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A quantitative genomics map of rice provides genetic insights and guides breeding

Nature Genetics volume 53, pages 243–253 (2021)Cite this article

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Abstract

Extensive allelic variation in agronomically important genes serves as the basis of rice breeding. Here, we present a comprehensive map of rice quantitative trait nucleotides (QTNs) and inferred QTN effects based on eight genome-wide association study cohorts. Population genetic analyses revealed that domestication, local adaptation and heterosis are all associated with QTN allele frequency changes. A genome navigation system, RiceNavi, was developed for QTN pyramiding and breeding route optimization, and implemented in the improvement of a widely cultivated indica variety. This work presents an efficient platform that bridges ever-increasing genomic knowledge and diverse improvement needs in rice.

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Fig. 1: Genotype matrix of 225 QTGs for a collection of 404 rice accessions.
Fig. 2: GWAS loci associated with important agronomic traits summarized from eight GWAS cohorts.
Fig. 3: Genomic distribution and linkage drag for QTGs in rice genome.
Fig. 4: Genetic investigation of the QTNs.
Fig. 5: Benchmarking for different backcrossing breeding designs.
Fig. 6: A schematic diagram for RiceNavi implementation and user operation.
Fig. 7: Improvement of the Huanghuazhan cultivar by implementation of RiceNavi.

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Data availability

The raw DNA sequencing data of the QTN library are deposited with GenBank under the bioproject accession no. PRJNA623686. A web-based version of RiceNavi is available from the website http://www.xhhuanglab.cn/tool/RiceNavi.html (supporting most browsers including Chrome, Firefox and Safari, but not Internet Explorer). In this web-based application, all functions in RiceNavi (QTNmap, QTNpick, Simulation and SampleSelect) can be accessed with user-friendly graphical interfaces.

Code availability

The source code of RiceNavi is available from both our laboratory website (http://www.xhhuanglab.cn/tool/RiceNavi.html) and the GitHub repository (https://github.com/xhhuanglab/RiceNavi). The other codes for the QTN-related analyses are also provided in the GitHub repository (https://github.com/xhhuanglab/QTN_scripts).

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Acknowledgements

We are grateful to the China National Rice Research Institute, Institute of Crop Sciences of Chinese Academy of Agricultural Sciences, Institute of Plant Protection of Chinese Academy of Agricultural Sciences, Chinese Academy of Sciences Center for Excellence of Molecular Plant Sciences and Huazhong Agricultural University for providing valuable rice varieties (see Supplementary Dataset 2 for details). We thank P. Xu and J. Murray for their advice and assistance in the paper writing. This work was funded by the National Natural Science Foundation of China (grant nos. 91935301 and 31825015), Innovation Program of Shanghai Municipal Education Commission (grant no. 2017-01-07-00-02-E00039) and Program of Shanghai Academic Research Leader (grant no. 18XD1402900) to X.H. and the US National Science Foundation (Plant Genome Research Program, IOS-1947609) to K.M.O.

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Author notes

  1. These authors contributed equally: Xin Wei, Jie Qiu.

Authors and Affiliations

  1. Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai, China

    Xin Wei, Jie Qiu, Kaicheng Yong, Jiongjiong Fan, Qi Zhang, Hua Hua, Jie Liu, Qin Wang & Xuehui Huang

  2. Department of Biology, Washington University in St Louis, St Louis, MO, USA

    Kenneth M. Olsen

  3. National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China

    Bin Han

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  1. Xin Wei
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Contributions

X.H. designed these studies and contributed to the original concept of the project. X.W., K.Y., J.F., Q.Z. and H.H. contributed to the collection, planting and phenotyping of the QTN library. Q.W. and J.L. performed the genome sequencing of the QTN library and breeding populations. J.Q., X.W. and X.H. performed QTN analysis, developed the RiceNavi system and implemented RiceNavi in practical breeding. X.H., J.Q., X.W., K.M.O. and B.H. analyzed the data and wrote the paper.

Corresponding author

Correspondence to Xuehui Huang.

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Competing interests

A patent on the QTN-based breeding selection method has been filed by Shanghai Normal University with X.H., X.W. and J.Q. as inventors. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Makoto Matsuoka and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The pipeline for 348 QTN site discovery and population genotyping.

The procedure includes determination of QTNs according to research papers and QTN genotyping from whole-genome sequence data of rice accessions.

Extended Data Fig. 2 Genotype matrix of 225 QTGs for QTN library colored by effect direction.

