“Objective: Hereditary hearing impairment is a genetically heterogeneous disorder. In spite of this, mutations in the GJB2 gene, encoding connexin 26 (Cx26). are a major cause of nonsyndromic recessive IPI-549 nmr hearing loss in many countries and are largely dependent on ethnic groups. The purpose of our study was to characterize the type and prevalence of GJB2 mutations among Azeri population of Iran.
Methods: Fifty families presenting autosomal recessive nonsyndromic hearing loss from Ardabil province of Iran were studied for mutations in GJB2 gene. All DNA samples were screened for c.35delG mutation by ARMS PCR. Samples from patients
who were normal for c.35delG were analyzed for the other variations in GJB2 by direct sequencing. In the absence of mutation detection, GJB6 was screened for the del(GJB6-D13S1830) and del(GJB6-D13S1854).
Result: Thirteen families demonstrated alteration in the Cx26 (26%). The 35delG mutation was the most common one, accounting for 69.2% (9 out of 13 families). All the detected families were homozygous for this mutation. Two families were homozygous for delE120 and 299-300delAT mutations. We also identified a novel mutation: c.463-464 delTA in 2 families resulting in a frame shift mutation.
Conclusion: Our results suggest that c.35delG mutation in the GJB2 gene is the most important
cause of GJB2 related deafness in Iranian Azeri population. (C) 2011 Elsevier Geneticin mw Ireland Ltd. All rights reserved.”
“Background and Objectives: As a result of the development of sophisticated MM-102 techniques, such as multiple imputation, the interest in handling missing data in longitudinal studies has increased enormously in past years. Within the field of longitudinal data analysis, there is a current debate on whether it is necessary to
use multiple imputations before performing a mixed-model analysis to analyze the longitudinal data. In the current study this necessity is evaluated.
Study Design and Setting: The results of mixed-model analyses with and without multiple imputation were compared with each other. Four data sets with missing values were created one data set with missing completely at random, two data sets with missing at random, and one data set with missing not at random). In all data sets, the relationship between a continuous outcome variable and two different covariates were analyzed: a time-independent dichotomous covariate and a time-dependent continuous covariate.
Results: Although for all types of missing data, the results of the mixed-model analysis with or without multiple imputations were slightly different, they were not in favor of one of the two approaches. In addition, repeating the multiple imputations 100 times showed that the results of the mixed-model analysis with multiple imputation were quite unstable.