Advertisement

Genetic and Genomic Resources in White Lupin and the Application of Genomic Selection

Chapter
Part of the Compendium of Plant Genomes book series (CPG)

Abstract

Landraces represent extremely valuable and largely untapped genetic resources for white lupin improvement. The development of high-throughput, relatively low-cost genotyping techniques, such as genotyping-by-sequencing (GBS), has allowed to develop dense genetic maps and to explore the application of genomic selection to predict breeding values of inbred lines or germplasm accessions for complex polygenic traits. We provide an unprecedented assessment of genomic selection in lupins, by assessing the ability of two selection models (Ridge regression BLUP and Bayesian Lasso) to predict grain yield and other traits of 83 landraces from nine historical cropping regions and eight varieties of white lupin that were autumn-sown in Northern Italy. GBS was applied to 3–4 genotypes per landrace and two genotypes per variety, analyzing cultivar allele frequencies for 6,578 polymorphic SNP markers. The two selection models displayed similar predictive ability. Predictions proved highly accurate for grain yield, winter survival and onset of flowering, which displayed predictive abilities of 0.865, 0.852 and 0.838, respectively, based on cross-validation results. Moderately high predictive ability (0.626–0.495) emerged for pod fertility, individual seed weight, plant height, leaf size, and mainstem proportion of seeds and number of leaves. Genomic selection holds high promise for white lupin based on these results.

Notes

Acknowledgements

This work was part of the project ‘Legumes for the agriculture of tomorrow (LEGATO)’, which received funding from EU’s 7th Framework Programme under Grant Agreement No. 613551.

