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Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize

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Maize Improvement

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Abstract

Maize is a highly important crop, not only because it is a staple crop for humans but also because it is a major source of feed for animals and has immense industrial potential. Existence of vast genetic diversity in maize germplasm makes it an ideal model plant for plant genetic studies. This diversity could play a vital role in maize breeding programmes aimed to enhance its agronomic performance under changing climate. Genome-wide association study takes advantage of such genetic diversity to reveal the genetics underlying a complex phenotypic trait. With the emergence of next-generation sequencing (NGS) and high-throughput phenotyping techniques, the significance of GWAS has been increased. In the last decade, a huge number of GWA studies have been performed in different crops for different phenotypic traits. Extensive natural variations, rapid linkage disequilibrium (LD) decay, wide climatic adaptability and availability of reference genome make maize an ideal crop for GWAS. GWAS in maize identified thousands of genomic regions associated with various phenotypic traits. In general, agronomic traits are polygenic and get affected by different types of stresses, which result in reduced yield and quality. Efforts have been made to improve agronomic traits in maize using traditional breeding and marker-assisted selection breeding. However, due to the low resolution of trait mapping, limited success has been achieved. In this chapter, we discuss how GWAS could take advantage of natural diversity and can play a chief role in improving the agronomic traits in maize. We also shed a light on the importance of functional validation of genes that are found to be associated with a specific trait using GWAS.

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References

  • Aci MM, Lupini A, Mauceri A, Morsli A, Khelifi L, Sunseri F (2018) Genetic variation and structure of maize populations from Saoura and Gourara oasis in Algerian Sahara. BMC Genet 19(1):1–10

    Article  Google Scholar 

  • Aflitos S, Schijlen E, De Jong H et al (2014) Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. https://doi.org/10.1111/tpj.12616

  • Almerekova S, Sariev B, Abugalieva A, Chudinov V, Sereda G, Tokhetova L et al (2019) Association mapping for agronomic traits in six-rowed spring barley from the USA harvested in Kazakhstan. PLoS One 14(8):e0221064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Alonso-Blanco C, Aarts MG, Bentsink L, Keurentjes JJ, Reymond M, Vreugdenhil D, Koornneef M (2009) What has natural variation taught us about plant development, physiology, and adaptation? Plant Cell 21(7):1877–1896

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • An Y, Chen L, Li YX, Li C, Shi Y, Zhang D et al (2020) Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize. BMC Plant Biol 20(1):1–11

    Article  CAS  Google Scholar 

  • Baird NA, Etter PD, Atwood TS et al (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. https://doi.org/10.1371/journal.pone.0003376

  • Baral K, Coulman B, Biligetu B, Fu YB (2018) Genotyping-by-sequencing enhances genetic diversity analysis of crested wheatgrass [Agropyron cristatum (L.) gaertn.]. Int J Mol Sci. https://doi.org/10.3390/ijms19092587

  • Beló A, Zheng P, Luck S, Shen B, Meyer DJ, Li B et al (2008) Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol Gen Genomics 279(1):1–10

    Article  Google Scholar 

  • Beyene Y, Botha AM, Myburg AA (2006) Genetic diversity in traditional Ethiopian highland maize accessions assessed by AFLP markers and morphological traits. Biodivers Conserv 15(8):2655–2671

    Article  Google Scholar 

  • Boutet G, Alves Carvalho S, Falque M et al (2016) SNP discovery and genetic mapping using genotyping by sequencing of whole genome genomic DNA from a pea RIL population. BMC Genomics. https://doi.org/10.1186/s12864-016-2447-2

  • Brachi B, Morris GP, Borevitz JO (2011) Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol 12(10):1–8

    Article  Google Scholar 

  • Burghardt LT, Young ND, Tiffin P (2017) A guide to genome-wide association mapping in plants. Curr Protoc Plant Biol 2(1):22–38

    Article  PubMed  Google Scholar 

  • Cai C, Zhu G, Zhang T, Guo W (2017) High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. BMC Genomics. https://doi.org/10.1186/s12864-017-4062-2

