Towards the genetic analysis of mulfactorial diseases: the estimation of allele frequency and epistasis.
OBJECTIVE
The general aim of this paper is to reactivate the original intention behind the Elston-Stewart algorithm: i.e. physiological characterisation of the effects of individual loci underlying quantitative variation. The specific aim is the estimation of allele frequency and epistasis in multifactorial genetic diseases.


METHODS
In a general genetic model, the probability of disease is a sigmoid function of the number of disease alleles summed over all loci. This model has just 4 parameters: the number of loci; the population frequency of disease alleles; a threshold expressed as a proportion of disease alleles; and the slope of the sigmoid curve. Assuming 10 loci, the remaining parameters can be estimated from empirical data: population frequency of the disease, monozygotic twin concordance rates, and disease frequency in sibs of affected probands.


RESULTS
For 10 typical multifactorial diseases, the estimates of allele frequency are generally high, of the order of 20%, with strong epistatic interactions between loci. It follows that the frequencies of subphenotypes specific for a single disease locus will also be high, and only about two-fold greater in affected individuals than in normal controls.


CONCLUSIONS
Because of allelic heterogeneity, purely genomic approaches are unlikely to succeed in unravelling the genetics of multifactorial diseases; this will rather require articulation with physiology and the identification of biologically meaningful subphenotypes.
Stewart
John
J
Instituto Gulbenkian de Ciência, Oeiras, Portugal. John.Stewart@utc.fr
D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D004843:Epistasis, Genetic; D005787:Gene Frequency; D006801:Humans; D020412:Multifactorial Inheritance / Q000235:genetics*; D010641:Phenotype; D012107:Research Design