VIGoR: Variational Bayesian Inference for Genome-Wide Regression

Software Paper English OPEN
Onogi, Akio; Iwata, Hiroyoshi;
  • Publisher: Ubiquity Press
  • Journal: Journal of Open Research Software (eissn: 2049-9647)
  • Related identifiers: doi: 10.5334/jors.80
  • Subject: Linear regression, variational Bayesian inference, genome-wide association, genomic prediction, variable selection
    mesheuropmc: food and beverages
    acm: ComputingMethodologies_PATTERNRECOGNITION

Genome-wide regression using a number of genome-wide markers as predictors is now widely used for genome-wide association mapping and genomic prediction. We developed novel software for genome-wide regression which we named VIGoR (variational Bayesian inference for geno... View more
  • References (42)
    42 references, page 1 of 5

    Date­published 10. Hickey, J M and Tier, B 2009 AlphaBayes (Beta):

    20/05/2015 Software for polygenic and whole genome analysis.

    11. Legarra, A, Ricardi, A and Filangi, O 2010 GS3: (3) Reuse potential Genomic Selection, Gibbs Sampling, Gauss-Seidel (and Because both the CLPs and R functions run by specifying BayesCπ and Bayesian Lasso). Available at http://snp. only a few arguments, these programs will be approach- able for geneticists who are interested in association map- 12. Janss, L L G 2010 Bayz manual version version 2.03 ping or genomic prediction. In addition, both the CLP Janss Biostatistics, Leiden, The Netherlands. Available and R functions vigor and hyperpara can accept predic- at tor variables other than the marker genotypes. Therefore, 13. Perez, P and de Los Campos, G 2014 Genome-Wide although we focus on genome-wide regression here, Regression and Prediction with the BGLR Statistical VIGoR can be applied into various problems where vari- Package. Genetics 198:483-495. DOI: http://dx.doi. able selection is required for huge data. Thus, VIGoR will org/10.1534/genetics.114.164442 have a wide reuse potential. 14. Park, T and Casella, G 2008 The Bayesian lasso. J

    Am Stat Assoc, 103: 681-686. DOI: http://dx.doi. Competing Interests org/10.1198/016214508000000337 The authors declare that they have no competing interests. 15. Mutshinda, C M and Sillanpaa, M J 2010 Extended

    Bayesian LASSO for multiple quantitative trait loci mapReferences ping and unobserved phenotype prediction. Genetics 1. Meuwissen, T H, Hayes, B J and Goddard, M E 2001 186:1067-1075. DOI:

    Prediction of total genetic value using genome-wide genetics.110.119586

    dense marker maps. Genetics, 157: 1819-1829. 16. Hayashi, T and Iwata, H 2010 EM algorithm for 2. Karkkainen, H P and Sillanpaa, M J 2012 Back to Bayesian estimation of genomic breeding values. BMC

    basics for Bayesian model building in genomic se- Genet, 11: 3. DOI:

    lection. Genetics, 191: 969-987. DOI: http://dx.doi. 2156-11-3

    org/10.1534/genetics.112.139014 17. George, E I and McCulloch, R E 1993 Variable selection 3. de los Campos, G, Hickey, J M, Pong-Wong, R, via Gibbs sampling. J Am Stat Assoc, 88: 881-889. DOI:

  • Related Research Results (1)
  • Metrics
    No metrics available
Share - Bookmark