The figure is another display mode of Fig. 1a (colored by effect direction, rather than alternative/reference in Fig. 1a). Here, dark green, dark blue and light green, yellow and gray boxes represent the genotype for the reduced allele, increased allele, heterozygous, NA and deletion, respectively.

Extended Data Fig. 3 The matrix of QTN of different types for a collection of 404 rice accessions.

a, QTGs with multiple (≥3) QTNs. QTNs related to heading date, biotic stress and abiotic stress are highlighted with blue, purple and pink bars, respectively. b, Rare allele QTNs. QTNs with low percentage of samples (≤2%) with alternative or heterozygous alleles are illustrated. c, QTNs differentiated between japonica (including tropical and temperate japonica) and indica. QTNs with allele frequency differentiated (AF > 0.4) between japonica and indica are shown. Light blue, dark blue and light green, yellow and gray boxes represent the genotype for the reference (MSUv7.0), alternative, heterozygous, multiple alleles and deletion, respectively.

Extended Data Fig. 4 The matrix of QTN for 3023 rice accessions.

Light blue, dark blue and light green, yellow and gray boxes represent the genotype for the reference (MSUv7.0), alternative, heterozygous, and deletion, respectively.

Extended Data Fig. 5 Estimated phenotypic effects for QTGs controlling four agronomic traits.

a, Geographical locations for 9 different environments in China. Longitude (° E) and latitude (° N) of the locations are shown. b–d, The estimated phenotypic effects of homozygous alternative alleles relative to homozygous Nipponbare are jointly shown for each QTG. The phenotypes displayed include heading date (b), plant height (c), grain length (d) and grain width (e). Colors represent different environments. The bars indicate standard errors estimated by GCTA package. The QTG effects from CNmix population in Beijing and from NE population in Lingshui are not showed. For QTNs in Lingshui, the QTNs with the peak p-value are selected.

Extended Data Fig. 6 Genomic distribution of linkage drag in the rice genome for QTN library.

The candidate linkage drag (superior and inferior alleles located physically less than 2 Mb in distance) are labeled across the rice genome. The blue dots indicate the percentage of drag for the 404 QTN library accessions.

Extended Data Fig. 7 Genomic characteristics for the QTNs in the UTR and promoter regions.

a, Percentage of upstream QTNs of different distances to translational start site (ATG). b, Upstream QTN sites which resides in the open chromatin regions identified by ATAC- and FAIRE-seq.

Extended Data Fig. 8 The QTNs involved in the domestication and improvement.

a, QTNs allele frequency change during the domestication & early variety improvement and modern variety improvement. QTNs with greatest allele changes are shown. Threshold is determined by the 4DTv sites and is indicated by dotted line. b, Groups of the domestication and improvement-related QTNs. QTNs shared by two kinds of domestication or improvement are shown. The color of the QTN names represents traits and is in line with Fig. 1a. c, Percentage of domesticated and improved QTGs in different agronomic traits. d, Number of QTGs with superior and inferior alleles.

Extended Data Fig. 9 The genotypes for the selected individuals of each generation during improvement of HHZ.

The superior alleles of three QTGs (OsSOC1, Badh2 and TAC1) are targeted during the breeding process for improvement of HHZ. The locations of the three QTGs are indicated by the red arrows. From BC1F1 to BC3F1, the numbers of selected individuals are 138, 10 and 3, respectively. The genotypes for the HHZ background, donor Basmati, and heterozygous are color coded as dark green, red, and yellow respectively.

Extended Data Fig. 10 An examination for the extent to which introgressed segments from donor parents could match expected phenotypes.

a, The genotypes of the 217 BC3F1 CSSLs that constructed by HHZ and Basmati. The genotypes for the HHZ background, donor Basmati, and heterozygous are color coded as dark green, red, and yellow respectively. Positon of the introduced QTNs is shown on the top. Number of QTNs that introduced into HHZ is shown on the right. b–d, Genotypes of three individuals of the CSSLs. QTNs are indicated by solid circles. The color represents the group of agronomic traits and is line with Fig. 1a. The change direction of the phenotype value is indicated by arrows. Red and blow arrows indicate increase and decrease of the traits, respectively.

Supplementary information

Supplementary Information

Supplementary Note, Fig. 1 and Tables 1–4

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Supplementary Data

Supplementary Datasets 1–9

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Wei, X., Qiu, J., Yong, K. et al. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat Genet 53, 243–253 (2021). https://doi.org/10.1038/s41588-020-00769-9

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