Abstract
Key message
The identified 30 functional nucleotide polymorphisms or genic SNP markers would offer essential information for marker-assisted breeding in groundnut.
Abstract
A genome-wide association study (GWAS) on component traits of LLS resistance in an eight-way multiparent advance generation intercross (MAGIC) population of groundnut in the field and in a light chamber (controlled conditions) was performed via an Affymetrix 48 K single-nucleotide polymorphism (SNP) ‘Axiom Arachis’ array. Multiparental populations with high-density genotyping enable the detection of novel alleles. In total, five quantitative trait loci (QTLs) with marker − log10(p value) scores ranging from 4.25 to 13.77 for the incubation period (IP) and six QTLs with marker − log10(p value) scores ranging from 4.33 to 10.79 for the latent period (LP) were identified across the A- and B-subgenomes. A total of 62 markers‒trait associations (MTAs) were identified across the A- and B-subgenomes. Markers for LLS scores and the area under the disease progression curve (AUDPC) recorded for plants in the light chamber and under field conditions presented − log10 (p value) scores ranging from 4.22 to 27.30. The highest number of MTAs (six) was identified on chromosomes A05, B07 and B09. Out of a total of 73 MTAs, 37 and 36 MTAs were detected in subgenomes A and B, respectively. Taken together, these results suggest that both subgenomes have equal potential genomic regions contributing to LLS resistance. A total of 30 functional nucleotide polymorphisms or genic SNP markers were detected, among which eight genes were found to encode leucine-rich repeat (LRR) receptor-like protein kinases and putative disease resistance proteins. These important SNPs can be used in breeding programmes for the development of cultivars with improved disease resistance.
Similar content being viewed by others
Data availability
The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Abdurakhmonov I, Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genom. https://doi.org/10.1155/2008/574927
Ahmad S, Nawade B, Bosamia TC et al (2020) Identification of novel QTLs for late leaf spot resistance and validation of a major rust QTL in peanut (Arachis hypogaea L.). 3 Biotech 10:458. https://doi.org/10.1007/s13205-020-02446-4
Anco DJ, Hiers JB, Thomas JS (2020) Improved management efficacy of late leaf spot on peanut through combined application of prothioconazole with fluxapyroxad and pyraclostrobin. Agronomy-Basel 10(2):298. https://doi.org/10.3390/agronomy10020298
Aravind L, Koonin EV (2001) The DNA-repair protein AlkB, EGL-9, and leprecan define new families of 2-oxoglutarate- and iron-dependent dioxygenases. Genome Biol 2(3):1–11
Backer R, Naidoo S, van den Berg N (2019) The nonexpressor of pathogenesis-related genes 1 (NPR1) and related family: Mechanistic insights in plant disease resistance. Front Plant Sci 10:102. https://doi.org/10.3389/fpls.2019.00102
Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT et al (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6:11. https://doi.org/10.1186/1939-8433-6-11
Baron KN (2013) GRAM genes and abscisic acid (ABA) metabolism in the reproductive development of Arabidopsis thaliana. Dissertation, University of Manitoba, Winnipeg, pp. 23–44
Bertioli DJ, Cannon SB, Froenicke L, Huang G, Farmer AD, Cannon EK, Liu X, Gao D, Clevenger J, Dash S, Ren L, Moretzsohn MC, Shirasawa K, Huang W, Vidigal B, Abernathy B, Chu Y, Niederhuth CE, Umale P, Araújo AC, Kozik A, Kim KD, Burow MD, Varshney RK, Wang X, Zhang X, Barkley N, Guimarães PM, Isobe S, Guo B, Liao B, Stalker HT, Schmitz RJ, Scheffler BE, Leal-Bertioli SC, Xun X, Jackson SA, Michelmore R, Ozias-Akins P (2016) The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet 48:438–446. https://doi.org/10.1038/ng.3517