  • Causse M, Desplat N, Pascual L et al (2013) Whole genome resequencing in tomato reveals variation associated with introgression and breeding events. BMC Genomics. https://doi.org/10.1186/1471-2164-14-791

  • Celik I, Gurbuz N, Uncu AT et al (2017) Genome-wide SNP discovery and QTL mapping for fruit quality traits in inbred backcross lines (IBLs) of solanum pimpinellifolium using genotyping by sequencing. BMC Genomics. https://doi.org/10.1186/s12864-016-3406-7

  • Chen J, Zhang L, Liu S, Li Z, Huang R, Li Y et al (2016) The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS One 11(4):e0153428

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen Q, Yang CJ, York AM, Xue W, Daskalska LL, DeValk CA et al (2019) TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize. Genetics 213(3):1065–1078

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen X, Wang L, Niu Z, Zhang M, Li J (2020) The effects of projected climate change and extreme climate on maize and rice in the Yangtze River Basin. China Agric Forest Meteorol 282:107867

    Article  Google Scholar 

  • Cheng H, Liu J, Wen J et al (2019) Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol. https://doi.org/10.1186/s13059-019-1744-x

  • Choi JK, Sa KJ, Park DH, Lim SE, Ryu SH, Park JY et al (2019) Construction of genetic linkage map and identification of QTLs related to agronomic traits in DH population of maize (Zea mays L.) using SSR markers. Genes Genomics 41(6):667–678

    Article  CAS  PubMed  Google Scholar 

  • Chung YS, Choi SC, Jun TH, Kim C (2017) Genotyping-by-sequencing: a promising tool for plant genetics research and breeding. Hortic Environ Biotechnol 58(5):425–431

    Article  CAS  Google Scholar 

  • Contreras-Soto RI, Mora F, de Oliveira MAR, Higashi W, Scapim CA, Schuster I (2017) A genome-wide association study for agronomic traits in soybean using SNP markers and SNP-based haplotype analysis. PLoS One 12(2):e0171105

    Article  PubMed  PubMed Central  Google Scholar 

  • Cui Z, Luo J, Qi C, Ruan Y, Li J, Zhang A et al (2016) Genome-wide association study (GWAS) reveals the genetic architecture of four husk traits in maize. BMC Genomics 17(1):1–14

    Article  Google Scholar 

  • D’Agostino N, Tripodi P (2017) NGS-based genotyping, high-throughput phenotyping and genome-wide association studies laid the foundations for next-generation breeding in horticultural crops. Diversity 9(3):38

    Article  Google Scholar 

  • Diepenbrock CH, Kandianis CB, Lipka AE, Magallanes-Lundback M, Vaillancourt B, Góngora-Castillo E et al (2017) Novel loci underlie natural variation in vitamin E levels in maize grain. Plant Cell 29(10):2374–2392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dittberner H, Korte A, Mettler-Altmann T, Weber AP, Monroe G, de Meaux J (2018) Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Mol Ecol 27(20):4052–4065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dong YB, Zhang ZW, Shi QL, Wang QL, Qiang ZHOU, Fei DENG et al (2015) QTL consistency for agronomic traits across three generations and potential applications in popcorn. J Integr Agric 14(12):2547–2557

    Article  Google Scholar 

  • Duvick DN (2005) The contribution of breeding to yield advances in maize (Zea mays L.). Adv Agron 86:83–145

    Article  Google Scholar 

  • Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. https://doi.org/10.1371/journal.pone.0019379

  • Fu YB, Peterson GW, Dong Y (2016) Increasing genome sampling and improving SNP genotyping for genotyping-by-sequencing with new combinations of restriction enzymes. G3 Genes, Genomes, Genet. https://doi.org/10.1534/g3.115.025775

  • Garrido-Cardenas JA, Mesa-Valle C, Manzano-Agugliaro F (2018) Trends in plant research using molecular markers. Planta 247(3):543–557

