Singh, S. B., Singh, K., Butola, S. S., Rawat, S. & Arunachalam, K. Determination of macronutrients, micronutrients and heavy metals present in Spilanthes acmella Hutch and Dalz: possible health effects. Nat. Prod. Sci. 26(1), 50–58 (2020).
Google Scholar
Singh, U. M., Sareen, P., Sengar, R. S. & Kumar, A. Plant ionomics: a newer approach to study mineral transport and its regulation. Acta Physiol. Plant. 35(9), 2641–2653 (2013).
Google Scholar
Khan, M. et al. Trace elements in abiotic stress tolerance. In Plant Nutrients and Abiotic Stress Tolerance (ed. Hasanuzzaman, M., Fujita, M., Oku, H., Nahar, K. & Hawrylak-Nowak, B.) 137–151 (Springer, Singapore, 2018).
Narwal, R. P., Malik, R. S., Malhotra, S. K. & Singh, B. R. Micronutrients and human health. In Encyclopedia of Soil Science (ed. Lal, R.) 1443–1448 (CRC Press, 2017).
Pecora, F., Persico, F., Argentiero, A., Neglia, C. & Esposito, S. The role of micronutrients in support of the immune response against viral infections. Nutrients 12(10), 3198 (2020).
Google Scholar
Shariatipour, N. & Heidari, B. Genetic-based biofortification of staple food crops to meet zinc and iron deficiency-related challenges. In Plant Micronutrients: Deficiency and Toxicity Management (ed. Aftab, T. & Hakeem, K.R.) 173–223 (Springer, Cham, 2020).
Salgueiro, M. J. et al. Zinc status and immune system relationship. Biol. Trace Elem. Res. 76, 193–205 (2000).
Google Scholar
Maxfield, L., & Crane, J. S. Zinc deficiency. In StatPearls. Treasure Island (FL: StatPearls Publishing, 2020).
Hodge, J. Hidden hunger: approaches to tackling micronutrient deficiencies. In Nourishing Millions: Stories of Change in Nutrition (ed. Gillespie, S., Hodge, J., Yosef, S. & Pandya-Lorch, R.) 35–43 (Washington: International Food Policy Research Institute (IFPRI), 2016).
Salt, D. E., Baxter, I. & Lahner, B. Ionomics and the study of the plant ionome. Annu. Rev. Plant. Biol. 59, 709–733 (2008).
Google Scholar
Baxter, I. Ionomics: the functional genomics of elements. Brief. Funct. Genomics 9(2), 149–156 (2010).
Google Scholar
Barh, D. OMICS Applications in Crop Science (CRC Press, 2013).
Google Scholar
Borém, A. & Fritsche-Neto, R. Omics in plant breeding. (Wiley Blackwell, 2014).
Vreugdenhil, D., Aarts, M. G., Koornneef, M., Nelissen, H. & Ernst, W. H. Natural variation and QTL analysis for cationic mineral content in seeds of Arabidopsis thaliana. Plant Cell Environ. 27(7), 828–839 (2004).
Google Scholar
Norton, G. J. et al. Genetic mapping of the rice ionome in leaves and grain: identification of QTLs for 17 elements including arsenic, cadmium, iron and selenium. Plant Soil 329(1–2), 139–153 (2010).
Google Scholar
Ghandilyan, A., Kutman, U. B., Kutman, B. Y., Cakmak, I. & Aarts, M. G. Genetic analysis of the effect of zinc deficiency on Arabidopsis growth and mineral concentrations. Plant Soil 361(1–2), 227–239 (2012).
Google Scholar
Gu, R. et al. Comprehensive phenotypic analysis and quantitative trait locus identification for grain mineral concentration, content, and yield in maize (Zea mays L.). Theor Appl Genet 128(9), 1777–1789 (2015).
Google Scholar
Liu, J., Wu, B., Singh, R. P. & Velu, G. QTL mapping for micronutrients concentration and yield component traits in a hexaploid wheat mapping population. J. Cereal Sci. 88, 57–64 (2019).
Google Scholar
Wang, C. et al. Genetic mapping of ionomic quantitative trait loci in rice grain and straw reveals OsMOT1; 1 as the putative causal gene for a molybdenum QTL qMo8. Mol. Genet. Genom. 295(2), 391–407 (2020).
Google Scholar
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(2), 477–492 (2015).
Google Scholar
Goffinet, B. & Gerber, S. Quantitative trait loci: a meta-analysis. Genetics 155(1), 463–473 (2000).
