature‐smart plants: A new horizon with omics‐driven plant breeding

Raza,

Bashir,

Khare

et al. 2024

Physiologia Plantarum

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The adverse effects of mounting environmental challenges, including extreme temperatures, threaten the global food supply due to their impact on plant growth and productivity. Temperature extremes disrupt plant genetics, leading to significant growth issues and eventually damaging phenotypes. Plants have developed complex signaling networks to respond and tolerate temperature stimuli, including genetic, physiological, biochemical, and molecular adaptations. In recent decades, omics tools and other molecular strategies have rapidly advanced, offering crucial insights and a wealth of information about how plants respond and adapt to stress. This review explores the potential of an integrated omics‐driven approach to understanding how plants adapt and tolerate extreme temperatures. By leveraging cutting‐edge omics methods, including genomics, transcriptomics, proteomics, metabolomics, miRNAomics, epigenomics, phenomics, and ionomics, alongside the power of machine learning and speed breeding data, we can revolutionize plant breeding practices. These advanced techniques offer a promising pathway to developing climate‐proof plant varieties that can withstand temperature fluctuations, addressing the increasing global demand for high‐quality food in the face of a changing climate.

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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

Cited by 12 publications

References 104 publications

Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data

Phenotyping Peanut Drought Stress with Aerial Remote-Sensing and Crop Index Data

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Temperature‐smart plants: A new horizon with omics‐driven plant breeding

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