    Article  CAS  PubMed  Google Scholar 

  • Gedil M, Menkir A (2019) An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa. Front Plant Sci 10:1430

    Article  PubMed  PubMed Central  Google Scholar 

  • Gostimsky SA, Kokaeva ZG, Konovalov FA (2005) Studying plant genome variation using molecular markers. Russ J Genet 41(4):378–388

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • He J, Zhao X, Laroche A et al (2014) Genotyping-by-sequencing (GBS), An ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484

    Article  PubMed  PubMed Central  Google Scholar 

  • Hellin J, Bellon MR, Hearne SJ (2014) Maize landraces and adaptation to climate change in Mexico. J Crop Improv 28(4):484–501

    Article  Google Scholar 

  • Henning JA, Gent DH, Twomey MC et al (2016) Genotyping-by-sequencing of a bi-parental mapping population segregating for downy mildew resistance in hop (Humulus lupulus L.). Euphytica. https://doi.org/10.1007/s10681-015-1600-3

  • Hindu V, Palacios-Rojas N, Babu R, Suwarno WB, Rashid Z, Usha R et al (2018) Identification and validation of genomic regions influencing kernel zinc and iron in maize. Theor Appl Genet 131(7):1443–1457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hufford MB, Xu X, Van Heerwaarden J, Pyhäjärvi T, Chia JM, Cartwright RA et al (2012) Comparative population genomics of maize domestication and improvement. Nat Genet 44(7):808–811

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ibrahim AK, Zhang L, Niyitanga S, Afzal MZ, Xu Y, Zhang L et al (2020) Principles and approaches of association mapping in plant breeding. Trop Plant Biol 13:212–224

    Article  Google Scholar 

  • Jamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AM et al (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19(1):1–18

    Article  Google Scholar 

  • Jiang S, Zhang H, Ni P, Yu S, Dong H, Zhang A et al (2020) Genome-wide association study dissects the genetic architecture of maize husk tightness. Front Plant Sci 11:861

    Article  PubMed  PubMed Central  Google Scholar 

  • Ju C, Zhang W, Liu Y, Gao Y, Wang X, Yan J et al (2018) Genetic analysis of seedling root traits reveals the association of root trait with other agronomic traits in maize. BMC Plant Biol 18(1):1–15

    Article  Google Scholar 

  • Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet. https://doi.org/10.1038/ng.548

  • Khan B, Nawab NN, Qamar M, Abbas M, Haroon M, Intikhab A et al (2017) Genetic variability in different maize (Zea mays L.) genotypes for comparative yield performance under local conditions of Rawalakot, Azad Jammu and Kashmir. Int J Biosci 11(3):102–107

    Article  Google Scholar 

  • Kim OG, Sa KJ, Lee JR, Lee JK (2017) Genetic analysis of maize germplasm in the Korean Genebank and association with agronomic traits and simple sequence repeat markers. Genes Genomics 39(8):843–853

    Article  CAS  Google Scholar 

  • Klopfenstein TJ, Erickson GE, Berger LL (2013) Maize is a critically important source of food, feed, energy and forage in the USA. Field Crop Res 153:5–11

    Article  Google Scholar 

  • Kong WQ, Kim C, Zhang D et al (2018) Genotyping by sequencing of 393 sorghum bicolor BTx623 × IS3620C recombinant inbred lines improves sensitivity and resolution of QTL detection. G3 Genes, Genomes, Genet. https://doi.org/10.1534/g3.118.200173

  • Lemay MA, Torkamaneh D, Rigaill G et al (2019) Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants. BMC Genomics. https://doi.org/10.1186/s12864-019-5998-1

  • Li N, Lin B, Wang H, Li X, Yang F, Ding X et al (2019) Natural variation in Zm FBL41 confers banded leaf and sheath blight resistance in maize. Nat Genet 51(10):1540–1548

    Article  CAS  PubMed  Google Scholar 

  • Listgarten J, Lippert C, Kadie CM et al (2012) Improved linear mixed models for genome-wide association studies. Nat Methods 9(6):525–526

