Abstract
Key message
The genetic architecture of five flag leaf morphology traits was dissected by the functional haplotype-based GWAS and a standard SNP-based GWAS in a diverse population consisting of 197 varieties.
Abstract
Flag leaf morphology (FLM) is a critical factor affecting plant architecture and grain yield in wheat. The genetic architecture of FLM traits has been extensively studied with QTL mapping in bi-parental populations, while few studies exploited genome-wide association studies (GWAS) in diverse populations. In this study, a panel of 197 elite and historical varieties from China was evaluated for five FLM traits including the length (FLL), width (FLW), ratio (FLR), area (FLA) and angle (FLANG) as well as yield in nine environments. Based on the phenotypic correlation between yield and FLL (-0.43), FLA (− 0.32) and FLW (0.11), an empirical FLM index combining the three FLM traits proved to be a good predictor for yield. Two GWAS approaches were applied to dissect the genetic architecture of five FLM traits with a Wheat660K SNP array. The functional haplotype-based GWAS revealed 6, 5 and 7 QTL for FLANG, FLL and FLR, respectively, whereas two QTL for FLW and one for FLR were identified by the standard SNP-based GWAS. Due to co-localization, there were 18 independent QTL and 10 of them were close to known ones. One co-localized QTL on chromosome 5A was associated with FLL, FLANG and FLR. Moreover, both GWAS approaches identified a novel QTL for FLR on chromosome 6B which was not reported in previous studies. This study provides new insights into the relationship between FLM and yield and broadens our understanding of the genetic architecture of FLM traits in wheat.
Similar content being viewed by others
Abbreviations
- BLUE:
-
Best linear unbiased estimation
- GWAS:
-
Genome-wide association study
- YHW:
-
Yellow and Huai Valley Winter Wheat Region
- FH-GWAS:
-
Functional haplotype-based GWAS
- FLL:
-
Flag leaf length
- FLW:
-
Flag leaf width
- FLANG:
-
Flag leaf angle
- FLR:
-
Flag leaf ratio
- FLA:
-
Flag leaf area
References
Biswal AK, Kohli A (2013) Cereal flag leaf adaptations for grain yield under drought: knowledge status and gaps. Mol Breed 31:749–766
Börner A, Schumann E, Fürste A, Cöster H, Leithold B, Röder M, Weber W (2002) Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat (Triticum aestivum L.). Theor Appl Genet 105:921–936
Chen S, Cheng X, Yu K, Chang X, Bi H, Xu H, Wang J, Pei X, Zhang Z, Zhan K (2019) Genome-wide association study of differences in 14 agronomic traits under low- and high-density planting models based on the 660k SNP array for common wheat. Plant Breed 139:272–283
Donald CM (1968) The breeding of crop ideotypes. Euphytica 17:385–403
Duncan W (1971) Leaf angles, leaf area, and canopy photosynthesis 1. Crop Sci 11:482–485
Fan X, Cui F, Zhao C, Zhang W, Yang L, Zhao X, Han J, Su Q, Ji J, Zhao Z (2015) QTLs for flag leaf size and their influence on yield-related traits in wheat (Triticum aestivum L.). Mol Breed 35(1):24
Farooq M, Tagle AG, Santos RE, Ebron LA, Fujita D, Kobayashi N (2010) Quantitative trait loci mapping for leaf length and leaf width in rice cv. IR64 derived lines. J Integr Plant Biol 52:578–584
Gage JL, White MR, Edwards JW, Kaeppler S, Natalia DL (2018) Selection signatures underlying dramatic male inflorescence transformation during modern hybrid maize breeding. Genetics 210:1125–1138
Haslett SJ, Puntanen S (2011) On the equality of the BLUPs under two linear mixed models. Metrika 74:381–395
Hill WG, Weir BS (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33:54–78
Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theoret Appl Genetics 38:226–231
Huang SS, Sun LQ, Hu X, Wang YH, Zhang YJ, Nevo E, Peng JH, Sun DF (2018) Associations of canopy leaf traits with SNP markers in durum wheat (Triticum turgidum L. durum (Desf.)). Plos One. 13(10):e0206226
Hussain W, Baenziger PS, Belamkar V, Guttieri MJ, Venegas JP, Easterly A, Sallam A, Poland J (2017) Genotyping-by-sequencing derived high-density linkage map and its application to QTL mapping of flag leaf traits in bread wheat. Sci Rep 7:1–15
