Elucidating the genetics of grain yield and stress-resilience in bread wheat using a large-scale genome-wide association mapping study with 55,568 lines.
Alleles
Bread
Chromosome Mapping
Droughts
Edible Grain
/ genetics
Genetic Linkage
/ genetics
Genome, Plant
/ genetics
Genome-Wide Association Study
Genotype
Linkage Disequilibrium
/ genetics
Phenotype
Plant Breeding
Polymorphism, Single Nucleotide
/ genetics
Quantitative Trait Loci
/ genetics
Triticum
/ genetics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 03 2021
04 03 2021
Historique:
received:
16
11
2020
accepted:
15
02
2021
entrez:
5
3
2021
pubmed:
6
3
2021
medline:
15
12
2021
Statut:
epublish
Résumé
Wheat grain yield (GY) improvement using genomic tools is important for achieving yield breakthroughs. To dissect the genetic architecture of wheat GY potential and stress-resilience, we have designed this large-scale genome-wide association study using 100 datasets, comprising 105,000 GY observations from 55,568 wheat lines evaluated between 2003 and 2019 by the International Maize and Wheat Improvement Center and national partners. We report 801 GY-associated genotyping-by-sequencing markers significant in more than one dataset and the highest number of them were on chromosomes 2A, 6B, 6A, 5B, 1B and 7B. We then used the linkage disequilibrium (LD) between the consistently significant markers to designate 214 GY-associated LD-blocks and observed that 84.5% of the 58 GY-associated LD-blocks in severe-drought, 100% of the 48 GY-associated LD-blocks in early-heat and 85.9% of the 71 GY-associated LD-blocks in late-heat, overlapped with the GY-associated LD-blocks in the irrigated-bed planting environment, substantiating that simultaneous improvement for GY potential and stress-resilience is feasible. Furthermore, we generated the GY-associated marker profiles and analyzed the GY favorable allele frequencies for a large panel of 73,142 wheat lines, resulting in 44.5 million datapoints. Overall, the extensive resources presented in this study provide great opportunities to accelerate breeding for high-yielding and stress-resilient wheat varieties.
Identifiants
pubmed: 33664297
doi: 10.1038/s41598-021-84308-4
pii: 10.1038/s41598-021-84308-4
pmc: PMC7933281
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5254Références
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. U. S. A. 112, 6931–6936 (2015).
pubmed: 25964323
pmcid: 4460489
doi: 10.1073/pnas.1415181112
Hatfield, J. L. & Dold, C. Agroclimatology and wheat production: coping with climate change. Front. Plant Sci. 9, 1–5 (2018).
doi: 10.3389/fpls.2018.00224
Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).
pubmed: 23908229
doi: 10.1126/science.1239402
Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).
pubmed: 23840465
pmcid: 3686737
doi: 10.1371/journal.pone.0066428
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293–1297 (2012).
pubmed: 23250423
doi: 10.1038/ncomms2296
Zampieri, M., Ceglar, A., Dentener, F. & Toreti, A. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett. 12, 064008 (2017).
doi: 10.1088/1748-9326/aa723b
Semenov, M. A. & Shewry, P. R. Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci. Rep. 1, 1–5 (2011).
doi: 10.1038/srep00066
Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).
doi: 10.1038/nclimate3115
Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4, 637–643 (2014).
doi: 10.1038/nclimate2242
Juliana, P. et al. Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics. Nat. Genet. 51, 1530–1539 (2019).
pubmed: 31548720
doi: 10.1038/s41588-019-0496-6
Snape, J. W. et al. Dissecting gene x environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154, 401–408 (2007).
doi: 10.1007/s10681-006-9208-2
Griffiths, S. et al. Genetic dissection of grain size and grain number trade-offs in CIMMYT wheat germplasm. PLoS ONE 10, 1–18 (2015).
doi: 10.1371/journal.pone.0118847
Jiang, Y., Schmidt, R. H., Zhao, Y. & Reif, J. C. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nature 49, 1741–1746 (2017).
Kuchel, H. et al. Genetic dissection of grain yield in bread wheat. II. QTL-by-environment interaction. TAG Theor. Appl. Genet. 115, 1015–1027 (2007).
pubmed: 17712541
doi: 10.1007/s00122-007-0628-8
Groos, C., Robert, N., Bervas, E. & Charmet, G. Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theor. Appl. Genet. 106, 1032–1040 (2003).
pubmed: 12671751
doi: 10.1007/s00122-002-1111-1
Kuchel, H., Williams, K. J. J., Langridge, P., Eagles, H. A. A. & Jefferies, S. P. P. Genetic dissection of grain yield in bread wheat. I. QTL analysis. TAG Theor. Appl. Genet. 115, 1029–1041 (2007).
pubmed: 17713755
doi: 10.1007/s00122-007-0629-7
Assanga, S. O. et al. Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs. PLoS ONE 12, 1–21 (2017).