Bertioli DJ, Jenkins J, Clevenger J, Dudchenko O, Gao D, Seijo G et al (2019) The genome sequence of segmental allotetraploid peanut Arachis hypogaea. Nat Genet 51:877–884. https://doi.org/10.1038/s41588-019-0405-z
Bertioli DJ, Clevenger J, Godoy IJ, Stalker HT, Wood S, Santos JF, Ballén-Taborda C, Abernathy B, Azevedo V, Campbell J, Chavarro C (2021) Legacy genetics of Arachis cardenasii in the peanut crop shows the profound benefits of international seed exchange. Proc Natl Acad Sci U.S.A 118(38):e2104899118. https://doi.org/10.1073/pnas.2104899118
Buckler E, Casstevens T, Bradbury P, Zhang Z, Kroon D, Glaubitz J (2014) (2014) User manual for TASSEL, trait analysis by association, evolution and linkage [Internet]. Comell University, New York
Burgos E et al (2020) Validated MAGIC and GWAS populations mapping reveal the link between vitamin E contents and natural variation in chorismate metabolism in tomato. Plant J 105:907–923. https://doi.org/10.1111/tpj.15077
Butler DG., Cullis BR, Gilmour AR, Gogel BG, Thompson R (2017) ASReml-R Reference Manual Version 4 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK
Chen X, Li H, Pandey MK, Yang Q, Wang X, Garg V, Li H, Chi X, Doddamani D, Hong Y, Upadhyaya HD, Guo H, Khan AW, Zhu F, Zhang X, Pan L, Pierce GJ, Zhou G, Krishnamohan KAVS, Chen M, Zhong N, Agarwal G, Li S, Chitikineni A, Zhang G, Sharma S, Chen N, Liu N, Janila P, Li S, Wang M, Wang T, Sun J, Li X, Li C, Wang M, Yu L, Wen S, Singh S, Yang Z, Zhao J, Zhang C, Yu Y, Bi J, Zhang X, Liu Z, Paterson AH, Wang S, Liang X, Varshney RK, Yu S (2016) Draft genome of the peanut A-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens. Proc Natl Acad Sci USA 113:6785–6790. https://doi.org/10.1073/pnas.1600899113
Chen X, Lu Q, Liu H, Zhang J, Hong Y, Lan H, Li H, Wang J, Liu H, Li H, Pandey MK, Zhang Z, Zhou G, Yu J, Zhang G, Yuan J, Li X, Wen S, Meng F, Yu S, Wang X, Siddique KHM, Liu Z-J, Paterson AH, Varshney RK, Liang X (2019) Sequencing of cultivated peanut, Arachis hypogaea, yields insights into genome evolution and oil improvement. Mol Plant 12(7):920–934. https://doi.org/10.1016/j.molp.2019.03.005
Chu Y, Chee P, Culbreath A, Isleib TG, Holbrook CC, Ozias-Akins P (2019) Major QTLs for resistance to early and late leaf spot diseases are identified on chromosomes 3 and 5 in peanut (Arachis hypogaea). Front Plant Sci 10:883. https://doi.org/10.3389/fpls.2019.00883
Clevenger J, Chu Y, Chavarro C, Botton S, Culbreath AK, Isleib TG, Holbrook CC, Ozias-Akins P (2018a) Mapping late leaf spot resistance in peanut (Arachis hypogaea) using QTL-seq reveals markers for marker assisted selection. Front Plant Sci 9:83. https://doi.org/10.3389/fpls.2018.00083
Clevenger JP, Korani W, Ozias-Akins P, Jackson S (2018b) Haplotype-based genotyping in polyploids. Front Plant Sci. https://doi.org/10.3389/fpls.2018.00564
Coelho AC, Pires R, Schütz G, Santa C, Manadas B, Pinto P (2021) Disclosing proteins in the leaves of cork oak plants associated with the immune response to Phytophthora cinnamomi inoculation in the roots: A long-term proteomics approach. PLoS One 16(1):e0245148. https://doi.org/10.1371/journal.pone.0245148
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393. https://doi.org/10.1198/108571106X154443
Dang PM, Lamb MC, Bowen KL, Chen CY (2019) Identification of expressed R-genes associated with leaf spot diseases in cultivated peanut. Mol Biol Rep 46:225–239. https://doi.org/10.1007/s11033-018-4464-5
Dao TTH, Linthorst HJM, Verpoorte R (2011) Chalcone synthase and its functions in plant resistance. Phytochem Rev 10(3):397–412. https://doi.org/10.1007/s11101-011-9211-7
Delfini J, Moda-Cirino V, dos Santos NJ et al (2021) Population structure, genetic diversity and genomic selection signatures among a Brazilian common bean germplasm. Sci Rep. https://doi.org/10.1038/s41598-021-82437-4
