Multi-locus nonparametric linkage analysis of complex trait loci with neural networks

Paul Lucek, Jens Hanke, Jens Reich, Sara A. Solla, Jürg Ott*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


Complex traits are generally taken to be under the influence of multiple genes, which may interact with each other to confer susceptibility to disease. Statistical methods in current use for localizing such genes essentially work under single-gene models, either implicitly or explicitly. In genomic screens for complex disease genes, some of the marker loci must be in tight linkage with disease susceptibility genes. We developed a general multi-locus approach to identify sets of such marker loci. Our approach focuses on affected sib pair data and employs a nonparametric pattern recognition technique using artificial neural networks. This technique analyzes all markers simultaneously in order to detect patterns of locus interactions. When applied to previously published sib pair data on type I diabetes, our approach finds the same genes as in the published report in addition to some new loci. For a specific two-locus model of inheritance, the power of our approach is higher than that of the currently used analysis standard.

Original languageEnglish (US)
Pages (from-to)275-284
Number of pages10
JournalHuman Heredity
Issue number5
StatePublished - Sep 1998


  • Affected sib pairs
  • Complex trait
  • Gene mapping
  • Linkage analysis
  • Neural networks

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics


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