Genetica

, Volume 140, Issue 10, pp 421–427

Power of a reproducing kernel-based method for testing the joint effect of a set of single-nucleotide polymorphisms

  • Hong He
  • Hongmei Zhang
  • Arnab Maity
  • Yubo Zou
  • James Hussey
  • Wilfried Karmaus
Article

DOI: 10.1007/s10709-012-9690-5

Cite this article as:
He, H., Zhang, H., Maity, A. et al. Genetica (2012) 140: 421. doi:10.1007/s10709-012-9690-5

Abstract

This study explored a semi-parametric method built upon reproducing kernels for estimating and testing the joint effect of a set of single nucleotide polymorphisms (SNPs). The kernel adopted is the identity-by-state kernel that measures SNP similarity between subjects. In this article, through simulations we first assessed its statistical power under different situations. It was found that in addition to the effect of sample size, the testing power was impacted by the strength of association between SNPs and the outcome of interest, and by the SNP similarity among the subjects. A quadratic relationship between SNP similarity and testing power was identified, and this relationship was further affected by sample sizes. Next we applied the method to a SNP-lung function data set to estimate and test the joint effect of a set of SNPs on forced vital capacity, one type of lung function measure. The findings were then connected to the patterns observed in simulation studies and further explored via variable importance indices of each SNP inferred from a variable selection procedure.

Keywords

Reproducing kernelsSNPMixed linear modelsTesting powerVariable selection

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Hong He
    • 1
  • Hongmei Zhang
    • 1
  • Arnab Maity
    • 2
  • Yubo Zou
    • 1
  • James Hussey
    • 1
  • Wilfried Karmaus
    • 1
  1. 1.Department of Epidemiology and Biostatistics, Norman J Arnold School of Public HealthUniversity of South CarolinaColumbiaUSA
  2. 2.Department of StatisticsNorth Carolina State UniversityRaleighUSA