Multiplex QTL editing of grain-related genes improves yield in elite rice varieties View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-08-29

AUTHORS

Jianping Zhou, Xuhui Xin, Yao He, Hongqiao Chen, Qian Li, Xu Tang, Zhaohui Zhong, Kejun Deng, Xuelian Zheng, Sayed Abdul Akher, Guangze Cai, Yiping Qi, Yong Zhang

ABSTRACT

KEY MESSAGE: Significant yield increase has been achieved by simultaneous introduction of three trait-related QTLs in three rice varieties with multiplex editing by CRISPR-Cas9. Using traditional breeding approaches to develop new elite rice varieties with high yield and superior quality is challenging. It usually requires introduction of multiple trait-related quantitative trait loci (QTLs) into an elite background through multiple rounds of crossing and selection. CRISPR-Cas9-based multiplex editing of QTLs represents a new breeding strategy that is straightforward and cost effective. To test this approach, we simultaneously targeted three yield-related QTLs for editing in three elite rice varieties, namely J809, L237 and CNXJ. The chosen yield-related QTL genes are OsGS3, OsGW2 and OsGn1a, which have been identified to negatively regulate the grain size, width and weight, and number, respectively. Our approach rapidly generated all seven combinations of single, double and triple mutants for the target genes in elite backgrounds. Detailed analysis of these mutants revealed differential contributions of QTL mutations to yield performance such as grain length, width, number and 1000-grain weight. Overall, the contributions are additive, resulting in 68 and 30% yield per panicle increase in triple mutants of J809 and L237, respectively. Our data hence demonstrates a promising genome editing approach for rapid breeding of QTLs in elite crop varieties. More... »

PAGES

1-11

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00299-018-2340-3

DOI

http://dx.doi.org/10.1007/s00299-018-2340-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106416287

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30159598


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