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lu X, Wang J, Wang Y, Wen W, Zhang Y, Du J et al (2021) Genome-wide association study of maize aboveground dry matter accumulation at seedling stage. Front Genet 11:1679

    Article  Google Scholar 

  • Malvar Pintos RA, Jiménez Galindo JC, Butrón Gómez AM, Santiago Carabelos R, Ordás López B (2018) GWAS for resistance to Mediterranean corn borer and agronomic traits in a MAGIC population of maize. Maize Genetics Conference, March

    Google Scholar 

  • Mammadov J, Buyyarapu R, Guttikonda SK, Parliament K, Abdurakhmonov IY, Kumpatla SP (2018) Wild relatives of maize, rice, cotton, and soybean: treasure troves for tolerance to biotic and abiotic stresses. Front Plant Sci 9:886

    Article  PubMed  PubMed Central  Google Scholar 

  • Mandić V, Bijelić Z, Krnjaja V, Đorđević S, Brankov M, Mićić N, Stanojković A (2021) Harvest time effect on quantitative and qualitative parameters of forage maize. J Anim Plant Sci 31(1)

    Google Scholar 

  • Maqbool A, Bakhsh A, Aksoy E (2021) Effects of natural variations on biofortification. In: Wild germplasm for genetic improvement in crop plants. Academic Press, pp 115–138

    Chapter  Google Scholar 

  • Mazaheri M, Heckwolf M, Vaillancourt B, Gage JL, Burdo B, Heckwolf S et al (2019) Genome-wide association analysis of stalk biomass and anatomical traits in maize. BMC Plant Biol 19(1):1–17

    Article  Google Scholar 

  • Menkir A, Rocheford T, Maziya-Dixon B, Tanumihardjo S (2015) Exploiting natural variation in exotic germplasm for increasing provitamin-A carotenoids in tropical maize. Euphytica 205(1):203–217

    Article  CAS  Google Scholar 

  • Mishra A, Singh A, Sharma M et al (2016) Development of EMS-induced mutation population for amylose and resistant starch variation in bread wheat (Triticum aestivum) and identification of candidate genes responsible for amylose variation. BMC Plant Biol. https://doi.org/10.1186/s12870-016-0896-z

  • Murray-Tortarolo GN, Jaramillo VJ, Larsen J (2018) Food security and climate change: the case of rainfed maize production in Mexico. Agric For Meteorol 253:124–131

    Article  Google Scholar 

  • Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21(8):2194–2202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nelimor C, Badu-Apraku B, Nguetta SP, Tetteh AY, Garcia-Oliveira AL (2020) Phenotypic characterization of maize landraces from Sahel and Coastal West Africa reveals marked diversity and potential for genetic improvement. J Crop Improv 34(1):122–138

    Article  Google Scholar 

  • Owens BF, Mathew D, Diepenbrock CH, Tiede T, Wu D, Mateos-Hernandez M et al (2019) Genome-wide association study and pathway-level analysis of kernel color in maize. G3: Genes, Genomes, Genetics 9(6):1945–1955

    Article  CAS  PubMed  Google Scholar 

  • Pandey MK, Agarwal G, Kale SM et al (2017) Development and evaluation of a high density genotyping “Axiom-Arachis” Array with 58 K SNPs for accelerating genetics and breeding in groundnut. Sci Rep. https://doi.org/10.1038/srep40577

  • Pereira-Dias L, Vilanova S, Fita A et al (2019) Genetic diversity, population structure, and relationships in a collection of pepper (Capsicum spp.) landraces from the Spanish centre of diversity revealed by genotyping-by-sequencing (GBS). Hortic Res. https://doi.org/10.1038/s41438-019-0132-8

  • Poland J, Endelman J, Dawson J et al (2012a) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome. https://doi.org/10.3835/plantgenome2012.06.0006

