title: An artificial neural network for estimating haplotype frequencies.
abstract: The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.
authors.affiliation: Case Western Reserve University, Department of Epidemiology and Biostatistics, Cleveland, OH, USA. kcartier@darwin.case.edu
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Cartier
authors.forename: Daniel
authors.initials: D
authors.lastname: Baechle
mesh_terms: D000465:Algorithms; D005787:Gene Frequency / Q000235:genetics*; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated