P Lucek | Search PubMed

PubMed1998

Complex traits on the map.

The lod score method for localizing mendelian disease genes in human genetics is reviewed. Current ways of applying this technique to hypothesized genes underlying complex traits are discussed. In contrast to these parametric methods, allele-sharing methods using affected sib pairs are reviewed. Based on such allele-sharing data, a particular type of analysis is outlined which can identify sets of disease loci. This method makes use of pattern-recognition techniques as implemented in artificial neural networks (ANNs).

Alleles↗

PubMedDate not supplied

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

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.

Chromosome Mapping↗