Descalsota GIL, Swamy BPM, Zaw H, Inabangan-Asilo MA, Amparado A, Mauleon R, Chadha-Mohanty P, Arocena EC, Raghavan C et al (2018) Genome-wide association mapping in a rice magic plus population detects QTLs and genes useful for biofortification. Frontiers Plant Sci 9:1347. https://doi.org/10.3389/fpls.2018.01347
Deshmukh DB, Marathi B, Sudini HK, Variath MT, Chaudhari S, Manohar SS, Rani CV, Pandey MK, Pasupuleti J (2020) Combining high oleic acid trait and resistance to late leaf spot and rust diseases in groundnut (Arachis hypogaea L.). Front Plant Sci 10(11):514. https://doi.org/10.3389/fgene.2020.00514
Dwivedi SL, Pande S, Rao JN, Nigam SN (2002) Components of resistance to late leaf spot and rust among interspecific derivatives and their significance in a foliar disease resistance breeding in groundnut (Arachis hypogaea L.). Euphytica 125:81–88. https://doi.org/10.1023/A:1015707301659
Earl DA, von Holdt BM (2012) Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Conserv Genet Resour 4:359–361. https://doi.org/10.1007/s12686-011-9548-7
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
FAOSTAT (2020) FAOSATAT statistical data base. Rome: food and agricultural organizations of the united nations. Accessed 25 Jan 2023
Foster DJ, Wynne JC, Beute MK (1980) Evaluation of detached leaf culture for screening peanuts for leaf spot resistance. Peanut Sci 7:98–100. https://doi.org/10.3146/i0095-3679-7-2-10
Fu ZQ, Dong X (2013) Systemic acquired resistance: turning local infection into global defense. Annu Rev Plant Biol 64:839–863. https://doi.org/10.1146/annurev-arplant-042811-105606
Gong L, Han S, Yuan M, Ma X, Hagan A, He G (2020) Transcriptomic analyses reveal the expression and regulation of genes associated with resistance to early leaf spot in peanut. BMC Res Notes 13(1):381. https://doi.org/10.1186/s13104-020-05225-9
Han S, Yuan M, Clevenger JP et al (2018) A SNP-based linkage map revealed QTLs for resistance to early and late leaf spot diseases in peanut (Arachis hypogaea L.). Front Plant Sci. 74(789):631. https://doi.org/10.3389/fpls.2018.01012
Hossain Z, Hajika M, Komatsu S (2012) Comparative proteome analysis of high and low cadmium accumulating soybeans under cadmium stress. Amino Acids 43:2393–2416. https://doi.org/10.1007/s00726-012-1319-6
Huynh BL, Ehlers JD, Huang BE, Muñoz-Amatriaín M, Lonardi S, Santos JRP, Ndeve A, Batieno BJ, Boukar O, Cisse N, Drabo I, Fatokun C, Kusi F, Agyare RY, Guo YN, Herniter I, Lo S, Wanamaker SI, Xu S, Close TJ, Roberts PA (2018) A multi-parent advanced generation inter-cross (MAGIC) population for genetic analysis and improvement of cowpea (Vigna unguiculata L. Walp.). Plant J 93:1129–1142. https://doi.org/10.1111/tpj.13827
Islam MS, Thyssen GN, Jenkins JN, Zeng L, Delhom CD, McCarty JC et al (2016) A MAGIC population-based genome-wide association study reveals functional association of GhRBB1_A07 gene with superior fiber quality in cotton. BMC Genom 17:903. https://doi.org/10.1186/s12864-016-3249-2
Janila P, Nigam SN, Abhishek R et al (2014) Iron and zinc concentrations in peanut (Arachis hypogaea L.) seeds and their relationship with other nutritional and yield parameters. J Agric Sci 153:975–994. https://doi.org/10.1017/s0021859614000525
Janila P, Manohar SS, Patne N, Variath MT, Nigam SN (2016) Genotype × environment interactions for oil content in peanut and stable high-oil-yielding sources. Crop Sci 56:2506–2515. https://doi.org/10.2135/cropsci2016.01.0005
Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42:348–354. https://doi.org/10.1038/ng.548
