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 Map** 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.
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16 February 2018
Unfortunately, the Fig. 1 of this original article was incorrectly published. The corrected Fig. 1 is given below.
Abbreviations
- WAMI:
-
The wheat association Map** Initiative
- BLUPs:
-
Best linear unbiased predictions
- MLM:
-
Mixed linear models
- GLM:
-
Generalized linear models
References
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 6:743
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 55:1749–1764
Bonneau J, Taylor J, Parent B et al (2013) Multi-environment analysis and improved map** of a yield-related QTL on chromosome 3B of wheat. Theor Appl Genet 126:747–761
Börner A, Schumann E, Fürste A, Cöster H, Leithold B, Röder MS, Weber WE (2002) Map** 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
Bradbury PJ, Zhang Z, Kroon DE et al (2007) TASSEL: software for association map** of complex traits in diverse samples. Bioinformatics 23:2633–2635
Braun H-J, Rajaram S, Ginkel M (1996) CIMMYT’s approach to breeding for wide adaptation. Euphytica 92:175–183
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 6:1–6
Brinton J, Simmonds J, Minter F et al (2017b) Increased pericarp cell length underlies a major QTL for grain weight in hexaploid wheat. bioRxiv 6:1–6
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 57:789–801
Edae EA, Byrne PF, Manmathan H, Haley SD, Moragues M, Lopes MS, Reynolds MP (2013) Association map** 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
Edae EA, Byrne PF, Haley SD et al (2014) Genome-wide association map** of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet 127:791–807
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 105:1038–1042
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587
Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes 7:574–578
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 10:1–18
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 56:2223–2236
Hardy OJ, Vekemans X (2002) SPAGeDI: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620
Huang X, Wei X, Sang T et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967
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 10:1–15
Kato K, Miura H, Sawada S (1999) QTL map** of genes controlling ear emergence time and plant height on chromosome 5A of wheat. TAG Theor Appl Genet 98:472–477
Kirigwi FM, Ginkel VM, Brown-Guedira G, Gill BS (2007) Markers associated with a QTL for grain yield in wheat under drought. Mol Breed 20:401–413
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
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 202:94–107
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
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 128:129–136
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 126:971–984
Lopes MS, Dreisigacker S, Peña RJ et al (2015) Genetic characterization of the wheat association map** initiative (WAMI) panel for dissection of complex traits in spring wheat. Theor Appl Genet 128:453–464
Maccaferri M, El-Feki W, Nazemi G et al (2016) Prioritizing quantitative trait loci for root system architecture in tetraploid wheat. J Exp Bot 67:1161–1178
Pask AJD, Pietragalla J, Mullan DM, Reynolds MP (2012) Physiological breeding II: a field guide to wheat phenoty**. Cimmyt
Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228
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 89:794–800
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 57:2627–2637
Ramya P, Chaubal A, Kulkarni K et al (2010) QTL map** of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.). J Appl Genet 51:421–429
Ray DK, Ramankutty N, Mueller ND et al (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3:1293
Reynolds M, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171
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):257
Röder MS, Huang XQ, Börner A (2008) Fine map** of the region on wheat chromosome 7D controlling grain weight. Funct Integr Genom 8:79–86
Sayre KD, Rajaram S, Fischer RA (1997) Yield potential progress in short bread wheats in northwest Mexico. Crop Sci 37:36
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 map** of yield and correlated traits of wheat (Triticum aestivum L.). J Ex Bot 68(15):4089–4101
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 7:41578
Sharma RC, Crossa J, Velu G et al (2012) Genetic gains for grain yield in CIMMYT spring bread wheat across international environments. Crop Sci 52:1522
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 14:191
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 129:1099–1112
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 map** in soya bean. Plant Biotechnol J 13:211–221
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 122:211–223
Sukumaran S, ** for grain quality in a diverse sorghum collection. Plant Genome J 5:126. https://doi.org/10.3835/plantgenome2012.07.0016
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 128:353–363
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 55:2609–2619
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 56:2962–2972
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
Sun G, Zhu C, Kramer MH et al (2010) Variation explained in mixed-model association map**. Heredity (Edinb) 105:333–340
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 127:1463–1489
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):271–283
Vargas M, Crossa J, Sayre KD et al (1998) Interpreting genotype × environment interaction in wheat by partial least squares regression. Crop Sci 38:679–689
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):11–19
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
Yan L, Loukoianov A, Tranquilli G et al (2003) Positional cloning of the wheat vernalization gene VRN1. Proc Natl Acad Sci USA 100:6263–6268
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 125:1057–1068
Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association map** that accounts for multiple levels of relatedness. Nat Genet 38:203–208
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14(6):415–421
Zanke CD, Ling J, Plieske J et al (2014) Whole genome association map** of plant height in winter wheat (Triticum aestivum L). PLoS One 9(11):e113287
Zhang Z, Ersoz E, Lai C-QQ et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360
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 192:347–357
Zhu C, Yu J (2009) Nonmetric multidimensional scaling corrects for population structure in association map** with different sample types. Genetics 182:875–888
Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association map** in plants. Plant Genome J 1:5
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 34:1023–1033
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.
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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.
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Communicated by Mark E. Sorrells.
A correction to this article is available online at https://doi.org/10.1007/s00122-018-3066-x.
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122_2017_3037_MOESM1_ESM.tif
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
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
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)
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Sukumaran, S., Lopes, M., Dreisigacker, S. et al. Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number. Theor Appl Genet 131, 985–998 (2018). https://doi.org/10.1007/s00122-017-3037-7
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DOI: https://doi.org/10.1007/s00122-017-3037-7