QTL identification in backcross population for brace-root-related traits in maize

Sun, Nana; Liu, Chaoxian; Mei, Xiupeng; Jiang, Dandan; Wang, Xu; Dong, Erfei; Zhang, Jing; Cai, Yingfan

doi:10.1007/s10681-020-2561-8

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…The images were processed using the WinRhizoPro 2009 software (Wang & Zhang, 2009). This software was chosen due to its good performance in root assessments (Sun et al, 2020;Puspasari et al, 2020).…”

Section: Databasementioning

confidence: 99%

Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions

Duarte

Ferreira

et al. 2022

Semina: Ciênc. Agrár.

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The objective of this study was to evaluate the performance of four machine learning models, as well as multitask learning, to predict soybean root variables from simpler variables, under two water availability conditions. In order to do so, 100 soybean cultivars were conducted in a greenhouse under a control condition and a stress condition. Aerial part and root variables were evaluated. The machine learning models used to predict complex root variables were artificial neural network (ANN), random forest (RF), extreme gradient boosting (EGBoost) and support vector machine (SVM). A linear model was used for comparison purposes. Multitask learning was employed for ANN and RF. In addition, feature importance was defined using RF and XGBoost algorithms. All the machine learning models performed better than the linear model. In general, SVM had the greatest potential for the prediction of most of the root variables, with better values of RMSE, MAE and R2. Dry weight of the aerial part and root volume exhibited the greatest importance in the predictions. The models developed using multitask learning performed similarly to the ones conventionally developed. Finally, it is concluded that the machine learning models evaluated can be used to predict root variables of soybean from easily measurable variables, such as dry weight of the aerial part and root volume.

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Section: Databasementioning

confidence: 99%

Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions

Duarte

Ferreira

et al. 2022

Semina: Ciênc. Agrár.

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“…A decrease in cost for genotyping and high precision in phenotyping using modern approaches has led to the identification of several QTLs/genes associated with different traits in maize [ 17 ]. Identifying QTLs and markers linked to RSA is essential for exploiting root traits in maize breeding programs through marker-assisted selection [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Karnatam

Chhabra

Saini

et al. 2023

IJMS

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Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions.

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“…QTL for similar traits have been found in wild rice (Long et al 2020 ). In maize, stalks with higher flexibility are associated with resistance to lodging by wind and a QTL has been found for stalk fracture angle before tasselling (Wang et al 2020 ) and others for brace-root development which help with anchorage (Sun et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-donor × elite-based populations reveal QTL for low-lodging wheat

Dreccer

Macdonald²,

Farnsworth

et al. 2022

Theor Appl Genet

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Key message Low-lodging high-yielding wheat germplasm and SNP-tagged novel alleles for lodging were identified in a process that involved selecting donors through functional phenotyping for underlying traits with a designed phenotypic screen, and a crossing strategy involving multiple-donor × elite populations. Abstract Lodging is a barrier to achieving high yield in wheat. As part of a study investigating the potential to breed low-lodging high-yielding wheat, populations were developed crossing four low-lodging high-yielding donors selected based on lodging related traits, with three cultivars. Lodging was evaluated in single rows in an early generation and subsequently in plots in 2 years with contrasting lodging environment. A large number of lines lodged less than their recurrent parents, and some were also higher yielding. Heritability for lodging was high, but the genetic correlation between contrasting environments was intermediate-low. Lodging genotypic rankings in single rows did not correlate well with plots. Populations from the highest lodging background were genotyped (90 K iSelect BeadChip array). Fourteen markers on nine chromosomes were associated with lodging, differing under high- versus low-lodging conditions. Of the fourteen markers, ten were found to co-locate with previously identified QTL for lodging-related traits or at homoeologous locations for previously identified lodging-related QTL, while the remaining four markers (in chromosomes 2D, 4D, 7B and 7D) appear to map to novel QTL for lodging. Lines with more favourable markers lodged less, suggesting value in these markers as a selection tool. This study demonstrates that the combination of donor functional phenotyping, screen design and crossing strategy can help identify novel alleles in germplasm without requiring extensive bi-parental populations.

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QTL identification in backcross population for brace-root-related traits in maize

Cited by 6 publications

References 22 publications

Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions

Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding

Multi-donor × elite-based populations reveal QTL for low-lodging wheat

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