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.

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Fifty families (491 individuals in 137 sibships) with more than one living case of isolated, nonsyndromic spina bifida (SB) were analyzed genetically. There were twice as many gene-carrier females (56) as gene-carrier males (28) (P < 0.005). This was not an artifact of ascertainment bias because the sex ratio of gene-carriers was the same whether the pedigree was obtained through the proband's father or mother. Also, this effect was not observed in other disorders analyzed by the same method. Neither was the effect due to differential fertility because the number and sex of affected and unaffected children per gene-carrier parent were not different for male or female gene-carrier parents. There was no evidence that the missing male gene-carriers were lost by selective spontaneous abortion. There was no deficit of male-to-male or male-to-female transmission, excluding simple X-linked or simple mitochondrial inheritance. If genomic imprinting plays a role in the unequal female and male carrier frequencies in SB, penetrance should differ with parental sex. Penetrance was higher for offspring of female parents than of male parents, but the difference was not statistically significant. In addition, both male and female gene-carriers were frequently found in the same pedigree. Thus, the present data suggest a possible role for imprinting in SB.

A recently developed approach that employs artificial neural networks (ANNs) was applied to the simulated data set to identify sets of marker loci involved in disease etiology. In this implementation, ANNs are trained to predict the disease state (output) from the given genetic marker data (input). A contribution value (CV) for each locus is calculated from the weights that represent the strength of the connections for the trained ANN; a higher CV indicates a higher probability of linkage. The highest CV values were chosen as the most likely candidate regions involved in the disease.1999 Wiley-Liss, Inc.

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