Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Bayesian LASSO, scale space and decision making in association genetics.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read Online Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Genetics*; Computational Biology/*methods; Bayes Theorem ; Decision Making ; Hordeum/genetics ; Quantitative Trait Loci/genetics ; Regression Analysis ; Triticum/genetics
    • Abstract:
      Background: LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection.
      Results: We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.
    • References:
      Genet Epidemiol. 2010 Jul;34(5):455-62. (PMID: 20568276)
      Genetics. 2009 Sep;183(1):347-63. (PMID: 19620397)
      Int J Plant Genomics. 2010;2010:893206. (PMID: 20631902)
      Genetics. 2012 Jul;191(3):969-87. (PMID: 22554888)
      Bioinformatics. 2011 Feb 15;27(4):516-23. (PMID: 21156729)
      Genetics. 2010 May;185(1):349-59. (PMID: 20157003)
      Heredity (Edinb). 2010 Nov;105(5):483-94. (PMID: 20051978)
      Stat Appl Genet Mol Biol. 2012 Jan 06;11(2):. (PMID: 22499691)
      Theor Appl Genet. 2012 Aug;125(3):419-35. (PMID: 22622521)
      BMC Bioinformatics. 2011 May 23;12:186. (PMID: 21605355)
      Genetics. 2013 Feb;193(2):327-45. (PMID: 22745228)
      Genetics. 2007 Jun;176(2):1169-85. (PMID: 17435239)
      Am J Hum Genet. 2003 Feb;72(2):351-63. (PMID: 12525994)
      Genetics. 2008 Jun;179(2):1045-55. (PMID: 18505874)
      Biometrics. 2007 Jun;63(2):513-21. (PMID: 17688503)
      Heredity (Edinb). 2006 Jul;97(1):4-18. (PMID: 16670709)
      Genetics. 1994 Nov;138(3):963-71. (PMID: 7851788)
      Genet Res (Camb). 2011 Aug;93(4):303-18. (PMID: 21767461)
      J R Stat Soc Series B Stat Methodol. 2008;70(5):903. (PMID: 19603084)
      BMC Proc. 2010 Mar 31;4 Suppl 1:S12. (PMID: 20380755)
      Philos Trans R Soc Lond B Biol Sci. 1994 Jun 29;344(1310):345-50; discussion 350-1. (PMID: 7800704)
      Theor Appl Genet. 2012 Nov;125(7):1575-87. (PMID: 22824967)
      Biostatistics. 2011 Jan;12(1):51-67. (PMID: 20577014)
      BMC Bioinformatics. 2011 May 26;12:211. (PMID: 21615941)
      Genetics. 2003 Feb;163(2):789-801. (PMID: 12618414)
      Heredity (Edinb). 2012 Feb;108(2):134-46. (PMID: 21792229)
      Genetics. 2012 Dec;192(4):1483-91. (PMID: 22982577)
      Genetics. 2010 Oct;186(2):713-24. (PMID: 20813882)
    • Publication Date:
      Date Created: 20150410 Date Completed: 20160329 Latest Revision: 20240326
    • Publication Date:
      20250114
    • Accession Number:
      PMC4391919
    • Accession Number:
      10.1371/journal.pone.0120017
    • Accession Number:
      25856391