Advertisement

Theoretical and Applied Genetics

, Volume 131, Issue 4, pp 985–998 | Cite as

Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number

  • Sivakumar Sukumaran
  • Marta Lopes
  • Susanne Dreisigacker
  • Matthew Reynolds
Original Article

Abstract

Key message

GWAS on multi-environment data identified genomic regions associated with trade-offs for grain weight and grain number.

Abstract

Grain yield (GY) can be dissected into its components thousand grain weight (TGW) and grain number (GN), but little has been achieved in assessing the trade-off between them in spring wheat. In the present study, the Wheat Association Mapping Initiative (WAMI) panel of 287 elite spring bread wheat lines was phenotyped for GY, GN, and TGW in ten environments across different wheat growing regions in Mexico, South Asia, and North Africa. The panel genotyped with the 90 K Illumina Infinitum SNP array resulted in 26,814 SNPs for genome-wide association study (GWAS). Statistical analysis of the multi-environmental data for GY, GN, and TGW observed repeatability estimates of 0.76, 0.62, and 0.95, respectively. GWAS on BLUPs of combined environment analysis identified 38 loci associated with the traits. Among them four loci—6A (85 cM), 5A (98 cM), 3B (99 cM), and 2B (96 cM)—were associated with multiple traits. The study identified two loci that showed positive association between GY and TGW, with allelic substitution effects of 4% (GY) and 1.7% (TGW) for 6A locus and 0.2% (GY) and 7.2% (TGW) for 2B locus. The locus in chromosome 6A (79–85 cM) harbored a gene TaGW2-6A. We also identified that a combination of markers associated with GY, TGW, and GN together explained higher variation for GY (32%), than the markers associated with GY alone (27%). The marker-trait associations from the present study can be used for marker-assisted selection (MAS) and to discover the underlying genes for these traits in spring wheat.

Abbreviations

WAMI

The wheat association Mapping Initiative

BLUPs

Best linear unbiased predictions

MLM

Mixed linear models

GLM

Generalized linear models

Notes

Acknowledgements

This work was implemented by CIMMYT as part of the MasAgro in collaboration with CIMMYT, made possible by the generous support of SAGARPA, IWYP, and ARCADIA Any opinions, findings, conclusion, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of SAGARPA, IWYP, and ARCADIA.

Author contributions

SS, MR, ML conceived the study. SS, MR, SD genotyped the panel. SS did the genetic analysis and wrote the manuscript. All authors read, made constructive comments, and approved the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

122_2017_3037_MOESM1_ESM.tif (1.4 mb)
Supplementary material 1 (TIFF 1449 kb) Supplementary Fig. 1. Linkage disequilibrium (LD) plot of the chromosome 3B region showing high LD of the markers associated with the traits. Markers used were RAC875_c1997_2590 (85 cM), RAC875_c5427_447 (91 cM), BobWhite_c35398_181 (95 cM), and wsnp_CAP12_c2297_1121142 (119 cM)
122_2017_3037_MOESM2_ESM.tif (1.7 mb)
Supplementary material 2 (TIFF 1770 kb) Supplementary Fig. 2. Linkage disequilibrium (LD) plot of the chromosome 6A showing the high LD region (77–85 cM). Markers in chromosome 6A were wsnp_Ra_c61979_62215037 (77 cM), wsnp_Ku_rep_c72681_72356010 (78 cM), wsnp_Ra_rep_c100410_86374467 (79 cM), wsnp_Ku_rep_c112734_95776957 (80 cM), wsnp_Ex_c34545_42832894 (81 cM), wsnp_RFL_Contig4424_5193532 (82 cM), wsnp_Ex_c341_667884 (83 cM), wsnp_Ku_c4296_7807837 (84 cM), wsnp_Ra_c11269_18309313 (85 cM) and Excalibur_rep_c111263_307 (86 cM)
122_2017_3037_MOESM3_ESM.tif (1.7 mb)
Supplementary material 3 (TIFF 1756 kb) Supplementary Fig. 3. Linkage disequilibrium plot (LD) of the 5A region 90–98 cM showing the SNP at 98 cM is not in high LD with the SNPs from 89–98 cM. Markers in chromosome 5A were wsnp_Ra_c12183_19587379 (89 cM), wsnp_Ex_c5998_10513766 (90 cM), wsnp_Ex_rep_c66689_65010988 (91 cM), wsnp_RFL_Contig2265_1693968 (92 cM), wsnp_Ex_rep_c109532_92292121 (93 cM), wsnp_Ra_c3966_7286546 (94 cM), IAAV108 (95 cM), wsnp_BF484028B_Td_2_1 (96 cM), wsnp_Ex_c790_1554988 (97 cM), and wsnp_Ku_c42416_50159250 (98 cM)
122_2017_3037_MOESM4_ESM.tif (1.3 mb)
Supplementary material 4 (TIFF 1366 kb)
122_2017_3037_MOESM5_ESM.docx (15 kb)
Supplementary material 5 (DOCX 14 kb)
122_2017_3037_MOESM6_ESM.docx (15 kb)
Supplementary material 6 (DOCX 14 kb)
122_2017_3037_MOESM7_ESM.docx (17 kb)
Supplementary material 7 (DOCX 17 kb)
122_2017_3037_MOESM8_ESM.docx (14 kb)
Supplementary material 8 (DOCX 13 kb)
122_2017_3037_MOESM9_ESM.docx (14 kb)
Supplementary material 9 (DOCX 14 kb)
122_2017_3037_MOESM10_ESM.docx (14 kb)
Supplementary material 10 (DOCX 13 kb)
122_2017_3037_MOESM11_ESM.docx (14 kb)
Supplementary material 11 (DOCX 14 kb)
122_2017_3037_MOESM12_ESM.docx (14 kb)
Supplementary material 12 (DOCX 13 kb)
122_2017_3037_MOESM13_ESM.xlsx (10 kb)
Supplementary material 13 (XLSX 10 kb)

