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Estimating and Correcting Optimism Bias in Multivariate PLS Regression: Application to the Study of the Association Between Single Nucleotide Polymorphisms and Multivariate Traits in Attention Deficit Hyperactivity Disorder

  • Erica Cunningham
  • Antonio Ciampi
  • Ridha Joober
  • Aurélie Labbe
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 173)

Abstract

In studies involving genetic data, the correlations between X and Y scores obtained from PLS regression models can be used as measures of association between genome-level measurements, X, and phenotype-level measurements, Y. These correlations may be overestimated due to potential overfitting (i.e., they may be vulnerable to optimism bias). We evaluate the optimism bias through simulations and examine the effect of increasing sample size and strength of correlation. We assess the effectiveness of bootstrap-based and permutation-based bias correction methods. We also investigate the selection of the appropriate number of components for PLS regression. We include an analysis of genetic data consisting of genotypes and phenotypes related to Attention Deficit Hyperactivity Disorder (ADHD).

Keywords

Partial least square regression (PLSR) Optimism bias Overfitting SNPs Bootstrap 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Erica Cunningham
    • 1
  • Antonio Ciampi
    • 1
  • Ridha Joober
    • 2
  • Aurélie Labbe
    • 1
  1. 1.Department of Epidemiology, Biostatistics, and Occupational HealthMcGill UniversityMontrealCanada
  2. 2.Douglas Mental Health University InstituteVerdunCanada

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