Abstract
The present study was conducted to study the genetic architecture of grain micronutrients (Zn, Fe and β-carotene contents), grain protein content and four yield traits in a spring wheat reference set comprising 246 genotypes. Phenotypic data on these traits recorded at two locations and the genotyping data for 17,937 SNP markers (obtained through outsourcing) were used for genome wide association study, which gave following results after Bonferroni correction using four methods: (1) single locus single trait analysis gave 136 marker-trait associations; (2) multi-locus mixed model gave 587 MTAs; (3) multi-trait mixed model gave 28 MTAs and (4) matrix-variate linear mixed model gave 33 MTAs. As many as 73 epistatic interactions were also detected. Keeping all the results in mind, nine most important MTAs were selected for biofortification. These markers were associated with three traits (GPC, GFeC and GYPP). These MTAs can be used in wheat improvement programs either using marker-assisted recurrent selection or pseudo-backcrossing method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ain QU, Rasheed A, Anwar A, Mahmood T, Imtiaz M, Mahmood T et al (2015) Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci 6:743
Allard RW (1999) Principles of plant breeding. Wiley, New York
Bolormaa S, Pryce JE, Reverter A et al (2014) A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet 10:e1004198. https://doi.org/10.1371/journal.pgen.1004198
Boone C, Bussey H, Andrews BJ (2007) Exploring genetic interactions and networks with yeast. Nat Rev Genet 8:437–449
Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177. https://doi.org/10.1534/genetics.105.044586
Chattha MU, Hassan MU, Khan I et al (2017) Biofortification of wheat cultivars to combat zinc deficiency. Front Plant Sci 8:281
Colasuonno P, Lozito ML, Marcotuli I et al (2017) The carotenoid biosynthetic and catabolic genes in wheat and their association with yellow pigments. BMC Genomics 18:122. https://doi.org/10.1186/s12864-016-3395-6
Crispim AC, Kelly MJ, Guimarães SEF et al (2015) Multi-trait GWAS and new candidate genes annotation for growth curve parameters in brahman cattle. PLoS One 10:e0139906. https://doi.org/10.1371/journal.pone.0139906
Diapari A, Bett K, Deokar A, Warkentin TD, Tar’an BM (2014) Genetic diversity and association mapping of iron and zinc concentrations in chickpea (Cicer arietinum L.). Genome 57:459–468. https://doi.org/10.1139/gen-2014-0108
Diapari M, Sindhu A, Warkentin TD et al (2015) Population structure and marker-trait association studies of iron, zinc and selenium concentrations in seed of field pea (Pisum sativum L.). Mol Breed 35:30. https://doi.org/10.1007/s11032-015-0252-2
Esuma W, Herselman L, Labuschagne MT et al (2016) Genome-wide association mapping of provitamin A carotenoid content in cassava. Euphytica 212:97–110. https://doi.org/10.1007/s10681-016-1772-5
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x
Furlotte NA, Eskin E (2015) Efficient multiple trait association and estimation of genetic correlation using the matrix-variate linear mixed-model. Genetics 200:59–68. https://doi.org/10.1534/genetics.114.171447
González JR, Armengol L, Solé X et al (2007) SNPassoc: an R package to perform whole genome association studies. Bioinformatics 23:644–645. https://doi.org/10.1093/bioinformatics/btm025
Gorafi YSA, Ishii T, Kim JS et al (2016) Genetic variation and association mapping of grain iron and zinc contents in synthetic hexaploid wheat germplasm. Plant Genet Resour 1:9. https://doi.org/10.1017/S1479262116000265
Graham R, Senadhira D, Beebe S et al (1999) Breeding for micronutrient density in edible portions of staple food crops: conventional approaches. Field Crop Res 60:57–80. https://doi.org/10.1016/S0378-4290(98)00133-6
