Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 115, Issue 32, Pages E7559-E7567Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1806110115
Keywords
high-yield gene; pedigree analysis; Green Revolution; gene knockout
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Funding
- National Major Special Project on New Varieties Cultivation for Transgenic Organisms Grant [2016ZX08009001-003]
- National Natural Science Foundation of China [91731308, 31571267]
- European Research Council Advanced Grant [EvoGenMed 669207-EvoGenMed-ERC-2014-ADG/ERC-2014-ADG]
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Repeated artificial selection of a complex trait facilitates the identification of genes underlying the trait, especially if multiple selected descendant lines are available. Here we developed a pedigree-based approach to identify genes underlying the Green Revolution (GR) phenotype. From a pedigree analysis, we selected 30 cultivars including the miracle rice IR8, a GR landmark, its ancestors and descendants, and also other related cultivars for identifying high-yield genes. Through sequencing of these genomes, we identified 28 ancestral chromosomal blocks that were maintained in all the high-yield cultivars under study. In these blocks, we identified six genes of known function, including the GR gene sd1, and 123 loci with genes of unknown function. We randomly selected 57 genes from the 123 loci for knockout or knockdown studies and found that a high proportion of these genes are essential or have phenotypic effects related to rice production. Notably, knockout lines have significant changes in plant height (P < 0.003), a key GR trait, compared with wild-type lines. Some gene knockouts or knockdowns were especially interesting. For example, knockout of Os10g0555100, a putative glucosyltransferase gene, showed both reduced growth and altered panicle architecture. In addition, we found that in some retained chromosome blocks several GR-related genes were clustered, although they have unrelated sequences, suggesting clustering of genes with similar functions. In conclusion, we have identified many high-yield genes in rice. Our method provides a powerful means to identify genes associated with a specific trait.
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