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PLS Regression and Hybrid Methods in Genomics Association Studies

  • Antonio Ciampi
  • Lin Yang
  • Aurélie Labbe
  • Chantal Mérette
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Abstract

Using data from a case-control study on schizophrenia, we demonstrate the use of PLS regression in constructing predictors of a phenotype from Single Nucleotide Polymorphisms (SNPs). We consider straightforward application of PLS regression as well as two hybrid methods, in which PLS regression scores are used as input for a tree-growing algorithm and a clustering algorithm respectively. We compare these approaches with other classic predictors used in statistical learning, showing that our PLS-based hybrid methods outperform both classic predictors and straightforward PLS regression.

Key words

PLS Regression Bagging SNP GWAS 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Antonio Ciampi
    • 1
  • Lin Yang
    • 2
  • Aurélie Labbe
    • 1
  • Chantal Mérette
    • 3
  1. 1.Department of EpidemiologyBiostatistics, and Occupational HealthMontréalCanada
  2. 2.Division of Clinical EpidemiologyMcGill University Health CentreMontréalCanada
  3. 3.Faculty of Medicine, Department of Psychiatry and NeurosciencesUniversité LavalQuebec CityCanada

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