Current Status and Prospects of Genomic Selection in Legumes



Legumes play a major role in food and nutritional security across the world. The current rate of genetic gains in legume breeding programs is not enough to meet the food and nutritional requirement of an ever increasing global population. To feed this growing population, it is essential to enhance the rate of genetic gains for increased productivity of these legumes. Genomics tools have great potential in developing improved cultivars faster and more precisely by deploying modern breeding approaches. Marker-assisted backcrossing (MABC) and marker-assisted recurrent selection (MARS) approaches have been successfully deployed in several legume crops for improving traits with simple genetic behaviour. However, it is difficult to address the complex traits using MABC and MARS as several large and small effect quantitative trait loci (QTLs) are involved in their expression. Genomic selection (GS) has potential to capture small and large effect genetic factors and deal with the complex traits. Over the last decade, large scale genomic resources have been developed in majority of the legume crops, which provide a perfect platform to deploy genome-wide information in selecting breeding material for enhancing the rate of genetic gain. Many legume breeders have already took initiatives towards deploying GS breeding by developing training populations, standardizing the GS models, studying effect of marker density, size of training population, and genotype and environment interaction. This chapter presents an overview on the current status of GS and presents the future prospects of its deployment in some legume breeding programs.


Legumes Genetic gain Genomics-assisted breeding Cross validation Population size 


  1. Albrecht T, Wimmer V, Auinger H, Erbe M, Knaak C et al (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350CrossRefPubMedGoogle Scholar
  2. Annicchiarico P, Nazzicari N, Li X, Wei Y, Pecetti L et al (2015) Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 16:1020CrossRefPubMedCentralPubMedGoogle Scholar
  3. Araújo SS, Beebe S, Crespi M, Delbreil B, González EM et al (2015) Abiotic stress responses in Legume crops: strategies used to cope with environmental challenges. Crit Rev Plant Sci 34:237–280CrossRefGoogle Scholar
  4. Asoro FG, Newell MA, Beavis WD, Scott MP, Jannink J (2011) Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. The Plant Genome 4:132–144CrossRefGoogle Scholar
  5. Ates D, Sever T, Aldemir S, Yagmur B, Temel HY et al (2016) Identification QTLs Controlling Genes for Se Uptake in Lentil Seeds. PLOS ONE 11(4): e0154054Google Scholar
  6. Bernardo R, Charcosset A (2006) Usefulness of gene information in marker-assisted recurrent selection: a simulation appraisal. Crop Sci 46:614–621CrossRefGoogle Scholar
  7. Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  8. Bertioli DJ, Cannon SB, Froenicke L, Huang G, Farmer AD et al (2016) The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet 48:438–446CrossRefPubMedGoogle Scholar
  9. Bohra A, Pandey MK, Jha UC, Singh B, Singh IP et al (2014) Genomics-assisted breeding in four major pulse crops of developing countries: present status and prospects. Theor Appl Genet 127:1263–1291CrossRefPubMedCentralPubMedGoogle Scholar
  10. Bordat A, Savois V, Nicolas M, Salse J, Chauveau A et al (2011) Translational genomics in legumes allowed placing in silico 5460 unigenes on the pea functional map and identified candidate genes in Pisum sativum L. Genes Genome Genet 1:93–103Google Scholar
  11. Breiman L (2001) Random forests. Mach Learn 45:5–32. doi: 10.1023/A:1010933404324 CrossRefGoogle Scholar
  12. Burstin J, Salloignon P, Chabert-Martinello M, Magnin-Robert JB, Siol M et al (2015) Genetic diversity and trait genomic prediction in a pea diversity panel. BMC Genomics 16:105CrossRefPubMedCentralPubMedGoogle Scholar
