考虑上位性的全基因组关联分析为高产优质水稻的高效育种提供了见解

doi:10.1016/j.jia.2023.07.021

Journal of Integrative Agriculture ›› 2024, Vol. 23 ›› Issue (8): 2541-2556.DOI: 10.1016/j.jia.2023.07.021

考虑上位性的全基因组关联分析为高产优质水稻的高效育种提供了见解

收稿日期:2023-03-27 接受日期:2023-07-03 出版日期:2024-08-20 发布日期:2024-07-29

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Xiaogang He¹, Zirong Li¹, Sicheng Guo¹, Xingfei Zheng², Chunhai Liu¹, Zijie Liu¹, Yongxin Li¹, Zheming Yuan¹, Lanzhi Li^1#

¹Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making/College of Plant Protection, Hunan Agricultural University, Changsha 410128, China
² Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement/Food Crop Institute, Hubei Academy of Agricultural Sciences, Wuhan 430064, China

Received:2023-03-27 Accepted:2023-07-03 Online:2024-08-20 Published:2024-07-29
About author:Xiaogang He, E-mail: hexiaogang97@163.com; #Correspondence Lanzhi Li, Tel: +86-731-84618163, E-mail: lancy0829@163.com
Supported by:
This work was partially supported by the Science and Technology Innovation Program of Hunan Province, China (2023NK2001), the Hubei Key Laboratory of Food Crop Germplasm and Genetic Improvement, China (2022LZJJ08), the Special Funds for Construction of Innovative Provinces in Hunan Province, China (2021NK1011), the Natural Science Foundation of Hunan Province, China (2020JJ4039), and the Key Research and Development Program of Hubei Province, China (2021BBA223).

摘要/Abstract

摘要：

标记辅助选择（MAS）和基因组选择（GS）育种极大地提高了水稻育种效率。由于上位性和基因多效性的影响，如何保证MAS和GS育种的实际效果仍是一个需要攻克的问题。本研究对113个籼稻品种（V）及565个杂交种（TC）的12个品质性状和9个农艺性状进行了全基因组关联分析，探讨了12个品质性状和9个农艺性状的遗传基础。共检测到381个主效显著相关位点（SAL）和1759个与这些主效SALs发生了上位性互作的微效SALs。筛选出322个位于SALs内或附近的候选基因，其中204个为克隆基因。通过对候选基因的优势单倍型和微效SALs的理想等位基因进行聚合分析，挖掘了39个有利于性状改良的MAS分子模块。通过构建遗传网络，鉴定到91个多效性位点。此外，本研究比较了将主效SALs、微效SALs和上位性SALs作为GS预测模型的协变量的预测精度与不使用SAL作为协变量的预测精度的差异。在TC数据集的大多性状中4种模型的预测准确性无显著差异。但在V数据集中，加入主效SALs、微效SALs和上位性SALs分别显著提高了5(26%)、3(16%)和11(58%)个性状的预测准确性。这些结果表明，上位性SALs可为亲本品系预测提供相当高的预测能力。这些结果为复杂性状的遗传基础提供了新的见解，为水稻分子育种提供了有价值的信息。

Abstract:

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influences of epistasis and gene pleiotropy, ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits. The original traits and general combining ability of the parents, as well as the original traits and mid-parent heterosis of TC, were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected. Among these loci, 322 candidate genes located within or nearby the SALs were screened, 204 of which were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracies of the four models were generally not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracies of 5 (26%), 3 (16%), and 11 (58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines. They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Key words: rice , genome-wide association study , epistasis , gene pleiotropy , maker-associated selection , genome selection

Xiaogang He, Zirong Li, Sicheng Guo, Xingfei Zheng, Chunhai Liu, Zijie Liu, Yongxin Li, Zheming Yuan, Lanzhi Li. 考虑上位性的全基因组关联分析为高产优质水稻的高效育种提供了见解[J]. Journal of Integrative Agriculture, 2024, 23(8): 2541-2556.

Xiaogang He, Zirong Li, Sicheng Guo, Xingfei Zheng, Chunhai Liu, Zijie Liu, Yongxin Li, Zheming Yuan, Lanzhi Li. Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice[J]. Journal of Integrative Agriculture, 2024, 23(8): 2541-2556.

参考文献

Awika H O, Cochran K, Joshi V, Bedre R, Mandadi K K, Avila C A. 2020. Single-marker and haplotype-based association analysis of anthracnose (Colletotrichum dematium) resistance in spinach (Spinacia oleracea). Plant Breeding, 139, 402–418.

Bai W, Zhang H, Zhang Z, Teng F, Wang L, Tao Y, Zheng Y. 2010. The evidence for non-additive effect as the main genetic component of plant height and ear height in maize using introgression line populations. Plant Breeding, 129, 376–384.

Bello B K, Hou Y X, Zhao J, Jiao G A, Wu Y W, Li Z Y, Wang Y F, Tong X H, Wang W, Yuan W Y. 2019. NF-YB1-YC12-bHLH144 complex directly activates Wx to regulate grain quality in rice (Oryza sativa L.). Plant Biotechnology Journal, 17, 1222–1235.

