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Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency
Author(s) -
van Bezouw Roel F. H. M.,
Keurentjes Joost J. B.,
Harbinson Jeremy,
Aarts Mark G. M.
Publication year - 2019
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.14190
Subject(s) - phenomics , biology , forward genetics , genetic architecture , identification (biology) , genomics , trait , quantitative trait locus , plant genetics , genetic variation , computational biology , plant breeding , microbiology and biotechnology , evolutionary biology , genetics , phenotype , gene , genome , ecology , botany , computer science , programming language
Summary 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.