Familial Mediterranean fever associated with MEFV mutations in a large cohort of Cypriot patients.

Familial Mediterranean fever (FMF) is caused by mutations in the MEFV gene and the spectrum of mutations among Greek-Cypriots with FMF-related symptoms was examined. Sequence analysis for exons 2, 3, 5, and 10 of the MEFV gene was performed in a cohort of 593 patients. A total of 70 patients carried mutations in the homozygote or compound heterozygote state, 128 were identified with one MEFV mutation and 395 had no mutations. Of the 268 identified alleles, p.Val726Ala (27.61%) was the most frequent followed by p.Met694Val (19.40%). The missense mutations p.Arg761His (3.73%) and p.Ala744Ser (2.24%) were identified as the rarest. An interesting finding is the high frequency (18.28%) of the complex p.Phe479Leu-p.Glu167Asp that was identified in 49 of the mutated alleles. The MEFV genotypes did not follow a binomial distribution and proved not to satisfy the HWE (P < 0.001). The high percentage (66.61%) of patients with unidentified mutations could be due to mutations in the rest of the coding or noncoding MEFV gene or due to mutations in other genes that are also causing Hereditary Recurrent Fevers. Results from this work indicate the high incidence of FMF in Cyprus and describe the spectrum of the mutations which occur in the country.

Median bias reduction in random-effects meta-analysis and meta-regression.

The reduction of the mean or median bias of the maximum likelihood estimator in regular parametric models can be achieved through the additive adjustment of the score equations. In this paper, we derive the adjusted score equations for median bias reduction in random-effects meta-analysis and meta-regression models and derive efficient estimation algorithms. The median bias-reducing adjusted score functions are found to be the derivatives of a penalised likelihood. The penalised likelihood is used to form a penalised likelihood ratio statistic which has known limiting distribution and can be used for carrying out hypothesis tests or for constructing confidence intervals for either the fixed-effect parameters or the variance component. Simulation studies and real data applications are used to assess the performance of estimation and inference based on the median bias-reducing penalised likelihood and compare it to recently proposed alternatives. The results provide evidence on the effectiveness of median bias reduction in improving estimation and likelihood-based inference.