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An example of the utility of genomic analysis for fast and accurate clinical diagnosis of complex rare phenotypes.

ABSTRACT: We describe molecular diagnosis in a complex consanguineous family: four offspring presented with combinations of three distinctive phenotypes; non-syndromic hearing loss (NSHL), an unusual skeletal phenotype comprising multiple fractures, cranial abnormalities and diaphyseal expansion, and significant developmental delay with microcephaly. We performed Chromosomal Microarray Analysis on the offspring with either the skeletal or developmental delay phenotypes, and linkage analysis and whole exome sequencing (WES) on all four children, parents and maternal aunt.Chromosomal microarray and FISH analysis identified a de novo unbalanced translocation as a cause of the microcephaly and severe developmental delay. WES identified a NSHL-causing splice variant in an autosomal recessive deafness gene PDZD7 which resided in a linkage region and affected three of the children. In the two children diagnosed with an unusual skeletal phenotype, WES eventually disclosed a heterozygous COL1A1 variant which affects C-propetide cleavage site of COL1. The variant was inherited from an apparently unaffected mosaic father in an autosomal dominant fashion. After the discovery of the COL1A1 variant, the skeletal phenotype was diagnosed as a high bone mass form of osteogenesis imperfecta.Next generation sequencing offers an unbiased approach to molecular genetic diagnosis in highly heterogeneous and poorly characterised disorders and enables early diagnosis as well as detection of mosaicism.

SUBMITTER: Le Quesne Stabej P

PROVIDER: S-EPMC5297239 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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An example of the utility of genomic analysis for fast and accurate clinical diagnosis of complex rare phenotypes.

Le Quesne Stabej Polona P James Chela C Ocaka Louise L Tekman Mehmet M Grunewald Stephanie S Clement Emma E Stanescu Horia C HC Kleta Robert R Morrogh Deborah D Calder Alistair A Williams Hywel J HJ Bitner-Glindzicz Maria M

Orphanet journal of rare diseases 20170207 1

<h4>Background</h4>We describe molecular diagnosis in a complex consanguineous family: four offspring presented with combinations of three distinctive phenotypes; non-syndromic hearing loss (NSHL), an unusual skeletal phenotype comprising multiple fractures, cranial abnormalities and diaphyseal expansion, and significant developmental delay with microcephaly. We performed Chromosomal Microarray Analysis on the offspring with either the skeletal or developmental delay phenotypes, and linkage anal ...[more]

PMID: 28173822

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Project description:There is an increasing need to use genome and transcriptome sequencing to genetically diagnose patients suffering from suspected monogenic rare diseases. The proper detection of compound heterozygous variant combinations as disease-causing candidates is a challenge in diagnostic workflows as haplotype information is lost by currently used next-generation sequencing technologies. Consequently, computational tools are required to phase, or resolve the haplotype of, the high number of heterozygous variants in the exome or genome of each patient. Here we present SmartPhase, a phasing tool designed to efficiently reduce the set of potential compound heterozygous variant pairs in genetic diagnoses pipelines. The phasing algorithm of SmartPhase creates haplotypes using both parental genotype information and reads generated by DNA or RNA sequencing and is thus well suited to resolve the phase of rare variants. To inform the user about the reliability of a phasing prediction, it computes a confidence score which is essential to select error-free predictions. It incorporates existing haplotype information and applies logical rules to determine variants that can be excluded as causing a recessive, monogenic disease. SmartPhase can phase either all possible variant pairs in predefined genetic loci or preselected variant pairs of interest, thus keeping the focus on clinically relevant results. We compared SmartPhase to WhatsHap, one of the leading comparable phasing tools, using simulated data and a real clinical cohort of 921 patients. On both data sets, SmartPhase generated error-free predictions using our derived confidence score threshold. It outperformed WhatsHap with regard to the percentage of resolved pairs when parental genotype information is available. On the cohort data, SmartPhase enabled on average the exclusion of approximately 22% of the input variant pairs in each singleton patient and 44% in each trio patient. SmartPhase is implemented as an open-source Java tool and freely available at http://ibis.helmholtz-muenchen.de/smartphase/.

