Genomic selection and enablers for agronomic traits in maize (<scp><i>Zea mays</i></scp>): A review

Gunundu, Rodreck; Shimelis, Hussein; Mashilo, Jacob

doi:10.1111/pbr.13127

Cited by 3 publications

(1 citation statement)

References 154 publications

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“…It is possible that performance in low-GY sites may not translate to GY in high-GY sites. Adopting accurate and efficient phenotyping strategies, with optimal replication over years in environments with known levels of reproducibility for target agroecological zones, could enhance the efficiency in selection for advancement ( Duvick, 2005 ; Gunundu et al., 2023 ). The focus of the KHMP breeding pipeline has been to improve GY, which averaged 4,530 kg ha −1 in the PVT and 6,100 kg ha −1 in the AVT.…”

Section: Discussionmentioning

confidence: 99%

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020

Ligeyo,

Saina,

Awalla

et al. 2024

Front. Plant Sci.

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Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999–2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha−1 year−1 (1.94% year−1) and 26 kg ha−1 year−1 (0.42% year−1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of −2.65% year−1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with −1.19% and −0.27% year−1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of −0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya’s highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development.

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Section: Discussionmentioning

confidence: 99%

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020

Ligeyo,

Saina,

Awalla

et al. 2024

Front. Plant Sci.

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An efficient subsampling method for estimating corn root characteristics with scanner‐based image analysis

Ampong,

Penn,

Camberato

et al. 2024

Agronomy Journal

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Quantifying root length, surface area, average diameter, and volume of fully‐matured corn (Zea mays L.) is labor intensive, time consuming, and costly. Accurate and efficient subsampling techniques are needed to overcome these limitations. In this study, eight corn root systems were grown to maturity in a sand‐culture hydroponics system to develop and test root system subsampling techniques for accuracy (uncertainty assessment) and efficiency (time). Each entire root system was separated into coarse and fine roots, which were then composited into 65 subsamples, either visually or by mass, followed by subsample scanning to quantify root characteristics. A bootstrap non‐parametric procedure was used to determine the sample size needed to represent the total root system and quantify uncertainty based on the number of subsamples analyzed. When subsamples were composited visually, as many as 60 subsamples (92% of the total root system) were necessary to represent the characteristics of the root system within ±5% of the true mean at a 95% confidence level. In contrast, when subsamples were composited by equal mass, a maximum of 15 subsamples (23% of the total root system) were needed to be representative, requiring 2 h and 15 min per root system. The findings show that separating the entire root system by coarse and fine roots and then weighing into equal mass subsamples before scanning decreased the number of subsamples and time required to accurately estimate corn root characteristics. Thus, this subsampling approach considerably reduced the effort and cost of processing corn root systems.

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Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China

Luo,

Yang,

Zhang

et al. 2024

Agronomy

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Genomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China.

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Genomic selection and enablers for agronomic traits in maize (Zea mays): A review

Cited by 3 publications

References 154 publications

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020

An efficient subsampling method for estimating corn root characteristics with scanner‐based image analysis

Genomic Prediction of Kernel Water Content in a Hybrid Population for Mechanized Harvesting in Maize in Northern China

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