Gupta PK, Harindra SB, Pawan LK et al (2007) QTL analysis for some quantitative traits in bread wheat. J Zhejiang Univ Sci B 8:807–814. https://doi.org/10.1631/jzus.2007.B0807
Gupta PK, Kulwal PL, Jaiswal V (2014) Association mapping in crop plants: opportunities and challenges. Adv Genet. https://doi.org/10.1016/b978-0-12-800271-1.00002-0
Heid IM, Winkler TW (2017) A multitrait GWAS sheds light on insulin resistance. Nat Genet 49:7–8
Hentschel V, Kranl K, Hollmann J et al (2002) Spectrophotometric determination of yellow pigment content and evaluation of carotenoids by high-performance liquid chromatography in durum wheat grain. J Agric Food Chem 50:6663–6668. https://doi.org/10.1021/jf025701p
Hidalgo A, Brandolini A, Pompei C, Piscozzi R (2006) Carotenoids and tocols of einkorn wheat (Triticum monococcum ssp. monococcum L.). J Cereal Sci 44:182–193. https://doi.org/10.1016/j.jcs.2006.06.002
Jaiswal V, Gahlaut V, Meher PK et al (2016) Genome wide single locus single trait, multi-locus and multi-trait association mapping for some important agronomic traits in common wheat (T. aestivum L.). PLoS One 11:e0159343. https://doi.org/10.1371/journal.pone.0159343
Jones ES, Sullivan H, Bhattramakki D, Smith JSC (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet 115:361–371. https://doi.org/10.1007/s00122-007-0570-9
Joshi AK, Crossa J, Arun B et al (2010) Genotype × environment interaction for zinc and iron concentration of wheat grain in eastern Gangetic plains of India. Field Crop Res 116:268–277. https://doi.org/10.1016/j.fcr.2010.01.004
Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216
Korte A, Vilhjálmsson BJ, Segura V et al (2012) A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet 44:1066–1071. https://doi.org/10.1038/ng.2376
Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant methods 9:29
Langer SM, Longin CFH, Würschum T (2014) Flowering time control in European winter wheat. Front Plant Sci 5:537. https://doi.org/10.3389/fpls.2014.00537
Leenhardt F, Lyan B, Rock E et al (2006) Genetic variability of carotenoid concentration, and lipoxygenase and peroxidase activities among cultivated wheat species and bread wheat varieties. Eur J Agron 25:170–176. https://doi.org/10.1016/j.eja.2006.04.010
Leplat F, Pedas PR, Rasmussen SK, Husted S (2016) Identification of manganese efficiency candidate genes in winter barley (Hordeum vulgare) using genome wide association mapping. BMC Genomics 17:805. https://doi.org/10.1186/s12864-016-3129-9
Li WH, Wei LIU, Li LIU, You MS, Liu GT, Li BY (2011) QTL mapping for wheat flour color with additive, epistatic, and QTL × environmental interaction effects. Agric Sci China 10:651–660. https://doi.org/10.1016/s1671-2927(11)60047-3
Li G, Xu X, Bai G, Carver BF, Hunger R, Bonman JM et al (2016) Genome-wide association mapping reveals novel qtl for seedling leaf rust resistance in a worldwide collection of winter wheat. Plant Genome 9:0
Loladze I (2014) Hidden shift of the ionome of plants exposed to elevated CO2 depletes minerals at the base of human nutrition. Elife 3:e02245. https://doi.org/10.7554/eLife.02245
Manickavelu A, Hattori T, Yamaoka S (2017) Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS One 12:e0169416
Marza F, Bai GH, Carver BF, Zhou WC (2006) Quantitative trait loci for yield and related traits in the wheat population Ning7840 x Clark. Theor Appl Genet 112:688–698. https://doi.org/10.1007/s00122-005-0172-3
Mogga M, Sibiya J, Shimelis H, Lamo J, Yao N (2018) Diversity analysis and genomewide association studies of grain shape and eating quality traits in rice (Oryza sativa L.) using DArT markers. PLoS One 13:e0198012
Mulki MA, Jighly A, Ye GY, Emebiri LC, Moody D, Ansari O, Ogbonnaya FC (2013) Association mapping for soilborne pathogen resistance in synthetic hexaploid wheat. Mol Breed 31:299–311
Myers SS, Zanobetti A, Kloog I et al (2014) Increasing CO2 threatens human nutrition. Nature 510:139–142. https://doi.org/10.1038/nature13179
