Display options
Cite
Kaler AS, Gillman JD, Beissinger T, et al. Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize. Front Plant Sci. 2020;10:1794doi: 10.3389/fpls.2019.01794.
Kaler, A. S., Gillman, J. D., Beissinger, T., & Purcell, L. C. (2019). Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize. Frontiers in plant science, 101794. https://doi.org/10.3389/fpls.2019.01794
Kaler, Avjinder S, et al. "Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize." Frontiers in plant science vol. 10 (2019): 1794. doi: https://doi.org/10.3389/fpls.2019.01794
Kaler AS, Gillman JD, Beissinger T, Purcell LC. Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize. Front Plant Sci. 2020 Feb 25;10:1794. doi: 10.3389/fpls.2019.01794. eCollection 2019. PMID: 32158452; PMCID: PMC7052329.
Copy Download .nbib Format: NLM
Share it on

Front Plant Sci. 2020 Feb 25;10:1794. doi: 10.3389/fpls.2019.01794. eCollection 2019.

Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

Frontiers in plant science

Avjinder S Kaler, Jason D Gillman, Timothy Beissinger, Larry C Purcell

Affiliations

  1. Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States.
  2. Plant Genetic Research Unit, USDA-ARS, Columbia, MO, United States.
  3. Division of Plant Breeding Methodology, Center for Integrated Breeding Research, Georg-August-Universität, Göttingen, Germany.

PMID: 32158452 PMCID: PMC7052329 DOI: 10.3389/fpls.2019.01794

Abstract

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (

Copyright © 2020 Kaler, Gillman, Beissinger and Purcell.

Keywords: association mapping; genome-wide association analyses; multiple testing correction; quantitative trait loci; statistical model analysis

References

  1. Nat Commun. 2011 Sep 13;2:467 - PubMed
  2. Front Plant Sci. 2018 Feb 02;9:69 - PubMed
  3. Sci Rep. 2016 Jan 20;6:19444 - PubMed
  4. Genetics. 2003 Jan;163(1):295-309 - PubMed
  5. BMC Genomics. 2019 Jul 29;20(1):618 - PubMed
  6. Plant Biotechnol J. 2018 Feb;16(2):688-695 - PubMed
  7. Nat Genet. 2006 Aug;38(8):904-9 - PubMed
  8. Genetics. 2000 Jun;155(2):945-59 - PubMed
  9. Nat Genet. 2010 Apr;42(4):355-60 - PubMed
  10. Plant Cell. 2004 Apr;16(4):819-35 - PubMed
  11. Plant J. 2005 Dec;44(6):1054-64 - PubMed
  12. Plant Cell. 2005 Oct;17(10):2619-32 - PubMed
  13. G3 (Bethesda). 2015 Sep 15;5(11):2383-90 - PubMed
  14. Curr Opin Biotechnol. 1998 Dec;9(6):578-94 - PubMed
  15. Annu Rev Genet. 2001;35:303-39 - PubMed
  16. Plant Mol Biol. 2002 Sep;50(2):187-96 - PubMed
  17. Genetics. 2008 Mar;178(3):1745-54 - PubMed
  18. Genetics. 2015 Feb;199(2):379-98 - PubMed
  19. Annu Rev Plant Biol. 2003;54:357-74 - PubMed
  20. PLoS One. 2014 Sep 23;9(9):e107684 - PubMed
  21. Nature. 2010 Jan 14;463(7278):178-83 - PubMed
  22. PLoS Genet. 2010 May 06;6(5):e1000940 - PubMed
  23. PLoS Comput Biol. 2017 Jan 31;13(1):e1005357 - PubMed
  24. BMC Plant Biol. 2012 Jul 18;12:109 - PubMed
  25. Brief Bioinform. 2002 Jun;3(2):146-53 - PubMed
  26. Nat Genet. 2005 Jun;37 Suppl:S5-10 - PubMed
  27. PLoS One. 2009 Dec 24;4(12):e8451 - PubMed
  28. G3 (Bethesda). 2015 Jul 28;5(10):1999-2006 - PubMed
  29. BMC Genomics. 2009 Feb 27;10:94 - PubMed
  30. Nat Genet. 2012 Jun 17;44(7):825-30 - PubMed
  31. Am J Hum Genet. 2000 Dec;67(6):1544-54 - PubMed
  32. Genome. 2004 Oct;47(5):819-31 - PubMed
  33. Am J Hum Genet. 2001 Jul;69(1):1-14 - PubMed
  34. Trends Genet. 2002 Feb;18(2):83-90 - PubMed
  35. Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11479-84 - PubMed
  36. Genome. 2006 Jun;49(6):565-71 - PubMed
  37. Science. 1996 Sep 13;273(5281):1516-7 - PubMed
  38. BMC Biol. 2014 Oct 17;12:73 - PubMed
  39. Nat Genet. 2003 Mar;33 Suppl:228-37 - PubMed
  40. Proc Natl Acad Sci U S A. 2012 Jun 5;109(23):8872-7 - PubMed
  41. BMC Genomics. 2015 Sep 03;16:671 - PubMed
  42. Nat Rev Genet. 2009 Jun;10(6):381-91 - PubMed
  43. Bioinformatics. 2012 Sep 15;28(18):2397-9 - PubMed
  44. Genome. 2003 Aug;46(4):659-64 - PubMed
  45. BMC Genet. 2010 Oct 15;11:94 - PubMed
  46. Brief Bioinform. 2018 Jul 20;19(4):700-712 - PubMed
  47. PLoS Genet. 2016 Feb 01;12(2):e1005767 - PubMed
  48. J Hered. 2010 Nov-Dec;101(6):757-68 - PubMed
  49. Nat Rev Genet. 2008 May;9(5):356-69 - PubMed
  50. Genetics. 2007 Apr;175(4):1937-44 - PubMed
  51. Theor Appl Genet. 2017 Oct;130(10):2203-2217 - PubMed
  52. BMC Plant Biol. 2010 Jul 28;10:155 - PubMed
  53. Nat Genet. 2006 Feb;38(2):203-8 - PubMed
  54. PLoS One. 2013;8(1):e54985 - PubMed

Publication Types