Send to:

Choose Destination
See comment in PubMed Commons below
J Data Mining Genomics Proteomics. 2013 Oct 20;4. doi: 10.4172/2153-0602.1000143.

Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data.

Author information

  • 1Department of Psychology, University of Notre Dame, Notre Dame, IN, USA ; Department of Biological Psychology, VU University Amsterdam, Amsterdam Netherlands.
  • 2Department of Psychology, University of Notre Dame, Notre Dame, IN, USA.
  • 323 and Me, Inc., Mountain View, CA, USA.
  • 4Twin Research and Genetic Epidemiology, Genetic Epidemiologist, King's College London, London, England.
  • 5Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia.
  • 6Department of Biological Psychology, VU University Amsterdam, Amsterdam Netherlands.


Typically, genome-wide association studies consist of regressing the phenotype on each SNP separately using an additive genetic model. Although statistical models for recessive, dominant, SNP-SNP, or SNP-environment interactions exist, the testing burden makes an evaluation of all possible effects impractical for genome-wide data. We advocate a two-step approach where the first step consists of a filter that is sensitive to different types of SNP main and interactions effects. The aim is to substantially reduce the number of SNPs such that more specific modeling becomes feasible in a second step. We provide an evaluation of a statistical learning method called "gradient boosting machine" (GBM) that can be used as a filter. GBM does not require an a priori specification of a genetic model, and permits inclusion of large numbers of covariates. GBM can therefore be used to explore multiple GxE interactions, which would not be feasible within the parametric framework used in GWAS. We show in a simulation that GBM performs well even under conditions favorable to the standard additive regression model commonly used in GWAS, and is sensitive to the detection of interaction effects even if one of the interacting variables has a zero main effect. The latter would not be detected in GWAS. Our evaluation is accompanied by an analysis of empirical data concerning hair morphology. We estimate the phenotypic variance explained by increasing numbers of highest ranked SNPs, and show that it is sufficient to select 10K-20K SNPs in the first step of a two-step approach.


Boosting; GCTA; GWAS

Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Write to the Help Desk