Up to 80% of rare disease patients remain undiagnosed after genomic sequencing1, with many probably involving pathogenic variants in yet to be discovered disease-gene associations. To search for such associations, we developed a rare variant gene burden analytical framework for Mendelian diseases, and applied it to protein-coding variants from whole-genome sequencing of 34,851 cases and their family members recruited to the 100,000 Genomes Project2. A total of 141 new associations were identified, including five for which independent disease-gene evidence was recently published. Following in silico triaging and clinical expert review, 69 associations were prioritized, of which 30 could be linked to existing experimental evidence. The five associations with strongest overall genetic and experimental evidence were monogenic diabetes with the known β cell regulator3,4 UNC13A, schizophrenia with GPR17, epilepsy with RBFOX3, Charcot-Marie-Tooth disease with ARPC3 and anterior segment ocular abnormalities with POMK. Further confirmation of these and other associations could lead to numerous diagnoses, highlighting the clinical impact of large-scale statistical approaches to rare disease-gene association discovery.

• Publikační typ
• časopisecké články MeSH

Prediction methods have become an integral part of biomedical and biotechnological research. However, their clinical interpretations are largely based on biochemical or molecular data, but not clinical data. Here, we focus on improving the reliability and clinical applicability of prediction algorithms. We assembled and curated two large non-overlapping large databases of clinical phenotypes. These phenotypes were caused by missense variations in 44 and 63 genes associated with Mendelian diseases. We used these databases to establish and validate the model, allowing us to improve the predictions obtained from EVmutation, SNAP2 and PoPMuSiC 2.1. The predictions of clinical effects suffered from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, although predictions mediated by these methods are associated with nearly absolute sensitivity. We introduced evidence-based tailoring of the default settings of the prediction methods; this tailoring substantially improved the prediction outcomes. Additionally, the comparisons of the clinically observed and theoretical variations led to the identification of large previously unreported pools of variations that were under negative selection during molecular evolution. The evolutionary variation analysis approach described here is the first to enable the highly specific identification of likely disease-causing missense variations that have not yet been associated with any clinical phenotype.

• MeSH
• algoritmy MeSH
• ektodysplasiny genetika   MeSH
• fenotyp MeSH
• genetická variace MeSH
• genetické nemoci vrozené genetika   MeSH
• genomika MeSH
• glukosa-6-fosfátdehydrogenasa genetika   MeSH
• hemoglobiny genetika   MeSH
• hepatocytární jaderný faktor 4 genetika   MeSH
• lidé MeSH
• missense mutace MeSH
• modely genetické * MeSH
• molekulární evoluce MeSH
• mutace * MeSH
• pravděpodobnostní funkce MeSH
• proteomika MeSH
• tyrosinfosfatasa nereceptorového typu 11 genetika   MeSH
• výpočetní biologie metody   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• EDA protein, human MeSH Prohlížeč
• ektodysplasiny MeSH
• G6PD protein, human MeSH Prohlížeč
• glukosa-6-fosfátdehydrogenasa MeSH
• hemoglobin B MeSH Prohlížeč
• hemoglobiny MeSH
• hepatocytární jaderný faktor 4 MeSH
• HNF4A protein, human MeSH Prohlížeč
• PTPN11 protein, human MeSH Prohlížeč
• tyrosinfosfatasa nereceptorového typu 11 MeSH

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.

• Klíčová slova
• Faces, data protection, data sharing, patient information, phenotyping, rare disease,
• Publikační typ
• časopisecké články MeSH