doi: 10.1371/journal.pone.0189669
Korte, A. & Farlow, A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9, 1–9 (2013).
doi: 10.1186/1746-4811-9-29
Remington, D. L. et al. Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc. Natl. Acad. Sci. U. S. A. 98, 11479–11484 (2001).
pubmed: 11562485
pmcid: 58755
doi: 10.1073/pnas.201394398
Yu, J. & Buckler, E. S. Genetic association mapping and genome organization of maize. Curr. Opin. Biotechnol. 17, 155–160 (2006).
pubmed: 16504497
doi: 10.1016/j.copbio.2006.02.003
Flint-Garcia, S. A., Thornsberry, J. M. & Buckler, E. S. Structure of linkage disequilibrium in plants. Annu. Rev. Plant Biol. 54, 357–374 (2003).
pubmed: 14502995
doi: 10.1146/annurev.arplant.54.031902.134907
IWGSC. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, 6403 (2018).
Chapman, J. A. et al. A whole-genome shotgun approach for assembling and anchoring the hexaploid bread wheat genome. Genome Biol. 16, 26 (2015).
pubmed: 25637298
pmcid: 4373400
doi: 10.1186/s13059-015-0582-8
Li, F. et al. Genetic architecture of grain yield in bread wheat based on genome-wide association studies. BMC Plant Biol. 19, 1–19 (2019).
Su, Q. et al. QTL detection for kernel size and weight in bread wheat (Triticum aestivum L.) using a high-density SNP and SSR-Based linkage map. Front. Plant Sci. 9, 1–13 (2018).
doi: 10.3389/fpls.2018.01484
Ma, D., Yan, J., He, Z., Wu, L. & Xia, X. Characterization of a cell wall invertase gene TaCwi-A1 on common wheat chromosome 2A and development of functional markers. Mol. Breed. 29, 43–52 (2012).
doi: 10.1007/s11032-010-9524-z
Díaz, A., Zikhali, M., Turner, A. S., Isaac, P. & Laurie, D. A. Copy number variation affecting the Photoperiod-B1 and Vernalization-A1 genes is associated with altered flowering time in wheat (Triticum aestivum). PLoS ONE 7, e33234 (2012).
pubmed: 22457747
pmcid: 3310869
doi: 10.1371/journal.pone.0033234
Groos, C., Robert, N., Bervas, E. & Charmet, G. Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. TAG Theor. Appl. Genet. 106, 1032–1040 (2003).
pubmed: 12671751
doi: 10.1007/s00122-002-1111-1
Dilbirligi, M. et al. High-density mapping and comparative analysis of agronomically important traits on wheat chromosome 3A. Genomics 88, 74–87 (2006).
pubmed: 16624516
doi: 10.1016/j.ygeno.2006.02.001
Ma, L. et al. TaGS5-3A, a grain size gene selected during wheat improvement for larger kernel and yield. Plant Biotechnol. J. 14, 1269–1280 (2016).
pubmed: 26480952
doi: 10.1111/pbi.12492
Rustgi, S. et al. Genetic dissection of yield and its component traits using high-density composite map of wheat chromosome 3A: bridging gaps between QTLs and underlying genes. PLoS ONE 8, e70526 (2013).
pubmed: 23894667
pmcid: 3722237
doi: 10.1371/journal.pone.0070526
Mengistu, N. et al. Validation of QTL for grain yield-related traits on wheat chromosome 3A using recombinant inbred chromosome lines. Crop Sci. 52, 1622–1632 (2012).
doi: 10.2135/cropsci2011.12.0677
Wang, R. X. et al. QTL mapping for grain filling rate and yield-related traits in RILs of the Chinese winter wheat population Heshangmai x Yu8679. Theor. Appl. Genet. 118, 313–325 (2009).
pubmed: 18853131
doi: 10.1007/s00122-008-0901-5
Jiang, Y. et al. A yield-associated gene TaCWI, in wheat: its function, selection and evolution in global breeding revealed by haplotype analysis. Theor. Appl. Genet. 128, 131–143 (2015).
pubmed: 25367379
doi: 10.1007/s00122-014-2417-5
Yan, L. et al. Positional cloning of the wheat vernalization gene VRN1. Proc. Natl. Acad. Sci. 100, 6263–6268 (2003).
pubmed: 12730378
doi: 10.1073/pnas.0937399100
pmcid: 156360
Wang, S.-X. et al. Genome-wide association study for grain yield and related traits in elite wheat varieties and advanced lines using SNP markers. PLoS ONE 12, e0188662 (2017).
pubmed: 29176820
pmcid: 5703539
doi: 10.1371/journal.pone.0188662
Qin, L. et al. TaGW2, a good reflection of wheat polyploidization and evolution. Front. Plant Sci. 8, 318 (2017).
