SNP & InDel



     Rice (Oryza sativa L.) is the staple food resource for half the world's population. By 2030, rice production must increase by at least 25% to keep pace with population growth. Accelerated genetic gains in rice improvement are needed to mitigate the effects of climate change and loss of arable land and to ensure global food security.
    Here, we present data from an international joint effort to resequence a set of core collection of about 3,000 rice accessions (3K rice genome) from 89 countries as a global public good. The 3K rice genomes had an average sequencing depth of 14X, averaged genome coverage and mapping rates of 94.0% and 92.5%, respectively.
    This data set provides a base for the large-scale novel allele mining for important traits in rice with various bioinformatics and/or genetic approaches. It can also serve as an window to show more details of the hidden diversity within O. sativa. With the release of this data set, we are looking forward to a global joint mining of this data set, establishing a global, public rice genetic/genomic database and information platform for the innovation of rice breeding technology.

How to cite
Wang, C. , Yu, H. , Huang, J. , Wang, W. , Faruquee, M. , Zhang, F. , Zhao, X. , Fu, B. , Chen, K. , Zhang, H. , Tai, S. , Wei, C. , McNally, K. L., Alexandrov, N. , Gao, X. , Li, J. , Li, Z. , Xu, J. and Zheng, T. (2020) Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0. Plant Biotechnol J. 18(1):14-16 doi:10.1111/pbi.13215