  13. Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Ani Breed Genet 124:362–368CrossRefGoogle Scholar
  14. Chen X, Sullivan PF (2003) Single nucleotide polymorphism genotyping: biochemistry, protocol, cost and throughput. Pharmacogenomics J 3:77–96CrossRefPubMedGoogle Scholar
  15. Chen X, Li H, Pandey MK, Yang Q, Wang X et al (2016) Draft genome of the peanut A-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens. Proc Nat Acad Sci 113:6785–6790CrossRefPubMedCentralPubMedGoogle Scholar
  16. Combs E, Bernardo R (2013) Accuracy of genomewide selection for different traits with constant population size, heritability and number of markers. The Plant Genome 6:1CrossRefGoogle Scholar
  17. Cottage A, Gostkiewicz K, Thomas JE, Borrows R, Torres AM et al (2012) Heterozygosity and diversity analysis using mapped SNPs in a faba bean inbreeding programme. Mol Breed 30:1799–1809CrossRefGoogle Scholar
  18. Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3:e3395CrossRefPubMedCentralPubMedGoogle Scholar
  19. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510CrossRefPubMedGoogle Scholar
  20. de los Campos G, Gianola D, GJM R (2009a) Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. J Anim Sci 87:1883–1887CrossRefGoogle Scholar
  21. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A et al (2009b) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385CrossRefPubMedCentralPubMedGoogle Scholar
  22. de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic breeding values across multiple populations. Genetics 183:1545–1553. doi: 10.1534/genetics.109.104935 CrossRefPubMedCentralPubMedGoogle Scholar
  23. Deokar AA, Ramsay L, Sharpe AG, Diapari M, Sindhu A et al (2014) Genome wide SNP identification in chickpea for use in development of a high density genetic map and improvement of chickpea reference genome assembly. BMC Genomics 15:708CrossRefPubMedCentralPubMedGoogle Scholar
  24. Deulvot C, Charrel H, Marty A, Jacquin F, Donnadieu C et al (2010) Highly-multiplexed SNP genotyping for genetic mapping and germplasm diversity studies in pea. BMC Genomics 11:468CrossRefPubMedCentralPubMedGoogle Scholar
  25. Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik V (1997) Support vector regression machines. Adv Neural Info Process Syst 9:155–161Google Scholar
  26. Duarte J, Rivière N, Baranger A et al (2014) Transcriptome sequencing for high throughput SNP development and genetic mapping in pea. BMC Genomics 15:126CrossRefPubMedCentralPubMedGoogle Scholar
  27. Egbadzor KF, Ofori K, Yeboah M, Aboagye LM, Opoku-Agyeman MO et al (2014) Diversity in 113 cowpea [Vigna unguiculata (L) Walp] accessions assessed with 458 SNP markers. Springer Plus 3:541CrossRefPubMedCentralPubMedGoogle Scholar
  28. Fedoruk M (2013) Linkage and association mapping of seed size and shape in lentil. Thesis (Masters of Science), University of Saskatchewan, SaskatoonGoogle Scholar
  29. Gautami B, Pandey MK, Vadez V, Nigam SN, Ratnakumar P et al (2012) Quantitative trait locus analysis and construction of consensus genetic map for drought tolerance traits based on three recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.) Mol Breed 30:757–772CrossRefPubMedGoogle Scholar
  30. Graham PH, Vance CP (2003) Legume crops: importance and constraints to greater use. Plant Physiol 131:872–877CrossRefPubMedCentralPubMedGoogle Scholar
  31. Grattapaglia D, Resende MDV, Resende MR, Sansaloni CP, Petroli CD et al (2011) Genomic selection for growth traits in eucalyptus: accuracy within and across breeding populations. BMC Proc 5:O16. doi: 10.1186/1753-6561-5-S7-O16 CrossRefPubMedCentralGoogle Scholar
  32. Gujaria N, Kumar A, Dauthal P, Dubey A, Hiremath P et al (2011) Development and use of genic molecular markers (GMMs) for construction of a transcript map of chickpea (Cicer arietinum L.) Theor Appl Genet 122:1577–1589CrossRefPubMedCentralPubMedGoogle Scholar
