Efficient localization of mutations by interval haplotype analysis

Efficient localization of mutations by interval haplotype analysis We have developed a mathematical algorithm to implement a method for localizing mutations using haplotype analysis. Our strategy infers haplotypes based on the determination of genotypes of a proximal and a distal marker for 21 chromosomal intervals distributed across the mouse genome (corresponding to two intervals for Chromosomes (Chrs) 1 and 2 and one for the remaining 17 autosomes). To simulate the analysis of mice homozygous for recessive mutations, we tested the efficacy of our method on over 200 data sets generated from two independent mapping panel data sets containing the genotypes of 46 F2 progeny of an intercross and 94 F2 progeny of a backcross. In all cases we were able to identify the chromosomal interval carrying the recessive mutation despite the fact that some of the data sets consisted of as few as 10 meioses. Our strategy proved sensitive and expedient, since the simulated genome-wide screen could be executed by genotype analysis of 40 microsatellite markers in small numbers of intercross or backcross progeny. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mammalian Genome Springer Journals

Efficient localization of mutations by interval haplotype analysis

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Publisher
Springer-Verlag
Copyright
Copyright © 1998 by Springer-Verlag New York Inc
Subject
Life Sciences; Cell Biology; Anatomy; Zoology
ISSN
0938-8990
eISSN
1432-1777
D.O.I.
10.1007/s003359900706
Publisher site
See Article on Publisher Site

Abstract

We have developed a mathematical algorithm to implement a method for localizing mutations using haplotype analysis. Our strategy infers haplotypes based on the determination of genotypes of a proximal and a distal marker for 21 chromosomal intervals distributed across the mouse genome (corresponding to two intervals for Chromosomes (Chrs) 1 and 2 and one for the remaining 17 autosomes). To simulate the analysis of mice homozygous for recessive mutations, we tested the efficacy of our method on over 200 data sets generated from two independent mapping panel data sets containing the genotypes of 46 F2 progeny of an intercross and 94 F2 progeny of a backcross. In all cases we were able to identify the chromosomal interval carrying the recessive mutation despite the fact that some of the data sets consisted of as few as 10 meioses. Our strategy proved sensitive and expedient, since the simulated genome-wide screen could be executed by genotype analysis of 40 microsatellite markers in small numbers of intercross or backcross progeny.

Journal

Mammalian GenomeSpringer Journals

Published: Mar 28, 2009

References

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