Literature DB >> 12508257

Haplotype sharing analysis with SNPs in candidate genes: the Genetic Analysis Workshop 12 example.

Christine Fischer1, Lars Beckmann, Paul Majoram, Gerard te Meerman, Jenny Chang-Claude.   

Abstract

Haplotype sharing analysis was used to investigate the association of affection status with single nucleotide polymorphism (SNP) haplotypes within candidate gene 1 in one sample each from the isolated and the general population of Genetic Analysis Workshop (GAW) 12 simulated data. Gene 1 has direct influence on affection and harbors more than 70 SNPs. Haplotype sharing analysis depends heavily on previous haplotype estimation. Using GENEHUNTER haplotypes, strong evidence was found for most SNPs in the isolated population sample, thus providing evidence for an involvement of this gene, but the maximum -log(10)(p) values for the haplotype sharing statistics (HSS) test statistic did not correspond to the location of the true variant in either population. In comparison, transmission disequilibrium test (TDT) analysis showed the strongest results at the disease-causing variant in both populations, and these were outstanding in the general population. In this example, TDT analysis appears to perform better than HSS in identifying the disease-causing variant, using SNPs within a candidate gene in an outbred population. Simulations showed that the performance of HSS is hampered by closely spaced SNPs in strong linkage disequilibrium with the functional variant and by ambiguous haplotypes. Copyright 2003 Wiley-Liss, Inc.

Mesh:

Year:  2003        PMID: 12508257     DOI: 10.1002/gepi.10207

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  2 in total

1.  More powerful haplotype sharing by accounting for the mode of inheritance.

Authors:  Andreas Ziegler; Adel Ewhida; Michael Brendel; André Kleensang
Journal:  Genet Epidemiol       Date:  2009-04       Impact factor: 2.135

2.  Haplotype-sharing analysis using Mantel statistics for combined genetic effects.

Authors:  Lars Beckmann; Christine Fischer; Markus Obreiter; Michael Rabes; Jenny Chang-Claude
Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

  2 in total

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