Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 546140
Title Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency
Author(s) Bezouw, Roel F.H.M. van; Keurentjes, Joost J.B.; Harbinson, Jeremy; Aarts, Mark G.M.
Source The Plant Journal 97 (2018)1. - ISSN 0960-7412 - p. 112 - 133.
DOI https://doi.org/10.1111/tpj.14190
Department(s) Laboratory of Genetics
EPS
PE&RC
Horticulture & Product Physiology
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) genome-wide association study - genomics - high-throughput phenotyping - phenomics - photosynthesis - trait discovery
Abstract

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.

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