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Inferring haplotypes at the NAT2 locus: the computational approach.

Abstract : BACKGROUND: Numerous studies have attempted to relate genetic polymorphisms within the N-acetyltransferase 2 gene (NAT2) to interindividual differences in response to drugs or in disease susceptibility. However, genotyping of individuals single-nucleotide polymorphisms (SNPs) alone may not always provide enough information to reach these goals. It is important to link SNPs in terms of haplotypes which carry more information about the genotype-phenotype relationship. Special analytical techniques have been designed to unequivocally determine the allocation of mutations to either DNA strand. However, molecular haplotyping methods are labour-intensive and expensive and do not appear to be good candidates for routine clinical applications. A cheap and relatively straightforward alternative is the use of computational algorithms. The objective of this study was to assess the performance of the computational approach in NAT2 haplotype reconstruction from phase-unknown genotype data, for population samples of various ethnic origin. RESULTS: We empirically evaluated the effectiveness of four haplotyping algorithms in predicting haplotype phases at NAT2, by comparing the results with those directly obtained through molecular haplotyping. All computational methods provided remarkably accurate and reliable estimates for NAT2 haplotype frequencies and individual haplotype phases. The Bayesian algorithm implemented in the PHASE program performed the best. CONCLUSION: This investigation provides a solid basis for the confident and rational use of computational methods which appear to be a good alternative to infer haplotype phases in the particular case of the NAT2 gene, where there is near complete linkage disequilibrium between polymorphic markers.
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Submitted on : Thursday, August 31, 2006 - 3:01:39 PM
Last modification on : Wednesday, September 16, 2020 - 4:57:35 PM
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Audrey Sabbagh, Pierre Darlu. Inferring haplotypes at the NAT2 locus: the computational approach.. BMC Genetics, BioMed Central, 2005, 6, pp.30. ⟨10.1186/1471-2156-6-30⟩. ⟨inserm-00090461⟩

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