Enabling global clinical collaborations on identifiable patient data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.PhenoScore is an open-source machine-learning tool that combines facial image recognition with Human Phenotype Ontology for genetic syndrome identification without genomic data, with applications to subgroup analysis and variants of unknown significance classification.Genetics of disease, diagnosis and treatmen

Hotspots of missense mutation identify neurodevelopmental disorder genes and functional domains

Genetics of disease, diagnosis and treatmen

Rare variants in the genetic background modulate cognitive and developmental phenotypes in individuals carrying disease-associated variants.

To assess the contribution of rare variants in the genetic background toward variability of neurodevelopmental phenotypes in individuals with rare copy-number variants (CNVs) and gene-disruptive variants. We analyzed quantitative clinical information, exome sequencing, and microarray data from 757 probands and 233 parents and siblings who carry disease-associated variants. The number of rare likely deleterious variants in functionally intolerant genes ("other hits") correlated with expression of neurodevelopmental phenotypes in probands with 16p12.1 deletion (n=23, p=0.004) and in autism probands carrying gene-disruptive variants (n=184, p=0.03) compared with their carrier family members. Probands with 16p12.1 deletion and a strong family history presented more severe clinical features (p=0.04) and higher burden of other hits compared with those with mild/no family history (p=0.001). The number of other hits also correlated with severity of cognitive impairment in probands carrying pathogenic CNVs (n=53) or de novo pathogenic variants in disease genes (n=290), and negatively correlated with head size among 80 probands with 16p11.2 deletion. These co-occurring hits involved known disease-associated genes such as SETD5, AUTS2, and NRXN1, and were enriched for cellular and developmental processes. Accurate genetic diagnosis of complex disorders will require complete evaluation of the genetic background even after a candidate disease-associated variant is identified

Truncating Variants in NAA15 Are Associated with Variable Levels of Intellectual Disability, Autism Spectrum Disorder, and Congenital Anomalies

Genetics of disease, diagnosis and treatmen

Cellular differentiation and gentic aspects of the trypanosome life cycle.

This collection of papers (submitted as a doctoral thesis) considers the various aspects of cellular differentiation and genetics of the trypanosome life cycle. The first chapters gives a general introduction to African trypanosomiasis. The next 4 discuss the DNA content of the genus Trypanosoma, identification of a facultative haploid life cycle stage in African trypanosomes, cell cycle synchronization of Trypanosoma brucei cultured in vitro, with aphidicolin measured by flow cytometry, and the karyotypes and comparative DNA contents of parental and hybrid T. brucei. Several of the sections in the book have been, or will be published elsewhere and are multi-authored

A de novo balanced t(2;6)(p15;p22.3) in a patient with West Syndrome disrupts a lnc-RNA.

Contains fulltext : 110552.pdf (publisher's version ) (Closed access)In a male patient with West Syndrome we identified a perfectly balanced, de novo balanced translocation 46,XY,t(2;6)(p15;p22.3). No known protein coding genes were disrupted by the translocation and positional effects on nearby genes were excluded by expression studies. A putative long non-coding RNA, BX118339, spans the breakpoint on chromosome 6. It can be hypothesized that disruption of this non-coding transcript plays a role in the pathogenesis of the patient.1 mei 201