Bonnafous F, Fievet G, Blanchet N, Boniface M C, Carrère S, Gouzy J, Legrand L, Marage G, Bret-Mestries E, Munos S. 2018. Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids. Theoretical and Applied Genetics, 131, 319–332.

Bouvet J M, Makouanzi G, Cros D, Vigneron P H. 2016. Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications. Heredity, 116, 146–157.

Desta Z A, Ortiz R. 2014. Genomic selection: Genome-wide prediction in plant improvement. Trends in Plant Science, 19, 592–601.

Dormatey R, Sun C, Ali K, Coulter J A, Bi Z, Bai J. 2020. Gene pyramiding for sustainable crop improvement against biotic and abiotic stresses. Agronomy, 10, 1255.

Fan C C, Xing Y Z, Mao H L, Lu T T, Han B, Xu C, Li X H, Zhang Q F. 2006. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theoretical and Applied Genetics, 112, 1164–1171.

Fang C, Ma Y M, Wu S W, Liu Z, Wang Z, Yang R, Hu G H, Zhou Z K, Yu H, Zhang M. 2017. Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biology, 18, 1–14.

Fang F, Ye S, Tang J Y, Bennett M J, Liang W Q. 2020. DWT1/DWL2 act together with OsPIP5K1 to regulate plant uniform growth in rice. New Phytologist, 225, 1234–1246.

Feng S J, Zhang J P, Mu Z H, Wang Y J, Wen C L, Wu T, Yu C, Li Z, Wang H. 2020. Recent progress on the molecular breeding of Cucumis sativus L. in China. Theoretical and Applied Genetics, 133, 1777–1790.

Galli G, Alves F C, Morosini J S, Fritsche-Neto R, 2020. On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids. PLoS ONE, 15, e0228724

Guo Z L, Yang W N, Chang Y, Ma X S, Tu H F, Xiong F, Jiang N, Feng H, Huang C L, Yang P. 2018. Genome-wide association studies of image traits reveal genetic architecture of drought resistance in rice. Molecular Plant, 11, 789–805.

Hill W G, Robertson A. 1968. Linkage disequilibrium in finite populations. Theoretical and Applied Genetics, 38, 226–231.

Hori K, Suzuki K, Iijima K, Ebana K. 2016. Variation in cooking and eating quality traits in Japanese rice germplasm accessions. Breeding Science, 66, 309–318.

Hsu Y C, Chiu C H, Yap R, Tseng Y C, Wu Y P. 2020. Pyramiding bacterial blight resistance genes in Tainung82 for broad-spectrum resistance using marker-assisted selection. International Journal of Molecular Sciences, 21, 1281.

Huang M, Liu X L, Zhou Y, Summers R M, Zhang Z W. 2019. BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience, 8, giy154.

Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25, 1754–1760.

Liu H J, Yan J B. 2019. Crop genome-wide association study: A harvest of biological relevance. The Plant Journal, 97, 8–18.

Liu Q, Han R, Wu K, Zhang J Q, Ye Y F, Wang S S, Chen J F, Pan Y J, Li Q, Xu X P. 2018. G-protein βγ subunits determine grain size through interaction with MADS-domain transcription factors in rice. Nature Communications, 9, 852.

Lou J, Chen L, Yue G H, Lou Q J, Mei H W, Xiong L, Luo L J. 2009. QTL mapping of grain quality traits in rice. Journal of Cereal Science, 50, 145–151.

Lu X M, Zhang N, Chen J F, Qian C T. 2017. The research progress in crops pyramiding breeding, Molecular Plant Breeding, 15, 1445–1454. (in Chinese)

Lv C F, Lu W J, Quan M Y, Xiao L, Li L Z, Zhou J X, Li P, Zhang D Q, Du Q Z. 2021. Pyramiding superior haplotypes and epistatic alleles to accelerate wood quality and yield improvement in poplar breeding. Industrial Crops and Products, 171, 113891.

Mackay T F. 2014. Epistasis and quantitative traits: Using model organisms to study gene–gene interactions. Nature Reviews Genetics, 15, 22–33.

Mathew I E, Priyadarshini R, Mahto A, Jaiswal P, Parida S K, Agarwal P. 2020. SUPER STARCHY1/ONAC025 participates in rice grain filling. Plant Direct, 4, e00249.

Matsushima R, Maekawa M, Kusano M, Tomita K, Kondo H, Nishimura H, Crofts N, Fujita N, Sakamoto W. 2016. Amyloplast membrane protein SUBSTANDARD STARCH GRAIN6 controls starch grain size in rice endosperm. Plant Physiology, 170, 1445–1459.

Nevame A Y M, Emon R M, Malek M A, Hasan M M, Alam M, Muharam F M, Aslani F, Rafii M Y, Ismail M R. 2018. Relationship between high temperature and formation of chalkiness and their effects on quality of rice. BioMed Research International, 2018, 1–18.

Park I M, Ibáñez A M, Shoemaker C F. 2007. Rice starch molecular size and its relationship with amylose content. Starch-Stärke, 59, 69–77.

