A Path to Implement Precision Child Health Cardiovascular Medicine.

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Diagnostic applications of next generation sequencing: working towards quality standards

Over the past 6 years, next generation sequencing (NGS) has been established as a valuable high-throughput method for research in molecular genetics and has successfully been employed in the identification of rare and common genetic variations. All major NGS technology companies providing commercially available instruments (Roche 454, Illumina, Life Technologies) have recently marketed bench top sequencing instruments with lower throughput and shorter run times, thereby broadening the applications of NGS and opening the technology to the potential use for clinical diagnostics. Although the high expectations regarding the discovery of new diagnostic targets and an overall reduction of cost have been achieved, technological challenges in instrument handling, robustness of the chemistry and data analysis need to be overcome. To facilitate the implementation of NGS as a routine method in molecular diagnostics, consistent quality standards need to be developed. Here the authors give an overview of the current standards in protocols and workflows and discuss possible approaches to define quality criteria for NGS in molecular genetic diagnostics

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment

Dystrophic Epidermolysis Bullosa: COL7A1 Mutation Landscape in a Multi-Ethnic Cohort of 152 Extended Families with High Degree of Customary Consanguineous Marriages

Dystrophic epidermolysis bullosa is a heritable skin disease manifesting with sub-lamina densa blistering, erosions, and chronic ulcers. COL7A1, encoding type VII collagen, has been identified as the candidate gene for dystrophic epidermolysis bullosa. In this study, we have identified COL7A1 mutations in a large multi-ethnic cohort of 152 extended Iranian families with high degree of consanguinity. The patients were diagnosed by clinical manifestations, histopathology, and immunoepitope mapping. Mutation detection consisted of a combination of single nucleotide polymorphism-based whole-genome homozygosity mapping, Sanger sequencing, and gene-targeted next-generation sequencing. A total of 104 distinct mutations in COL7A1 were identified in 149 of 152 families (98%), 56 (53%) of them being previously unreported. Ninety percent of these mutations were homozygous recessive, reflecting consanguinity in these families. Three recurrent mutations were identified in five or more families, and haplotype analysis suggested a founder effect in two of them. In conclusion, COL7A1 harbored mutations in the overwhelming majority of patients with dystrophic epi-dermolysis bullosa, and most of them in this Iranian cohort were consistent with autosomal recessive inheri-tance. The mutation profile attests to the impact of consanguinity in these families

Ontology-guided data preparation for discovering genotype-phenotype relationships

International audienceComplexity of post-genomic data and multiplicity of mining strategies are two limits to Knowledge Discovery in Databases (KDD) in life sciences. Because they provide a semantic frame to data and because they benefit from the progress of semantic web technologies, bio-ontologies should be considered for playing a key role in the KDD process. In the frame of a case study relative to the search of genotype-phenotype relationships, we demonstrate the capability of bio-ontologies to guide data selection during the preparation step of the KDD process. We propose three scenarios to illustrate how domain knowledge can be taken into account in order to select or aggregate data to mine, and consequently how it can facilitate result interpretation at the end of the process