TY - JOUR
T1 - Identifying a large number of high-yield genes in rice by pedigree analysis, whole genome sequencing and CRISPR-Cas9 gene knockout
T2 - High-yield genes detected by pedigree analysis
AU - Huang, Ju
AU - Li, Jing
AU - Zhou, Jun
AU - Wang, Long
AU - Yang, Sihai
AU - Hurst, Laurence
AU - Li, Wen-Hsiung
AU - Tian, Dacheng
PY - 2018/8/7
Y1 - 2018/8/7
N2 - 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.
AB - 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.
KW - Gene knockout
KW - Green Revolution
KW - High-yield gene
KW - Pedigree analysis
UR - http://www.scopus.com/inward/record.url?scp=85053503221&partnerID=8YFLogxK
U2 - 10.1073/pnas.1806110115
DO - 10.1073/pnas.1806110115
M3 - Article
C2 - 30037991
SN - 0027-8424
VL - 115
SP - E7559-E7567
JO - Proceedings of the National Academy of Sciences of the United States of America (PNAS)
JF - Proceedings of the National Academy of Sciences of the United States of America (PNAS)
IS - 32
ER -