Qi H H, Huang J, Zheng Q, Huang Y Q, Shao R X, Zhu L Y, Zhang Z X, Qiu F Z, Zhou G C, Zheng Y L. 2013. Identification of combining ability loci for five yield-related traits in maize using a set of testcrosses with introgression lines. Theoretical and Applied Genetics, 126, 369–377.

Rice B, Lipka A E. 2019. Evaluation of RR-BLUP genomic selection models that incorporate peak genome-wide association study signals in maize and sorghum. The Plant Genome, 12, 180052.

Roberts A, McMillan L, Wang W, Parker J, Rusyn I, Threadgill D. 2007. Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics, 23, i401–i407.

Sehgal D, Rosyara U, Mondal S, Singh R, Poland J, Dreisigacker S. 2020. Incorporating genome-wide association mapping results into genomic prediction models for grain yield and yield stability in CIMMYT spring bread wheat. Frontiers in Plant Science, 11, 197.

Sinha P, Singh V K, Saxena R K, Khan A W, Abbai R, Chitikineni A, Desai A, Molla J, Upadhyaya H D, Kumar A. 2020. Superior haplotypes for haplotype-based breeding for drought tolerance in pigeonpea (Cajanus cajan L.). Plant Biotechnology Journal, 18, 2482–2490.

Smoot M E, Ono K, Ruscheinski J, Wang P L, Ideker T. 2011. Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics, 27, 431–432.

Spindel J E, Begum H, Akdemir D, Collard B, Redoña E, Jannink J L, McCouch S. 2016. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity, 116, 395–408.

Wang J B, Zhang Z W. 2021. GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genomics, Proteomics & Bioinformatics, 19, 629–640.

Wang Q, Tang J L, Han B, Huang X H. 2020. Advances in genome-wide association studies of complex traits in rice. Theoretical and Applied Genetics, 133, 1415–1425.

Wang W S, Mauleon R, Hu Z Q, Chebotarov D, Tai S S, Wu Z C, Li M, Zheng T Q, Fuentes R R, Zhang F. 2018. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature, 557, 43–49.

Wang Y, Li H H, Zhang L Y, Lü W Y, Wang J K. 2012. On the use of mathematically-derived traits in QTL mapping. Molecular Breeding, 29, 661–673.

Xia D, Zhou H, Liu R J, Dan W H, Li P B, Wu B, Chen J X, Wang L Q, Gao G J, Zhang Q L. 2018. GL3.3, a novel QTL encoding a GSK3/SHAGGY-like kinase, epistatically interacts with GS3 to produce extra-long grains in rice. Molecular Plant, 11, 754–756.

Xu K, Zheng X F, Zhang H Y, Hu Z L, Ning Z L, Li L Z. 2023. Genome-wide association analysis of indica-rice heading date based on NCII genetic mating design. Acta Agronomica Sinic, 49, 89-96. (in Chinese)

Xu X, Ye J H, Yang Y Y, Li R S, Li Z, Wang S, Sun Y F, Zhang M C, Xu Q, Feng Y, Wei X H, Yang Y L. 2022. Genetic diversity analysis and GWAS reveal the adaptive loci of milling and appearance quality of japonica rice (Oryza sativa L.) in Northeast China. Journal of Integrative Agriculture, 21, 1539-1550.

Xue W Y, Xing Y Z, Weng X Y, Zhao Y, Tang W J, Wang L, Zhou H J, Yu S B, Xu C G, Li X H. 2008. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nature Genetics, 40, 761–767.

Yang J, Lee S H, Goddard M E, Visscher P M. 2011. GCTA: A tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 88, 76–82.

Yao X Y, Li Q, Liu J, Jiang S K, Yang S L, Wang J Y, Xu Z J. 2015. Dissection of QTLs for plant height and panicle length traits in rice under different environment. Scientia Agricultura Sinica, 48, 407-414. (in Chinese)

Yi Q, Liu Y H, Hou X B, Zhang X G, Li H M, Zhang J J, Liu H M, Hu Y F, Yu G W, Li Y P. 2019. Genetic dissection of yield-related traits and mid-parent heterosis for those traits in maize (Zea mays L.). BMC plant Biology, 19, 1–20.

Yi X H, Zhang Z J, Zeng S Y, Tian C Y, Peng J C, Li M, Lu Y, Meng Q C, Gu M H, Yan C J. 2011. Introgression of qPE9-1 allele, conferring the panicle erectness, leads to the decrease of grain yield per plant in japonica rice (Oryza sativa L.). Journal of Genetics and Genomics, 38, 217–223.Zhou X, Stephens M. 2012. Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 44, 821–824.

Zhou Y, Zhu J Y, Li Z Y, Yi C D, Liu J, Zhang H G, Tang S Z, Gu M H, Liang G H. 2009. Deletion in a quantitative trait gene qPE9-1 associated with panicle erectness improves plant architecture during rice domestication. Genetics, 183, 315–324.

考虑上位性的全基因组关联分析为高产优质水稻的高效育种提供了见解

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics