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Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice

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Abstract

Genomic selection is an efficient tool for breeding selection, especially for quantitative traits controlled by multiples genes with low heritability. To validate the application of genomic selection in hybrid rice breeding, the yield and grain quality traits of 404 hybrid rice breeding lines were investigated, and the same accessions were genotyped by using a 56 K SNP chip. There were wide variances among the tested accessions for all the measured traits, and most of the traits were correlated. A total of 67 significant loci were identified for the yield-related traits, and 123 significant loci were identified for the grain quality traits by GWAS. Two of these loci associated with increasing grain yield but decreasing grain quality. The GEBVs of all the yield and grain quality traits were calculated by using 15 different prediction algorithms. The plant height, panicle length, thousand grain weight, grain length and width ratio, amylose content, and alkali value have higher predictability than other traits. However, the predictive accuracy of different GS models is different for different traits. This study provided useful information for genomic selection of specific trait using proper markers and prediction models.

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The data supporting the findings of this study are available within the article and its supplementary materials.

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References

  • Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using Ime4. J Stat Softw 67(1):1–48

    Article  Google Scholar 

  • Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23(19):2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Chen J, Zhou H, Xie W, Xia D, Gao G, Zhang Q, Wang G, Lian X, Xiao J, He Y (2019) Genome-wide association analyses reveal the genetic basis of combining ability in rice. Plant Biotechnol J 17(11):2211–2222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Covarrubias-Pazaran G (2016) Genome-assisted prediction of quantitative traits using the R package sommer. PLOS ONE 11(6):e0156744

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, Campos G, Burgueño J, González-Camacho J, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney R (2017a) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975

    Article  CAS  PubMed  Google Scholar 

  • Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, Campos G, Burgueño J, González-Camacho M, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney K (2017b) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975

    Article  CAS  PubMed  Google Scholar 

  • Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, Ali J, Xu S (2020) Hybrid breeding of rice via genomic selection. Plant Biotechnol J 18(1):57–67

    Article  CAS  PubMed  Google Scholar 

  • Doyle J, Doyle J (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemistry Bulletin 19:11–15

    Google Scholar 

  • García-Ruiz A, Cole J, anRaden P, Wiggans G, Ruiz-López F, Tassell C, (2016) Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. PANS 113(28):3995–4004

    Google Scholar 

  • Grenier C, Cao T, Ospina Y, Quintero C, Châtel M, Tohme J, Courtois B, Ahmadi N (2015) Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding. PLOS ONE 10(8):e0136594

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Hassen M, Cao T, Bartholome J, Orasen G, Colombi C, Rakotomalala J, Razafinimpiasa L, Bertone C, Biselli C, Volante A, Desiderio F, Jacquin L, Vale G, Ahmadi N (2018) Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. Theor Appl Genet 131:417–435

    Article  PubMed  Google Scholar 

  • Huang X, Yang S, Gong J, Zhao Y, Feng Q, Gong H, Li W, Zhan Q, Cheng B, Xia J, Chen N, Hao Z, Liu K, Zhu C, Huang T, Zhao Q, Zhang L, Fan D, Zhou C, Lu Y, Weng Q, Wang Z, Li J, Han B (2015) Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nat Commun 6:6258

    Article  CAS  PubMed  Google Scholar 

  • Huang M, Balimponya E, Mgonja E, McHale L, Kihupi A, Wang G, Sneller C (2019) Use of genomic selection in breeding rice (Oryza sativa L.) for resistance to rice blast (Magnaporthe oryzae). Molecular Breeding 39:114

  • International Rice Genome Sequencing Project, Sasaki T (2005) The map-based sequence of the rice genome. Nature 436:793–800

    Article  CAS  Google Scholar 

  • Isidro J, Jannink J, Akdemir D, Poland J, Heslot N, Sorrells M (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128:145–158

    Article  PubMed  Google Scholar 

  • Iwata H, Ebana K, Uga Y, Hayashi T (2015) Genomic prediction of biological shape: elliptic Fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.). PLOS ONE 10(3):e0120610

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jena K, Mackill D (2008) Molecular markers and their use in marker-assisted selection in rice. Crop Sci 48(4):1266–1276