  33. Guo Z, Tucker D, Lu J, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275CrossRefPubMedGoogle Scholar
  34. Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E et al (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762CrossRefPubMedGoogle Scholar
  35. Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12:186CrossRefPubMedCentralPubMedGoogle Scholar
  36. Hascoët E, Jaminon O, Devaux C, Blassiau C, Bahrman N, Bochard A-M et al (2014) Towards fine mapping of frost tolerance QTLs in pea, in 2nd PeaMUST Annual Meeting (Dijon)Google Scholar
  37. Hayes B, Bowman P, Chamberlain A, Goddard M (2009) Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92:433–443CrossRefPubMedGoogle Scholar
  38. Heffner EL, Me S, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  39. Heffner EI, Jannink JL, Iwata H, Souza E, Sorrells ME (2011) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606CrossRefGoogle Scholar
  40. Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160CrossRefGoogle Scholar
  41. Heslot N, Rutkoski J, Poland J, Jannink JL, Sorrells ME (2013) Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity. PLoS One 8:e74612CrossRefPubMedCentralPubMedGoogle Scholar
  42. Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R et al (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54:1476–1488CrossRefGoogle Scholar
  43. Hiremath PJ, Farmer A, Cannon SB, Woodward J, Kudapa H et al (2011) Large-scale transcriptome analysis in chickpea (Cicer arietinum L.), an orphan legume crop of the semi-arid tropics of Asia and Africa. Plant Biotechnol J 9:922–931. doi: 10.1111/j.1467-7652.2011.00625.x CrossRefPubMedCentralPubMedGoogle Scholar
  44. Hiremath PJ, Kumar A, Penmetsa RV, Farmer A, Schlueter JA et al (2012) Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnol J 10:716–732CrossRefPubMedCentralPubMedGoogle Scholar
  45. Hospital F (2005) Selection in backcross programmes. Philos Trans Roy Soc Lond B Biol Sci 360:1503–1511CrossRefGoogle Scholar
  46. Huynh BL, Close TJ, Roberts PA, Hu Z, Wanamaker S et al (2013) Gene pools and the genetic architecture of domesticated cowpea. The Plant Genome 6:3CrossRefGoogle Scholar
  47. Jaganathan D, Thudi M, Kale S, Azam S, Roorkiwal M et al (2015) Genotyping-by-sequencing based intra-specific genetic map refines a “QTL-hotspot” region for drought tolerance in chickpea. Mol Gen Genomics 290:559–571CrossRefGoogle Scholar
  48. Jain M, Misra G, Patel RK, Priya P, Jhanwar S et al (2013) A draft genome sequence of the pulse crop chickpea (Cicer arietinum L.) Plant J 74:715–729. doi: 10.1111/tpj.12173 CrossRefPubMedGoogle Scholar
  49. Janila P, Pandey MK, Shasidhar Y, Variath MT, Sriswathi M et al (2016) Molecular breeding for introgression of fatty acid desaturase mutant alleles (ahFAD2A and ahFAD2B) enhances oil quality in high and low oil containing peanut genotypes. Plant Sci 242:203–213CrossRefPubMedGoogle Scholar
  50. Jarquín D, Kocak K, Posadas L, Hyma K, Jedlicka J et al (2014) Genotyping by sequencing for genomic prediction in a soybean breeding population. BMC Genomics 15:740CrossRefPubMedCentralPubMedGoogle Scholar
  51. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118–127. doi: 10.1093/biostatistics/kxj037 CrossRefPubMedGoogle Scholar
  52. Kang YJ, Kim SK, Kim MY, Lestari P, Kim KH et al (2014) Genome sequence of mungbean and insights into evolution within Vigna species. Nat Commun 5:5443CrossRefPubMedCentralPubMedGoogle Scholar
  53. Kaur S, Cogan NO, Stephens A, Noy D, Butsch M et al (2014a) EST-SNP discovery and dense genetic mapping in lentil (Lens culinaris Medik.) enable candidate gene selection for boron tolerance. Theor Appl Genet 127:703–713CrossRefPubMedGoogle Scholar