Google Scholar
Veyrieras, J. B., Goffinet, B. & Charcosset, A. MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinform. 8(1), 49 (2007).
Google Scholar
Hanocq, E., Laperche, A., Jaminon, O., Lainé, A. L. & Le Gouis, J. Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis. Theor. Appl. Genet. 114(3), 569–584 (2007).
Google Scholar
Arcade, A. et al. BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics 20(14), 2324–2326 (2004).
Google Scholar
Sosnowski, O., Charcosset, A. & Joets, J. BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics 28(15), 2082–2083 (2012).
Google Scholar
Li, W. T. et al. Meta-analysis of QTL associated with tolerance to abiotic stresses in barley. Euphytica 189(1), 31–49 (2013).
Google Scholar
Wang, Y., Wang, Y., Wang, X. & Deng, D. Integrated meta-QTL and genome-wide association study analyses reveal candidate genes for maize yield. J. Plant Growth Regul. 39, 229–238 (2019).
Google Scholar
Safdar, L. B. et al. Genome-wide association study and QTL meta-analysis identified novel genomic loci controlling potassium use efficiency and agronomic traits in bread wheat. Front Plant Sci 11, 70 (2020).
Google Scholar
Yang, Y. et al. Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat. Theor. Appl. Genet. https://doi.org/10.1007/s00122-021-03881-4 (2021).
Google Scholar
Jin, T. et al. The genetic architecture of zinc and iron content in maize grains as revealed by QTL mapping and meta-analysis. Breed Sci. 63(3), 317–324 (2013).
Google Scholar
Chardon, F. et al. QTL meta-analysis in Arabidopsis reveals an interaction between leaf senescence and resource allocation to seeds. J. Exp. Bot. 65(14), 3949–3962 (2014).
Google Scholar
Martinez, A. K. et al. Yield QTLome distribution correlates with gene density in maize. Plant Sci. 242, 300–309 (2016).
Google Scholar
Zhang, Y. et al. QTL meta-analysis of root traits in Brassica napus under contrasting phosphorus supply in two growth systems. Sci. Rep. 6, 33113 (2016).
Google Scholar
Abdelraheem, A., Liu, F., Song, M. & Zhang, J. F. A meta-analysis of quantitative trait loci for abiotic and biotic stress resistance in tetraploid cotton. Mol. Genet. Genom. 292(6), 1221–1235 (2017).
Google Scholar
Zhang, X., Shabala, S., Koutoulis, A., Shabala, L. & Zhou, M. Meta-analysis of major QTL for abiotic stress tolerance in barley and implications for barley breeding. Planta 245(2), 283–295 (2017).
Google Scholar
Avni, R. et al. Genome based meta-QTL analysis of grain weight in tetraploid wheat identifies rare alleles of GRF4 associated with larger grains. Genes 9(12), 636 (2018).
Google Scholar
Izquierdo, P. et al. Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.). Theor. Appl. Genet. 131(8), 1645–1658 (2018).
Google Scholar
Islam, M., Ontoy, J. & Subudhi, P. K. Meta-analysis of quantitative trait loci associated with seedling-stage salt tolerance in rice (Oryza sativa L.). Plants 8(2), 33 (2019).
Google Scholar
Raza, Q., Riaz, A., Sabar, M., Atif, R. M. & Bashir, K. Meta-analysis of grain iron and zinc associated QTLs identified hotspot chromosomal regions and positional candidate genes for breeding biofortified rice. Plant Sci. 288, 110214 (2019).
Google Scholar
Chen, X., Yuan, L. & Ludewig, U. Natural genetic variation of seed micronutrients of Arabidopsis thaliana grown in Zinc-deficient and Zinc-amended soil. Front. Plant. Sci. 7, 1070 (2016).
Google Scholar
Buescher, E. et al. Natural genetic variation in selected populations of Arabidopsis thaliana is associated with ionomic differences. PLoS ONE 5(6), e11081 (2010).
Google Scholar
Ghandilyan, A. et al. Genetic analysis identifies quantitative trait loci controlling rosette mineral concentrations in Arabidopsis thaliana under drought. New Phytol. 184(1), 180–192 (2009).
Google Scholar
Ghandilyan, A. et al. A strong effect of growth medium and organ type on the identification of QTLs for phytate and mineral concentrations in three Arabidopsis thaliana RIL populations. J. Exp. Bot. 60(5), 1409–1425 (2009).
Google Scholar
Waters, B. M. & Grusak, M. A. Quantitative trait locus mapping for seed mineral concentrations in two Arabidopsis thaliana recombinant inbred populations. New Phytol. 179(4), 1033–1047 (2008).
