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Inferring haplotypes at the NAT2 locus: the computational approach.

Sabbagh A, Darlu P - BMC Genet. (2005)

Bottom Line: However, molecular haplotyping methods are labour-intensive and expensive and do not appear to be good candidates for routine clinical applications.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Unité de Recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. sabbagh@vjf.inserm.fr

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.

Show MeSH
The ambiguous gametic phase of haplotypes for a given multilocus genotype. To illustrate the relevance of linkage phase ascertainment, let us consider the following case of a four-site heterozygous individual at positions 191, 341, 481 and 803 within the NAT2 coding sequence. Eight possible combinations of haplotypes can be inferred from this multilocus genotype, two of whom are shown here. Depending on the location of mutations to either DNA strand, the individual's NAT2 genotype composed of two multilocus haplotypes will not be the same. Moreover, an incorrect resolution of mutation linkage patterns may entail an error in individual phenotype prediction: the subject will be classified either as a slow or as a rapid acetylator depending on the haplotypic combination chosen. Symbol (*) points at mutations leading to a decrease in NAT2 enzyme activity, while symbol (×) indicates those with no impact on the acetylator phenotype.
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Figure 1: The ambiguous gametic phase of haplotypes for a given multilocus genotype. To illustrate the relevance of linkage phase ascertainment, let us consider the following case of a four-site heterozygous individual at positions 191, 341, 481 and 803 within the NAT2 coding sequence. Eight possible combinations of haplotypes can be inferred from this multilocus genotype, two of whom are shown here. Depending on the location of mutations to either DNA strand, the individual's NAT2 genotype composed of two multilocus haplotypes will not be the same. Moreover, an incorrect resolution of mutation linkage patterns may entail an error in individual phenotype prediction: the subject will be classified either as a slow or as a rapid acetylator depending on the haplotypic combination chosen. Symbol (*) points at mutations leading to a decrease in NAT2 enzyme activity, while symbol (×) indicates those with no impact on the acetylator phenotype.

Mentions: Problems may occur when individual multi-site NAT2 genotypes have to be assigned correctly to a particular combination of two multilocus haplotypes. Indeed, current routine genotyping and sequencing methods typically do not provide haplotype information in diploid organisms such as humans, and the gametic phase of haplotypes is inherently ambiguous when individuals are heterozygous at more than one locus. As illustrated in Figure 1, a subject carrying two inactivating mutations can be either rapid or slow acetylator depending on whether these mutations are located in the same or different chromosome, respectively. It is thus crucial to unequivocally assess mutation linkage patterns, this step being a prerequisite to obtain accurate haplotype frequency estimates in populations and reliable genotype-phenotype predictions.


Inferring haplotypes at the NAT2 locus: the computational approach.

Sabbagh A, Darlu P - BMC Genet. (2005)

The ambiguous gametic phase of haplotypes for a given multilocus genotype. To illustrate the relevance of linkage phase ascertainment, let us consider the following case of a four-site heterozygous individual at positions 191, 341, 481 and 803 within the NAT2 coding sequence. Eight possible combinations of haplotypes can be inferred from this multilocus genotype, two of whom are shown here. Depending on the location of mutations to either DNA strand, the individual's NAT2 genotype composed of two multilocus haplotypes will not be the same. Moreover, an incorrect resolution of mutation linkage patterns may entail an error in individual phenotype prediction: the subject will be classified either as a slow or as a rapid acetylator depending on the haplotypic combination chosen. Symbol (*) points at mutations leading to a decrease in NAT2 enzyme activity, while symbol (×) indicates those with no impact on the acetylator phenotype.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1173101&req=5

Figure 1: The ambiguous gametic phase of haplotypes for a given multilocus genotype. To illustrate the relevance of linkage phase ascertainment, let us consider the following case of a four-site heterozygous individual at positions 191, 341, 481 and 803 within the NAT2 coding sequence. Eight possible combinations of haplotypes can be inferred from this multilocus genotype, two of whom are shown here. Depending on the location of mutations to either DNA strand, the individual's NAT2 genotype composed of two multilocus haplotypes will not be the same. Moreover, an incorrect resolution of mutation linkage patterns may entail an error in individual phenotype prediction: the subject will be classified either as a slow or as a rapid acetylator depending on the haplotypic combination chosen. Symbol (*) points at mutations leading to a decrease in NAT2 enzyme activity, while symbol (×) indicates those with no impact on the acetylator phenotype.
Mentions: Problems may occur when individual multi-site NAT2 genotypes have to be assigned correctly to a particular combination of two multilocus haplotypes. Indeed, current routine genotyping and sequencing methods typically do not provide haplotype information in diploid organisms such as humans, and the gametic phase of haplotypes is inherently ambiguous when individuals are heterozygous at more than one locus. As illustrated in Figure 1, a subject carrying two inactivating mutations can be either rapid or slow acetylator depending on whether these mutations are located in the same or different chromosome, respectively. It is thus crucial to unequivocally assess mutation linkage patterns, this step being a prerequisite to obtain accurate haplotype frequency estimates in populations and reliable genotype-phenotype predictions.

Bottom Line: However, molecular haplotyping methods are labour-intensive and expensive and do not appear to be good candidates for routine clinical applications.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Unité de Recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. sabbagh@vjf.inserm.fr

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.

Show MeSH