References

  1. Ain Q-U, Rasheed A, Anwar A et al (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci 6:743CrossRefPubMedPubMedCentralGoogle Scholar
  2. Aisawi KAB, Reynolds MP, Singh RP, Foulkes MJ (2015) The physiological basis of the genetic progress in yield potential of CIMMYT spring wheat cultivars from 1966–2009. Crop Sci 55:1749–1764CrossRefGoogle Scholar
  3. Bonneau J, Taylor J, Parent B et al (2013) Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat. Theor Appl Genet 126:747–761CrossRefPubMedGoogle Scholar
  4. Börner A, Schumann E, Fürste A, Cöster H, Leithold B, Röder MS, Weber WE (2002) Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat (Triticum aestivum L.). Theor Appl Genet 105(6–7):921–936.  https://doi.org/10.1007/s00122-002-0994-1 PubMedGoogle Scholar
  5. Bradbury PJ, Zhang Z, Kroon DE et al (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635CrossRefPubMedGoogle Scholar
  6. Braun H-J, Rajaram S, Ginkel M (1996) CIMMYT’s approach to breeding for wide adaptation. Euphytica 92:175–183CrossRefGoogle Scholar
  7. Brinton J, Simmonds J, Minter F et al (2017a) Increased pericarp cell length underlies a major quantitative trait locus for grain weight in hexaploid wheat. New Phytol 6:1–6Google Scholar
  8. Brinton J, Simmonds J, Minter F et al (2017b) Increased pericarp cell length underlies a major QTL for grain weight in hexaploid wheat. bioRxiv 6:1–6Google Scholar
  9. Crespo-Herrera LA, Crossa J, Huerta-Espino J et al (2017) Genetic yield gains in CIMMYT’S international elite spring wheat yield trials by modeling the genotype × environment interaction. Crop Sci 57:789–801CrossRefGoogle Scholar
  10. Edae EA, Byrne PF, Manmathan H, Haley SD, Moragues M, Lopes MS, Reynolds MP (2013) Association mapping and nucleotide sequence variation in five drought tolerance candidate genes in spring wheat. Plant Genome 6(2).  https://doi.org/10.3835/plantgenome2013.04.0010
  11. Edae EA, Byrne PF, Haley SD et al (2014) Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807CrossRefPubMedGoogle Scholar
  12. Ellis MH, Spielmeyer W, Gale KR et al (2002) “Perfect” markers for the Rht-B1b and Rht-D1b dwarfing genes in wheat. Theor Appl Genet 105:1038–1042CrossRefPubMedGoogle Scholar
  13. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedPubMedCentralGoogle Scholar
  14. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes 7:574–578CrossRefPubMedPubMedCentralGoogle Scholar
  15. Griffiths S, Wingen L, Pietragalla J et al (2015) Genetic dissection of grain size and grain number trade-offs in CIMMYT wheat germplasm. PLoS ONE 10:1–18Google Scholar
  16. Grogan SM, Anderson J, Stephen Baenziger P et al (2016) Phenotypic plasticity of winter wheat heading date and grain yield across the US great plains. Crop Sci 56:2223–2236CrossRefGoogle Scholar
  17. Hardy OJ, Vekemans X (2002) SPAGeDI: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620CrossRefGoogle Scholar
  18. Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967CrossRefPubMedGoogle Scholar
  19. Jaiswal V, Gahlaut V, Mathur S et al (2015) Identification of novel SNP in promoter sequence of TaGW2-6A associated with grain weight and other agronomic traits in wheat (Triticum aestivum L.). PLoS One 10:1–15Google Scholar
  20. Kato K, Miura H, Sawada S (1999) QTL mapping of genes controlling ear emergence time and plant height on chromosome 5A of wheat. TAG Theor Appl Genet 98:472–477CrossRefGoogle Scholar
  21. Kirigwi FM, Ginkel VM, Brown-Guedira G, Gill BS (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20:401–413CrossRefGoogle Scholar
  22. Kuchel H, Williams KJ, Langridge P, Eagles HA, Jefferies SP (2007) Genetic dissection of grain yield in bread wheat. I. QTL analysis. Theor Appl Genet 115(8):1029–1041.  https://doi.org/10.1007/s00122-007-0629-7 CrossRefPubMedGoogle Scholar
  23. Kumar U, Laza MR, Soulié JC et al (2015) Analysis and simulation of phenotypic plasticity for traits contributing to yield potential in twelve rice genotypes. Field Crop Res 202:94–107CrossRefGoogle Scholar
