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 in | Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 32; pp. E7559 - E7567 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
07.08.2018
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Series | PNAS 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. |
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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 |
Author_xml | – sequence: 1 givenname: Ju surname: Huang fullname: Huang, Ju organization: State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 210023 Nanjing, China – sequence: 2 givenname: Jing surname: Li fullname: Li, Jing organization: State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 210023 Nanjing, China – sequence: 3 givenname: Jun surname: Zhou fullname: Zhou, Jun organization: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138 – sequence: 4 givenname: Long surname: Wang fullname: Wang, Long organization: State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 210023 Nanjing, China – sequence: 5 givenname: Sihai surname: Yang fullname: Yang, Sihai organization: State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 210023 Nanjing, China – sequence: 6 givenname: Laurence D. surname: Hurst fullname: Hurst, Laurence D. organization: The Milner Centre for Evolution, University of Bath, BA2 7AY Bath, United Kingdom – sequence: 7 givenname: Wen-Hsiung surname: Li fullname: Li, Wen-Hsiung organization: Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637 – sequence: 8 givenname: Dacheng surname: Tian fullname: Tian, Dacheng organization: State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, 210023 Nanjing, China |
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Copyright | Volumes 1–89 and 106–115, copyright as a collective work only; author(s) retains copyright to individual articles Copyright National Academy of Sciences Aug 7, 2018 2018 |
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Keywords | Green Revolution gene knockout high-yield gene pedigree analysis |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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) |
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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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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 |
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