  • Poland JA, Brown PJ, Sorrells ME, Jannink JL (2012b) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. https://doi.org/10.1371/journal.pone.0032253

  • Prasanna BM (2012) Diversity in global maize germplasm: characterization and utilization. J Biosci 37(5):843–855

    Article  CAS  PubMed  Google Scholar 

  • Rahman S, Mia MM, Quddus T, Hassan L, Haque MA (2015) Assessing genetic diversity of maize (Zea mays L.) genotypes for agronomic traits. Res Agric Livest Fish 2(1):53–61

    Article  Google Scholar 

  • Ranum P, Peña-Rosas JP, Garcia-Casal MN (2014) Global maize production, utilization, and consumption. Ann N Y Acad Sci 1312(1):105–112

    Article  PubMed  Google Scholar 

  • Raut SK, Ghimire SK, Kharel R, Kuwar CB, Sapkota M, Kushwaha UKS (2017) Study of yield and yield attributing traits of maize. Am J Food Sci Health 3(6):123–129

    Google Scholar 

  • Rosegrant MR, Ringler C, Sulser TB, Ewing M, Palazzo A, Zhu T et al (2009) Agriculture and food security under global change: prospects for 2025/2050. International Food Policy Research Institute, Washington, DC, pp 145–178

    Google Scholar 

  • Rowan BA, Seymour DK, Chae E et al (2017) Methods for genotyping-by-sequencing. In: Genotyping. Humana Press, New York, pp 221–242

    Chapter  Google Scholar 

  • Ruggieri V, Anzar I, Paytuvi A et al (2017) Exploiting the great potential of sequence capture data by a new tool, SUPER-CAP. DNA Res. https://doi.org/10.1093/dnares/dsw050

  • Sa KJ, Hong TK, Lee JK (2018) Genetic diversity and association analyses of Canadian maize inbred lines with agronomic traits and simple sequence repeat markers. Plant Breed Biotechnol 6(2):159–169

    Article  Google Scholar 

  • Sahu PK, Sao R, Mondal S et al (2020) Next generation sequencing based forward genetic approaches for identification and mapping of causal mutations in crop plants: a comprehensive review. Plants 9(10):1355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shah TR, Prasad K, Kumar P (2016) Maize—a potential source of human nutrition and health: a review. Cogent Food Agric 2(1):1166995

    Google Scholar 

  • Shiferaw B, Prasanna BM, Hellin J, Bänziger M (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur 3(3):307–327

    Article  Google Scholar 

  • Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B (2021) Genome-wide association mapping in maize: status and prospects. 3. Biotech 11(5):1–10

    Google Scholar 

  • Sim SC, Durstewitz G, Plieske J et al (2012) Development of a large snp genotyping array and generation of high-density genetic maps in tomato. PLoS One. https://doi.org/10.1371/journal.pone.0040563

  • Singh B, Kukreja S, Goutam U (2018) Milestones achieved in response to drought stress through reverse genetic approaches. F1000Res 7:1311. https://doi.org/10.12688/f1000research.15606.1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Smith SM, Maughan PJ (2015) SNP genotyping using KASPar assays. Methods Mol Biol. https://doi.org/10.1007/978-1-4939-1966-6_18

  • Sood S, Flint-Garcia S, Willcox M, Holland J (2014) Mining natural variation for maize improvement: selection on phenotypes and genes. In: Tuberosa R, Graner A, Frison E (eds) Genomics of plant genetic resources. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7572-5_25

    Chapter  Google Scholar 

  • Sul JH, Martin LS, Eskin E (2018) Population structure in genetic studies: confounding factors and mixed models. PLoS Genet 14(12):e1007309

    Article  PubMed  PubMed Central  Google Scholar 

  • Sun J, Gao J, Wang Z, Hu S, Zhang F, Bao H, Fan Y (2019) Maize canopy photosynthetic efficiency, plant growth, and yield responses to tillage depth. Agronomy 9(1):3