Khedikar YP, Gowda MVC, Sarvamangala C, Patgar KV, Upadhyaya HD, Varshney RK (2010) A QTL study on late leaf spot and rust revealed one major QTL for molecular breeding for rust resistance in groundnut (Arachis hypogaea L.). Theor Appl Genet 121:971–984. https://doi.org/10.1007/s00122-010-1366-x
Khera P, Pandey MK, Wang H et al (2016) Mapping quantitative trait loci of resistance to tomato spotted wilt virus and leaf spots in a recombinant inbred line population of peanut (Arachis hypogaea L.) from SunOleic 97R and NC94022. PLoS One 11:e0158452. https://doi.org/10.1371/journal.pone.0158452
Kolekar RM, Sujay V, Shirasawa K, Sukruth M, Gowda MVC, Pandey MK, Varshney RK, Bhat RS (2016) QTL mapping for late leaf spot and rust resistance using an improved genetic map and extensive phenotypic data on a recombinant inbred line population in peanut (Arachis hypogaea L.). Euphytica 209(1):147–156. https://doi.org/10.1007/s10681-016-1651-0
Korani W, Clevenger JP, Chu Y, Ozias-Akins P (2019) Machine learning as an effective method for identifying true single nucleotide polymorphisms in polyploid plants. Plant Genome 12:180023. https://doi.org/10.3835/plantgenome2018.05.0023
Korte A, Farlow A (2013) The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9:29
Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM et al (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5(7):e1000551. https://doi.org/10.1371/journal.pgen.1000551
Liu X, Huang M, Fan B et al (2016) Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet 12:e1005767. https://doi.org/10.1371/journal.pgen.1005767
Liu S, Zou W, Lu X, Bian J, He H, Chen J, Ye G (2021) Genome-wide association study using a multiparent advanced generation intercross (MAGIC) population identified SNPs and candidate genes to predict shoot and grain zinc contents in rice. Agriculture 11(1):70. https://doi.org/10.3390/agriculture11010070
López-Malvar A, Butron A, Malvar RA, McQueen-Mason SJ, Faas L, Gómez LD et al (2021) Association mapping for maize Stover yield and saccharification efficiency using a multiparent advanced generation intercross (MAGIC) population. Sci Rep 11:1–9. https://doi.org/10.1038/s41598-021-83107-1
Meng L, Guo L, Ponce K, Zhao X, Ye G (2016) Characterization of three indica rice multiparent advanced generation intercross (MAGIC) populations for quantitative trait loci identification. Plant Genome. https://doi.org/10.3835/plantgenome2015.10.0109
Merrick LF, Burke AB, Chen X, Carter AH (2021) Breeding with major and minor genes: genomic selection for quantitative disease resistance. Front Plant Sci 12:13667. https://doi.org/10.3389/fpls.2021.713667
Novakazi F, Krusell L, Jensen JD, Orabi J, Jahoor A, Bengtsson T, on behalf of the PPP Barley Consortium (2020) You had me at “MAGIC”!: four Barley MAGIC populations reveal novel resistance QTL for powdery mildew. Genes 11:1512. https://doi.org/10.3390/genes11121512
Ongom PO, Ejeta G (2018) Mating design and genetic structure of a multi-parent advanced generation intercross (MAGIC) population of sorghum (Sorghum bicolor(L.) Moench). G3 (Bethesda) 8:331–34. https://doi.org/10.1534/g3.117.300248
Otyama PI, Wilkey A, Kulkarni R et al (2019) Evaluation of linkage disequilibrium, population structure, and genetic diversity in the U. S. peanut mini core collection. BMC Genom 20:481. https://doi.org/10.1186/s12864-019-5824-9
Pande S, Upadhyaya HD, Rao JN, Reddy PL, Rao PP (2005) Promotion of integrated disease management of ICGV 91114, a dual-purpose, early maturing groundnut variety for rainfed areas. Inf Bull, 68
Pandey MK, Upadhyaya HD, Rathore A et al (2014) Genomewide association studies for 50 agronomic traits in peanut using the ‘reference set’ comprising 300 genotypes from 48 countries of the semi-arid tropics of the world. PLoS One 9:105228. https://doi.org/10.1371/journal.pone.0105228
