The genetic algorithm applied to haplotype data at the LDL receptor locus

Conventional statistical methods based upon single restriction fragment len gth polymorphisms often prove inadequate in studies of genetic variation. C ladistic analysis has been suggested as an alternative, but requires basic assumptions that usually cannot be met. We wanted to test whether it could be a workable approach to apply the genetic algorithm, an artificial intell igence method, to haplotype data. The genetic algorithm creates in-computer artificial 'individuals', all having 'genes' coding for solutions to a pro blem. The individuals are allowed to compete and 'mate', individuals with g enes coding for better solutions mating more often. Genes coding for good s olutions survive through generations of the genetic algorithm. At the end o f the run, the best solutions can be extracted. We applied the genetic algo rithm to data consisting of cholesterol values and haplotypes made up of se ven restriction sites at the LDL receptor locus. The persons included were 114 FH (familial hypercholesterolemia) patients and 61 normals. The genetic algorithm found the restriction sites 1 (Sph1 in intron 6), 2 (StuI in exo n 8), and 7 (ApaLI site in the 3' flanking region) were associated with hig h cholesterol levels. As a validity check we used runs of the genetic algor ithm applied to 'artificial patients', i.e. artificially generated haplotyp es linked to artificially generated cholesterol values. This demonstrated t he genetic algorithm consistently found the appropriate haplotype. We concl ude that the genetic algorithm may be a useful tool for studying genetic va riation. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.