Neeraja CN, Babu VR, Ram S, Hossain F et al (2017) Biofortification in cereals: progress and prospects. Curr Sci 113:6
Norton GJ, Douglas A, Lahner B et al (2014) Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites. PLoS One 9:e89685. https://doi.org/10.1371/journal.pone.0089685
Ogbonnaya FC, Rasheed A, Okechukwu EC, Jighly A et al (2017) Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor Appl Genet 130:1819–1835
Ortiz-Monasterio JI, Palacios-Rojas N, Meng E et al (2007) Enhancing the mineral and vitamin content of wheat and maize through plant breeding. J Cereal Sci 46:293–307. https://doi.org/10.1016/j.jcs.2007.06.005
Paltridge NG, Palmer LJ, Milham PJ et al (2012) Energy-dispersive X-ray fluorescence analysis of zinc and iron concentration in rice and pearl millet grain. Plant Soil 361:251–260. https://doi.org/10.1007/s11104-011-1104-4
Pausch H, Emmerling R, Schwarzenbacher H, Fries R (2016) A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle. Genet Sel Evol 48:14. https://doi.org/10.1186/s12711-016-0190-4
Peleg Z, Cakmak I, Ozturk L et al (2009) Quantitative trait loci conferring grain mineral nutrient concentrations in durum wheat × wild emmer wheat RIL population. Theor Appl Genet 119:353–369. https://doi.org/10.1007/s00122-009-1044-z
Phillips PC (2008) Epistasis–the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9:855–867. https://doi.org/10.1038/nrg2452
Pozniak CJ, Knox RE, Clarke FR, Clarke JM (2007) Identification of QTL and association of a phytoene synthase gene with endosperm colour in durum wheat. Theor Appl Genet 114:525–537. https://doi.org/10.1007/s00122-006-0453-5
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. https://doi.org/10.1111/j.1471-8286.2007.01758.x
Rabbi IY, Udoh LI, Wolfe M et al (2017) Genome-wide association mapping of correlated traits in cassava: dry matter and total carotenoid content. Plant Genome 03:18. https://doi.org/10.3835/plantgenome2016.09.0094
Reif JC, Maurer HP, Korzun V et al (2011) Mapping QTLs with main and epistatic effects underlying grain yield and heading time in soft winter wheat. Theor Appl Genet 123:283–292. https://doi.org/10.1007/s00122-011-1583-y
Roshanzamir H, Kordenaeej A, Bostani A (2013) Mapping QTLs related to Zn and Fe concentrations in bread wheat (Triticum aestivum L.) grain using microsatellite markers. IJGPB 2:10–17
Segura V, Vilhjálmsson BJ, Platt A et al (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44:825–830. https://doi.org/10.1038/ng.2314
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. https://doi.org/10.1038/srep41578
Sharma P, Sheikh I, Kumar S, Verma SK et al (2018) Precise transfers of genes for high grain iron and zinc from wheat-Aegilops substitution lines into wheat through pollen irradiation. Mol Breed 38:81
Shi R, Li H, Tong Y et al (2008) Identification of quantitative trait locus of zinc and phosphorus density in wheat (Triticum aestivum L.) grain. Plant Soil 306:95–104
Srinivasa J, Arun B, Mishra VK et al (2014) Zinc and iron concentration QTL mapped in a Triticum spelta × T. Aestivum cross. Theor Appl Genet 127:1643–1651. https://doi.org/10.1007/s00122-014-2327-6
Suwarno WB, Pixley KV, Palacios-Rojas N et al (2015) Genome-wide association analysis reveals new targets for carotenoid biofortification in maize. Theor Appl Genet 128:851–864. https://doi.org/10.1007/s00122-015-2475-3
Tadesse W, Ogbonnaya FC, Jighly A et al (2015) Genome-wide association mapping of yield and grain quality traits in winter wheat genotypes. PLoS One 10:e0141339. https://doi.org/10.1371/journal.pone.0141339
Thoen MPM, Davila Olivas NH, Kloth KJ et al (2017) Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping. New Phytol 213:1346–1362. https://doi.org/10.1111/nph.14220
