The Results of CHD7 Analysis in Clinically Well-Characterized Patients with Kallmann Syndrome

Item does not contain fulltextCONTEXT: Kallmann syndrome (KS) and CHARGE syndrome are rare heritable disorders in which anosmia and hypogonadotropic hypogonadism co-occur. KS is genetically heterogeneous, and there are at least eight genes involved in its pathogenesis, whereas CHARGE syndrome is caused by autosomal dominant mutations in only one gene, the CHD7 gene. Two independent studies showed that CHD7 mutations can also be found in a minority of KS patients. OBJECTIVE: We aimed to investigate whether CHD7 mutations can give rise to isolated KS or whether additional features of CHARGE syndrome always occur. DESIGN: We performed CHD7 analysis in a cohort of 36 clinically well-characterized Dutch patients with KS but without mutations in KAL1 and with known status for the KS genes with incomplete penetrance, FGFR1, PROK2, PROKR2, and FGF8. RESULTS: We identified three heterozygous CHD7 mutations. The CHD7-positive patients were carefully reexamined and were all found to have additional features of CHARGE syndrome. CONCLUSION: The yield of CHD7 analysis in patients with isolated KS seems very low but increases when additional CHARGE features are present. Therefore, we recommend performing CHD7 analysis in KS patients who have at least two additional CHARGE features or semicircular canal anomalies. Identifying a CHD7 mutation has important clinical implications for the surveillance and genetic counseling of patients

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Dynamic metabolomic data analysis: a tutorial review

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a ‘dynamic’ method. Some of the methods are illustrated with real-life metabolomics examples

Characteristics of small breast and/or ovarian cancer families with germline mutations in BRCA1 and BRCA2

For families with a small number of cases of breast and/or ovarian cancer, limited data are available to predict the likelihood of genetic predisposition due to mutations in BRCA1 or BRCA2. In 104 families with three or more affected individuals (average 3.8) seeking counselling at family cancer clinics, mutation analysis was performed in the open reading frame of BRCA1 and BRCA2 by the protein truncation test and mutation-specific assays. In 31 of the 104 families tested, mutations were detected (30%). The majority of these mutations (25) occurred in BRCA1. Mutations were detected in 15 out of 25 families (60%) with both breast and ovarian cancer and in 16 out of 79 families (20%) with exclusively cases of breast cancer. Thus, an ovarian cancer case strongly predicted finding a mutation (P < 0.001). Within the group of small breast-cancer-only families, a bilateral breast cancer case or a unilateral breast cancer case diagnosed before age 40 independently predicted finding a BRCA1 or BRCA2 mutation (P = 0.005 and P = 0.02, respectively). Therefore, even small breast/ovarian cancer families with at least one case of ovarian cancer, bilateral breast cancer, or a case of breast cancer diagnosed before age 40, should be referred for mutation screening. © 1999 Cancer Research Campaig