Genome-wide screening for localization of disease genes necessitates the efficient reconstruction of haplotypes of members of a pedigree from genotype data at multiple loci. We propose a genetic algorithmic approach to haplotyping and show that it works fast, efficiently and reliably. This algorithm uses certain principles of biological evolution to find optimal solutions to complex problems. The optimality criterion used in the present problem is the minimum number of recombinations over possible haplotype configurations of members of a pedigree. The proposed algorithm is much less demanding in terms of data and assumption requirements compared to the currently used likelihood-based methods of haplotype reconstruction. It also provides multiple optimal haplotype configurations of a pedigree, if such multiple optima exist.

1.
Wijsman EM: A deductive method of haplotype analysis in pedigrees. Am J Hum Genet 1987;41:356–373.
2.
Haines JL: Chromlook: An interactive program for error detection and mapping in reference linkage data. Genomics 1992;14:517–519.
3.
Lange K, Matthysse M: Simulation of pedigree genotypes by random walks. Am J Hum Genet 1989;45:959–970.
4.
Lange K, Sobel E: A random walk method for computing genetic location scores. Am J Hum Genet 1991;49:1320–1334.
5.
Weeks DE, Sobel E, O’Connell JR, Lange K: Computer programs for multilocus haplotyping of general pedigrees. Am J Hum Genet 1995;56:1506–1507.
6.
O’Connell JR, Weeks DE: The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set recording and fuzzy inheritance. Nat Genet 1995;11:402–408.
7.
Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES: Parametric and nonparametric linkage analysis: A unified multipoint approach. Am J Hum Genet 1996;58:1347–1363.
8.
Sobel E, Lange K, O’Connell JR, Weeks DE: Haplotyping algorithms; in Speed TP, Waterman MS (eds): Genetic Mapping and DNA Sequencing. IMA Volumes in Mathematics and Its Applications (edited by Friedman A, Gulliver R). New York, Springer, 1995.
9.
Davis L (ed): Genetic Algorithms and Simulated Annealing. London, Pitman, 1987.
10.
Goldberg DE: Genetic Algorithms in Search, Optimization and Machine Learning. Reading, Addison Wesley, 1989.
11.
DeJong KA: Adaptive system design: A genetic approach. IEEE Trans Syst Man Cyber 1980;10:566–574.
12.
Srinivas M, Patnaik LM: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cyber 1994;24:656–667.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.