Confirmation of Clinical Diagnosis in Requests for Prenatal Prediction of SMA Type I

The recent discovery of a major SMA-locus in the chromosomal region 5q makes it possible to carry out prenatal DNA studies in families in which a child with SMA type I has been born. Since direct mutation analysis is not yet possible, the reliability of prenatal prediction of SMA type I usually depends on the certainty of the clinical diagnosis in the index patient. Sixteen requests were received for DNA studies in couples who had had a previous child with SMA type I. After re-evaluation, the performance of prenatal diagnosis was rejected in four cases. Among the other twelve families prenatal DNA analysis of chorion villus biopsies has been carried out in three families. In all three cases the fetus had inherited the high-risk haplotypes from both parents, and the parents chose to terminate the pregnancy. An illustration of the prenatal DNA studies in one family is given. The importance of confirmation of the diagnosis SMA type I before performing DNA studies is emphasised

Delay in diagnosis of muscle disorders depends on the subspecialty of the initially consulted physician

<p>Abstract</p> <p>Background</p> <p>New therapeutic strategies in muscular dystrophies will make a difference in prognosis only if they are begun early in the course of the disease. Therefore, we investigated factors that influence the time to diagnosis in muscle dystrophy patients.</p> <p>Methods</p> <p>A sample of 101 patients (mean age 49 years; range 19-80; 44% women) with diagnosed muscle dystrophies from neurological practices and the neuromuscular specialty clinic in Berlin, Germany, was invited to participate. Time from first consultation to diagnosis, subspecialty of physician, and sociodemographic data were assessed with self-report questionnaires. The association between time to diagnosis and potential predictors (subspecialty of initially consulted physician, diagnoses, gender, and age at onset) was modeled with linear regression analysis.</p> <p>Results</p> <p>The mean time span between first health-care contact and diagnosis was 4.3 years (median 1). The diagnostic delay was significantly longer if patients were initially seen by a non-neurological specialist compared to a general practitioner (5.2 vs. 3.5 years, p = 0.047). Other factors that were independently associated with diagnostic delay were female gender and inherited muscle disease.</p> <p>Conclusion</p> <p>Action to improve clinical awareness of muscle diseases in non-neurological specialists is needed.</p

Skewed X-inactivation is common in the general female population

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants

Mining the human phenome using allelic scores that index biological intermediates

J. Kaprio ja M-L. Lokki työryhmien jäseniä.It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.Peer reviewe