Summary
The objective of gene mapping is to localize genes responsible for a particular disease or trait. We consider association-based gene mapping, where the data consist of markers genotyped for a sample of independent case and control individuals. In this chapter we give a generic framework for nonparametric gene mapping based on pattern discovery. We have previously introduced two instances of the framework: haplotype pattern mining (HPM) for case—control haplotype material and QHPM for quantitative trait and covariates. In our experiments, HPM has proven to be very competitive compared to other methods. Geneticists have found the output of HPM useful, and today HPM is routinely used for analyses by several research groups. We review these methods and present a novel instance, HPM-G, suitable for directly analyzing phase-unknown genotype data. Obtaining haplotypes is more costly than obtaining phase-unknown genotypes, and our experiments show that although larger samples are needed with HPMG, it is still in many cases more cost-effective than analysis with haplotype data.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Sevon, P., Toivonen, H.T.T., Onkamo, P. (2005). Gene Mapping by Pattern Discovery. In: Wu, X., Jain, L., Wang, J.T., Zaki, M.J., Toivonen, H.T., Shasha, D. (eds) Data Mining in Bioinformatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-059-1_6
Download citation
DOI: https://doi.org/10.1007/1-84628-059-1_6
Publisher Name: Springer, London
Print ISBN: 978-1-85233-671-4
Online ISBN: 978-1-84628-059-7
eBook Packages: Computer ScienceComputer Science (R0)