pubmed: 28326096
pmcid: 5339256
doi: 10.3389/fpls.2017.00318
Sukumaran, S., Dreisigacker, S., Lopes, M., Chavez, P. & Reynolds, M. P. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor. Appl. Genet. 128, 353–363 (2014).
pubmed: 25490985
doi: 10.1007/s00122-014-2435-3
Tadesse, W. et al. Genome-wide association mapping of yield and grain quality traits in winter wheat genotypes. PLoS ONE 10, 1–18 (2015).
doi: 10.1371/journal.pone.0141339
Azadi, A. et al. QTL mapping of yield and yield components under normal and salt-stress conditions in bread wheat (Triticum aestivum L.). Plant Mol. Biol. Rep. 33, 102–120 (2015).
doi: 10.1007/s11105-014-0726-0
Zanke, C. D. et al. Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping. Front. Plant Sci. 6, 1–14 (2015).
doi: 10.3389/fpls.2015.00644
Yan, L. et al. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proc. Natl. Acad. Sci. 103, 19581–19586 (2006).
pubmed: 17158798
doi: 10.1073/pnas.0607142103
pmcid: 1748268
Acuña-Galindo, M. A., Mason, R. E., Subramanian, N. K. & Hays, D. B. Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Sci. 55, 477–492 (2015).
doi: 10.2135/cropsci2013.11.0793
Hou, J. et al. Global selection on sucrose synthase haplotypes during a century of wheat breeding. Plant Physiol. 164, 1918–1929 (2014).
pubmed: 24402050
pmcid: 3982753
doi: 10.1104/pp.113.232454
Schmidt, J. et al. Novel alleles for combined drought and heat stress tolerance in wheat. Front. Plant Sci. 10, 1–14 (2020).
doi: 10.3389/fpls.2019.01800
Chen, Y., Carver, B. F., Wang, S., Cao, S. & Yan, L. Genetic regulation of developmental phases in winter wheat. Mol. Breed. 26, 573–582 (2010).
doi: 10.1007/s11032-010-9392-6
Röder, M. S., Huang, X. Q. & Börner, A. Fine mapping of the region on wheat chromosome 7D controlling grain weight. Funct. Integr. Genomics 8, 79–86 (2008).
pubmed: 17554574
doi: 10.1007/s10142-007-0053-8
Whittal, A., Kaviani, M., Graf, R., Humphreys, G. & Navabi, A. Allelic variation of vernalization and photoperiod response genes in a diverse set of North American high latitude winter wheat genotypes. PLoS ONE 13, e0203068 (2018).
pubmed: 30161188
pmcid: 6117032
doi: 10.1371/journal.pone.0203068
Singh, R. P., Huerta-Espino, J., Sharma, R., Joshi, A. K. & Trethowan, R. High yielding spring bread wheat germplasm for global irrigated and rainfed production systems. Euphytica 157, 351–363 (2007).
doi: 10.1007/s10681-006-9346-6
Juliana, P. et al. Prospects and challenges of applied genomic selection—a new paradigm in breeding for grain yield in bread wheat. Plant Genome 11, 1–17 (2018).
doi: 10.3835/plantgenome2018.03.0017
Juliana, P. et al. Retrospective quantitative genetic analysis and genomic prediction of global wheat yields. Front. Plant Sci. 11, 1328 (2020).
doi: 10.3389/fpls.2020.580136
Juliana, P. et al. Genomic selection for grain yield in the CIMMYT wheat breeding program—status and perspectives. Front. Plant Sci. 11, 1418 (2020).
doi: 10.3389/fpls.2020.564183
Huang, M., Liu, X., Zhou, Y., Summers, R. M. & Zhang, Z. BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience 8, giy154 (2018).
pmcid: 6365300
Gilmour, A. R. ASREML for testing fixed effects and estimating multiple trait variance components. Proc. Assoc. Adv. Anim. Breed. Genet. 12, 386–390 (1997).
Poland, J. A., Brown, P. J., Sorrells, M. E. & Jannink, J. L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253 (2012).
pubmed: 22389690
pmcid: 3289635
doi: 10.1371/journal.pone.0032253
Glaubitz, J. C. et al. TASSEL-GBS : a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9, e90346 (2014).
pubmed: 24587335
pmcid: 3938676
doi: 10.1371/journal.pone.0090346
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).
pubmed: 26748515
pmcid: 4716681
doi: 10.1016/j.ajhg.2015.11.020
Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).
pubmed: 16380716
doi: 10.1038/ng1702
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
pubmed: 16862161
doi: 10.1038/ng1847
Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).
pubmed: 20208535
pmcid: 2931336
doi: 10.1038/ng.546
LiLin-Yin. CMplot: Circle Manhattan Plot. R package version 3.6.0. (2020).
Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 1–7 (2011).
Warnes, G. R. et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.0.3. (2020).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, 2016).