  54. Kaur S, Kimber RBE, Cogan NOI, Materne M, Forster JW et al (2014b) SNP discovery and high-density genetic mapping in faba bean (Vicia faba L.) permits identification of QTLs for ascochyta blight resistance. Plant Sci 217–218:47–55CrossRefPubMedGoogle Scholar
  55. Khera P, Upadhyaya HD, Pandey MK, Roorkiwal M, Sriswathi M et al (2013) SNP-based genetic diversity in the reference set of peanut (Arachis spp.) by developing and applying cost-effective KASPar genotyping assays. Plant Genome 6:1–11CrossRefGoogle Scholar
  56. Kim KH, Kim MY, Van K, Moon JK, Kim DH et al (2008) Marker-assisted foreground and background selection of near isogenic lines for bacterial leaf pustule resistant gene in soybean. J Crop Sci Biotechnol 11:263–268Google Scholar
  57. Kumar S, Banks TW, Cloutier S (2012) SNP discovery through next-generation sequencing and its applications. Int J Plant Genomics 15Google Scholar
  58. Kumar V, Rani A, Rawal R, Mourya V (2015) Marker assisted accelerated introgression of null allele of kunitz trypsin inhibitor in soybean. Breed Sci 65:447–452CrossRefPubMedCentralPubMedGoogle Scholar
  59. Lavaud C, Lesne A, Piriou C, Le Roy G, Boutet G et al (2015) Validation of QTL for resistance to Aphanomyces euteiches in different pea genetic backgrounds using near-isogenic lines. Theor Appl Genet 128:2273–2288CrossRefPubMedGoogle Scholar
  60. Lee YG, Jeong N, Kim JH, Lee K, Kim KH et al (2015) Development, validation and genetic analysis of a large soybean SNP genotyping array. Plant J 81:625–636CrossRefPubMedGoogle Scholar
  61. Legarra A, Robert-Granie P, Croiseau G, Guillaume F, Fritz S (2011) Improved LASSO for genomic selection. Genet Res 93:77–87CrossRefGoogle Scholar
  62. Li X, Wei Y, Acharya A, Hansen JL, Crawford JL et al (2015) Genomic prediction of biomass yield in two selection cycles of a tetraploid alfalfa breeding population. Plant Genome 8Google Scholar
  63. Liu XQ, Rong JY, Liu XY (2008) Best linear unbiased prediction for linear combinations in general mixed linear models. J Multivariate Analysis 99:1503–1517CrossRefGoogle Scholar
  64. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161CrossRefPubMedGoogle Scholar
  65. Lucas MR, Ehlers JD, Huynh BL, Diop NN, Roberts PA et al (2013) Markers for breeding heat-tolerant cowpea. Mol Breed 31:529–536CrossRefGoogle Scholar
  66. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedCentralPubMedGoogle Scholar
  67. Moser G, Tier B, Crump RE, Khatkar MS, Raadsma HW (2009) A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genet Sel Evol 41:56CrossRefPubMedCentralPubMedGoogle Scholar
  68. Mujibi FD, Nkrumah JD, Durunna ON, Stothard P, Mah J et al (2011) Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle. J Anim Sci 89:3353–3361CrossRefPubMedGoogle Scholar
  69. Muñoz-Amatriaín M, Mirebrahim H, Xu P, Wanamaker SI, Luo M et al (2016) Genome resources for climate-resilient cowpea, an essential crop for food security. Plant J. doi:
  70. Nakaya A, Isobe SN (2012) Will genomic selection be a practical method for plant breeding? Ann Bot 110:1303–1316CrossRefPubMedCentralPubMedGoogle Scholar
  71. Pandey MK, Rathore A, Das RR, Khera P, Upadhyaya HD et al (2014a) Selection of appropriate genomic selection model in an unstructured germplasm set of peanut (Arachis hypogaea L.). 6th international Food Legumes Research conference & 7th international conference on Legume Genetics and Genomics on 7–11 July 2014, SaskatoonGoogle Scholar
  72. Pandey MK, Upadhyaya HD, Rathore A, Vadez V, Sheshshayee MS et al (2014b) Genome-wide association studies for 50 agronomic traits in peanut using the ‘reference set’ comprising 300 genotypes from 48 countries of semi-arid tropics of the world. PLoS One 9:e113326CrossRefGoogle Scholar
