9.9
CiteScore
7.1
Impact Factor
Turn off MathJax
Article Contents

Population genomics reveals selection signatures and favorable haplotypes for low chalkiness in indica rice

doi: 10.1016/j.jgg.2026.04.013
Funds:

2024YG01), The Introduction of Young Key Talents of Guangdong Academy of Agricultural Sciences (R2023YJ-QC001), Guangdong Provincial Key Laboratory of New Technology in Rice Breeding (2023B1212060042), National Key R&

This research was supported by Guangdong Provincial Key R&

D Fund (JSGGKQTD20221101115652015) and Guangdong Laboratory for Lingnan Modern Agriculture (NT25002).

D Program Project (2025B0202050001), the National Natural Science Foundation of China (32400512 and 32470347), Guangdong Pearl River Talents Program (2024QN11N336), The GuangDong Basic and Applied Basic Research Foundation (2024A1515011981), Youth S&

D Program of China (2024YFD1200801), Shenzhen Science and Technology R&

T Talent Support Programme of Guangdong Provincial Association for Science and Technology (SKXRC2025531), The “YouGu” Plan of Rice Research Institute of Guangdong Academy of Agricultural Sciences (2023YG04&

  • Received Date: 2025-11-20
  • Accepted Date: 2026-04-19
  • Rev Recd Date: 2026-04-18
  • Available Online: 2026-04-28
  • Grain chalkiness compromises rice appearance quality and market value. The genetic basis of chalkiness reduction in indica rice remains less well understood than that in japonica. Modern Guangdong indica varieties released since the 1980s have long, translucent grains with low chalkiness, making them a suitable system to dissect quality improvement in indica. Here, we analyse whole-genome variation in 154 Guangdong indica varieties and 229 genetically diverse international indica accessions. Integrating population genomics, haplotype association analysis, and endosperm expression profiling, we identify four major chalkiness-associated genes, Wx, Chalk5, OsDER1, and OsATG8b, showing strong genetic differentiation consistent with breeding selection in Guangdong germplasm. Superior haplotypes at all four loci are significantly associated with reduced chalkiness in multi-year phenotyping in Guangzhou and in independent trials from the Philippines. Analysis of HuangHuaZhan pedigree further reveals stepwise pyramiding and fixation of these haplotypes during grain-quality improvement. Across other indica-growing provinces in southern China, favourable haplotypes at Wx and OsDER1 are largely fixed in cultivated varieties, whereas those at Chalk5 and OsATG8b remain less deployed. Variants in these four genes also enable efficient prediction of chalkiness across multiple machine learning models. Together, these results illustrate how breeding enriched favourable haplotypes for low chalkiness in indica rice.
  • loading
  • Alam, M., Lou, G., Abbas, W., Osti, R., Ahmad, A., Bista, S., Ahiakpa, J.K., He, Y., 2024. Improving rice grain quality through ecotype breeding for enhancing food and nutritional security in Asia-Pacific region. Rice 17, 47.
    Bradbury, P.J., Zhang, Z., Kroon, D.E., Casstevens, T.M., Ramdoss, Y., Buckler, E.S., 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinform. 23, 2633-2635.
    Brown, J., Pirrung, M., McCue, L.A., 2017. FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinform. 33, 3137-3139.
    Calvanese, E., Gu, Y., 2022. Towards understanding inner nuclear membrane protein degradation in plants. J. Exp. Bot. 73, 2266-2274.
    Chen, L., Li, X., Zheng, M., Hu, R., Dong, J., Zhou, L., Liu, W., Liu, D., Yang, W., 2024. Genes controlling grain chalkiness in rice. Crop J. 12, 979-991.
    Chen, S., 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2, e107.
    Chen, X., Ren, Y., Dong, H., Jiang, X., Zheng, X., Duan, E., Teng, X., Wang, Y., Gu, C., Chen, R., et al., 2025. Natural variation in OsTPS8 confers differential regulation of chalkiness and seed vigor in indica and japonica rice. Nat. Genet. 58, 206-217.
    Chen, Z., Bu, Q., Liu, G., Wang, M., Wang, H., Liu, H., Li, X., Li, H., Fang, J., Liang, Y., et al., 2023. Genomic decoding of breeding history to guide breeding-by-design in rice. Natl. Sci. Rev. 10, nwad029.
