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research: Biometrics and statistics

CIMMYT research publications sow seeds in academic world

Written by Mike Listman on . Posted in News.

Julio Huerta stands in a wheat field in Ciudad Obregon. Photo: Xochiquetzal Fonseca/CIMMYT.
Julio Huerta stands in a wheat field in northern Mexico. Photo: Xochiquetzal Fonseca/CIMMYT.

Based on publication records, CIMMYT scientists produce a lot more than just improved maize and wheat varieties, as important as that work has been for farmers, partners, and consumers.

In 2017, CIMMYT researchers contributed to nearly 300 peer-reviewed journal articles, many published in high-impact journals including Nature and Science. The articles emerged from partnerships with a broad range of international universities and research institutes and have been cited frequently by peers in recent years.

“CIMMYT is the world’s largest distributor of publicly-available maize and wheat ‘germplasm,’ which includes breeding lines and other genetic resources in the form of seed,” said Marianne BĂ€nziger, CIMMYT deputy director general for research and partnerships. “But the center’s researchers also publish high-quality, cutting-edge science articles, not to mention mentoring and training several hundred students and professionals mostly from national research systems every year and interacting with thousands of farmers.”

Multiple CIMMYT authors led by José Crossa, CIMMYT biometrician and distinguished scientist, published two papers in Heredity on genomic selection in maize and wheat that have been among those most often cited for that journal since 2013, having been mentioned in other papers 124 times.

Ravi Singh and Julio Huerta, CIMMYT wheat scientists, were recognized in 2017 among the top one percent of researchers for the frequency of citation of their articles by other science authors.

Among the many reports to which they contributed, Huerta and Singh were participants and co-authors in a study published in the eminent journal Science in 2009 and since cited by other researchers 441 times. The study described the molecular basis of a “wonder” gene that, in tandem with other resistance genes, has helped protect wheat from three deadly fungal diseases for more than 50 years, providing farmers benefits in excess of $5 billion in harvests saved, according to a CIMMYT report on the findings.

The two scientists share authorship on at least a half-dozen other articles on wheat disease breeding and genetics that have been cited over 100 times.

“These examples show that CIMMYT research substantially contributes to global science, in addition to the impact achieved in farmers’ fields,” said BĂ€nziger. “It all builds on high-value partnerships with hundreds of researchers and professionals worldwide.”

New Publications: Using prediction models to keep up with growing demand for wheat

Written by on . Posted in Publications.

Wheat harvest near IztaccĂ­huatl volcano in Juchitepec, Estado de MĂ©xico. (Photo: P. Lowe/CIMMYT)
Wheat harvest near IztaccĂ­huatl volcano in Juchitepec, Estado de MĂ©xico. (Photo: P. Lowe/CIMMYT)

With increasing global demand for wheat and increasing constraints (high temperatures, diseases) to wheat’s productivity, wheat breeders are looking for new methodologies to make breeding more efficient. A new study looks at refinements of genomic prediction models to help achieve this.

The authors write that genomic selection is becoming a standard approach to achieving genetic progress in plants, as it gets around the need to field-test the offspring at every cycle, but that the models commonly used in plant breeding are based on datasets of only a few hundred genotyped individual plants.

This study used pedigree and genomic data from nearly 59,000 wheat lines evaluated in different environments, as well as genomic and pedigree information in a model that incorporated genotype X environment interactions to predict the performance of wheat lines in Mexican and South Asian environments.

They found that models using markers (and pedigree) had higher prediction accuracies than models using only phenotypic data. Models that included genomic x environment had higher prediction accuracies than models that do not include interaction.

Read the full study “Single-Step Genomic and Pedigree Genotype × Environment Interaction Models for Predicting Wheat Lines in International Environments” and check out other publications by CIMMYT staff below:

  • Association mapping reveals loci associated with multiple traits that affect grain yield and adaptation in soft winter wheat. 2017. Lozada, D. N., Mason, E.R., Md Ali Babar, Carver, B. F., Guedira, G. B., Merrill, K., Arguello, M. N., Acuna, A., Vieira, L., Holder, A., Addison, C., Moon, D. E., Miller, R. G., Dreisigacker, S. In: Euphytica v. 213 : 222.
  • Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. 2017. Ao Zhang, Hongwu Wang, Beyene, Y., Fentaye Kassa Semagn, Yubo Liu, Shiliang Cao, Zhenhai Cui, Yanye Ruan, Burgueño, J., San Vicente, F.M., Olsen, M., Prasanna, B.M., Crossa, J., Haiqiu Yu, Zhang, X. In: Frontiers in Plant Science v. 8 : 1916.
  • Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. 2017. Hickey, J.M., Tinashe Chiurugwi, Mackay, I., Powell, W., Eggen, A., Kilian, A., Jones, C., Canales, C., Grattapaglia, D., Bassi, F., Atlin, G.N., Gorjanc, G., Dawson, I., Rabbi, I.,  Ribaut, J.M., Rutkoski, J., Benzie, J., Lightner, J., Mwacharo, J., Parmentier, J., Robbins, K., Skot, L., Wolfe, M., Rouard, M., Clark, M., Amer, P., Gardiner, P., Hendre, P., Mrode, R., Sivasankar, S., Rasmussen, S., Groh, S., Jackson, V., Thomas, W., Beyene, Y. In: Nature Genetics v. 49, no. 9, p. 1297–1303.
  • Genomic selection in plant breeding : methods, models and perspectives. 2017. Crossa, J., PĂ©rez-RodrĂ­guez, P., Cuevas, J., Montesinos-Lopez, O.A., JarquĂ­n, D., De los Campos, G., Burgueño, J., Camacho-GonzĂĄlez, J. M., Perez-Elizalde, S., Beyene, Y., Dreisigacker, S., Singh, R.P., Zhang, X., Gowda, M., Rutkoski, J., Varshney, R. K. In: Trends in Plant Science v. 22, no. 11, p. 961-975.
  • Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments. 2017. PĂ©rez-RodrĂ­guez, P., Crossa, J., Rutkoski, J.,  Singh, R.P., Legarra, A., Autrique, E., De los Campos, G., Burgueño, J., Dreisigacker, S. In: The Plant Genome v. 10, no. 2.