Tiwari VK, Rawat N, Chhuneja P et al (2009) Mapping of quantitative trait loci for grain iron and zinc concentration in diploid a genome wheat. J Hered 100:771–776. https://doi.org/10.1093/jhered/esp030
Tiwari C, Wallwork H, Arun B et al (2016) Molecular mapping of quantitative trait loci for zinc, iron and protein content in the grains of hexaploid wheat. Euphytica 207:563–570. https://doi.org/10.1007/s10681-015-1544-7
Velu G, Singh RP, Huerta-Espino J et al (2012) Performance of biofortified spring wheat genotypes in target environments for grain zinc and iron concentrations. Field Crops Res 137:261–267. https://doi.org/10.1016/j.fcr.2012.07.018
Wang D, Eskridge KM, Crossa J (2011) Identifying QTLs and epistasis in structured plant populations using adaptive mixed LASSO. J Agric Biol Environ Stat 16:170–184. https://doi.org/10.1007/s13253-010-0046-2
Wang L, Cui F, Wang J et al (2012) Conditional QTL mapping of protein content in wheat with respect to grain yield and its components. J Genet 91:303–312. https://doi.org/10.1007/s12041-012-0190-2
Wang X, Pang Y, Zhang J, Wu Z, Chen K, Ali J, Ye G, Xu J, Li Z (2017) Genome-wide and gene-based association mapping for rice eating and cooking characteristics and protein content. Sci Rep 7:17203
Welch RM, Graham RD (2004) Breeding for micronutrients in staple food crops from a human nutrition perspective. J Exp Bot 55:353–364
White PJ, Broadley MR (2009) Biofortification of crops with seven mineral elements often lacking in human diets - Iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol 182:49–84
World Health Organization (2016) World health statistics—monitoring health for the SDGs. World Heal Organ. https://doi.org/10.1017/cbo9781107415324.004
Xu Y, An D, Li H, Xu H (2011) Review: breeding wheat for enhanced micronutrients. Can J Plant Sci 91:231–237. https://doi.org/10.4141/CJPS10117
Xu Y, An D, Liu D et al (2012) Molecular mapping of QTLs for grain zinc, iron and protein concentration of wheat across two environments. Field Crops Res 138:57–62. https://doi.org/10.1016/j.fcr.2012.09.017
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 38:203–208. https://doi.org/10.1038/ng1702
Yu LX, Lorenz A, Rutkoski J et al (2011) Association mapping and gene-gene interaction for stem rust resistance in CIMMYT spring wheat germplasm. Theor Appl Genet 123:1257–1268. https://doi.org/10.1007/s00122-011-1664-y
Zhang Y, Ni Z, Yao Y, Nie X, Sun Q (2007) Gibberellins and heterosis of plant height in wheat (Triticum aestivum L.). BMC Genet 8:40
Zhao Y, Sun HY, Wang YY et al (2013) QTL mapping for the color, carotenoids and polyphenol oxidase activity of flour in recombinant inbred lines of wheat. Aust J Crop Sci 7:328–337
Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824. https://doi.org/10.1038/ng.2310
Zhou X, Stephens M (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407–409. https://doi.org/10.1038/nmeth.2848
Zhou K, Yin JJ, Yu L (2005) Phenolic acid, tocopherol and carotenoid compositions, and antioxidant functions of hard red winter wheat bran. J Agric Food Chem 53:3916–3922. https://doi.org/10.1021/jf050117c
Acknowledgements
JK received financial support in the form of a Senior Research Fellowship (SRF) from Indian Council of Medical Research (ICMR). Head, Genetics and Plant Breeding, CCS University, Meerut provided the facilities and Prof. V.K. Mishra, Banaras Hindu University Varanasi, India provided facility for zinc and iron estimations. CIMMYT gene bank is sincerely acknowledged for providing seed material and genotypic data for the present research.
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Kumar, J., Saripalli, G., Gahlaut, V. et al. Genetics of Fe, Zn, β-carotene, GPC and yield traits in bread wheat (Triticum aestivum L.) using multi-locus and multi-traits GWAS. Euphytica 214, 219 (2018). https://doi.org/10.1007/s10681-018-2284-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10681-018-2284-2