title: Contrasting linkage-disequilibrium patterns between cases and controls as a novel association-mapping method.
abstract: Identification and description of genetic variation underlying disease susceptibility, efficacy, and adverse reactions to drugs remains a difficult problem. One of the important steps in the analysis of variation in a candidate region is the characterization of linkage disequilibrium (LD). In a region of genetic association, the extent of LD varies between the case and the control groups. Separate plots of pairwise standardized measures of LD (e.g., D') for cases and controls are often presented for a candidate region, to graphically convey case-control differences in LD. However, the observed graphic differences lack statistical support. Therefore, we suggest the "LD contrast" test to compare whole matrices of disequilibrium between two samples. A common technique of assessing LD when the haplotype phase is unobserved is the expectation-maximization algorithm, with the likelihood incorporating the assumption of Hardy-Weinberg equilibrium (HWE). This approach presents a potential problem in that, in the region of genetic association, the HWE assumption may not hold when samples are selected on the basis of phenotypes. Here, we present a computationally feasible approach that does not assume HWE, along with graphic displays and a statistical comparison of pairwise matrices of LD between case and control samples. LD-contrast tests provide a useful addition to existing tools of finding and characterizing genetic associations. Although haplotype association tests are expected to provide superior power when susceptibilities are primarily determined by haplotypes, the LD-contrast tests demonstrate substantially higher power under certain haplotype-driven disease models.
authors.affiliation: National Institute of Environmental Health Sciences, National Institutes of Health. Electronic address: zaykind@niehs.nih.gov.
authors.forename: Dmitri V
authors.initials: DV
authors.lastname: Zaykin
authors.affiliation: Department of Biostatistics and Programming, Sanofi-Aventis, Bridgewater, NJ.
authors.forename: Zhaoling
authors.initials: Z
authors.lastname: Meng
authors.affiliation: Department of Genetics Research, GlaxoSmithKline, Research Triangle Park, NC.
authors.forename: Margaret G
authors.initials: MG
authors.lastname: Ehm
mesh_terms: D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation; D019389:Cytochrome P-450 CYP2D6 / Q000235:genetics* / Q000494:pharmacology; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable
chemical_list: D005819:Genetic Markers; D019389:Cytochrome P-450 CYP2D6