    Article  CAS  Google Scholar 

  • Sun C, Dong Z, Zhao L et al (2020) The Wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnol J 18(6):1354–1360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Taranto F, D’Agostino N, Greco B et al (2016) Genome-wide SNP discovery and population structure analysis in pepper (Capsicum annuum) using genotyping by sequencing. BMC Genomics. https://doi.org/10.1186/s12864-016-3297-7

  • Terracciano I, Cantarella C, D’Agostino N (2016) Hybridization-based enrichment and next generation sequencing to explore genetic diversity in plants. In: Dynamics of mathematical models in biology. Springer, Cham

    Google Scholar 

  • Terracciano I, Cantarella C, Fasano C et al (2017) Liquid-phase sequence capture and targeted re-sequencing revealed novel polymorphisms in tomato genes belonging to the MEP carotenoid pathway. Sci Rep. https://doi.org/10.1038/s41598-017-06120-3

  • Tibbs Cortes L, Zhang Z, Yu J (2021) Status and prospects of genome-wide association studies in plants. Plant Genome 14(1):e20077

    Article  CAS  PubMed  Google Scholar 

  • Van Bavel J (2013) The world population explosion: causes, backgrounds and -projections for the future. Facts Views Vis ObGyn 5(4):281–291

    PubMed  PubMed Central  Google Scholar 

  • Vathana Y, Sa KJ, Lim SE, Lee JK (2019) Genetic diversity and association analyses of Chinese maize inbred lines using SSR markers. Plant Breed Biotechnol 7(3):186–199

    Article  Google Scholar 

  • Verma RK, Chetia SK, Dey PC, Rahman A, Saikia S, Sharma V et al (2021) Genome-wide association studies for agronomical traits in winter rice accessions of Assam. Genomics 113(3):1037–1047

    Article  CAS  PubMed  Google Scholar 

  • Vos PG, Uitdewilligen JGAML, Voorrips RE et al (2015) Development and analysis of a 20K SNP array for potato (Solanum tuberosum): an insight into the breeding history. Theor Appl Genet 128:2387–2401. https://doi.org/10.1007/s00122-015-2593-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang H, Li K, Hu X, Liu Z, Wu Y, Huang C (2016) Genome-wide association analysis of forage quality in maize mature stalk. BMC Plant Biol 16(1):1–12

    Article  Google Scholar 

  • Wang B, Liu H, Liu Z, Dong X, Guo J, Li W et al (2018) Identification of minor effect QTLs for plant architecture related traits using super high density genotyping and large recombinant inbred population in maize (Zea mays). BMC Plant Biol 18(1):1–12

    Google Scholar 

  • Wang G, Zhao Y, Mao W, Ma X, Su C (2020) QTL analysis and fine mapping of a major QTL conferring kernel size in maize (Zea mays). Front Genet 11:603920

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang A, Jiang Y, Shu X, Zha Z, Yin D, Liu Y et al (2021) Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.). Genomics 113(3):1396–1406

    Article  CAS  PubMed  Google Scholar 

  • Warburton ML, Ribaut JM, Franco J, Crossa J, Dubreuil P, Betrán FJ (2005) Genetic characterization of 218 elite CIMMYT maize inbred lines using RFLP markers. Euphytica 142(1):97–106

    Article  CAS  Google Scholar 

  • Wickland DP, Battu G, Hudson KA et al (2017) A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow. BMC Bioinformatics, GB-eaSy. https://doi.org/10.1186/s12859-017-2000-6

    Book  Google Scholar 

  • Xiang K, Yang KC, Pan GT, Reid LM, Li WT, Zhu X, Zhang ZM (2010) Genetic diversity and classification of maize landraces from China’s Sichuan basin based on agronomic traits, quality traits, combining ability and SSR markers. Maydica 55(1):85–93

    Google Scholar 

  • Xu Y, Li P, Yang Z, Xu C (2017) Genetic mapping of quantitative trait loci in crops. Crop J 5(2):175–184