Project description:Purpose: There is a quest for novel non-invasive diagnostic markers for the detection of breast cancer. The goal of this study is to identify circulating microRNA signatures using a cohort of Asian Chinese breast cancer patients, and to compare microRNA profiles between tumour and serum samples. Experimental design: MicroRNAs from paired breast cancer tumours, normal tissue and serum samples derived from 32 patients were comprehensively profiled using microarrays (1300 microRNAs against tumour and normal tissues) or LNA RT-PCR panels (742 microRNAs against serum samples). Serum samples from healthy individuals (n=22) were also employed as normal controls. Significant serum microRNAs, identified by logistic regression, were validated in an independent set of serum samples from patients (n=82) and healthy controls (n=53). Results: The 20 most significant microRNAs differentially expressed in breast cancer tumours included miR-21, miR-10b, and miR-145, previously shown to be dysregulated in breast cancer. Interestingly, 16 of the 20 most significant microRNAs differentially expressed in serum samples were novel. MiR-1, miR-92a, miR-133a and miR-133b were identified as the most important diagnostic markers, and were successfully validated; receiver operating characteristic curves derived from combinations of these microRNAs exhibited areas under the curves of 0.944-0.946. Only seven microRNAs were overexpressed in both tumours and serum, suggesting that microRNAs may be released into the serum selectively. Conclusion: The clinical employment of microRNA signatures as a non-invasive diagnostic strategy is promising, but should be further validated for different subtypes of breast cancers. Blood samples were collected in Becton Dickinson (Franklin Lakes, NJ) Vacutainer SST tubes. Serum was harvested by centrifugation at 2200g after allowing blood to clot for 30mins. 32 patient samples and 22 samples from healthy controls were obtained for profiling. Sera samples were stored at -80oC.

Project description:Purpose: There is a quest for novel non-invasive diagnostic markers for the detection of breast cancer. The goal of this study is to identify circulating microRNA signatures using a cohort of Asian Chinese breast cancer patients, and to compare microRNA profiles between tumour and serum samples. Experimental design: MicroRNAs from paired breast cancer tumours, normal tissue and serum samples derived from 32 patients were comprehensively profiled using microarrays (1300 microRNAs against tumour and normal tissues) or LNA RT-PCR panels (742 microRNAs against serum samples). Serum samples from healthy individuals (n=22) were also employed as normal controls. Significant serum microRNAs, identified by logistic regression, were validated in an independent set of serum samples from patients (n=82) and healthy controls (n=53). Results: The 20 most significant microRNAs differentially expressed in breast cancer tumours included miR-21, miR-10b, and miR-145, previously shown to be dysregulated in breast cancer. Interestingly, 16 of the 20 most significant microRNAs differentially expressed in serum samples were novel. MiR-1, miR-92a, miR-133a and miR-133b were identified as the most important diagnostic markers, and were successfully validated; receiver operating characteristic curves derived from combinations of these microRNAs exhibited areas under the curves of 0.944-0.946. Only seven microRNAs were overexpressed in both tumours and serum, suggesting that microRNAs may be released into the serum selectively. Conclusion: The clinical employment of microRNA signatures as a non-invasive diagnostic strategy is promising, but should be further validated for different subtypes of breast cancers. "_A" and "_B" are two tissue sections of the same sample; "_1" and "_2" represents 2 runs of the same sample; na = not available All tissue samples were histologically confirmed by a pathologist using hematoxylin and eosin staining of cryosectioned specimens. One tumour sample was rejected due to failure to detect any tumour cells. Except for two samples (with 30% and 40% tumour cells), all tumour tissues employed had a minimum of 60% tumour cells, as estimated microscopically. Overall, the breast cancer tumour samples had an average of 71% tumour cells. The criteria for adjacent normal tissue were absence of tumour cells and presence of epithelial cells. Hence, after histological confirmation, 31 breast cancer tumours and 23 matched normal tissues were employed for microRNA extraction and profiling using microarray.

Dataset Information

An example of the utility of genomic analysis for fast and accurate clinical diagnosis of complex rare phenotypes.

Publications

An example of the utility of genomic analysis for fast and accurate clinical diagnosis of complex rare phenotypes.

Similar Datasets

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