Serious carbamazepine-induced hypersensitivity reactions associated with the HSP70 gene cluster.

OBJECTIVES: The use of carbamazepine (CBZ), the most commonly prescribed antiepileptic drug, is hampered by the occurrence of severe, potentially lethal hypersensitivity reactions. The pathogenesis of hypersensitivity is not yet known, but immune mechanisms are involved. Predisposition to CBZ hypersensitivity is likely to be genetically determined, and genes within the major histocompatibility complex (MHC) have been implicated. The heat shock protein (HSP70) gene cluster is located in the MHC class III region. METHODS: Using a case-control study design, we compared 61 patients with CBZ hypersensitivity (22 with a severe reaction) to 44 patients on CBZ with no signs of hypersensitivity and 172 healthy controls. The genotyping strategy involved identification of common and rare single nucleotide polymorphisms (SNPs) within the HSP70 gene cluster by sequencing, estimation of linkage disequilibrium (LD) and haplotype structure, and thereafter, analysis of SNP/haplotype frequencies in the cases and controls. Population substructure was evaluated by genotyping of 34 microsatellites. RESULTS: Twenty-five SNPs were detected across the three HSP70 genes. Analyses revealed that alleles G, T and C at the SNPs HSPA1A +1911 C/G, HSPA1A +438 C/T and HSPA1L +2437 T/C, respectively, were associated with protection from serious hypersensitivity reactions to CBZ, with the associated alleles falling on a common haplotype. We were unable to detect the presence of population stratification in our patients and controls. CONCLUSIONS: Our data show that HSP70 gene variants are associated with serious CBZ hypersensitivity reactions, but whether this is causal or reflects LD with another gene within the MHC requires further study.

Selecting cases from nuclear families for case-control association analysis.

We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AF aff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AF L). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.