References

  1. Adhikari K, Buirchell B, Yan G, Sweetingham M (2011) Two complementary dominant genes control flowering time in albus lupin (Lupinus albus L.). Plant Breed 130:496–499Google Scholar
  2. Adhikari KN, Thomas G, Diepeveen D, Trethowan R (2013) Overcoming the barriers of combining early flowering and anthracnose resistance in white lupin (Lupinus albus L.) for the Northern Agricultural Region of Western Australia. Crop Pasture Sci 64:914–921Google Scholar
  3. Annicchiarico P (2008) Adaptation of cool-season grain legume species across climatically-contrasting environments of southern Europe. Agron J 100:1647–1654Google Scholar
  4. Annicchiarico P, Carroni AM (2009) Diversity of white and narrow-leafed lupin genotype adaptive response across south-European environments and implications for selection. Euphytica 166:71–81Google Scholar
  5. Annicchiarico P, Iannucci A (2007) Winter survival of pea, faba bean and white lupin cultivars across contrasting Italian locations and sowing times, and implications for selection. J Agric Sci 145:611–622Google Scholar
  6. Annicchiarico P, Thami-Alami I (2012) Enhancing white lupin (Lupinus albus L.) adaptation to calcareous soils through lime-tolerant plant germplasm and Bradyrhizobium strains. Plant Soil 350:131–144Google Scholar
  7. Annicchiarico P, Harzic N, Carroni AM (2010) Adaptation, diversity, and exploitation of global white lupin (Lupinus albus L.) landrace genetic resources. Field Crops Res 119:114–124Google Scholar
  8. Annicchiarico P, Harzic N, Huyghe C, Carroni AM (2011) Ecological classification of white lupin landrace genetic resources. Euphytica 180:17–25Google Scholar
  9. Annicchiarico P, Nazzicari N, Li X, Wei Y, Pecetti L, Brummer EC (2015a) Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 16:1020PubMedPubMedCentralGoogle Scholar
  10. Annicchiarico P, Barrett B, Brummer EC, Julier B, Marshall AH (2015b) Achievements and challenges in improving temperate perennial forage legumes. Crit Rev Plant Sci 34:327–380Google Scholar
  11. Annicchiarico P, Nazzicari N, Pecetti L, Romani M, Ferrari B, Wei Y, Brummer EC (2017a) GBS-based genomic selection for pea grain yield under severe terminal drought. Plant Genome 10:10.3835Google Scholar
  12. Annicchiarico P, Nazzicari N, Wei Y, Pecetti L, Brummer EC (2017b) Genotyping-by-sequencing and its exploitation for forage and cool-season grain legume breeding. Front Plant Sci 8:679PubMedPubMedCentralGoogle Scholar
  13. Annicchiarico P, Romani M, Pecetti L (2018a) White lupin variation for adaptation to severe drought stress. Plant Breed 137:782–789Google Scholar
  14. Annicchiarico P, Carroni AM, Manunza P, Huyghe C, Pecetti L (2018b) Grain yield and morphology of dwarf vs tall white lupin in Mediterranean environments. In: Brazauskas G, Statkevičiūtė G, Jonavičienė K (eds) Breeding grasses and protein crops in the era of genomics. Springer, Dordrecht, The Netherlands, pp 113–117Google Scholar
  15. Annicchiarico P, Nazzicari N, Pecetti L, Romani M, Russi L (2019a) Pea genomic selection for Italian environments. BMC Genomics 20:603Google Scholar
  16. Annicchiarico P, Nazzicari N, Ferrari B, Harzic N, Carroni AM, Romani M, Pecetti L (2019b) Genomic prediction of grain yield in contrasting environments for white lupin genetic resources. Mol Breed 39:142Google Scholar
  17. Arnoldi A, Boschin G, Zanoni C, Lammi C (2015) The health benefits of sweet lupin seed flours and isolated proteins. J Funct Foods 18:550–563Google Scholar
  18. Atkins CA, Smith PMC, Gupta S, Jones MGK, Caligari PDS (1998) Genetics, cytology and biotechnology. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CABI, Wallingford, UK, pp 67–92Google Scholar
  19. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090Google Scholar
  20. Biazzi E, Nazzicari N, Pecetti L, Brummer EC, Palmonari A, Tava A, Annicchiarico P (2017) Genome-wide association mapping and genomic selection for alfalfa (Medicago sativa) forage quality traits. PLoS ONE 12:e0169234PubMedPubMedCentralGoogle Scholar
  21. Buirchell BJ, Cowling WA (1998) Genetic resources in lupins. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CABI, Wallingford, UK, pp 41–66Google Scholar
  22. Burstin J, Salloignon P, Chabert-Martinello M, Magnin-Robert J-B, Siol M, Jacquin F, Chauveau A, Pont C, Aubert G, Delaitre C, Truntzer C, Duc G (2015) Genetic diversity and trait genomic prediction in a pea diversity panel. BMC Genomics 16:105PubMedPubMedCentralGoogle Scholar
  23. Cowley R, Luckett DJ, Ash GJ, Harper JD, Vipin CA, Raman H, Ellwood S (2014) Identification of QTLs associated with resistance to Phomopsis pod blight (Diaporthe toxica) in Lupinus albus. Breed Sci 64:83–89PubMedPubMedCentralGoogle Scholar
  24. Croxford AE, Rogers T, Caligari PDS, Wilkinson MJ (2008) High-resolution melt analysis to identify and map sequence-tagged site anchor points onto linkage maps: a white lupin (Lupinus albus) map as an exemplar. New Phytol 180:594–607PubMedGoogle Scholar
