Satellite meeting

Eucarpia Biometrics 2022 - Satellite Workshop on Phenomic Selection

20^th September 2022 - ENS Paris-Saclay

Phenomic selection consists in replacing genotyping with high-throughput phenotyping such as near-infrared spectroscopy (NIRS) in the prediction models. Its interest lies in the fact that NIRS is low cost and able to capture non additive effects. Since the founding publication of Rincent et al. (2018) it has been tested on numerous species including cereals (Cuevas et al. 2019, Krause et al. 2019, Galan et al. 2020, Lane et al. 2021, Robert et al. 2022, Weiß et al. 2022) and other crops, forest and fruit trees (Brault et al. 2021) with promising results. Based on this emerging literature, we propose to organize a workshop on the 20^th of September 2022 as a satellite of the Eucarpia Biometrics congress that will be held in Paris-Saclay:

- The morning session (10:00 – 12:00) will be dedicated to a practical on the Rincent et al. datasets, with the objective that all participants analyse the spectra and generate their own phenomic and genomic predictions.

- The afternoon session (13:30 – 17:00) will be dedicated to oral presentations and discussions. Presentations based on ongoing work are more than welcome. Participants can attend one of the two sessions, or the full day at their convenience.

The workshop will take place at the same location than the Eucarpia congress (ENS Paris-Saclay). Lunch will be offered by the organizing committee. While the workshop attendance will be free, registration is mandatory and must be made with the following form by the 25^th of June 2022:

Click here to access Registration form

Contact: vincent.segura@inrae.fr and renaud.rincent@inrae.fr

References:

- Brault C, Lazerges J, Doligez A, et al (2021) Interest of phenomic prediction as an alternative to genomic prediction in grapevine. bioRxiv 2021.12.16.472608; doi: https://doi.org/10.1101/2021.12.16.472608

- Cuevas J, Montesinos-López O, Juliana P, et al. (2019) Deep Kernel for Genomic and Near Infrared Predictions in Multi-environment Breeding Trials. G3 Genes Genomes Genet 9:2913–2924. https://doi.org/10.1534/g3.119.400493

- Galán RJ, Bernal-Vasquez A-M, Jebsen C, et al. (2020) Integration of genotypic, hyperspectral, and phenotypic data to improve biomass yield prediction in hybrid rye. Theor Appl Genet 133: 3001–3015. https://doi.org/10.1007/s00122-020-03651-8

- Krause MR, González-Pérez L, Crossa J, et al. (2019) Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat. G3 Genes Genomes Genet 9: 1231–1247. https://doi.org/10.1534/g3.118.200856

- Lane HM, Murray SC, Montesinos‑López OA, Montesinos‑López A, et al. (2020) Phenomic selection and prediction of maize grain yield from near-infrared reflectance spectroscopy of kernels. Plant Phenome J 3:e20002. https://doi.org/10.1002/ppj2.20002

- Rincent R, Charpentier J-P, Faivre-Rampant P, et al (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|Genetics g3.200760.2018. https://doi.org/10.1534/g3.118.200760

- Robert, P., Auzanneau, J., Goudemand, et al. (2022). Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. Theoretical and Applied Genetics, 1-20.

- Weiß TM, Zhu X, Leiser WL, Li D, et al. (2022) Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.), G3 Genes|Genomes|Genetics, Volume 12, Issue 3, https://doi.org/10.1093/g3journal/jkab445.