Linkage disequilibrium structure and its impact on the localization of a candidate functional mutation.
We have used the unblinded MG1/Q1 Genetic Analysis Workshop 12 simulated data as a model system for investigating the use of linkage disequilibrium structure and simple genotype-phenotype associations to identify candidate functional mutations within a gene of interest. Analysis of the pattern of pairwise linkage disequilibrium indicated three groups of single-nucleotide polymorphisms for which the linkage disequilibrium was high between sites within a group, but lower between sites of different groups. Using linear regression to predict levels of the trait Q1 showed that the known functional site, 5782, was usually not the best genetic predictor of Q1, but sites belonging to the same group as 5782 (i.e., group 2) were always included in the prediction model. In 49 out of the 50 replicates, the functional site was not the best predictor of the trait. Finally, more detailed analyses demonstrate that the relationship between the adjusted R2 for the marker in the prediction model and its disequilibrium with 5782 was linear with the intercept at the origin and terminating at the R2 value for the known functional mutation when the disequilibrium is maximal. These data indicate that simple association studies will not identify the functional mutation, but rather will identify candidate functional mutations that are in very tight linkage disequilibrium with the functional mutation.
Huang
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Human Genetics Center, University of Texas Health Science Center at Houston, 6901 Bertner, Houston, TX 77030, USA.
Morrison
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Boerwinkle
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D002874:Chromosome Mapping / Q000706:statistics & numerical data; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis
D005819:Genetic Markers