  25. DeLacy IH, Basford KE, Cooper M, Bull IK, McLaren CG (1996) Analysis of multi-environment trials—an historical perspective. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CABI, Wallingford, UK, pp 39–124Google Scholar
  26. Duhnen A, Gras A, Teyssèdre S, Romestant M, Claustres B, Daydé J, Manfin B (2017) Genomic selection for yield and seed protein content in soybean: a study of breeding program data and assessment of prediction accuracy. Crop Sci 57:1325–1337Google Scholar
  27. Duranti M, Consonni A, Magni C, Sessa F, Scarafoni A (2008) The major proteins of lupin seed: characterization and molecular properties for use as functional and nutraceutical ingredients. Trends Food Sci Technol 19:624–633Google Scholar
  28. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:e19379PubMedPubMedCentralGoogle Scholar
  29. Gladstones JS (1998) Distribution, origin, taxonomy, history and importance. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CABI, Wallingford, UK, pp 1–39Google Scholar
  30. Gresta F, Wink M, Prins U, Abberton M, Capraro J, Scarafoni A, Hill G (2017) Lupins in European cropping systems. In: Murphy-Bokern D, Stoddard FL, Watson CA (eds) Legumes in cropping systems. CABI, Wallingford, UK, pp 88–108Google Scholar
  31. Hane JK, Ming Y, Kamphuis LG, Nelson MN, Garg G, Atkins CA, Bayer PE, Bravo A, Bringans S, Cannon S, Edwards D, Foley R, Gao L, Harrison MJ, Huang W, Hurgobin B, Li S, Liu C, McGrath A, Morahan G, Murray J, Weller J, Jian J, Singh KB (2017) A comprehensive draft genome sequence for lupin (Lupinus angustifolius), an emerging health food: insights into plant–microbe interactions and legume evolution. Plant Biotechnol J 15:318–330PubMedGoogle Scholar
  32. Harzic N, Huyghe C, Papineau J (1995) Dry matter accumulation and seed yield of dwarf autumn-sown white lupin (Lupinus albus L.). Can J Plant Sci 75:549–555.Google Scholar
  33. Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12Google Scholar
  34. Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690Google Scholar
  35. Heffner EL, Jannink J-L, Iwata H, Souza E, Sorrells ME (2011) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51:2597–2606Google Scholar
  36. Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, Cresko WA (2010) Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet 6:e1000862PubMedPubMedCentralGoogle Scholar
  37. Huyghe C (1997) White lupin (Lupinus albus L.). Field Crops Res 53:147–160Google Scholar
  38. Huyghe C, Papineau J (1990) Winter development of autumn-sown white lupin: agronomic and breeding consequences. Agronomie 10:709–716Google Scholar
  39. Jacob I, Feuerstein U, Heinz M, Schott M, Urbatzka P (2017) Evaluation of new breeding lines of white lupin with improved resistance to anthracnose. Euphytica 213:236Google Scholar
  40. Jarquín D, Kocak K, Posadas L, Hyma K, Jedlicka J, Graef G, Lorenz A (2014) Genotyping by sequencing for genomic prediction in a soybean breeding population. BMC Genomics 15:740PubMedPubMedCentralGoogle Scholar
  41. Jarquín D, Specht J, Lorenz A (2016) Prospects of genomic prediction in the USDA soybean germplasm collection: historical data creates robust models for enhancing selection of accessions. G3 (Bethesda) 6:2329–2341Google Scholar
  42. Julier B, Huyghe C (1993) Description and model of the architecture of four genotypes of determinate autumn-sown white lupin (Lupinus albus l.) as influenced by location, sowing date and density. Ann Bot 72:493–501Google Scholar
  43. Julier B, Huyghe C, Papineau J, Milford GFJ, Day JM, Billot C, Mangin P (1993) Seed yield and yield stability of determinate and indeterminate autumn-sown white lupins (Lupinus albus) grown at different locations in France and the UK. J Agric Sci 121:177–186Google Scholar
  44. Kerley SJ, Shield IF, Huyghe C (2001) Specific and genotypic variation in the nutrient content of lupin species in soils of neutral and alkaline pH. Aust J Agric Res 52:93–102Google Scholar
  45. Książkiewicz M, Nazzicari N, Yang H, Nelson N, Renshaw D, Rychel S, Ferrari B, Carelli M, Tomaszewska M, Stawiński S, Naganowska B, Wolko B, Annicchiarico P (2017) A high-density consensus linkage map of white lupin highlights synteny with narrow-leafed lupin and provides markers tagging key agronomic traits. Sci Rep 7:15335PubMedPubMedCentralGoogle Scholar
  46. Kurlovich BS (2002) Lupins (geography, classification, genetic resources and breeding). Publishing House Intan, St. Petersburg, RussiaGoogle Scholar
  47. Lagunes-Espinoza L, Huyghe C, Papineau J, Pacault D (1999) Effect of genotype and environment on pod wall proportion in white lupin: consequences to seed yield. Aust J Agric Res 50:575–582Google Scholar
  48. Lin R, Renshaw D, Luckett D, Clements J, Yan G, Adhikari K, Yang H (2009) Development of a sequence-specific PCR marker linked to the gene “pauper” conferring low-alkaloids in white lupin (Lupinus albus L.) for marker assisted selection. Mol Breed 23:153–161Google Scholar