Google Scholar
Hubert, S. & Hedgecock, D. Linkage maps of microsatellite DNA markers for the Pacific oyster Crassostrea gigas. Genetics 168(1), 351–362 (2004).
Google Scholar
Fishman, L., Kelly, A. J., Morgan, E. & Willis, J. H. A genetic map in the Mimulus guttatus species complex reveals transmission ratio distortion due to heterospecific interactions. Genetics 159(4), 1701–1716 (2001).
Google Scholar
Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, e251 (2020).
Google Scholar
Löffler, M., Schön, C. C. & Miedaner, T. Revealing the genetic architecture of FHB resistance in hexaploid wheat (Triticum aestivum L.) by QTL meta-analysis. Mol. Breed. 23(3), 473–488 (2009).
Google Scholar
Xu, Y., Li, P., Yang, Z. & Xu, C. Genetic mapping of quantitative trait loci in crops. Crop J. 5(2), 175–184 (2017).
Google Scholar
Gupta, P. K., Rustgi, S. & Kulwal, P. L. Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol. Biol. 57, 461–485 (2005).
Google Scholar
Korte, A. & Farlow, A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9, 29 (2013).
Google Scholar
Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11(7), 459–463 (2010).
Google Scholar
Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90(1), 7–24 (2012).
Google Scholar
Han, B. & Eskin, E. Interpreting meta-analyses of genome-wide association studies. PLoS Genet. 8(3), e1002555 (2012).
Google Scholar
Asins, M. J., Bernet, G. P., Villalta, I. & Carbonell, E. A. QTL analysis in plant breeding. In Molecular Techniques in Crop Improvement (ed. Mohan Jain, S. & Brar, D.S.) 3–21 (Springer, Dordrecht, 2010).
Serin, E. A. et al. Construction of a high-density genetic map from RNA-Seq data for an Arabidopsis bay-0× Shahdara RIL population. Front. Genet. 8, 201 (2017).
Google Scholar
Flint, J. & Mackay, T. F. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res. 19(5), 723–733 (2009).
Google Scholar
Salvi, S. & Tuberosa, R. The crop QTLome comes of age. Curr. Opin. Biotechnol. 32, 179–185 (2015).
Google Scholar
Gao, F., Robe, K., Gaymard, F., Izquierdo, E. & Dubos, C. The transcriptional control of iron homeostasis in plants: a tale of bHLH transcription factors?. Front. Plant. Sci. 10, 6 (2019).
Google Scholar
Thomine, S., Lelièvre, F., Debarbieux, E., Schroeder, J. I. & Barbier-Brygoo, H. AtNRAMP3, a multispecific vacuolar metal transporter involved in plant responses to iron deficiency. Plant J. 34(5), 685–695 (2003).
Google Scholar
Lanquar, V., Lelièvre, F., Barbier-Brygoo, H. & Thomine, S. Regulation and function of AtNRAMP4 metal transporter protein. Soil Sci. Plant Nutr. 50, 1141–1150 (2004).
Google Scholar
Lanquar, V. et al. Mobilization of vacuolar iron by AtNRAMP3 and AtNRAMP4 is essential for seed germination on low iron. EMBO J. 24(23), 4041–4051 (2005).
Google Scholar
Colangelo, E. P. & Guerinot, M. L. The essential basic helix-loop-helix protein FIT1 is required for the iron deficiency response. Plant Cell 16(12), 3400–3412 (2004).
Google Scholar
Jakoby, M., Wang, H. Y., Reidt, W., Weisshaar, B. & Bauer, P. FRU (BHLH029) is required for induction of iron mobilization genes in Arabidopsis thaliana. FEBS Lett. 577(3), 528–534 (2004).
Google Scholar
Yuan, Y. X., Zhang, J., Wang, D. W. & Ling, H. Q. AtbHLH29 of Arabidopsis thaliana is a functional ortholog of tomato FER involved in controlling iron acquisition in strategy I plants. Cell Res. 15(8), 613–621 (2005).
Google Scholar
Bauer, P., Ling, H. Q. & Guerinot, M. L. FIT, the FER-like iron deficiency induced transcription factor in Arabidopsis. Plant. Physiol. Biochem. 45(5), 260–261 (2007).
Google Scholar
Schwarz, B. & Bauer, P. FIT, a regulatory hub for iron deficiency and stress signaling in roots, and FIT-dependent and-independent gene signatures. J. Exp. Bot. 71(5), 1694–1705 (2020).
