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Dernière mise à jour : Mai 2018

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MCP3 - PHENO-MATH-INFO: METHODS AND TOOLS FOR ANALYSING PHENOTYPIC DATA

Leader : Nadine Hilgert (UMR MISTEA)

Contributors: MISTEA – I3M - VIRTUAL PLANT – all the partners associated to the facilities

We develop a set of methods based on computer science, mathematical modelling and statistics, to analyse the large phenotypic datasets in order to extract reproducible traits associated with each genotype.

  • Cleaning datasets for outlier points or plants is inevitable for generating high-quality datasets due to problems of sensors or devices, or wrong identification of plants or seeds. It is relatively easy, based on intuition, for small datasets but require artificial intelligence for thousands of plants and traits for, e.g. 90 days. We developed a software agent that cleans and validates large datasets by automatically mimicking the biologist's reasoning, allowing one to detect potential outliers that may or may not be eventually used in analyses. The detection of 'outlier plants or microplots', i.e. biological replicates deviating from the distribution of plants on a multi-criteria basis, requires elaborate methods combining prior knowledge and statistical tools. We have shown that the absence of cleaning may lead either to the detection of a large number of artefact genomic regions associated with traits of interest, or to the loss of interesting alleles if high threshold are used to avoid such artefacts.
MCP3 outlier

An example of identification of potentially outlier datapoints, which a user may or may not validate based on intuition and, may not use in further analyses

  • Combining phenomic datasets with models of plants or crops, and with statistical models, is an essential challenge for the use of phenomic datasets in the design of new varieties coping with climate change. Indeed, even ambitious phenotyping projects finally involve a limited number of experimental fields (usually 5-30 as a maximum) whereas a much higher number would be necessary for testing genotypes in the real world involving many combination of environmental conditions. Furthermore, many combinations of alleles need to be tested. It is now possible to envisage testing hundreds of genotypes in hundreds of fields in current or future climates. This requires combining genotype-specific traits obtained by phenomics, environmental characterization based on sensor networks or environmental grids, genomic prediction of traits and crop modelling (see highlights 6 and 7).

 

  • Developing tools for combining phenomics, genomic prediction, environmental characterization and modelling will be a priority for next years in the frame of our "big data" strategy.