  73. Pandey MK, Agarwal G, Rathore A, Janila P, Upadhyaya HD, et al. (2015). Development of high density 60K “Axiom_Arachis” SNP Chip and optimization of genomic selection model for enhancing breeding efficiency in peanut. Proceedings of 8th international conference of the Peanut Research Community on “Advances in Arachis through Genomics and Biotechnology”, Brisbane, 5–9 Nov 2015Google Scholar
  74. Pandey MK, Roorkiwal M, Singh VK, Ramalingam A, Kudapa H et al (2016) Emerging genomic tools for legume breeding: current status and future prospects. Front Plant Sci 7Google Scholar
  75. Pandey MK, Agarwal G, Kale SM, Clevenger J, Nayak SN et al (2017) Development and evaluation of a high density genotyping ‘Axiom_Arachis’ array with 58K SNPs for accelerating genetics and breeding in groundnut. Nat Sci Rep 7:40577. doi: 10.1038/srep40577 CrossRefGoogle Scholar
  76. Poland J, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5:92–102CrossRefGoogle Scholar
  77. Poland J, Endelman J, Dawson J, Rutkoski J, Wu S et al (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103–113CrossRefGoogle Scholar
  78. Power JF (1987) Legume crops: their potential role in agricultural production. Am J Alt Agri 2:69–73CrossRefGoogle Scholar
  79. Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11:459–463CrossRefPubMedCentralPubMedGoogle Scholar
  80. Rebello CJ, Greenway FL, Finley JW (2014) A review of the nutritional value of legumes and their effects on obesity and its related co-morbidities. Obesity Rev 15:392–407CrossRefGoogle Scholar
  81. Resende MDV, Resende MFR, Sansaloni CP, Petroli CD, Missiaggia AA et al (2012a) Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol 194:116–128CrossRefPubMedGoogle Scholar
  82. Resende MFR, Munoz P, Resende MDV, Garrick DJ, Fernando RL et al (2012b) Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda l.) Genetics 190:1503–1510CrossRefPubMedCentralPubMedGoogle Scholar
  83. Roorkiwal M, Rathore A, Das RR, Singh MK, Jain A, Srinivasan S, Gaur PM, Chellapilla B, Tripathi S, Li Y, Hickey JM, Lorenz A, Sutton T, Crossa J, Jannink J-L, Varshney RK (2016) Genome-Enabled prediction models for yield related traits in chickpea. Front Plant Sci 7Google Scholar
  84. Roorkiwal M, Sawargaonkar SL, Chitikineni A, Thudi M, Saxena RK et al (2013) Single nucleotide polymorphism genotyping for breeding and genetics applications in chickpea and pigeonpea using the BeadXpress platform. Plant Genome 6Google Scholar
  85. Roorkiwal M, Von Wettberg EJ, Upadhyaya HD, Warschefsky E, Rathore A et al (2014) Exploring germplasm diversity to understand the domestication process in Cicer spp. using SNP and DArT markers. PLoS One 9(7):e102016CrossRefPubMedCentralPubMedGoogle Scholar
  86. Rubiales D, Mikic A (2015) Introduction: legumes in sustainable agriculture. Crit Rev Plant Sci 34:2–3CrossRefGoogle Scholar
  87. Sato S, Nakamura Y, Kaneko T, Asamizu E, Kato T et al (2008) Genome structure of the legume, Lotus japonicus. DNA Res 15:227–239CrossRefPubMedCentralPubMedGoogle Scholar
  88. Saxena RK, Penmetsa RV, Upadhyaya HD, Kumar A, Carrasquilla-Garcia N et al (2012) Large-scale development of cost-effective single-nucleotide polymorphism marker assays for genetic mapping in pigeonpea and comparative mapping in legumes. DNA Res 19:449–461CrossRefPubMedCentralPubMedGoogle Scholar
  89. Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183CrossRefPubMedGoogle Scholar
  90. Sharpe AG, Ramsay L, Sanderson LA, Fedoruk MJ, Clarke WE et al (2013) Ancient orphan crop joins modern era: gene-based SNP discovery and mapping in lentil. BMC Genomics 14:192CrossRefPubMedCentralPubMedGoogle Scholar
  91. Solberg TR, Sonesson AK, Woolliams JA (2008) Genomic selection using different marker types and densities. J Anim Sci 86(10):2447–2454CrossRefPubMedGoogle Scholar
  92. Spindel J, Begum H, Akdemir D, Virk P, Collard B et al (2015) Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11:e1005350CrossRefPubMedCentralPubMedGoogle Scholar