Pandey MK, Khan AW, Singh VK, Vishwakarma MK, Shasidhar Y, Kumar V, Garg V, Bhat RS, Chitikineni A, Janila P (2017a) QTL-seq approach identified genomic regions and diagnostic markers for rust and late leaf spot resistance in groundnut (Arachis hypogaea L.). Plant Biotechnol J 15:927–941. https://doi.org/10.1111/pbi.12686
Pandey MK, Wang H, Khera P et al (2017) Genetic dissection of novel QTLs for resistance to leaf spots and tomato spotted wilt virus in peanut (Arachis hypogaea L.). Front Plant Sci. https://doi.org/10.3389/fpls.2017b.00025
Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L, Bouchet J-P, Le QH, Chauchard B, Verschave P, Causse M (2015) Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era. Plant Biotechnol J 13:565–577. https://doi.org/10.1111/pbi.12282
Pasupuleti J, Venuprasad R, Rathore A, Upakula A, Reddy RK, Waliyar F, Nigam SN (2013) Genetic analysis of resistance to late leaf spot in interspecific groundnuts. Euphytica. https://doi.org/10.1007/s10681-013-0881-7
Pasupuleti J, Pandey MK, Manohar SS, Variath MT, Nallathambi P, Nadaf HL, Sudini H, Varshney RK (2016) Foliar fungal disease-resistant introgression lines of groundnut (Arachis hypogaea L.) record higher pod and haulm yield in multilocation testing. Plant Breed 135:355–366. https://doi.org/10.1111/pbr.12358
Pritchard JK, Wen W, Falush D (2007) Documentation for structure software: version 2.2. University of Chicago, Chicago, USA
Puglisi D, Delbono S, Visioni A, Ozkan H, Kara I, Casas AM, Igartua E, Valè G, Piero ARL, Cattivelli L, Tondelli A (2021) Genomic prediction of grain yield in a barley MAGIC population modeling genotype per environment interaction. Front Plant Sci 12:1–18. https://doi.org/10.3389/fpls.2021.664148
Qanbari S (2020) On the extent of linkage disequilibrium in the genome of farm animals. Front Genet 10:1304. https://doi.org/10.3389/fgene.2019.01304
Rao MJV, Upadhyaya HD, Mehan VK, Nigam SN, McDonald D, Reddy NS (1995) Registration of peanut germplasm ICGV 88145 and ICGV 89104 resistant to seed infection by Aspergillus flavus. Crop Sci 35:1717. https://doi.org/10.2135/cropsci1995.0011183X003500060048x
Sannemann W, Lisker A, Maurer A et al (2018) Adaptive selection of founder segments and epistatic control of plant height in the MAGIC winter wheat population WM-800. BMC Genom 19:559. https://doi.org/10.1186/s12864-018-4915-3
SAS Institute Inc. (2018) SAS/STAT® 15.1 User’s Guide. Cary, NC: SAS Institute Inc
Scott MF, Ladejobi O, Amer S et al (2020) Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding. Heredity. https://doi.org/10.1038/s41437-020-0336-6
Shaner G, Finney R (1977) The effect of nitrogen fertilization on the expression of slow mildewing resistance in Knox wheat. Phytopathology 67:1051–1056
Singh BL, Erickson JE, Boote KJ, Tillman BL, Jones JW, Van Bruggen AH (2011) Late leaf spot effects on growth, Photosynthesis and yield in peanut cultivars of differing resistance. Agron J 103:85–91. https://doi.org/10.2134/agronj2010.0322
Stadlmeier M, Hartl L, Mohler V (2018) Usefulness of a multiparent advanced generation intercross population with a greatly reduced mating design for genetic studies in winter wheat. Front Plant Sci. https://doi.org/10.3389/fpls.2018.01825
Subrahmanyam P, McDonald D, Waliyar F, Reddy LJ, Nigam SN, Gibbons RW, Rao VR, Singh AK, Pande S, Reddy PM, Subba Rao PV (1995) Screening methods and sources of resistance to rust and late leaf spot of groundnut. In: Information Bulletin No 47. International crops research institute for the semi-arid tropics. Patancheru, Andhra Pradesh 502324, India
Sujay V, Gowda M, Pandey M, Bhat R, Khedikar Y, Nadaf H, Gautami B, Sarvamangala C, Lingaraju S, Radhakrishan T (2012) Quantitative trait locus analysis and construction of consensus genetic map for foliar disease resistance based on two recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breed 30:773–788. https://doi.org/10.1007/s11032-011-9661-z
