Classifying disease chromosomes arising from multiple founders, with application to fine-scale haplotype mapping

The availability of high‐density haplotype data has motivated several fine‐scale linkage disequilibrium mapping methods for locating disease‐causing mutations. These methods identify loci around which haplotypes of case chromosomes exhibit greater similarity than do those of control chromosomes. A d...

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Bibliographic Details
Published in:Genetic epidemiology 2004-11, Vol.27 (3), p.173-181
Main Authors: Yu, K., Martin, R.B., Whittemore, A.S.
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Chromosome Mapping - methods
Classical genetics, quantitative genetics, hybrids
clustering method
Computer Simulation
Cystic Fibrosis - genetics
Founder Effect
founder heterogeneity
Fundamental and applied biological sciences. Psychology
Genes, BRCA1
Genetics of eukaryotes. Biological and molecular evolution
haplotype fine-scale mapping
Haplotypes - genetics
Human
Humans
linkage disequilibrium
Linkage Disequilibrium - genetics
Methods, theories and miscellaneous
Models, Genetic
Mutation
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Summary:The availability of high‐density haplotype data has motivated several fine‐scale linkage disequilibrium mapping methods for locating disease‐causing mutations. These methods identify loci around which haplotypes of case chromosomes exhibit greater similarity than do those of control chromosomes. A difficulty arising in such mapping is the possibility that case chromosomes have inherited disease‐causing mutations from different ancestral chromosomes (founder heterogeneity). Such heterogeneity dilutes measures of case haplotype similarity. This dilution can be mitigated by separating case chromosomes into subsets according to their putative mutation origin, and searching for an area with excessive haplotype similarity within each subset. We propose a nonparametric method for identifying subsets of case chromosomes likely to share a common ancestral progenitor. By simulation studies and application to published data, we show that the method accurately identifies relatively large subsets of chromosomes that share a common founder. We also show that the method allows more precise estimates of the disease mutation loci than obtained by other fine‐scale mapping methods. © 2004 Wiley‐Liss, Inc.
ISSN:0741-0395
1098-2272
DOI:10.1002/gepi.20016