    Cingolani, P., 2012. Variant annotation and functional prediction: SnpEff, , in: Ng, C., Piscuoglio, S. (eds.), Variant calling: methods and protocols. Humana, New York, pp. 289-314.
    Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, R.E., Lunter, G., Marth, G.T., Sherry, S.T., et al., 2011. The variant call format and VCFtools. Bioinform. 27, 2156-2158.
    Danecek, P., Bonfield, J.K., Liddle, J., Marshall, J., Ohan, V., Pollard, M.O., Whitwham, A., Keane, T., McCarthy, S.A., Davies, R.M., et al., 2021. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008.
    Fan, G., Jiang, J., Long, Y., Wang, R., Liang, F., Liu, H., Xu, J., Qiu, X., Li, Z., 2024. Generation of two-line restorer line with low chalkiness using knockout of Chalk5 through CRISPR/cas9 editing. Biology 13, 617.
    Fan, T., Yang, W., Zeng, X., Xu, X., Xu, Y., Fan, X., Luo, M., Tian, C., Xia, K., Zhang, M., 2020. A rice autophagy gene OsATG8b is involved in nitrogen remobilization and control of grain quality. Front. Plant Sci. 11, 588.
    Gao, J., Gao, L., Chen, W., Huang, J., Qing, D., Pan, Y., Ma, C., Wu, H., Zhou, W., Li, J., et al., 2024. Genetic effects of grain quality enhancement in indica hybrid rice: insights for molecular design breeding. Rice 17, 39.
    Hang, Y., Yue, L., Bingrui, S., Qing, L., Xingxue, M., Liqun, J., Shuwei, L., Jing, Z., Pingli, C., Dajian, P., et al., 2023. Genetic Diversity and Breeding Signatures for Regional Indica Rice Improvement in Guangdong of Southern China. Rice 16, 25.
    He, F., Tao, H., Wang, R., Liu, J., Hao, Z., Wang, D., Shi, X., Zhang, F., Long, J., Zhang, H., et al., 2025. OsATG1 and OsATG8 exhibit autophagy-independent functions to oppositely regulate ROP GTPase-mediated plant immunity in rice. Mol. Plant 18, 1472-1489.
    Hong, J., Rosental, L., Xu, Y., Xu, D., Orf, I., Wang, W., Hu, Z., Su, S., Bai, S., Ashraf, M., et al., 2023. Genetic architecture of seed glycerolipids in Asian cultivated rice. Plant Cell Environ. 46, 1278-1294.
    Hu, Z., Liu, H., Guo, M., Han, X., Li, Y., Chen, R., Guo, Y., Yang, Y., Sun, S., Zhou, Y., et al., 2025. Natural variation of an E3 ubiquitin ligase encoding gene Chalk9 regulates grain chalkiness in rice. Nat. Commun. 16, 6653.
    Ishimaru, T., Horigane, A.K., Ida, M., Iwasawa, N., San-oh, Y.A., Nakazono, M., Nishizawa, N.K., Masumura, T., Kondo, M., Yoshida, M., 2009. Formation of grain chalkiness and changes in water distribution in developing rice caryopses grown under high-temperature stress. J. Cereal Sci. 50, 166-174.
    Jing, C.-Y., Zhang, F.-M., Wang, X.-H., Wang, M.-X., Zhou, L., Cai, Z., Han, J.-D., Geng, M.-F., Yu, W.-H., Jiao, Z.-H., et al., 2023. Multiple domestications of Asian rice. Nat. Plants 9, 1221-1235.
    Jung, Y., Han, D., 2022. BWA-MEME: BWA-MEM emulated with a machine learning approach. Bioinform. 38, 2404-2413.
    Kawahara, Y., de la Bastide, M., Hamilton, J.P., Kanamori, H., McCombie, W.R., Ouyang, S., Schwartz, D.C., Tanaka, T., Wu, J., Zhou, S., et al., 2013. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6, 4.
    Kramer, O., 2016. Scikit-learn, Machine learning for evolution strategies. Springer, Cham, pp. 45-53.
    Letunic, I., Bork, P., 2021. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293-W296.
    Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., Subgroup, G.P.D.P., 2009. The sequence alignment/map format and SAMtools. Bioinform. 25, 2078-2079.
    Li, W., Yang, K., Hu, C., Abbas, W., Zhang, J., Xu, P., Cheng, B., Zhang, J., Yin, W., Shalmani, A., et al., 2025. A natural gene on-off system confers field thermotolerance for grain quality and yield in rice. Cell 188, 3661-3678.
    Li, Y., Fan, C., Xing, Y., Yun, P., Luo, L., Yan, B., Peng, B., Xie, W., Wang, G., Li, X., et al., 2014. Chalk5 encodes a vacuolar H+-translocating pyrophosphatase influencing grain chalkiness in rice. Nat. Genet. 46, 398-404.
    Liao, Y., Smyth, G.K., Shi, W., 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinform. 30, 923-930.
