Novel candidate loci for morpho-agronomic and seed quality traits detected by targeted genotyping-by-sequencing in common bean.

Samson Ugwuanyi, Obi Sergius Udengwu, Rod J Snowdon, Christian Obermeier
Author Information
  1. Samson Ugwuanyi: Department of Plant Breeding, Justus Liebig University, Giessen, Germany.
  2. Obi Sergius Udengwu: Department of Plant Science and Biotechnology, University of Nigeria, Nsukka, Nigeria.
  3. Rod J Snowdon: Department of Plant Breeding, Justus Liebig University, Giessen, Germany.
  4. Christian Obermeier: Department of Plant Breeding, Justus Liebig University, Giessen, Germany.

Abstract

L., known as common bean, is one of the most important grain legumes cultivated around the world for its immature pods and dry seeds, which are rich in protein and micronutrients. Common bean offers a cheap food and protein sources to ameliorate food shortage and malnutrition around the world. However, the genetic basis of most important traits in common bean remains unknown. This study aimed at identifying QTL and candidate gene models underlying twenty-six agronomically important traits in common bean. For this, we assembled and phenotyped a diversity panel of 200 P genotypes in the greenhouse, comprising determinate bushy, determinate climbing and indeterminate climbing beans. The panel included dry beans and snap beans from different breeding programmes, elite lines and landraces from around the world with a major focus on accessions of African, European and South American origin. The panel was genotyped using a cost-conscious targeted genotyping-by-sequencing (GBS) platform to take advantage of highly polymorphic SNPs detected in previous studies and in diverse germplasm. The detected single nucleotide polymorphisms (SNPs) were applied in marker-trait analysis and revealed sixty-two quantitative trait loci (QTL) significantly associated with sixteen traits. Gene model identification a similarity-based approach implicated major candidate gene models underlying the QTL associated with ten traits including, flowering, yield, seed quality, pod and seed characteristics. Our study revealed six QTL for pod shattering including three new QTL potentially useful for breeding. However, the panel was evaluated in a single greenhouse environment and the findings should be corroborated by evaluations across different field environments. Some of the detected QTL and a number of candidate gene models only elucidate the understanding of the genetic nature of these traits and provide the basis for further studies. Finally, the study showed the possibility of using a limited number of SNPs in performing marker-trait association in common bean by applying a highly scalable targeted GBS approach. This targeted GBS approach is a cost-efficient strategy for assessment of the genetic basis of complex traits and can enable geneticists and breeders to identify novel loci and targets for marker-assisted breeding more efficiently.