Google Scholar
Guerinot, M. L. The ZIP family of metal transporters. Biochim. Biophys. Acta. Biomembr. 1465(1–2), 190–198 (2000).
Google Scholar
Grotz, N. et al. Identification of a family of zinc transporter genes from Arabidopsis that respond to zinc deficiency. Proc. Natl. Acad. Sci. USA. 95(12), 7220–7224 (1998).
Google Scholar
Colangelo, E. P. & Guerinot, M. L. Put the metal to the petal: metal uptake and transport throughout plants. Curr. Opin. Plant Biol. 9(3), 322–330 (2006).
Google Scholar
van de Mortel, J. E. et al. Large expression differences in genes for iron and zinc homeostasis, stress response, and lignin biosynthesis distinguish roots of Arabidopsis thaliana and the related metal hyperaccumulator Thlaspi caerulescens. Plant Physiol. 142, 1127–1147 (2006).
Google Scholar
Talke, I. N., Hanikenne, M. & Krämer, U. Zinc-dependent global transcriptional control, transcriptional deregulation, and higher gene copy number for genes in metal homeostasis of the hyperaccumulator Arabidopsis halleri. Plant Physiol. 142(1), 148–1467 (2006).
Google Scholar
Lin, Y. F. et al. Arabidopsis IRT3 is a zinc-regulated and plasma membrane localized zinc/iron transporter. New Phytol. 182(2), 392–404 (2009).
Google Scholar
Delhaize, E. et al. A role for the AtMTP11 gene of Arabidopsis in manganese transport and tolerance. Plant J. 51(2), 198–210 (2007).
Google Scholar
Han, Y. et al. WRKY12 represses GSH1 expression to negatively regulate cadmium tolerance in Arabidopsis. Plant Mol. Biol. 99(1–2), 149–159 (2019).
Google Scholar
Sheng, Y. et al. The WRKY transcription factor, WRKY13, activates PDR8 expression to positively regulate cadmium tolerance in Arabidopsis. Plant Cell Environ. 42(3), 891–903 (2019).
Google Scholar
Shaul, O. et al. Cloning and characterization of a novel Mg2+/H+ exchanger. EMBO J. 18(14), 3973–3980 (1999).
Google Scholar
Berezin, I. et al. Overexpression of AtMHX in tobacco causes increased sensitivity to Mg2+, Zn2+, and Cd2+ ions, induction of V-ATPase expression, and a reduction in plant size. Plant Cell. Rep. 27, 939–949 (2008).
Google Scholar
Gaash, R. et al. Phylogeny and a structural model of plant MHX transporters. BMC Plant Biol. 13(1), 1–20 (2013).
Google Scholar
Grusak, M. A. & DellaPenna, D. Improving the nutrient composition of plants to enhance human nutrition and health. Ann. Rev. Plant. Physiol. Plant. Mol. Biol. 50, 133–161 (1999).
Google Scholar
Garcia-Oliveira, A. L., Tan, L., Fu, Y. & Sun, C. Genetic identification of quantitative trait loci for contents of mineral nutrients in rice grain. J. Integr. Plant. Biol. 51(1), 84–92 (2009).
Google Scholar
Baxter, I. et al. Biodiversity of mineral nutrient and trace element accumulation in Arabidopsis thaliana. PLoS ONE 7(4), e35121 (2012).
Google Scholar
Manickavelu, A. et al. Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS ONE 12(1), e0169416 (2017).
Google Scholar
Hill, W. G. & Zhang, X. S. On the pleiotropic structure of the genotype–phenotype map and the evolvability of complex organisms. Genetics 190(3), 1131–1137 (2012).
Google Scholar
Descalsota-Empleo, G. I. et al. Genetic mapping of QTL for agronomic traits and grain mineral elements in rice. Crop J. 7(4), 560–572 (2019).
Google Scholar
Getahun, B. B., Visser, R. G. & van der Linden, C. G. Identification of QTLs associated with nitrogen use efficiency and related traits in a diploid potato population. Am. J. Potato. Res. 97(2), 185–201 (2020).
Google Scholar
Newton-Cheh, C. & Hirschhorn, J. N. Genetic association studies of complex traits: design and analysis issues. Mutat. Res.-Fund. Mol. M. 573(1–2), 54–69 (2005).
Google Scholar
König, I. R. Validation in genetic association studies. Brief. Bioinform. 12(3), 253–258 (2011).
Google Scholar
Raboy, V. Approaches and challenges to engineering seed phytate and total phosphorus. Plant Sci. 177(4), 281–296 (2009).
Google Scholar