Sul JH, Martin LS, Eskin E (2018) Population structure in genetic studies: confounding factors and mixed models. PLoS Genet 14:e1007309. https://doi.org/10.1371/journal.pgen.1007309
Varshney RK, Pandey MK, Janila P, Nigam SN, Sudini H et al (2014) Marker-assisted introgression of a QTL region to improve rust resistance in three elite and popular varieties of peanut (Arachis hypogaea L.). Theor Appl Genet 127:1771–1781. https://doi.org/10.1007/s00122-014-2338-3
Wang H, Pandey MK, Qiao L et al (2013) Genetic mapping and quantitative trait loci analysis for disease resistance using F2 and F5 generation-based genetic maps derived from ‘Tifrunner’ × ‘GT-C20’ in peanut. Plant Genome. https://doi.org/10.3835/plantgenome2013.05.0018
Wang YN, Tang L, Hou Y et al (2016) Differential transcriptome analysis of leaves of tea plant (Camellia sinensis) provides comprehensive insights into the defense responses to Ectropis oblique attack using RNA-Seq. Funct Integr Genom 16(4):383–398. https://doi.org/10.1007/s10142-016-0491-2
Wankhade AP, Kadirimangalam SR, Viswanatha KP, Deshmukh MP, Shinde VS, Deshmukh DB, Pasupuleti J (2021) Variability and trait association studies for late leaf spot resistance in a groundnut magic population. Agronomy 11(11):2193. https://doi.org/10.3390/agronomy11112193
Xue JY, Takken FL, Nepal MP, Maekawa T, Shao ZQ (2020) Evolution and functional mechanisms of plant disease resistance. Front Genet 11:1138. https://doi.org/10.3389/fgene.2020.593240
Zambetakkis C, Waliyar F, Bockelee-Morvan A, dePins O (1981) Results of four years of research on resistance of groundnut varieties to Aspergillus flavus. Oléagineux 36:377–385
Zhang C, Chen H, Zhuang R-R, Chen Y-T, Deng Y, Cai T-C et al (2019) Overexpression of the peanut CLAVATA1-like leucine-rich repeat receptor-like kinase AhRLK1 confers increased resistance to bacterial wilt in tobacco. J Exp Bot 70:5407–5421. https://doi.org/10.1093/jxb/erz274
Zhang H, Chu Y, Dang P, Tang Y, Jiang T, Clevenger JP et al (2020) Identification of QTLs for resistance to leaf spots in cultivated peanut (Arachis hypogaea L.) through GWAS analysis. Theor Appl Genet 133(7):2051–61. https://doi.org/10.1007/s00122-020-03576-2
Zhou X, Xia Y, Liao J, Liu K, Li Q, Dong Y, Ren X, Chen Y, Huang Y, Liao B, Lei Y, Yan L, Jiang H (2016) Quantitative trait locus analysis of late leaf spot resistance and plant-type-related traits in cultivated peanut (Arachis hypogaea L.) under multi-environments. PLoS One 11:e0166873. https://doi.org/10.1371/journal.pone.0166873
Funding
The research reported here was financially supported by OPEC Fund for International Development (OFID) with grant number 13161, and was conducted under CGIAR Research Program on Grain Legumes and Dryland Cereals (CRP-GLDC).
Author information
Authors and Affiliations
Contributions
APW recorded the phenotypic data, done GWAS analysis and prepared the manuscript. SY helped in GWAS analysis. KPV, VPC, HKS, MPD and VSS were involved in experiment design and manuscript revising. DBD and SG helped in recording the phenotypic data. AKV analysed the phenotypic data. JP conceptualized and supervised the whole study and provided assistance for manuscript preparation.
Corresponding author
Ethics declarations
Conflict of interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Communicated by Reyazul Rouf Mir.
Publisher's Note statement
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wankhade, A.P., Chimote, V.P., Viswanatha, K.P. et al. Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.). Theor Appl Genet 136, 43 (2023). https://doi.org/10.1007/s00122-023-04256-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00122-023-04256-7