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The Combinatorial Partitioning Method

  • Matthew R. Nelson
  • Sharon L. Kardia
  • Charles F. Sing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1848)

Abstract

Recent advances in genome technology have led to an exponential increase in the ability to identify and measure variation in a large number of genes in the human genome. However, statistical and computational methods to utilize this information on hundreds, and soon thousands, of variable DNA sites to investigate genotype-phenotype relationships have not kept pace. Because genotype-phenotype relationships are combinatoric and non-additive in nature, traditional methods, such as generalized linear models, are limited in their ability to search through the high-dimensional genotype space to identify genetic subgroups that are associated with phenotypic variation. We present here a combinatorial partitioning method (CPM) that identifies partitions of higher dimensional genotype spaces that predict variation in levels of a quantitative trait. We illustrate this method by applying it to the problem of genetically predicting interindividual variation in plasma triglyceride levels, a risk factor for atherosclerosis.

Keywords

Cross Validation Interindividual Variation Locus Genotype Phenotype Relationship Trait Variability 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Matthew R. Nelson
    • 1
  • Sharon L. Kardia
    • 2
  • Charles F. Sing
    • 3
  1. 1.Dept. of GenomicsEsperion Therapeutics, IncAnn ArborUSA
  2. 2.Dept. of EpidemiologyUniversity of MichiganAnn ArborUSA
  3. 3.Dept. of Human GeneticsUniversity of MichiganAnn ArborUSA

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