General background to the topic
With the acceleration of climate change, natural populations suffer abrupt changes in the climatic conditions to which they have adapted locally. For cultivated plants, one short-term strategy for facilitating rapid adaptation to changing environmental conditions is to locally replace the crop varieties that are predicted to become maladapted with varieties that are better suited to the future conditions through assisted migration (Aitken and Whitlock, 2013; Hällfors et al., 2014; Rhoné et al., 2020). The identification of accessions that will be better adapted (or less maladapted) can be done by combining genomic and environmental data from different locations or different periods to estimate their genomic vulnerability to environmental changes (e.g, spatial or temporal genomic offset [GO]; Capblancq et al., 2020). This approach enables to assess populations vulnerability to environmental change, taking into consideration their adaptive potential.
Problematics
Sorghum is the fifth most widely cultivated cereal and is a staple food for millions of inhabitants in the drier areas of Africa and India where it is mostly cultivated in small-scale family farming systems using landraces (a genetically diverse variety that has evolved in a certain environment and is therefore adapted to the local conditions; Villa et al., 2005). It is cultivated under a wide range of environmental conditions (elevation, temperature and rainfall gradients, soil types) and is sensitive to photoperiod. Sorghum is considered as a climate-resilient crop due to its ability to grow in areas too harsh for other major cereals, and as such, could become even more important in the future than it is now. However, despite its drought-tolerance, its cultivation may become problematic in the near future, especially in low latitude areas and regions mostly relying on agricultural systems more vulnerable to global change (Intergovernmental Panel on Climate Change (IPCC), 2022; Sultan and Gaetani, 2016). In this context, the aim of this M2 internship is to the aim of this M2 internship is to implement the first methodological steps to assess sorghum genomic vulnerability to climate change.
Course of the internship (data acquisition, modelling, expected results, …)
Sorghum accessions that will be analysed during the internship will be selected from available Whole Genome Sequencing dataset (either from published studies or from the sequencing of our collections), focussing on geo-referenced landraces from Africa and India as those sorghums are expected to be locally adapted. First, the student will look at the neutral population genetic structure of cultivated sorghum as well as the genetic bases of climate adaptation using landscape genomics and genotype-environment associations analyses. This will allow the identification of the genomic regions involved in local adaptation in sorghum. Following this, he/she will estimate the genomic offset of sorghum accessions, and identify the populations at risk in the future, and the accessions that would be better adapted locally to the future conditions.
The student will join the DDSE team (Dynamics of diversity, societies and environments) and will be based at ARCAD (AGAP Institut, Montpellier). The student will be mainly supervised by Aude Gilabert (CIRAD, UMR AGAP Institut, Montpellier). As part of the ANR project Afradapt (Genomic vulnerability of African crops to future climate), the work will be done in close collaboration with the DYNADIV team (UMR DIADE, Montpellier), which has strong expertise on cultivated plant evolution and genomic, and on genomic offset estimations. The student will also interact with members of the GE²pop team (located at ARCAD) working on the adaptation of crop populations to the environment and on genomic offset estimations.
Methods, techniques and tools to use
As the WGS sequences are already available, the work will consist in bioinformatic analyses to get the data formatted (genomic data as a vcf file, and environmental data) and to conduct the different analyses. Environmental data will be retrieved form the CHELSA and SoilGrids databases for the climatic and soil types data respectively. Photoperiod (or daylength) will be calculated using the R package geosphere. Because the raw genomic sequences will be retrieved from different dataset, the student will have to perform the mapping and SNP calling using bwa and GATK and the recently released V5 of the reference genome of Sorghum bicolor. Population genetic structure will be analysed using clustering methods (such as a PCA or the sNMF method available in the R package LEA Frichot and François, 2015). Complementary methods will be used for the GEA and GO analyses (BayPass (Gautier, 2015), LFMM (Caye et al., 2019), RDA (Capblancq and Forester, 2021)) to assess robustness between the methods.
It is essential to validate the biological meaningfulness of the genomic offset statistics through field trial experiments to test the negative correlation between GO statistics and plant fitness. However, due to timing constraints, the biological validation will not be conducted during the course of the internship but common gardens in Senegal and France for GO validation are planned for next year.
Keywords (max 5)
Landscape genomics, genotype-environment associations, genomic offset, assisted migration, sorghum
Duration: 5-6 months, starting from January
Website: https://umr-agap.cirad.fr/en/recherches/equipes-scientifiques/dynamiques-de-la-diversite-societes-et-environnements/contextes-et-enjeux
Contact: Aude Gilabert: aude.gilabert@cirad.fr
Bibliography
Aitken, S.N., Whitlock, M.C., 2013. Assisted Gene Flow to Facilitate Local Adaptation to Climate Change. Annual Review of Ecology, Evolution, and Systematics 44, 367–388. https://doi.org/10.1146/annurev-ecolsys-110512-135747
Capblancq, T., Fitzpatrick, M.C., Bay, R.A., Exposito-Alonso, M., Keller, S.R., 2020. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annual Review of Ecology, Evolution, and Systematics 51, 245–269. https://doi.org/10.1146/annurev-ecolsys-020720-042553
Capblancq, T., Forester, B.R., 2021. Redundancy analysis: A Swiss Army Knife for landscape genomics. Methods in Ecology and Evolution 12, 2298–2309. https://doi.org/10.1111/2041-210X.13722
Caye, K., Jumentier, B., Lepeule, J., François, O., 2019. LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies. Molecular Biology and Evolution 36, 852–860. https://doi.org/10.1093/molbev/msz008
Frichot, E., François, O., 2015. LEA: An R package for landscape and ecological association studies. Methods in Ecology and Evolution 6, 925–929. https://doi.org/10.1111/2041-210X.12382
Gautier, M., 2015. Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates. Genetics 201, 1555–1579. https://doi.org/10.1534/genetics.115.181453
Hällfors, M.H., Vaara, E.M., Hyvärinen, M., Oksanen, M., Schulman, L.E., Siipi, H., Lehvävirta, S., 2014. Coming to Terms with the Concept of Moving Species Threatened by Climate Change – A Systematic Review of the Terminology and Definitions. PLOS ONE 9, e102979. https://doi.org/10.1371/journal.pone.0102979
Intergovernmental Panel on Climate Change (IPCC), 2022. Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama. ed. Cambridge University Press, Cambridge, UK and New York, NY, USA. https://doi.org/10.1017/9781009325844
Rhoné, B., Defrance, D., Berthouly-Salazar, C., Mariac, C., Cubry, P., Couderc, M., Dequincey, A., Assoumanne, A., Kane, N.A., Sultan, B., Barnaud, A., Vigouroux, Y., 2020. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat Commun 11, 5274. https://doi.org/10.1038/s41467-020-19066-4
Sultan, B., Gaetani, M., 2016. Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Front. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.01262
Villa, T.C.C., Maxted, N., Scholten, M., Ford-Lloyd, B., 2005. Defining and identifying crop landraces. Plant Genetic Resources 3, 373–384. https://doi.org/10.1079/PGR200591
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