Skip to main content

Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2011 (IDEAL 2011)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Carrasquillo, M.M., et al.: Genome-wide association study and mouse model identify interaction between TET and EDNRB pathways in Hirschsprung disease. Nat. Genet. 32, 237–244 (2002)

    Article  Google Scholar 

  2. Motsinger, A.A., Ritchie, M.D., Reif, D.M.: Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics 8(9), 1229–1241 (2007)

    Article  Google Scholar 

  3. Musani, S.K., et al.: Detection of gene x gene interactions in genome-wide association studies of human population data. Human Heredity 63(2), 67–84 (2007)

    Article  Google Scholar 

  4. Elston, R.C., et al.: Haseman and Elston revisited. Genetic Epidemiology 19(1), 1–17 (2000)

    Article  MathSciNet  Google Scholar 

  5. Barber, M.J., et al.: Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairs. Genetic Epidemiology 26(2), 97–107 (2004)

    Article  Google Scholar 

  6. Haseman, J.K., Elston, R.C.: The investigation of linkage between a quantitative trait and a marker locus. Behavior Genetics 2(1), 3–19 (1972)

    Article  Google Scholar 

  7. Sham, P.C., et al.: Powerful regression-based quantitative-trait linkage analysis of general pedigrees. The American Journal of Human Genetics 71(2), 238–253 (2002)

    Article  Google Scholar 

  8. Hall, M.A.: Correlation-based feature selection of discrete and numeric class machine learning (2000)

    Google Scholar 

  9. Gütlein, M., et al.: Large-scale attribute selection using wrappers (2009)

    Google Scholar 

  10. Masters, T.: Practical neural network recipes in C++. Morgan Kaufmann, San Francisco (1993)

    MATH  Google Scholar 

  11. Montano, J.J., Palmer, A.: Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing & Applications 12(2), 119–125 (2003)

    Article  Google Scholar 

  12. Matchenko-Shimko, N., Dube, M.P.: Gene-Gene Interaction Tests Using SVM and Neural Network Modeling. In: CIBCB (2007)

    Google Scholar 

  13. Thomas, D.C.: Statistical Methods in Genetic Epidemiology. Oxford University Press, Oxford (2004)

    MATH  Google Scholar 

  14. Kaufman, J.M., et al.: Genome-wide linkage screen of bone mineral density (BMD) in European pedigrees ascertained through a male relative with low BMD values: evidence for quantitative trait loci on 17q21-23, 11q12-13, 13q12-14, and 22q11. Journal of Clinical Endocrinology & Metabolism 93(10), 3755 (2008)

    Article  Google Scholar 

  15. Richards, J.B., et al.: Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. The Lancet (2008)

    Google Scholar 

  16. Rivadeneira, F., et al.: Twenty loci associated with bone mineral density identified by large-scale meta-analysis of genome-wide association datasets. Bone 44, 230–231 (2009)

    Article  Google Scholar 

  17. Koller, D.L., et al.: Genome-Wide Association Study of Bone Mineral Density in Premenopausal European-American Women and Replication in African-American Women. Journal of Clinical Endocrinology & Metabolism

    Google Scholar 

  18. Styrkarsdottir, U., et al.: Multiple genetic loci for bone mineral density and fractures. New England Journal of Medicine 358(22), 2355 (2008)

    Article  Google Scholar 

  19. Zhang, F., et al.: A whole genome linkage scan for QTLs underlying peak bone mineral density. Osteoporosis International 19(3), 303–310 (2008)

    Article  MathSciNet  Google Scholar 

  20. Wilson, S.G., et al.: Comparison of genome screens for two independent cohorts provides replication of suggestive linkage of bone mineral density to 3p21 and 1p36. The American Journal of Human Genetics 72(1), 144–155 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, Q., MacGregor, A.J., Wang, W. (2011). Novel Data Mining Approaches for Detecting Quantitative Trait Loci of Bone Mineral Density in Genome-Wide Linkage Analysis. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23878-9_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23877-2

  • Online ISBN: 978-3-642-23878-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics