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Project properties |
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| Title | Data science and statistical genetical modelling of photosynthetic responses through time to improve yield |
| Group | Genetics, Laboratory of |
| Project type | thesis |
| Credits | 36 ECTS |
| Supervisor(s) | Fred van Eeuwijk, Mark Aarts and Tom Theeuwen |
| Examiner(s) | Fred van Eeuwijk, Mark Aarts and Tom Theeuwen |
| Contact info | fred.vaneeuwijk@wur.nl, mark.aarts@wur.nl, or tom.theeuwen@wur.nl |
| Begin date | 2023/09/04 |
| End date | 2024/08/30 |
| Description | Background
Photosynthesis has been identified as the last untapped resource to improve crop yield (Zhu et al., 2010). Improving photosynthesis has so far primarily been achieved via genetic modification routes. Nevertheless, it has been established that there is ample of genetic diversity for photosynthesis that can potentially also be used for crop improvements. Since the Green Revolution improvements in photosynthesis have only contributed minimally to crop improvements, despite the genetic diversity available. There are likely several interlinked reasons why the genetic diversity for photosynthesis has not been used to improve crop yield. Firstly the complex genetic architecture underlying photosynthetic traits makes breeding for improved photosynthesis difficult (Theeuwen et al., 2022a). Secondly, plant breeders often select on stable effects throughout the years, but physiological traits like photosynthesis are heavily influenced by environmental conditions (Prado et al., 2018; Welcker et al., 2022). Consequently, the genetic diversity and the interaction with the environmental conditions makes that photosynthesis has not been selected for. Lastly, there might be physiological bottlenecks that prevent an improvement in photosynthesis to result in higher yields. Ongoing research Studying how variation for photosynthesis is influenced by the environment and how it connects to yield is not trivial. To unravel the genetic architecture and the interaction with the environment we use the model species Arabidopsis thaliana. Using high-throughput phenotyping systems we can measure photosynthetic responses for thousands of plants in dynamic environmental conditions (Cruz et al., 2016). Such experiments yield data of hundreds of time points for dozens of photosynthetic parameters. Resulting from these experiments indeed a plethora of QTLs can be identified, however most QTLs have relatively small photosynthetic effect sizes and show a tight interaction with the environmental conditions (Theeuwen et al., 2022b). Momentarily statistical analyses are based on a per timepoint basis, and as a result multiple testing becomes an issue and general patterns might stay unobserved. Modelling the response over time, taking into account the environmental conditions, would allow to select for QTLs that positively contribute to photosynthesis or yield. QTLs with positive effect can then be used in further research, and markers with positive effects can be the starting point for genomic selection approaches. Project Phenotypic timeseries datasets of amongst others segregating biparental populations are available. This project would revolve around modelling the phenotypic responses through time and environmental conditions. In order to allow for statistical testing either parametrization or the whole phenotypic response can be used. Next, a method needs to be developed to assess what the effect size and direction of a marker is. The effect size can be measured on photosynthetic traits and yield separately, and discrepancies may hint at bottlenecks limiting crop yield improvement. Finally, it should be explored how such analyses can feed into genomic selection approaches. For information on the type of modelling see the statgen HTP package: https://cran.r-project.org/web/packages/statgenHTP/vignettes/Overview_HTP.html References Cruz JA, Savage LJ, Zegarac R, Hall CC, Satoh-Cruz M, Davis GA, Kovac WK, Chen J, Kramer DM. 2016. Dynamic Environmental Photosynthetic Imaging Reveals Emergent Phenotypes. Cell Systems 2, 365–377. Prado SA, Cabrera-Bosquet L, Grau A, Coupel-Ledru A, Millet EJ, Welcker C, Tardieu F. 2018. Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. Plant, Cell & Environment 41, 314–326. Theeuwen TPJM, Logie LL, Harbinson J, Aarts MGM. 2022a. Genetics as a key to improving crop photosynthesis. Journal Of Experimental Botany. Theeuwen TPJM, Logie LL, Put S, et al. 2022b. Plethora of QTLs found in Arabidopsis thaliana reveals complexity of genetic variation for photosynthesis in dynamic light conditions. BioRxiv, 2022.11.13.516256. Welcker C, Spencer NA, Turc O, et al. 2022. Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nature Communications 13, 3225. Zhu XG, Long SP, Ort DR. 2010. Improving photosynthetic efficiency for greater yield. Annual Review of Plant Biology 61, 235–261. |
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