Keywords

References

  1. Sci Rep. 2018 Apr 19;8(1):6261 [PMID: 29674702]
  2. PLoS One. 2015 Jan 28;10(1):e0116822 [PMID: 25629314]
  3. Theor Appl Genet. 2012 May;124(8):1539-47 [PMID: 22331140]
  4. Physiol Plant. 2016 Jan;156(1):97-107 [PMID: 26096810]
  5. Front Plant Sci. 2017 Apr 27;8:649 [PMID: 28496452]
  6. Annu Rev Plant Biol. 2013;64:189-217 [PMID: 23451786]
  7. Genetics. 2000 Jun;155(2):945-59 [PMID: 10835412]
  8. Plant Genome. 2016 Nov;9(3): [PMID: 27902795]
  9. J Hered. 2013 Mar;104(2):273-86 [PMID: 23235700]
  10. PLoS One. 2018 Jan 4;13(1):e0190303 [PMID: 29300788]
  11. Genet Mol Biol. 2011 Jan;34(1):88-102 [PMID: 21637550]
  12. Proc Natl Acad Sci U S A. 2014 Dec 16;111(50):17797-802 [PMID: 25468966]
  13. Front Plant Sci. 2022 Apr 25;13:830896 [PMID: 35557726]
  14. Plants (Basel). 2022 Jun 10;11(12): [PMID: 35736697]
  15. Genetics. 2015 Feb;199(2):379-98 [PMID: 25527288]
  16. BMC Plant Biol. 2021 Apr 17;21(1):184 [PMID: 33865309]
  17. New Phytol. 2013 Jan;197(1):300-313 [PMID: 23126683]
  18. Front Plant Sci. 2019 Jul 24;10:962 [PMID: 31428109]
  19. Plant J. 2008 Mar;53(5):814-27 [PMID: 18036197]
  20. Plant J. 2021 Nov;108(4):1193-1212 [PMID: 34562334]
  21. Plant J. 2019 Jan;97(1):8-18 [PMID: 30368955]
  22. BMC Genomics. 2019 Jul 29;20(1):618 [PMID: 31357925]
  23. G3 (Bethesda). 2015 Aug 28;5(11):2285-90 [PMID: 26318155]
  24. Nat Commun. 2014;5:3352 [PMID: 24549030]
  25. Sci Rep. 2016 Sep 20;6:33673 [PMID: 27646167]
  26. BMC Genomics. 2020 Nov 16;21(1):799 [PMID: 33198642]
  27. New Phytol. 2020 Jan;225(1):558-570 [PMID: 31486530]
  28. J Exp Bot. 2021 Feb 27;72(5):1617-1633 [PMID: 33247939]
  29. Bioinformatics. 2012 Aug 1;28(15):2086-7 [PMID: 22689388]
  30. Plant J. 2019 Feb;97(4):693-714 [PMID: 30422331]
  31. Genes (Basel). 2019 Dec 28;11(1): [PMID: 31905657]
  32. G3 (Bethesda). 2019 Jun 5;9(6):1881-1892 [PMID: 31167806]
  33. Genet Resour Crop Evol. 2019;66(3):707-722 [PMID: 30956400]
  34. Plant Cell. 2011 Nov;23(11):4041-53 [PMID: 22086088]
  35. New Phytol. 2022 Sep;235(6):2454-2465 [PMID: 35708662]
  36. Theor Appl Genet. 2010 Sep;121(5):801-13 [PMID: 20502861]
  37. Theor Appl Genet. 2006 Apr;112(6):1149-63 [PMID: 16432734]
  38. PLoS One. 2016 Jun 06;11(6):e0156391 [PMID: 27270627]
  39. Proc Natl Acad Sci U S A. 2013 Oct 29;110(44):18017-22 [PMID: 24127609]
  40. Sci Rep. 2020 Mar 27;10(1):5623 [PMID: 32221398]
  41. Genes (Basel). 2020 Oct 30;11(11): [PMID: 33143347]
  42. Theor Appl Genet. 2015 May;128(5):813-26 [PMID: 25740562]
  43. Front Plant Sci. 2017 Mar 03;8:251 [PMID: 28316606]
  44. Sci Rep. 2021 Feb 3;11(1):2964 [PMID: 33536468]
  45. Bioinformatics. 2012 Sep 15;28(18):2397-9 [PMID: 22796960]
  46. Bioinformatics. 2005 Jan 15;21(2):263-5 [PMID: 15297300]
  47. J Exp Bot. 2014 Aug;65(16):4505-13 [PMID: 24482369]
  48. Plant Cell. 2021 Apr 17;33(2):179-199 [PMID: 33793864]
  49. PLoS One. 2019 Feb 7;14(2):e0212140 [PMID: 30730982]
  50. Theor Appl Genet. 2021 Jan;134(1):313-325 [PMID: 33130953]
  51. PLoS Genet. 2016 Feb 01;12(2):e1005767 [PMID: 26828793]
  52. Genes (Basel). 2018 Dec 21;10(1): [PMID: 30583474]
  53. J Exp Bot. 2012 Oct;63(17):6139-47 [PMID: 23066145]
  54. Nat Genet. 2014 Jul;46(7):707-13 [PMID: 24908249]
  55. Bioinformatics. 2007 May 15;23(10):1294-6 [PMID: 17384015]
  56. BMC Genomics. 2014 Oct 16;15:903 [PMID: 25326146]
  57. Nat Genet. 2010 Apr;42(4):355-60 [PMID: 20208535]
  58. Front Plant Sci. 2021 Mar 05;12:636484 [PMID: 33763096]
  59. Plant Genome. 2015 Jul;8(2):eplantgenome2014.09.0059 [PMID: 33228312]
  60. Front Plant Sci. 2017 Jun 13;8:985 [PMID: 28659941]
  61. Genes (Basel). 2018 Oct 23;9(11): [PMID: 30360561]
  62. J Hered. 2002 Jan-Feb;93(1):77-8 [PMID: 12011185]

Word Cloud

Created with Highcharts 10.0.0traitsbeanQTLcommontargetedcandidatepaneldetectedseedimportantaroundworldgeneticbasisstudygenemodelsbeansbreedinggenotyping-by-sequencingGBSSNPssinglemarker-traitlociapproachqualitypoddryproteinfoodHoweverunderlyinggreenhousedeterminateclimbingdifferentmajorusinghighlystudiesnucleotidepolymorphismsrevealedassociatedincludingshatteringnumberassociationLknownonegrainlegumescultivatedimmaturepodsseedsrichmicronutrientsCommonofferscheapsourcesameliorateshortagemalnutritionremainsunknownaimedidentifyingtwenty-sixagronomicallyassembledphenotypeddiversity200 PgenotypescomprisingbushyindeterminateincludedsnapprogrammeselitelineslandracesfocusaccessionsAfricanEuropeanSouthAmericanorigingenotypedcost-consciousplatformtakeadvantagepolymorphicpreviousdiversegermplasmappliedanalysissixty-twoquantitativetraitsignificantlysixteenGenemodelidentificationsimilarity-basedimplicatedtenfloweringyieldcharacteristicssixthreenewpotentiallyusefulevaluatedenvironmentfindingscorroboratedevaluationsacrossfieldenvironmentselucidateunderstandingnatureprovideFinallyshowedpossibilitylimitedperformingapplyingscalablecost-efficientstrategyassessmentcomplexcanenablegeneticistsbreedersidentifynoveltargetsmarker-assistedefficientlyNovelmorpho-agronomicGWASPhaseolusvulgarisphenology

Similar Articles

Cited By