Treatment of epilepsy in Rett syndrome.

INTRODUCTION: Epilepsy is very frequent in Rett syndrome (RTT) patients and often difficult to treat. Because most cases of RTT are caused by mutations in the MECP2 gene it is reasonable to assume that convulsions are based on common pathogenetic mechanisms and thus should have a similar response to antiepileptic drugs. PURPOSE: To find the optimal treatment for epilepsy in RTT. METHODS: We performed a retrospective study on 110 female patients with confirmed MECP2 mutations. RESULTS: The median age was 10 years, 58% had a history of epilepsy and 55% received antiepileptic drugs (AEDs). Only sulthiame, carbamazepine and valproate were administered in an adequate frequency to allow statistical analysis. The best anticonvulsive results were seen in the RTT group that was treated with carbamazepine. Sulthiame was slightly less effective while valproate was significantly less effective. The rate of side effects was equivalent in all groups. In conclusion, carbamazepine should be recommended as first choice AED in RTT. If carbamazepine is not effective or not well tolerated sulthiame ought to be taken as second choice AED.

Hidden population substructures in an apparently homogeneous population bias association studies.

Linkage- and association-based approaches have been applied to attempt to unravel the genetic predisposition for complex diseases. However, studies often report contradictory results even when similar population backgrounds are investigated. Unrecognized population substructures could possibly explain these inconsistencies. In an apparently homogeneous German sample of 612 patients with type 2 diabetic and end-stage diabetic nephropathy and 214 healthy controls, we tested for hidden population substructures and their possible effects on association. Using a genetic vector space analysis of genotypes of 20 microsatellite markers, we identified four distinct subsets of cases and controls. The significance of these substructures was demonstrated by subsequent association analyses, using three genetic markers (UCSNP-43,-19,-63; intron 3 of the calpain-10 gene). In the undivided sample, we found no association between individual SNPs or any haplogenotypes (ie the genotype combination of two multilocus haplotypes) and type 2 diabetes. In contrast, when analyzing the four groups separately, we found that there was evidence for association of the common C allele of UCSNP-63 with the trait in the largest group (n=547 cases/101 controls; P=0.002). In this subset haplotype 112 was more frequent in controls than in cases (P=0.006; haplogenotype 112/121: odds ratio (OR)=0.27, 95% confidence intervals (CI)=0.13-0.57), indicating a protective effect against the development of type 2 diabetes. Our study demonstrates that unconsidered population substructures (ethnicity-dependent factors) can severely bias association studies.

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association.

For the identification of susceptibility loci in complex diseases the choice of the target phenotype is very important. We compared results of genome-wide searches for linkage or for association related to three phenotypes for alcohol use disorder. These are a behavioral score BQ, based on a 12-item questionnaire about drinking behavior and the subject's report of drinking-related health problems, and ERP pattern and ERP magnitude, both derived from the eyes closed resting ERP measures to quantify brain activity. Overall, we were able to identify 11 candidate regions for linkage. Only two regions were found to be related to both BQ and one of the ERP phenotypes. The genome-wide search for association using single-nucleotide polymorphisms did not yield interesting leads.

Nonparametric longitudinal allele-sharing model.

Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed.

Case-control studies with affected sibships.

Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index FST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well.

Summary of contributions to GAW15 Group 13: candidate gene association studies.

Here we summarize the contributions to Group 13 of the Genetic Analysis Workshop 15 held in St. Pete Beach, Florida, on November 12-14, 2006. The focus of this group was to identify candidate genes associated with rheumatoid arthritis or surrogate outcomes. The association methods proposed in this group were diverse, from better known approaches, such as logistic regression for single nucleotide polymorphism (SNP) analysis and haplotype sharing tests to methods less familiar to genetic epidemiologists, such as machine learning and visualization methods. The majority of papers analyzed Genetic Analysis Workshop 15 Problems 2 (rheumatoid arthritis data) and 3 (simulated data). The highlighted points of this group analyses were: (1) haplotype-based statistics can be more powerful than single SNP analysis for risk-locus localization; (2) considering linkage disequilibrium block structure in haplotype analysis may reduce the likelihood of false-positive results; and (3) visual representation of genetic models for continuous covariates may help identify SNPs associated with the underlying quantitative trait loci.

SNP-based analysis of genetic substructure in the German population.

OBJECTIVE: To evaluate the relevance and necessity to account for the effects of population substructure on association studies under a case-control design in central Europe, we analysed three samples drawn from different geographic areas of Germany. Two of the three samples, POPGEN (n = 720) and SHIP (n = 709), are from north and north-east Germany, respectively, and one sample, KORA (n = 730), is from southern Germany. METHODS: Population genetic differentiation was measured by classical F-statistics for different marker sets, either consisting of genome-wide selected coding SNPs located in functional genes, or consisting of selectively neutral SNPs from 'genomic deserts'. Quantitative estimates of the degree of stratification were performed comparing the genomic control approach [Devlin B, Roeder K: Biometrics 1999;55:997-1004], structured association [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945-959] and sophisticated methods like random forests [Breiman L: Machine Learning 2001;45:5-32]. RESULTS: F-statistics showed that there exists a low genetic differentiation between the samples along a north-south gradient within Germany (F(ST)(KORA/POPGEN): 1.7 . 10(-4); F(ST)(KORA/SHIP): 5.4 . 10(-4); F(ST)(POPGEN/SHIP): -1.3 . 10(-5)). CONCLUSION: Although the F(ST )-values are very small, indicating a minor degree of population structure, and are too low to be detectable from methods without using prior information of subpopulation membership, such as STRUCTURE [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945-959], they may be a possible source for confounding due to population stratification.

Indication for genetic testing: a checklist for Rett syndrome.

OBJECTIVE: We reevaluated 49 girls with either Rett syndrome (RTT) or features of RTT who had negative test results for mutations in the MECP2 gene and compared them with 49 girls who had positive test results. The girls with MECP2-positive results included 2 girls with forme fruste and 2 with congenital RTT. Study design Based on the original diagnostic criteria for RTT, we developed a 10-item checklist with a score ranging from 0 to 12. RESULTS: If only girls with a score of 8 or more had been tested, 46% of the girls without mutations would have been excluded from testing without missing a single girl with MECP2-positive results. CONCLUSIONS: This checklist provides a simple aid for deciding whether or not a genetic test for RTT should be performed with only a minimal risk of missing girls with MECP2-positive results.