    Article  Google Scholar 

  • Yang J, Liu Z, Chen Q, Qu Y, Tang J, Lübberstedt T, Li H (2020) Mapping of QtL for grain yield components based on a DH population in maize. Sci Rep 10(1):1–11

    Google Scholar 

  • 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. 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. https://doi.org/10.1007/s00122-011-1664-y

  • Yuan Y, Cairns JE, Babu R, Gowda M, Makumbi D, Magorokosho C et al (2019) Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Front Plant Sci 9:1919

    Article  PubMed  PubMed Central  Google Scholar 

  • Zebire DA (2020) Applications of molecular markers in genetic diversity studies of maize. Niger J Biotechnol 37(1):101–108

    Article  Google Scholar 

  • Zhang X, Qi Y (2021) Genetic architecture affecting maize agronomic traits identified by variance heterogeneity association mapping. Genomics 113(4):1681–1688

    Article  CAS  PubMed  Google Scholar 

  • Zhang Y, Wan J, He L, Lan H, Li L (2019) Genome-wide association analysis of plant height using the maize F1 population. Plan Theory 8(10):432

    CAS  Google Scholar 

  • Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y et al (2020) A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133(10):2881–2895

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y, Su C (2019) Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize. Sci Rep 9(1):1–10

    Google Scholar 

  • Zhao K, Aranzana MJ, Kim S et al (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet. https://doi.org/10.1371/journal.pgen.0030004

  • Zhao C, Zhang Y, Du J et al (2019a) Crop phenomics: current status and perspectives. Front Plant Sci 10:714

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhao Y, Wang H, Bo C, Dai W, Zhang X, Cai R et al (2019b) Genome-wide association study of maize plant architecture using f 1 populations. Plant Mol Biol 99(1–2):1–15

    Article  CAS  PubMed  Google Scholar 

  • Zheng Z, Hey S, Jubery T, Liu H, Yang Y, Coffey L et al (2020) Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant Physiol 182(2):977–991

    Article  CAS  PubMed  Google Scholar 

  • Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet. https://doi.org/10.1038/ng.2310

  • Zhou G, Hao D, Xue L, Chen G, Lu H, Zhang Z et al (2018) Genome-wide association study of kernel moisture content at harvest stage in maize. Breed Sci 68(5):622–628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome J 1:5. https://doi.org/10.3835/plantgenome2008.02.0089

    Article  CAS  Google Scholar 

  • Zhu LY, Chen JT, Li D, Zhang JH, Huang YQ, Zhao YF et al (2013) QTL mapping for stalk related traits in maize (Zea mays L.) under different densities. J Integr Agric 12(2):218–228

    Article  Google Scholar 

  • Zhu XM, Shao XY, Pei YH, Guo XM, Li J, Song XY, Zhao MA (2018) Genetic diversity and genome-wide association study of major ear quantitative traits using high-density SNPs in maize. Front Plant Sci 9:966

    Article  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India

    Baljeet Singh, Vijay Kumar & Umesh Goutam

  2. MRCFC Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir Hussain Wani

  3. Department of Agronomy, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India

    Sarvjeet Kukreja

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  1. Baljeet Singh
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  2. Shabir Hussain Wani
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  3. Sarvjeet Kukreja
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  4. Vijay Kumar
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  5. Umesh Goutam
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Editors and Affiliations

  1. Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology, Srinagar, Jammu and Kashmir, India

    Shabir Hussain Wani

  2. Dryland Agriculture Research Station, Sher-e-Kashmir University of Agricultural Sciences and Technology, Srinagar, Jammu and Kashmir, India

    Zahoor Ahmad Dar

  3. ICAR-National Bureau of Plant Genetic Resources, New Delhi, India

    Gyanendra Pratap Singh

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Singh, B., Wani, S.H., Kukreja, S., Kumar, V., Goutam, U. (2023). Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize. In: Wani, S.H., Dar, Z.A., Singh, G.P. (eds) Maize Improvement. Springer, Cham. https://doi.org/10.1007/978-3-031-21640-4_4

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