Genetic-based optimization of treatment prescription is becoming a central research focus in the management of chronic diseases, such as multiple sclerosis, which incur a prolonged drug-regimen adjustment. This study was aimed to identify genetic markers that can predict response to glatiramer acetate (Copaxone) immunotherapy for relapsing multiple sclerosis. For this purpose, we genotyped fractional cohorts of two glatiramer acetate clinical trials for HLA-DRB1*1501 and 61 single nucleotide polymorphisms within a total of 27 candidate genes. Statistical analyses included single nucleotide polymorphism-by-single nucleotide polymorphism and haplotype tests of drug-by-genotype effects in drug-treated versus placebo-treated groups. We report the detection of a statistically significant association between glatiramer acetate response and a single nucleotide polymorphism in a T-cell receptor beta (TRB@) variant replicated in the two independent cohorts (odds ratio=6.85). Findings in the Cathepsin S (CTSS) gene (P=0.049 corrected for all single nucleotide polymorphisms and definitions tested, odds ratio=11.59) in one of the cohorts indicate a possible association that needs to be further investigated. Additionally, we recorded nominally significant associations of response with five other genes, MBP, CD86, FAS, IL1R1 and IL12RB2, which are likely to be involved in glatiramer acetate's mode-of-action, both directly and indirectly. Each of these association signals in and of itself is consistent with the no-association null-hypothesis, but the number of detected associations is surprising vis-à-vis chance expectation. Moreover, the restriction of these associations to the glatiramer acetate-treated group, rather than the placebo group, clearly demonstrates drug-specific genetic effects. These findings provide additional progress toward development of pharmacogenetics-based personalized treatment for multiple sclerosis.

Autoimmune diseases seem to have strong genetic attributes, and are affected to some extent by shared susceptibility loci. The latter potentially amount to hundreds of candidate genes (CG), creating the need for a prioritization strategy in genetic association studies. To form such a strategy, 26 autoimmune-related CG were genotyped for a total of 72 single nucleotide polymorphisms (SNPs) in three distinct Israeli ethnic populations: Ashkenazi Jews, Sephardic Jews and Arabs. Four quantitative criteria reflecting population stratification were analyzed: allele frequencies, haplotype frequencies, the F st statistic for homozygotes distribution and linkage disequilibrium extents. According to the consequent interpopulation genomic diversity profiles, the genes were classified into conserved, intermediate and diversified gene groups. Our results demonstrate a correlation between the biological role of autoimmune-related CG and their interpopulation diversity profiles as classified by the different analyses. Annotation analysis suggests that genes more readily influenced by environmental conditions, such as immunological mediators, are 'population specific'. Conversely, genes showing genetic conservation across all populations are characterized by apoptotic and cleaving functions. We suggest a research strategy by which CG association studies should focus first on likely conserved gene categories, to increase the likelihood of attaining significant results and promote the development of gene-based therapies.

The genetic mapping of drug-response traits is often characterised by a poor signal-to-noise ratio that is placebo related and which distinguishes pharmacogenetic association studies from classical case-control studies for disease susceptibility. The goal of this study was to evaluate the statistical power of candidate gene association studies under different pharmacogenetic scenarios, with special emphasis on the placebo effect. Genotype/phenotype data were simulated, mimicking samples from clinical trials, and response to the drug was modelled as a binary trait. Association was evaluated by a logistic regression model. Statistical power was estimated as a function of the number of single nucleotide polymorphisms (SNPs) genotyped, the frequency of the placebo 'response', the genotype relative risk (GRR) of the response polymorphism, the strategy for selecting SNPs for genotyping, the number of individuals in the trial and the ratio of placebo-treated to drugtreated patients. We show that: (i) the placebo 'response' strongly affects the statistical power of association studies -- even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics; (ii) the power of a pharmacogenetic association study depends primarily on the penetrance of the response genotype and, when this penetrance is fixed, power decreases for larger placebo effects; (iii) power is dramatically increased when adding markers; (iv) an optimal study design includes a similar number of placebo- and drugtreated patients; and (v) in this setting, straightforward haplotype analysis does not seem to have an advantage over single marker analysis.