Journal of Data Mining in Genomics & Proteomics

Eriksson N

Publications

• Research Article
  Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data
  Author(s): Lubke GH, Laurin C, Walters R, Eriksson N, Hysi P, Spector TD, Montgomery GW, Martin NG, Medland SE and Boomsma DILubke GH, Laurin C, Walters R, Eriksson N, Hysi P, Spector TD, Montgomery GW, Martin NG, Medland SE and Boomsma DI
  
  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 ex.. View More»
  DOI: 10.4172/2153-0602.1000143

  Abstract PDF