    Article  Google Scholar 

  • Labroo M, Ali J, Aslam M, de Asis E, dela Paz M, Sevilla M, Lipka A, Studer A, Rutkoski J (2021) Genomic prediction of yield traits in single-cross hybrid rice (Oryza sativa L.). Frontiers in Genetics 12:692870

  • Li J, Wang J, Zeigler R (2014) The 3,000 rice genomes project: new opportunities and challenges for future rice research. GigaScience 3:8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Li F, Xie J, Zhu X, Wang X, Zhao Y, Ma X, Zhang Z, Rashid MAR, Zhang Z, Zhi L, Zhang S, Li J, Li Z, Zhang H (2018) Genetic basis underlying correlations among growth duration and yield traits revealed by GWAS in rice. Front Plant Sci 9:650

    Article  PubMed  PubMed Central  Google Scholar 

  • López M, Neira R, Yáñez J (2015) Applications in the search for genomic selection signatures in fish. Front Genet 5:458

    PubMed  PubMed Central  Google Scholar 

  • Lu F, Yang F, Fan T, Liu J, Li Q, Wang L, Long X (2019) Analysis of rice variety approval data from 1977 to 2018. China Seed Industry 2:29–40

    Google Scholar 

  • Ma G, Yuan L (2015) Hybrid rice achievements, development and prospect in China. J Integr Agric 14(2):197–205

    Article  Google Scholar 

  • McCouch S, Committee on Gene Symbolization NaL, Rice Genetics Cooperative, (2008) Gene nomenclature system for rice. Rice 1:72-84

  • Meuwissen T (2007) Genomic selection: marker assisted selection on a genome wide scale. J Anim Breed Genet 124(6):321–322

    Article  Google Scholar 

  • Meuwissen T, Hayes B, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Misra G, Anacleto R, Badoni S, Butardo V, Molina J, Graner A, Demont M, Morell M, Sreenivasulu N (2019) Dissecting the genome-wide genetic variants of milling and appearance quality traits in rice. J Exp Bot 70(19):5115–5130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Misztal I, Legarra A (2017) Invited review: efficient computation strategies in genomic selection. Animal 11(5):731–736

    Article  CAS  PubMed  Google Scholar 

  • Mucha S, Mrode R, MacLaren-Lee I, Coffey M, Conington J (2015) Estimation of genomic breeding values for milk yield in UK dairy goats. Dairy Science 98(11):8201–8208

    Article  CAS  Google Scholar 

  • Onogi A, Ideta O, Inoshita Y, Ebana K, Yoshioka T, Yamasaki M, Iwata H (2015) Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.). Theor Appl Genet 128:41–53

    Article  PubMed  Google Scholar 

  • Proost S, Mutwil M (2017) PlaNet: comparative co-expression network analyses for plants. Methods Mol Biol 1533:213–227

    Article  CAS  PubMed  Google Scholar 

  • Robertsen C, Hjotrtshøj R, Janss L (2019) Genomic selection in cereal breeding. Agronomy 9:1–16

    Article  Google Scholar 

  • Samore A, Fontanesi L (2015) Genomic selection in pigs: state of the art and perspectives. Ital J Anim Sci 15(2):211–232

    Article  CAS  Google Scholar 

  • Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redoña E, Atlin G, Jannink J, McCouch S (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite tropical rice breeding lines. PLOS Genetics 11(2):e1004982

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Su J, Xu K, Li Z, Hu Y, Hu Z, Zheng X, Song S, Tang Z, Li L (2021) Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential. Sci Rep 11:6894