  49. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J-L (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123Google Scholar
  50. Lu F, Lipka AE, Glaubitz J, Elshire R, Cherney JH, Casler MD, Buckler ES, Costich DE (2013) Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet 9:e1003215PubMedPubMedCentralGoogle Scholar
  51. Ma Y, Reif JC, Jiang Y, Wen Z, Wang D, Liu Z, Guo Y, Wei S, Wang S, Yang C, Wang H, Yang C, Lu W, Xu R, Zhou, Wang R, Sun Z, Chen H, Zhang W, Wu J, Hu G, Liu C, Luan X, Fu Y, Guo T, Han T, Zhang M, Sun B, Zhang L, Chen X, Han D, Yan H, Li W (2016) Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Mol Breed 36:113Google Scholar
  52. Mera M, Alcalde JM (2019) Lupinus albus is the species that achieves greatest grain and protein yields in chile. In: Developing lupin crop into a modern and sustainable food and feed source. Fundación PROINPA, Cochabamba, Bolivia, p 44Google Scholar
  53. Mera M, Beltran L, Miranda H, Rouanet JL (2006) Strong heritability across years and sites for pod wall proportion and specific weight in Lupinus albus and genotypic correlation with other pod and seed attributes. Plant Breed 125:161–166Google Scholar
  54. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  55. Naganowska B, Wolko B, Śliwińska E, Kaczmarek Z (2003) Nuclear DNA content variation and species relationships in the genus Lupinus (Fabaceae). Ann Bot 92:349–355PubMedPubMedCentralGoogle Scholar
  56. Nazzicari N, Biscarini F (2017) GROAN: genomic regression workbench (version 1.2.0). https://cran.r-project.org/package=GROAN. Accessed 19 July 2019
  57. Nazzicari N, Biscarini F, Cozzi P, Brummer EC, Annicchiarico P (2016) Marker imputation efficiency for genotyping-by-sequencing data in rice (Oryza sativa) and alfalfa (Medicago sativa). Mol Breed 16:69Google Scholar
  58. O’Rourke JA, Yang SS, Miller SS, Bucciarelli B, Liu J, Rydeen A, Gronwald JW (2013) An RNA-Seq transcriptome analysis of orthophosphate-deficient white lupin reveals novel insights into phosphorus acclimation in plants. Plant Physiol 161:705–724Google Scholar
  59. Papineau J, Huyghe C (2004) Le Lupin Doux Protéagineux. Editions France Agricole, ParisGoogle Scholar
  60. Park T, Casella G (2008) The Bayesian Lasso. J Am Stat Assoc 103:681–686Google Scholar
  61. Phan HTT, Ellwood SR, Adhikari K, Nelson MN, Oliver RP (2007) The first genetic and comparative map of white lupin (Lupinus albus L.): identification of QTLs for anthracnose resistance and flowering time, and a locus for alkaloid content. DNA Res 14:59–70PubMedPubMedCentralGoogle Scholar
  62. Raman R, Vipin C, Luckett DJ, Cowley RB, Ash GJ, Harper JD, Raman, H (2014) Localisation of loci involved in resistance to Diaporthe toxica and Pleiochaeta setosa in white lupin (Lupinus albus L.). Open J Genet 4:210Google Scholar
  63. Rodrigues ML, Pacheco CMA, Chaves MM (1995) Soil-plant water relations, root distribution and biomass partitioning in Lupinus albus L. under drought conditions. J Exp Bot 46:947–956Google Scholar
  64. 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 7:1666PubMedPubMedCentralGoogle Scholar
  65. Schwender H, Fritsch A (2013) Scrime: analysis of high-dimensional categorical data such as SNP data (version 1.3.5). https://cran.r-project.org/web/packages/scrime/index.html. Accessed 19 July 2019
  66. Searle SR, Casella G, McCulloch CE (2009) Variance components. Wiley, New York, USAGoogle Scholar
  67. Varshney RK, Kudapa H, Pazhamala L, Chitikineni A, Thudi M, Bohra A, Gaur PM, Janila P, Fikre A, Kimurto P, Ellis N (2015) Translational genomics in agriculture: some examples in grain legumes. Crit Rev Plant Sci 34:169–194Google Scholar
  68. Viana JMS, Piepho H-P, Silva FF (2017) Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations. Sci Agric 74:41–50Google Scholar
  69. Vipin CA, Luckett DJ, Harper JD, Ash GJ, Kilian A, Ellwood SR, Raman H (2013) Construction of integrated linkage map of a recombinant inbred line population of white lupin (Lupinus albus L.). Breed Sci 63:292–300PubMedPubMedCentralGoogle Scholar
  70. Wang X, Xu Y, Hu Z, Xu C (2018) Genomic selection methods for crop improvement: current status and prospects. Crop J 6:330–340Google Scholar
  71. Wiggans GR, Cole JB, Hubbard SM, Sonstegard TS (2017) Genomic selection in dairy cattle: the USDA experience. Annu Rev Anim Biosci 5:309–327PubMedGoogle Scholar
  72. Yang H, Lin R, Renshaw D, Li C, Adhikari K, Thomas G, Yan G (2010) Development of sequence-specific PCR markers associated with a polygenic controlled trait for marker-assisted selection using a modified selective genotyping strategy: a case study on anthracnose disease resistance in white lupin (Lupinus albus L.). Mol Breed 25:239–249Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Council for Agricultural Research and Economics (CREA), Centre for Animal Production and AquacultureLodiItaly

Personalised recommendations