  93. Tarawali G, Ogunbile OA (1995) Legumes for sustainable food production in semi-arid savannahs. ILEIA Newslett 11(4):18–23Google Scholar
  94. Tayeh N, Aluome C, Falque M, Jacquin F, Klein A et al (2015) Development of two major resources for pea genomics: the GenoPea 13.2 K SNP Array and a high-density, high-resolution consensus genetic map. Plant J 84:1257–1273CrossRefPubMedGoogle Scholar
  95. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc Series B 58:267–288Google Scholar
  96. Varshney RK, Chen W, Li Y, Bharti AK, Saxena RK et al (2012) Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nat Biotechnol 30:83–89CrossRefGoogle Scholar
  97. Varshney RK, Mohan SM, Gaur PM, Gangarao NVPR, Pandey MK et al (2013a) Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol Adv 31:1120–1134CrossRefPubMedGoogle Scholar
  98. Varshney RK, Song C, Saxena RK, Azam S, Yu S et al (2013b) Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat Biotechnol 31:240–246. doi: 10.1038/nbt.2491 CrossRefPubMedGoogle Scholar
  99. Varshney RK, Gaur PM, Chamarthi SK, Krishnamurthy L, Tripathi S et al (2013c) Fast-track introgression of “QTL-Hotspot” for root traits and other drought tolerance traits in JG 11, an elite and leading variety of chickpea. Plant Genome 6:3. doi: 10.3835/plantgenome2013.07.0022 Google Scholar
  100. Varshney RK, Mohan SM, Gaur PM, Chamarthi SK, Singh VK et al (2014) Marker-assisted backcrossing to introgress resistance to Fusarium wilt (FW) race 1 and Ascochyta blight (AB) in C 214, an elite cultivar of chickpea. Plant Genome. doi: 10.3835/plantgenome2013.10.0035
  101. Verma S, Gupta S, Bandhiwal N, Kumar T, Bharadwaj C et al (2015) High-density linkage map construction and mapping of seed trait QTLs in chickpea (Cicer arietinum L.) using genotyping-by-sequencing (GBS). Sci Rep 5:17512Google Scholar
  102. Wang J, Chu S, Zhang H, Zhu Y, Cheng H et al (2016) Development and application of a novel genome-wide SNP array reveals domestication history in soybean. Sci Rep 6Google Scholar
  103. Xu P, Wu XH, Wang BG, Luo J, Liu YH et al (2012) Genome wide linkage disequilibrium in Chinese asparagus bean (Vigna unguiculata ssp. sesquipedialis) germplasm: implications for domestication history and genome wide association studies. Heredity 109:34–40CrossRefPubMedCentralPubMedGoogle Scholar
  104. Yang H, Tao Y, Zheng Z, Zhang Q, Zhou G et al (2013) Draft genome sequence, and a sequence-defined genetic linkage map of the legume crop species Lupinus angustifolius L. PLoS One 8:e64799CrossRefPubMedCentralPubMedGoogle Scholar
  105. Young ND, Debellé F, Oldroyd GE, Geurts R, Cannon SB et al (2011) The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480:520–524CrossRefPubMedCentralPubMedGoogle Scholar
  106. Zhang Z, Liu J, Ding X, Bijma P, de Koning D-J et al (2010) Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS One 5:e12648. doi: 10.1371/journal.pone.0012648 CrossRefPubMedCentralPubMedGoogle Scholar
  107. Zhang Z, Ober U, Erbe M, Zhang H, Gao N et al (2014) Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies. PLoS One 9:e93017CrossRefPubMedCentralPubMedGoogle Scholar
  108. Zhang J, Song Q, Cregan PB, Jiang GL (2016) Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor Appl Genet 129:117–130CrossRefPubMedGoogle Scholar
  109. Zhong S, Dekkers JC, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364CrossRefPubMedCentralPubMedGoogle Scholar
  110. Zhu S, Walker DR, Warrington CV, Parrott WA, All JN et al (2007) Registration of four soybean germplasm lines containing defoliating insect resistance QTLs from PI 229358 introgressed into ‘Benning’. J Plant Reg 1:162–163CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)Patancheru, TelanganaIndia

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