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Lopes, MdAuthor

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May 27, 2021
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Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan

Publicated to: Plants-Basel. 10 (3): 558- - 2021-03-01 10(3), DOI: 10.3390/plants10030558

Authors: Tehseen, MM; Kehel, Z; Sansaloni, CP; Lopes, MD; Amri, A; Kurtulus, E; Nazari, K

Affiliations

Cultius Extensius Sostenibles. IRTA Investigación y Tecnología Agroalimentarias - Author
Cultius Extensius Sostenibles. Producció Vegetal - Author
Producció Vegetal. IRTA Investigación y Tecnología Agroalimentarias - Author
‎ Ege Univ, Dept Field Crops, POB 35100, Izmir, Turkey - Author
‎ Int Ctr Agr Res Dry Areas ICARDA, ICARDA Prebreeding & Genebank Operat, Biodivers & Crop Improvement Program, POB 10000, Rabat, Morocco - Author
‎ Int Maize & Wheat Improvement Ctr CIMMYT, Carretera Mexico Veracruz Km 45, El Batan 56237, Texcoco, Mexico - Author
‎ IRTA Inst Food & Agr Res & Technol, Sustainable Field Crops Programme, Lleida 25198, Spain - Author
‎ Reg Cereal Rust Res Ctr RCRRC, Biodivers & Crop Improvement Program, Int Ctr Agr Res Dry Areas ICARDA, POB 35661, Izmir, Turkey - Author
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Abstract

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV's for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.

Keywords

genomic predictionleaf ruststem rustwheat landracesGenomic predictionLeaf rustStem rustWheat landracesYellow rust

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Plants-Basel due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 39/239, thus managing to position itself as a Q1 (Primer Cuartil), in the category Plant Sciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-12-15:

  • WoS: 12
  • Europe PMC: 8

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-15:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 29.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 28 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/20.500.12327/1275

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Mexico; Morocco; Turkey.

Awards linked to the item

This research was funded by Delivering Genetic Gain in Wheat project supported by Bill and Melinda Gates Foundation (BMGF) and the UK Department for International Development (DFID), grant number OPP1133199.