  24. Lê S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. J Stat Softw 25(1):1–18.  https://doi.org/10.18637/jss.v025.i01 CrossRefGoogle Scholar
  25. Lopes MS, Reynolds MP, Jalal-Kamali MR et al (2012) The yield correlations of selectable physiological traits in a population of advanced spring wheat lines grown in warm and drought environments. Field Crop Res 128:129–136CrossRefGoogle Scholar
  26. Lopes MS, Reynolds MP, McIntyre CL et al (2013) QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions. Theor Appl Genet 126:971–984CrossRefPubMedGoogle Scholar
  27. Lopes MS, Dreisigacker S, Peña RJ et al (2015) Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat. Theor Appl Genet 128:453–464CrossRefPubMedGoogle Scholar
  28. Maccaferri M, El-Feki W, Nazemi G et al (2016) Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot 67:1161–1178CrossRefPubMedPubMedCentralGoogle Scholar
  29. Pask AJD, Pietragalla J, Mullan DM, Reynolds MP (2012) Physiological breeding II: a field guide to wheat phenotyping. CimmytGoogle Scholar
  30. Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228CrossRefPubMedPubMedCentralGoogle Scholar
  31. Quarrie SA, Gulli M, Calestani C et al (1994) Location of a gene regulating drought-induced abscisic acid production on the long arm of chromosome 5A of wheat. Theor Appl Genet 89:794–800CrossRefPubMedGoogle Scholar
  32. Quarrie SA, Pekic Quarrie S, Radosevic R et al (2006) Dissecting a wheat QTL for yield present in a range of environments: from the QTL to candidate genes. J Exp Bot 57:2627–2637CrossRefPubMedGoogle Scholar
  33. Ramya P, Chaubal A, Kulkarni K et al (2010) QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.). J Appl Genet 51:421–429CrossRefPubMedGoogle Scholar
  34. Ray DK, Ramankutty N, Mueller ND et al (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3:1293CrossRefPubMedGoogle Scholar
  35. Reynolds M, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171CrossRefPubMedGoogle Scholar
  36. Reynolds M, Pask A et al (2017) Strategic crossing of biomass and harvest index-source and sink- achieves genetic grain in wheat. Euphytica 213(11):257CrossRefGoogle Scholar
  37. Röder MS, Huang XQ, Börner A (2008) Fine mapping of the region on wheat chromosome 7D controlling grain weight. Funct Integr Genom 8:79–86CrossRefGoogle Scholar
  38. Sayre KD, Rajaram S, Fischer RA (1997) Yield potential progress in short bread wheats in northwest Mexico. Crop Sci 37:36CrossRefGoogle Scholar
  39. Schulthess AW, Reif JC, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Ganal MW, Röder (2017) The roles of pleiotropy and close linkage as revealed by association mapping of yield and correlated traits of wheat (Triticum aestivum L.). J Ex Bot 68(15):4089–4101CrossRefGoogle Scholar
  40. Sehgal D, Autrique E, Singh R et al (2017) Identification of genomic regions for grain yield and yield stability and their epistatic interactions. Sci Rep 7:41578CrossRefPubMedPubMedCentralGoogle Scholar
  41. Sharma RC, Crossa J, Velu G et al (2012) Genetic gains for grain yield in CIMMYT spring bread wheat across international environments. Crop Sci 52:1522CrossRefGoogle Scholar
  42. Simmonds J, Scott P, Leverington-Waite M et al (2014) Identification and independent validation of a stable yield and thousand grain weight QTL on chromosome 6A of hexaploid wheat (Triticum aestivum L.). BMC Plant Biol 14:191CrossRefPubMedPubMedCentralGoogle Scholar
  43. Simmonds J, Scott P, Brinton J et al (2016) A splice acceptor site mutation in TaGW2-A1 increases thousand grain weight in tetraploid and hexaploid wheat through wider and longer grains. Theor Appl Genet 129:1099–1112CrossRefPubMedPubMedCentralGoogle Scholar
  44. Sonah H, O’Donoughue L, Cober E et al (2015) Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnol J 13:211–221CrossRefPubMedGoogle Scholar
  45. Su Z, Hao C, Wang L et al (2011) Identification and development of a functional marker of TaGW2 associated with grain weight in bread wheat (Triticum aestivum L.). Theor Appl Genet 122:211–223CrossRefPubMedGoogle Scholar
