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Genetic association mapping via evolution-based clustering of haplotypes.

Tachmazidou I, Verzilli CJ, De Iorio M - PLoS Genet. (2007)

Bottom Line: We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates.Also, the method is computationally quicker than other multi-marker approaches.We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Imperial College London, United Kingdom. ioanna.tachmazidou03@ic.ac.uk

ABSTRACT
Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

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Posterior Density of Location of Causal Allele and 95% Credible IntervalsCredible intervals (95%) of causal location for two datasets simulated with 1.8 and 2.4 GRR(Aa) and all other simulation parameters at default values. The credible intervals are 150 kb and 15 kb wide, respectively.
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pgen-0030111-g010: Posterior Density of Location of Causal Allele and 95% Credible IntervalsCredible intervals (95%) of causal location for two datasets simulated with 1.8 and 2.4 GRR(Aa) and all other simulation parameters at default values. The credible intervals are 150 kb and 15 kb wide, respectively.

Mentions: For the proposed method, it is straightforward to construct credible intervals for the estimated location of the causative SNP. At each iteration of the MCMC algorithm, we obtain an estimate of the causal location by averaging the locations of the markers currently selected as cluster centres. Thus, upon convergence, we obtain a posterior distribution of locations, from which a credible interval can be constructed. Under the default simulation scenario, in 43 out of the 50 replicates, the 95% credible interval contained the true causal locus. Figure 10 shows the posterior densities of the putative location of the causative variants, together with 95% credible intervals for two datasets simulated with 1.8 and 2.4 GRR(Aa), with all other simulation parameters set at their default values. The credible intervals are 150 and 15 kb wide, respectively.


Genetic association mapping via evolution-based clustering of haplotypes.

Tachmazidou I, Verzilli CJ, De Iorio M - PLoS Genet. (2007)

Posterior Density of Location of Causal Allele and 95% Credible IntervalsCredible intervals (95%) of causal location for two datasets simulated with 1.8 and 2.4 GRR(Aa) and all other simulation parameters at default values. The credible intervals are 150 kb and 15 kb wide, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1913101&req=5

pgen-0030111-g010: Posterior Density of Location of Causal Allele and 95% Credible IntervalsCredible intervals (95%) of causal location for two datasets simulated with 1.8 and 2.4 GRR(Aa) and all other simulation parameters at default values. The credible intervals are 150 kb and 15 kb wide, respectively.
Mentions: For the proposed method, it is straightforward to construct credible intervals for the estimated location of the causative SNP. At each iteration of the MCMC algorithm, we obtain an estimate of the causal location by averaging the locations of the markers currently selected as cluster centres. Thus, upon convergence, we obtain a posterior distribution of locations, from which a credible interval can be constructed. Under the default simulation scenario, in 43 out of the 50 replicates, the 95% credible interval contained the true causal locus. Figure 10 shows the posterior densities of the putative location of the causative variants, together with 95% credible intervals for two datasets simulated with 1.8 and 2.4 GRR(Aa), with all other simulation parameters set at their default values. The credible intervals are 150 and 15 kb wide, respectively.

Bottom Line: We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates.Also, the method is computationally quicker than other multi-marker approaches.We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Imperial College London, United Kingdom. ioanna.tachmazidou03@ic.ac.uk

ABSTRACT
Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

Show MeSH
Related in: MedlinePlus