Jia H, Wan H, Yang S, Zhang Z, Kong Z, Xue S, Zhang L, Ma Z (2013) Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China’s wheat breeding. Theor Appl Genet 126:2123–2139
Jiang Y, Zhao Y, Rodemann B, Plieske J, Kollers S, Korzun V, Ebmeyer E, Argillier O, Hinze M, Ling J, Röder MS, Ganal MW, Mette MF, Reif JC (2015) Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.). Heredity 114:318–326
Jiang Y, Schmidt RH, Reif JC (2018) Haplotype-based genome-wide prediction models exploit local epistatic interactions among markers. G3: Genes. Genomes, Genetics 8:1687–1699
Khaliq I, Irshad A, Ahsan M (2008) Awns and flag leaf contribution towards grain yield in spring wheat (Triticum aestivum L.). Cereal Res Commun 36:65–76
Ku L, Zhao W, Zhang J, Wu L, Wang C, Wang P, Zhang W, Chen Y (2010) Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.). Theor Appl Genet 121:951–959
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) FaST linear mixed models for genome-wide association studies. Nat Methods 8:833–835
Liu G, Jia L, Lu L, Qin D, Zhang J, Guan P, Ni Z, Yao Y, Sun Q, Peng H (2014) Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor Appl Genet 127:2415–2432
Liu L, Sun G, Ren X, Li C, Sun D (2015) Identification of QTL underlying physiological and morphological traits of flag leaf in barley. BMC Genet 16:29
Liu Y, Lin Y, Gao S, Li Z, Ma J, Deng M, Chen G, Wei Y, Zheng Y (2017) A genome-wide association study of 23 agronomic traits in Chinese wheat landraces. Plant J 91:861–873
Liu KY, Xu H, Liu G, Guan PF, Zhou XY, Peng HR, Yao YY, Ni ZF, Sun QX, Du JK (2018a) QTL mapping of flag leaf-related traits in wheat (Triticum aestivum L.). Theor Appl Genet 131:839–849
Liu Y, Tao Y, Wang Z, Guo Q, Wu F, Yang X, Deng M, Ma J, Chen G, Wei Y (2018b) Identification of QTL for flag leaf length in common wheat and their pleiotropic effects. Mol Breed 38:11
Liu F, Schmidt RH, Reif JC, Jiang Y (2019) Selecting closely-linked SNPs based on local epistatic effects for haplotype construction improves power of association mapping. G3: Genes. Genomes, Genetics 9:4115–4126
Liu F, Jiang Y, Zhao Y, Schulthess AW, Reif JC (2020) Haplotype-based genome-wide association increases the predictability of leaf rust (Puccinia triticina) resistance in wheat. J Exp Bot 71:6958–6968
Ma J, Tu Y, Zhu J, Luo W, Liu H, Li C, Li SQ, Liu JJ et al (2019) Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits. Theor Appl Genet 133:297–315
Mantilla-Perez MB, Salas Fernandez MG (2017) Differential manipulation of leaf angle throughout the canopy: current status and prospects. J Exp Bot 68:5699–5717
Melchinger AE, Messmer MM, Lee M, Woodman WL, Lamkey KR (1991) Diversity and relationships among US maize inbreds revealed by restriction fragment length polymorphisms. Crop Sci 31:669–678
Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop Journal 3:269–283
Morinaka Y, Sakamoto T, Inukai Y, Agetsuma M, Kitano H, Ashikari M, Matsuoka M (2006) Morphological alteration caused by brassinosteroid insensitivity increases the biomass and grain production of rice. Plant Physiol 141:924–931
Pérez P, de Los CG (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198:483–495
Pérez-Pérez JM, Esteve-Bruna D, Micol JL (2010) QTL analysis of leaf architecture. J Plant Res 123:15–23
Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888
Pryce J, Bolormaa S, Chamberlain A, Bowman P, Savin K, Goddard M, Hayes B (2010) A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. J Dairy Sci 93:3331–3345
Team R (2009) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing:Vienna. Austria. Computing 14:12–21
Rabbee N, Speed T (2006) A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 22:7–12
Rajaram S, Borlaug N, Van Ginkel M (2002) CIMMYT international wheat breeding. Bread wheat improvement and production FAO, Rome, pp 103–117
Ramírez-González RH, Borrill P, Lang D, Harrington SA, Brinton J, Venturini L et al (2018) The transcriptional landscape of polyploid wheat. Science 361:eaar6089
Reif JC, Melchinger AE, Frisch M (2005) Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci 45:1–7
Rogers JS (1972) Measure of genetic similarity and genetic distance. Studies in genetics VII. Univ Texas Publ 7213:145–153