  46. Sukumaran S, Xiang W, Bean SR et al (2012) Association mapping for grain quality in a diverse sorghum collection. Plant Genome J 5:126.  https://doi.org/10.3835/plantgenome2012.07.0016 CrossRefGoogle Scholar
  47. Sukumaran S, Dreisigacker S, Lopes M et al (2015a) Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet 128:353–363CrossRefPubMedGoogle Scholar
  48. Sukumaran S, Reynolds MP, Lopes MS et al (2015b) Genome-Wide association study for adaptation to agronomic plant density: a component of high yield potential in spring wheat. Crop Sci 55:2609–2619CrossRefGoogle Scholar
  49. Sukumaran S, Lopes MS, Dreisigacker S et al (2016) Identification of earliness per se flowering time locus in spring wheat through a genome-wide association study. Crop Sci 56:2962–2972CrossRefGoogle Scholar
  50. Sukumaran S, Crossa J, Jarquin D, Lopes M, Reynolds MP (2017) Genomic prediction with pedigree and genotype × environment interaction in spring wheat grown in South and West Asia, North Africa, and Mexico. G3 (Bethesda) 7(2):481–495.  https://doi.org/10.1534/G3.116.036251
  51. Sun G, Zhu C, Kramer MH et al (2010) Variation explained in mixed-model association mapping. Heredity (Edinb) 105:333–340CrossRefGoogle Scholar
  52. Valluru R, Reynolds MP, Salse J (2014) Genetic and molecular bases of yield-associated traits: a translational biology approach between rice and wheat. Theor Appl Genet 127:1463–1489CrossRefPubMedGoogle Scholar
  53. Valluru R, Reynolds MP, Davies WJ, Sukumaran S (2017) Phenotypic and genome-wide association analysis of spike ethylene in diverse wheat genotypes under heat stress. New Phytol 214(1):271–283CrossRefPubMedGoogle Scholar
  54. Vargas M, Crossa J, Sayre KD et al (1998) Interpreting genotype × environment interaction in wheat by partial least squares regression. Crop Sci 38:679–689CrossRefGoogle Scholar
  55. Vargas M, Combs E, Alvarado G, Atlin G, Mathews K, Crossa J (2013) Meta: a suite of sas programs to analyze multienvironment breeding trials. Agron J 105(1):11–19CrossRefGoogle Scholar
  56. Wei T, Simko V (2017) R package “corrplot”: Visualization of a correlation matrix (Version 0.84). Available from https://github.com/taiyun/corrplot. Accessed 12 Dec 2017
  57. Yan L, Loukoianov A, Tranquilli G et al (2003) Positional cloning of the wheat vernalization gene VRN1. Proc Natl Acad Sci USA 100:6263–6268CrossRefPubMedPubMedCentralGoogle Scholar
  58. Yang Z, Bai Z, Li X et al (2012) SNP identification and allelic-specific PCR markers development for TaGW2, a gene linked to wheat kernel weight. Theor Appl Genet 125:1057–1068CrossRefPubMedGoogle Scholar
  59. Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208CrossRefPubMedGoogle Scholar
  60. Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14(6):415–421CrossRefGoogle Scholar
  61. Zanke CD, Ling J, Plieske J et al (2014) Whole genome association mapping of plant height in winter wheat (Triticum aestivum L). PLoS One 9(11):e113287CrossRefPubMedPubMedCentralGoogle Scholar
  62. Zhang Z, Ersoz E, Lai C-QQ et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360CrossRefPubMedPubMedCentralGoogle Scholar
  63. Zhang X, Chen J, Shi C et al (2013) Function of TaGW2-6A and its effect on grain weight in wheat (Triticum aestivum L.). Euphytica 192:347–357CrossRefGoogle Scholar
  64. Zhu C, Yu J (2009) Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types. Genetics 182:875–888CrossRefPubMedPubMedCentralGoogle Scholar
  65. Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome J 1:5CrossRefGoogle Scholar
  66. Zikhali M, Michelle L-W, Fish L et al (2014) Validation of a 1DL earliness per se (eps) flowering QTL in bread wheat (Triticum aestivum). Mol Breed 34:1023–1033CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Global Wheat ProgramInternational Maize and Wheat Improvement Center (CIMMYT)Mexico CityMexico
  2. 2.CIMMYTEmek, AnkaraTurkey

Personalised recommendations