# Performance of phenomic selection in sorghum: Exploring population structure and GXE effects on prediction accuracy

Nicolas Salas, Vincent Garin, Korothimi Thera, Chiaka Diallo, Mohamed Lamine Tekete, Baptiste Guitton, Karim Dagno, Abdoulaye Diallo,

Mamoutou Kouressy, Willmar L. Leiser, et al.

► To cite this version:

Nicolas Salas, Vincent Garin, Korothimi Thera, Chiaka Diallo, Mohamed Lamine Tekete, et al. Performance of phenomic selection in sorghum: Exploring population structure and GXE effects on prediction accuracy. Sorghum in the 21st Century, Global Sorghum Conference, CIRAD; Kansas State University; Collaborative Research on Sorghum and Millet; SorghumID; IRD; CERAAS, Jun 2023, Montpellier, France. pp.115. hal-05181923

HAL Id: hal-05181923

https://hal.science/hal-05181923v1

Submitted on 23 Jul 2025

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# Sorghum in the 21st Century
## Global Sorghum Conference

Resiliency and Sustainability in the Face of Climate Change

June 5-9 2023

The Corum Event Center, Montpellier, France

Lead Sponsor

KANSAS STATE
UNIVERSITY.

College of Agriculture

Supporting the

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INTERNATIONAL YEAR OF
MILLETS
2023

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## BOOK OF ABSTRACTS

PROGRAM - ABSTRACTS

List of posters - List of participants

List of exhibitors - Exhibition plan

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cirad

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Sorghum
Institut d'Études
Raisons et Développements
FRANCE

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CERAMS
Centre d'Etude régional
de rédaction à la sauveté
Performance of phenomic selection in sorghum : Exploring population structure and GXE effects on
prediction accuracy

Nicolas Salas¹ (nicolas.salas@cirad.fr), Garin Vincent¹, ², Korotimi Thera³, Chiaka Diallo⁴,
Tekete Mohamed Lamine³, Guitton Baptiste⁵, Karim Dagno³, Diallo Abdoulaye³, Kouressy Mamoutou³,
Willmar Leiser⁴, Rattunde Fred⁶, Ibrahima Sissoko⁴, Aboubacar Toure⁴, Baloua Nebie⁷, Moussa Samake⁸,
Jana Kholova², ⁹, Julien Frouin¹, Michel Vaksmann¹⁰, Eva Weltzien¹¹, ⁴, Niaba Teme³, Jean-François Rami⁵,
Vincent Segura¹², Hugues De Verdal¹, David Pot¹

¹ AGAP/GIV/BIOS, Cirad, Montpellier, France ; ² ICRISAT, Patancheru, India ; ³ IER, Bamako, Mali ; ⁴ ICRISAT, Bamako, Mali ;
⁵ AGAP/DDSE/BIOS, Cirad, Montpellier, France ; ⁶ Agronomy Department, University of Madison, Madison, Wisconsin, United States ;
⁷ International Maize and Wheat Improvement Center, Dakar, Senegal ; ⁸ Université des Sciences des Techniques et des Technologies de Bamako - Faculté d, Bamako, Mali ;
⁹ Department of Information Technologies, Faculty of Economics and Management, Cze, Prague, Czech Republic ; ¹⁰ Cirad, Montpellier, France ; ¹¹ Agronomy Department, University of Wisconsin, Madison, Wisconsin, United States ; ¹² AGAP/DAAV, INRAE, Montpellier, France

The concept of phenomic selection has been formalized by Rincent et al. (2018) based on the idea that spectral information (Near Infra Red Spectroscopy (NIRS), Hyperspectral Imaging...) acquired from animal or plant tissues contains genetic information that can be used to predict the genetic values of candidates to selection. Together with this genetic information, spectra also captures information linked to the environment and genotype by environment interaction effects, that can also prove to be useful to optimize the prediction of individual's performances in different environmental contexts. Furthermore, because spectral information corresponds to intermediate phenotype (endophenotypes) located between the genome and phenotypes of interest, it can also capture interaction effects between genes that are typically difficult to obtain from DNA polymorphism information. This novel approach of phenomic prediction has proven to be relevant in several plant species, achieving higher genetic gains in a variety of contexts than with classical phenotypic or genomic selection approaches. In this study, we explored the relevance of phenomic selection to predict various traits of agronomic interest in two sorghum population's types. First, a broad-based population (GWAS) that has been evaluated across more than ten environments on which near infrared spectra were acquired on grains (1 environment) and stems (5 environments) was used. In addition, a large multi-reference Back-Cross Nested Association Mapping population, based on 3 recurrent parents and more than 20 donor parents (Garin et al., 2023), including more than 3900 BC1F4 families characterized in diverse conditions and for which NIRS spectra have been acquired on grains (1 to 2 sites maximum depending on the families) was also mobilized. Based on these populations, two questions were addressed, the effect of population structure on the prediction accuracies of genomic and phenomic selection and the definition of strategies to capture GXE effects to maximize prediction accuracies.

References:

1. Garin, V., Diallo, C., Tekete, M.L., Thera, K., Guitton, B., Dagno, K., Diallo, A.G., Kouressy, M., Leiser, W., Rattunde, F., Sissoko, I., Toure, A., Nebie, B., Samake, M., Kholova, J., Frouin, J., Pot, D., Vaksmann, M., Weltzien, E., Teme, N., Rami, J.-F., 2023. Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping. https://doi.org/10.1101/2023.03.11.532173

2. Rincent, R., Charpentier, J.-P., Faivre-Rampant, P., Paux, E., Gouis, J.L., Bastien, C., Segura, V., 2018. Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar. G3 Genes Genomes Genet. g3.200760.2018. https://doi.org/10.1534/g3.118.200760