    Liu, J., Chen, J., Zheng, X., Wu, F., Lin, Q., Heng, Y., Tian, P., Cheng, Z., Yu, X., Zhou, K., et al., 2017. GW5 acts in the brassinosteroid signalling pathway to regulate grain width and weight in rice. Nat. Plants 3, 1-7.
    Liu, R., Zhao, D., Li, P., Xia, D., Feng, Q., Wang, L., Wang, Y., Shi, H., Zhou, Y., Chen, F., et al., 2025. Natural variation in OsMADS1 transcript splicing affects rice grain thickness and quality by influencing monosaccharide loading to the endosperm. Plant Commun. 6, 101178.
    Liu, Z., Yang, Q., Wu, P., Li, Y., Lin, Y., Liu, W., Guo, S., Liu, Y., Huang, Y., Xu, P., et al., 2023. Dynamic monitoring of TGW6 by selective autophagy during grain development in rice. New Phytol. 240, 2419-2435.
    Lv, Q., Li, W., Sun, Z., Ouyang, N., Jing, X., He, Q., Wu, J., Zheng, J., Zheng, J., Tang, S., et al., 2020. Resequencing of 1,143 indica rice accessions reveals important genetic variations and different heterosis patterns. Nat. Commun. 11, 4778.
    Ma, X., Wang, H., Yan, S., Zhou, C., Zhou, K., Zhang, Q., Li, M., Yang, Y., Li, D., Song, P., et al., 2025. Large-scale genomic and phenomic analyses of modern cultivars empower future rice breeding design. Mol. Plant 18, 651-668.
    Mahto, A., Yadav, A., PV, A., Parida, S.K., Tyagi, A.K., Agarwal, P., 2023. Cytological, transcriptome and miRNome temporal landscapes decode enhancement of rice grain size. BMC Biol. 21, 91.
    McCouch, S.R., Wright, M.H., Tung, C.-W., Maron, L.G., McNally, K.L., Fitzgerald, M., Singh, N., DeClerck, G., Agosto-Perez, F., Korniliev, P., et al., 2016. Open access resources for genome-wide association mapping in rice. Nat. Commun. 7, 10532.
    McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al., 2010. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297-1303.
    Misra, G., Badoni, S., Parween, S., Singh, R.K., Leung, H., Ladejobi, O., Mott, R., Sreenivasulu, N., 2021. Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness. Plant Biotechnol. J. 19, 910-925.
    Pan, L.-X., Sun, Z.-Z., Zhang, C.-Q., Li, B., Yang, Q.-Q., Chen, F., Fan, X.-L., Zhao, D.-S., Lv, Q.-M., Yuan, D.-Y., et al., 2022. Allelic diversification of the Wx and ALK loci in indica restorer lines and their utilisation in hybrid rice breeding in China over the last 50 years. Int. J. Mol. Sci. 23, 5941.
    Price, M.N., Dehal, P.S., Arkin, A.P., 2009. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641-1650.
    Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., De Bakker, P.I., Daly, M.J., et al., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575.
    Qian, D., Chen, G., Tian, L., Qu, L.Q., 2018. OsDER1 is an ER-associated protein degradation factor that responds to ER stress. Plant Physiol. 178, 402-412.
    Qiu, X., Yang, J., Zhang, F., Niu, Y., Zhao, X., Shen, C., Chen, K., Teng, S., Xu, J., 2021. Genetic dissection of rice appearance quality and cooked rice elongation by genome-wide association study. Crop J. 9, 1470-1480.
    Sehgal, A., Sita, K., Siddique, K.H., Kumar, R., Bhogireddy, S., Varshney, R.K., HanumanthaRao, B., Nair, R.M., Prasad, P.V., Nayyar, H., 2018. Drought or/and heat-stress effects on seed filling in food crops: impacts on functional biochemistry, seed yields, and nutritional quality. Front. Plant Sci. 9, 1705.
    Sera, Y., Hanamata, S., Sakamoto, S., Ono, S., Kaneko, K., Mitsui, Y., Koyano, T., Fujita, N., Sasou, A., Masumura, T., et al., 2019. Essential roles of autophagy in metabolic regulation in endosperm development during rice seed maturation. Sci. Rep. 9, 18544.
    Shao, Y., Peng, Y., Mao, B., Lv, Q., Yuan, D., Liu, X., Zhao, B., 2020. Allelic variations of the Wx locus in cultivated rice and their use in the development of hybrid rice in China. PloS ONE 15, e0232279.
    Sreenivasulu, N., Zhang, C., Tiozon, R.N., Liu, Q., 2022. Post-genomics revolution in the design of premium quality rice in a high-yielding background to meet consumer demands in the 21st century. Plant Commun. 3, 100271.
    Su, Y., Rao, Y., Hu, S., Yang, Y., Gao, Z., Zhang, G., Liu, J., Hu, J., Yan, M., Dong, G., et al., 2011. Map-based cloning proves qGC-6, a major QTL for gel consistency of japonica/indica cross, responds by Waxy in rice (Oryza sativa L.). Theor. Appl. Genet. 123, 859-867.