Sakamoto T, Morinaka Y, Ohnishi T, Sunohara H, Fujioka S, Ueguchi-Tanaka M, Mizutani M, Sakata K, Takatsuto S, Yoshida S (2006) Erect leaves caused by brassinosteroid deficiency increase biomass production and grain yield in rice. Nat Biotechnol 24:105–109
Sharma S, Sain R, Sharma R (2003) The genetic control of flag leaf length in normal and late sown durum wheat. J Agric Sci 141:323–331
Song J, Huang S, Dalmay T, Yang Z (2012) Regulation of leaf Morphology by MicroRNA394 and its target LEAF CURLING RESPONSIVENESS. Plant Cell Physiol 53:1283–1294
Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, Chen F (2017) Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnol J 15:953–969
Tang Y, Wu X, Li C, Yang W, Huang M, Ma X, Li S (2017) Yield, growth, canopy traits and photosynthesis in high-yielding, synthetic hexaploid-derived wheats cultivars compared with non-synthetic wheats. Crop Pasture Sci 68:115–125
Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154:1839–1849
Valassi A, Chierici R (2014) Information and treatment of unknown correlations in the combination of measurements using the BLUE method. Eur Phys J C 74:2717
Wang P, Zhou G, Yu H, Yu S (2011) Fine mapping a major QTL for flag leaf size and yield-related traits in rice. Theor Appl Genet 123:1319–1330
Wang P, Zhou G, Cui K, Li Z, Yu S (2012) Clustered QTL for source leaf size and yield traits in rice (Oryza sativa L.). Mol Breeding 29:99–113
Wang B, Lin Z, Li X, Zhao Y, Wang H, Wu G et al (2020) Genome-wide selection and genetic improvement during modern maize breeding. Nat Genet 52:565–571
Wu Q, Chen Y, Fu L, Zhou S, Chen J, Zhao X, Zhang D, Ouyang S, Wang Z, Li D (2016) QTL mapping of flag leaf traits in common wheat using an integrated high-density SSR and SNP genetic linkage map. Euphytica 208:337–351
Xue S, Xu F, Li G, Zhou Y, Lin M, Gao Z, Su X, Xu X, Jiang G, Zhang S (2013) Fine mapping TaFLW1, a major QTL controlling flag leaf width in bread wheat (Triticum aestivum L.). Theor Appl Genet 126:1941–1949
Yan X, Zhao L, Ren Y, Zhang N, Dong Z, Chen F (2020) Identification of genetic loci and a candidate gene related to flag leaf traits in common wheat by genome-wide association study and linkage mapping. Mol Breed. https://doi.org/10.1007/s11032-020-01135-7
Yu JM, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208
Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin FH, Ranc N, Reif JC (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776
Zhao C, Bao Y, Wang X, Yu H, Ding A, Guan C, Cui J, Wu Y, Sun H, Li X (2018) QTL for flag leaf size and their influence on yield-related traits in wheat. Euphytica 214:209
Zhou Y, Srinivasan S, Mirnezami SV, Kusmec A, FuQ A, L, et al (2018) Semi-automated feature extraction from rgb images for sorghum panicle architecture gwas. Plant Physiol 179:24–37
Acknowledgements
The authors thank Pro. Thorsten Schnurbusch (IPK) for support, advice and revisions regarding this manuscript. We thank Yusuf Yilmaz Ata and Roop Kamel for their help in GWAS. And we also show our appreciation for the China Scholarship Council (CSC) for sponsoring Shulin Chen as guest scientist studying in Leibniz Institute of Plant Genetics and Crop Plant Research (IPK).
Funding
The design of the study and collection, analysis and interpretation of data were supported by the Scientific and technological research projects (30602021) and the National Basic Research Program of China (2014CB138105).
Author information
Authors and Affiliations
Contributions
K.Z. and Y.J. conceived the project and planned the trials. S.C. and F.L. analysed the data and were a major contributor in writing the manuscript. W.W. performed the statistical analyses and edited the manuscript. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
All the authors have no conflicts of interest, and they approved the publication.
Additional information
Communicated by Jessica Rutkoski.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Chen, S., Liu, F., Wu, W. et al. A SNP-based GWAS and functional haplotype-based GWAS of flag leaf-related traits and their influence on the yield of bread wheat (Triticum aestivum L.). Theor Appl Genet 134, 3895–3909 (2021). https://doi.org/10.1007/s00122-021-03935-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-021-03935-7