Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.

Paulo Izquierdo, Rie Sadohara, Jason Wiesinger, Raymond Glahn, Carlos Urrea, Karen Cichy
Author Information
  1. Paulo Izquierdo: Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States.
  2. Rie Sadohara: Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States.
  3. Jason Wiesinger: USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States.
  4. Raymond Glahn: USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States.
  5. Carlos Urrea: 3 Department of Agronomy and Horticulture, Panhandle Research and Extension Center, University of Nebraska-Lincoln, Scottsbluff, NE, United States.
  6. Karen Cichy: Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States.

Abstract

Dry bean is a nutrient-dense food targeted in biofortification programs to increase seed iron and zinc levels. The underlying assumption of breeding for higher mineral content is that enhanced iron and zinc levels will deliver health benefits to the consumers of these biofortified foods. This study characterized a diversity panel of 275 genotypes comprising the Yellow Bean Collection (YBC) for seed Fe and Zn concentration, Fe bioavailability (FeBio), and seed yield across 2 years in two field locations. The genetic architecture of each trait was elucidated via genome-wide association studies (GWAS) and the efficacy of genomic prediction (GP) was assessed. Moreover, 82 yellow breeding lines were evaluated for seed Fe and Zn concentrations as well as seed yield, serving as a prediction set for GP models. Large phenotypic variability was identified in all traits evaluated, and variations of up to 2.8 and 13.7-fold were observed for Fe concentration and FeBio, respectively. Prediction accuracies in the YBC ranged from a low of 0.12 for Fe concentration, to a high of 0.72 for FeBio, and an accuracy improvement of 0.03 was observed when a QTN, identified through GWAS, was used as a fixed effect for FeBio. This study provides evidence of the lack of correlation between FeBio estimated and Fe concentration and highlights the potential of GP in accurately predicting FeBio in yellow beans, offering a cost-effective alternative to the traditional assessment of using Caco2 cell methodologies.

Keywords

References

  1. Theor Appl Genet. 2012 Aug;125(4):759-71 [PMID: 22566067]
  2. PLoS Genet. 2013;9(7):e1003608 [PMID: 23874214]
  3. Plant Genome. 2016 Nov;9(3): [PMID: 27902799]
  4. Front Plant Sci. 2021 Sep 09;12:715910 [PMID: 34589099]
  5. Theor Appl Genet. 2019 Mar;132(3):669-686 [PMID: 30569365]
  6. Genet Res (Camb). 2010 Aug;92(4):295-308 [PMID: 20943010]
  7. Nat Genet. 2014 Jul;46(7):707-13 [PMID: 24908249]
  8. J Nutr. 1998 Sep;128(9):1555-61 [PMID: 9732319]
  9. Theor Appl Genet. 2021 Sep;134(9):2795-2811 [PMID: 34027567]
  10. BMC Genomics. 2016 Aug 31;17 Suppl 5:498 [PMID: 27585926]
  11. Genomics Proteomics Bioinformatics. 2021 Aug;19(4):629-640 [PMID: 34492338]
  12. Nutrients. 2019 Aug 01;11(8): [PMID: 31374868]
  13. Front Plant Sci. 2013 Jul 29;4:275 [PMID: 23908660]
  14. New Phytol. 2018 Aug;219(3):1112-1123 [PMID: 29897103]
  15. Geohealth. 2017 Aug 02;1(6):248-257 [PMID: 32158990]
  16. Plant Genome. 2022 Mar;15(1):e20173 [PMID: 34817119]
  17. Genetics. 2022 Aug 30;222(1): [PMID: 35924977]
  18. Am J Hum Genet. 2018 Sep 6;103(3):338-348 [PMID: 30100085]
  19. J Nutr. 2020 Nov 19;150(11):3013-3023 [PMID: 32678427]
  20. J Agric Food Chem. 2007 Sep 19;55(19):7950-6 [PMID: 17705438]
  21. Nutrients. 2018 Nov 01;10(11): [PMID: 30388772]
  22. Theor Appl Genet. 2012 Sep;125(5):1015-31 [PMID: 22718301]
  23. Front Plant Sci. 2021 May 10;12:670965 [PMID: 34040625]
  24. Sci Rep. 2020 May 18;10(1):8195 [PMID: 32424224]
  25. Theor Appl Genet. 2018 Aug;131(8):1645-1658 [PMID: 29752522]
  26. Sci Rep. 2020 Nov 13;10(1):19775 [PMID: 33188249]
  27. J Agric Food Chem. 2016 Nov 16;64(45):8592-8603 [PMID: 27754657]
  28. Front Genet. 2022 Mar 14;13:750814 [PMID: 35391791]
  29. Bioinformatics. 2019 Nov 1;35(22):4716-4723 [PMID: 31099384]
  30. Bioinformatics. 2011 Nov 1;27(21):2987-93 [PMID: 21903627]
  31. Genetics. 2021 May 17;218(1): [PMID: 33748861]
  32. Plant Genome. 2018 Jul;11(2): [PMID: 30025029]
  33. Plant Genome. 2023 Jun;16(2):e20328 [PMID: 37082832]
  34. Nat Methods. 2012 Mar 04;9(4):357-9 [PMID: 22388286]
  35. Theor Appl Genet. 2011 Feb;122(3):511-21 [PMID: 21113704]
  36. Genetics. 2001 Apr;157(4):1819-29 [PMID: 11290733]
  37. PLoS One. 2015 Sep 18;10(9):e0138479 [PMID: 26381264]
  38. Proc Nutr Soc. 2019 Aug;78(3):380-387 [PMID: 30688178]
  39. Front Plant Sci. 2020 Jul 07;11:1001 [PMID: 32774338]
  40. Front Genet. 2014 Oct 16;5:363 [PMID: 25360145]
  41. Lancet. 2019 Feb 2;393(10170):447-492 [PMID: 30660336]
  42. Heredity (Edinb). 2021 Nov;127(5):423-432 [PMID: 34564692]
  43. Plant Genome. 2022 Dec;15(4):e20254 [PMID: 36043341]
  44. Front Plant Sci. 2021 Mar 05;12:636484 [PMID: 33763096]
  45. 3 Biotech. 2017 Oct;7(5):295 [PMID: 28868222]
  46. J Nutr. 2023 Jul;153(7):2125-2132 [PMID: 37182693]

Word Cloud

Created with Highcharts 10.0.0FeFeBioseedironconcentrationpredictionzincbioavailabilitygenomicGPyellow0biofortificationlevelsbreedingstudydiversitypanelYBCZnyieldassociationGWASevaluatedidentifiedobservedbeansDrybeannutrient-densefoodtargetedprogramsincreaseunderlyingassumptionhighermineralcontentenhancedwilldeliverhealthbenefitsconsumersbiofortifiedfoodscharacterized275genotypescomprisingYellowBeanCollectionacross2 yearstwofieldlocationsgeneticarchitecturetraitelucidatedviagenome-widestudiesefficacyassessedMoreover82linesconcentrationswellservingsetmodelsLargephenotypicvariabilitytraitsvariations28137-foldrespectivelyPredictionaccuraciesrangedlow12high72accuracyimprovement03QTNusedfixedeffectprovidesevidencelackcorrelationestimatedhighlightspotentialaccuratelypredictingofferingcost-effectivealternativetraditionalassessmentusingCaco2cellmethodologiesGenome-widecollectiondryGWAS-assistedPhaseolusvulgarisL

Similar Articles

Cited By