    Wang, C., Han, B., 2022. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. Mol. Plant 15, 593-619.
    Wang, D., Wang, J., Sun, W., Qiu, X., Yuan, Z., Yu, S., 2022. Verifying the breeding value of a rare haplotype of Chalk7, GS3, and Chalk5 to improve grain appearance quality in rice. Plants 11, 1470.
    Wang, F., 2024. Overview of the main achievements in rice science and technology innovation of Guangdong rice over the past century. China Rice 30, 1.
    Wang, J., Yang, W., Zhang, S., Hu, H., Yuan, Y., Dong, J., Chen, L., Ma, Y., Yang, T., Zhou, L., et al., 2023. A pangenome analysis pipeline provides insights into functional gene identification in rice. Genome Biol. 24, 19.
    Wang, W., Mauleon, R., Hu, Z., Chebotarov, D., Tai, S., Wu, Z., Li, M., Zheng, T., Fuentes, R.R., Zhang, F., et al., 2018. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43-49.
    Wickham, H., 2011. ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics 3, 180-185.
    Wickham, H., 2016. Programming with ggplot2, ggplot2: elegant graphics for data analysis. Springer, New York, pp. 241-253.
    Xu, Y., Lin, Q., Li, X., Wang, F., Chen, Z., Wang, J., Li, W., Fan, F., Tao, Y., Jiang, Y., et al., 2021. Fine-tuning the amylose content of rice by precise base editing of the Wx gene. Plant Biotechnol. J. 19, 11-13.
    Yang, W., Liang, J., Hao, Q., Luan, X., Tan, Q., Lin, S., Zhu, H., Liu, G., Liu, Z., Bu, S., et al., 2021. Fine mapping of two grain chalkiness QTLs sensitive to high temperature in rice. Rice 14, 33.
    Ye, J., Zhang, M., Yuan, X., Hu, D., Zhang, Y., Xu, S., Li, Z., Li, R., Liu, J., Sun, Y., et al., 2022. Genomic insight into genetic changes and shaping of major inbred rice cultivars in China. New Phytol. 236, 2311-2326.
    Yu, L., Turner, M., Fitzgerald, M., Stokes, J., Witt, T., 2017. Review of the effects of different processing technologies on cooked and convenience rice quality. Trends Food Sci. Technol. 59, 124-138.
    Zhan, P., Ma, S., Xiao, Z., Li, F., Wei, X., Lin, S., Wang, X., Ji, Z., Fu, Y., Pan, J., et al., 2022. Natural variations in grain length 10 (GL10) regulate rice grain size. J. Genet. Genomics 49, 405-413.
    Zhang, C., Yang, Y., Chen, S., Liu, X., Zhu, J., Zhou, L., Lu, Y., Li, Q., Fan, X., Tang, S., et al., 2021. A rare Waxy allele coordinately improves rice eating and cooking quality and grain transparency. J. Integr. Plant Biol. 63, 889-901.
    Zhang, C., Zhu, J., Chen, S., Fan, X., Li, Q., Lu, Y., Wang, M., Yu, H., Yi, C., Tang, S., et al., 2019. Wxlv, the ancestral allele of rice Waxy gene. Mol. Plant 12, 1157-1166.
    Zhang, R., Jia, G., Diao, X., 2023. geneHapR: an R package for gene haplotypic statistics and visualization. BMC Bioinform. 24, 199.
    Zheng, X., Wei, F., Cheng, C., Qian, Q., 2024. A historical review of hybrid rice breeding. J. Integr. Plant Biol. 66, 532-545.
    Zhou, D., Chen, W., Lin, Z., Chen, H., Wang, C., Li, H., Yu, R., Zhang, F., Zhen, G., Yi, J., et al., 2016a. Pedigree-based analysis of derivation of genome segments of an elite rice reveals key regions during its breeding. Plant Biotechnol. J. 14, 638-648.
    Zhou, H., Wang, L., Liu, G., Meng, X., Jing, Y., Shu, X., Kong, X., Sun, J., Yu, H., Smith, S.M., et al., 2016b. Critical roles of soluble starch synthase SSIIIa and granule-bound starch synthase Waxy in synthesizing resistant starch in rice. Proc. Natl. Acad. Sci. U. S. A. 113, 12844-12849.
    Zhou, X.-Q., Chen, D.-G., Guo, J., Chen, P.-L., Li, L.-J., Chen, K., Chen, Y.-D., Liu, C.-G., Zhang, Z.-M., 2022a. Genetic improvement of grain quality traits in indica inbred rice cultivars developed in South China during 1956-2020. Euphytica 218, 8.
    Zhou, Y.-F., Qing, T., Shu, X.-L., Liu, J.-X., 2022b. Unfolded protein response and storage product accumulation in rice grains. Seed Biol. 1, 1-5.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (67) PDF downloads (0) Cited by ()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return