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118:302–310

    Article  CAS  PubMed  Google Scholar 

  • Wang W, Mauleon R, Hu Z, Chebotarov D, Tai S, Wu Z, Li M, Zheng T, Fuentes R, Zhang F, Mansueto L, Copetti D, Sanciangco M, Palis K, Xu J, Sun C, Fu B, Zhang H, Gao Y, Zhao X, Shen F, Cui X, Yu H, Li Z, Chen M, Detras J, Zhou Y, Zhang X, Zhao Y, Kudrna D, Wang C, Li R, Jia B, Lu J, He X, Dong Z, Xu J, Li Y, Wang M, Shi J, Li J, Zhang D, Lee S, Hu W, Poliakov A, Dubchak I, Ulat V, Borja F, Mendoza J, Ali J, Li J, Gao Q, Niu Y, Yue Z, Naredo M, Talag J, Wang X, Li J, Fang X, Yin Y, Glaszmann J, Zhang J, Li J, Hamilton R, Wing R, Ruan J, Zhang G, Wei C, Alexandrov N, McNally K, Li Z, Leung H (2018) Genomic variation in 3010 diverse accessions of Asian cultivated rice. Nature 557:43–49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wilson S, Zheng C, Maliepaard C, Mulder H, Visser R, Burgt A, Eeuwijk F (2021) Understanding the effectiveness of genomic prediction in tetraploid potato. Front Plant Sci 12:1–13

    Google Scholar 

  • Wolc A, Zhao H, Arango J, Settar P, Fulton J, O’Sullivan N, Preisinger R, Stricker C, Habier D, Fernando R, Garrick D, Lamont S, Dekkers J (2015) Response and inbreeding from a genomic selection experiment in layer chickens. Genet Sel Evol 47:59

    Article  PubMed  PubMed Central  Google Scholar 

  • Xiao N, Pan C, Li Y, Wu Y, Cai Y, Lu Y, Wang R, Yu L, Shi W, Kang H, Zhu Z, Huang N, Zhang X, Chen Z, Liu J, Yang Z, Ning Y, Li A (2021) Genomic insight into balancing high yield, good quality, and blast resistance of japonica rice. Genome Biol 22:283

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. PNAS 111(34):12456–12461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Wang X, Ding X, Zheng X, Yang Z, Hu Z (2018) Genomic selection of agronomic traits in hybrid rice using an NCII population. Rice 11(1):32

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu S (2017) Predicted residual error sum of squares of mixed models: an application for genomic pPrediction. G3 (Bethesda) 7 (3):895–909

  • Yabe S, Yoshida H, Kajiya-Kanegae H, Yamasaki M, Iwata H, Ebana K, Hayashi T, Nakagawa H (2018) Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice. PLOS ONE 13(11):e0207627

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Yao W, Li G, Yu Y, Ouyang Y (2018) FunRiceGenes dataset for comprehensive understanding and application of rice functional genes. GigaScience 7(1):gix119

    Article  CAS  Google Scholar 

  • Zhang Z, Zhang Q, Ding X (2011) Advances in genomic selection in domestic animals. Chin Sci Bull 56(25):2655–2663

    Article  Google Scholar 

  • Zhen G, Qin P, Liu K, Nie D, Yang Y, Deng X, He H (2017) Genome-wide dissection of heterosis for yield traits in two-line hybrid rice populations. Sci Rep 7:7635

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

This study was financially supported by the National Key Research and Development Project of Ministry of Science and Technology (2017YFD0102002-4).

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Authors and Affiliations

  1. Huazhi Biotechnology Co. Ltd, Changsha, 410125, Hunan, China

    Peiyi Yu, Changrong Ye, Le Li, Hexing Yin, Jian Zhao, Yongka Wang, Zhe Zhang, Weiguo Li, Yu Long, Xueyi Hu, Jinhua Xiao, Gaofeng Jia & Bingchuan Tian

Authors
  1. Peiyi Yu
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  2. Changrong Ye
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  3. Le Li
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  4. Hexing Yin
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  5. Jian Zhao
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  6. Yongka Wang
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  7. Zhe Zhang
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  8. Weiguo Li
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  9. Yu Long
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  10. Xueyi Hu
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  11. Jinhua Xiao
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  12. Gaofeng Jia
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  13. Bingchuan Tian
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Contributions

P Yu and C Ye wrote the original draft; L Li, H Yin, J Zhao, and Y Wang performed the experiments; Z Zhang, W Li, and Y Long analyzed the data; X Hu, J Xiao, G Jia, and B Tian designed and supervised this study.

Corresponding authors

Correspondence to Jinhua Xiao, Gaofeng Jia or Bingchuan Tian.

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Peiyi Yu and Changrong Ye are contributed equally.

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Yu, P., Ye, C., Li, L. et al. Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice. Mol Breeding 42, 16 (2022). https://doi.org/10.1007/s11032-022-01289-6

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