Identifying a large number of high-yield genes in rice by pedigree analysis, whole-genome sequencing, and CRISPR-Cas9 gene knockout

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...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 115; no. 32; pp. E7559 - E7567
Main Authors Huang, Ju, Li, Jing, Zhou, Jun, Wang, Long, Yang, Sihai, Hurst, Laurence D., Li, Wen-Hsiung, Tian, Dacheng
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 07.08.2018
SeriesPNAS Plus
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Abstract 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.
AbstractList 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.
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 , 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 ( < 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.
Finding the genes that control a complex trait is difficult because each gene may have only minor phenotypic effects. Quantitative trait loci mapping and genome-wide association study techniques have been developed for this purpose but are laborious and time-consuming. Here we developed a method combining pedigree analysis, whole-genome sequencing, and CRISPR-Cas9 technology. By sequencing the parents and descendants of IR8, the Green Revolution “miracle rice,” we identified many genes that had been retained in the pedigree by selection for high yield. Knockout and knockdown studies showed that a large proportion of the identified genes are essential or have phenotypic effects related to production. Our approach provides a powerful means for identifying genes involved in a complex trait. 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.
Author Wang, Long
Yang, Sihai
Zhou, Jun
Huang, Ju
Li, Wen-Hsiung
Hurst, Laurence D.
Li, Jing
Tian, Dacheng
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Issue 32
Keywords Green Revolution
gene knockout
high-yield gene
pedigree analysis
Language English
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1J.H. and J.L. contributed equally to this work.
Reviewers: M.D.P., New York University; M.B.T., Berea College; and J.Z., University of Michigan.
Author contributions: J.H., J.L., L.D.H., W.-H.L., and D.T. designed research; J.H., J.L., J.Z., and D.T. performed research; J.Z. contributed new reagents/analytic tools; J.H., J.L., L.W., and S.Y. analyzed data; and J.H., J.L., S.Y., L.D.H., W.-H.L., and D.T. wrote the paper.
Contributed by Wen-Hsiung Li, June 18, 2018 (sent for review April 11, 2018; reviewed by Michael D. Purugganan, Milton Brian Traw, and Jianzhi Zhang)
ORCID 0000-0001-5697-9527
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Snippet Repeated artificial selection of a complex trait facilitates the identification of genes underlying the trait, especially if multiple selected descendant lines...
Finding the genes that control a complex trait is difficult because each gene may have only minor phenotypic effects. Quantitative trait loci mapping and...
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proquest
crossref
pubmed
jstor
SourceType Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage E7559
SubjectTerms Agricultural production
Agriculture - methods
Biological Sciences
Clustering
CRISPR
CRISPR-Cas Systems - genetics
Crop yield
Cultivars
Gene Knockout Techniques - methods
Gene sequencing
Genes
Genome, Plant - physiology
Genomes
Genotype & phenotype
Glucosyltransferase
Green revolution
Oryza
Oryza - genetics
Oryza - physiology
Pedigree
Phenotype
Phenotypes
Plants, Genetically Modified - genetics
Plants, Genetically Modified - physiology
PNAS Plus
Quantitative Trait Loci
Quantitative Trait, Heritable
Rice
Selection, Genetic - genetics
Sequence Analysis, DNA - methods
Title Identifying a large number of high-yield genes in rice by pedigree analysis, whole-genome sequencing, and CRISPR-Cas9 gene knockout
URI https://www.jstor.org/stable/26530105
https://www.ncbi.nlm.nih.gov/pubmed/30037991
https://www.proquest.com/docview/2110230053
https://search.proquest.com/docview/2075546676
https://pubmed.ncbi.nlm.nih.gov/PMC6094097
Volume 115
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