Novel data mining approaches for detecting quantitative trait loci of bone mineral density in genome-wide linkage analysis

  • Authors:

  • Qiao Li;Alexander J. MacGregor;Wenjia Wang

  • Affiliations:

  • School of Public Health, Imperial College London, London, UK;School of Medicine, University of East Anglia, Norwich, UK;School of Computing Sciences, University of East Anglia, Norwich, UK

  • Venue:
  • IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
  • Year:
  • 2011

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Abstract

Haseman-Elston (H-E) regression is a commonly used conventional approach for detecting quantitative trait loci (QTLs), which regulate the quantitative phenotype based on the Identical-By-Descent (IBD) information between twins in Genome-wide scan. However, this approach only considers genetic effect at individual loci, but not any interaction between genes. A Pair-Wise H-E regression (PWH-E) and a Feature Screening Approach (FSA) are proposed in this paper to take gene-gene interaction into account when detecting QTLs. After testing these approaches with several series of simulation studies, they are applied to a real-world bone mineral density (BMD) dataset, and find three site specific sets of potential QTLs. Further comparison analyses show that our results not only corroborate the 14 findings from previous published studies, but also suggest 22 new QTLs of BMD.