From Big Data to Precision Medicine.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.Wellcome Trus

Undiagnosed diseases: Needs and opportunities in 20 countries participating in the Undiagnosed Diseases Network International

Introduction: Rare diseases (RD) are a health priority worldwide, overall affecting hundreds of millions of people globally. Early and accurate diagnosis is essential to support clinical care but remains challenging in many countries, especially the low- and medium-income ones. Hence, undiagnosed RD (URD) account for a significant portion of the overall RD burden. Methods: In October 2020, the Developing Nations Working Group of the Undiagnosed Diseases Network International (DNWG-UDNI) launched a survey among its members, belonging to 20 countries across all continents, to map unmet needs and opportunities for patients with URD. The survey was based on questions with open answers and included eight different domains. Conflicting interpretations were resolved in contact with the partners involved. Results: All members responded to the survey. The results indicated that the scientific and medical centers make substantial efforts to respond to the unmet needs of patients. In most countries, there is a high awareness of RD issues. Scarcity of resources was highlighted as a major problem, leading to reduced availability of diagnostic expertise and research. Serious equity in accessibility to services were highlighted both within and between participating countries. Regulatory problems, including securing informed consent, difficulties in sending DNA to foreign laboratories, protection of intellectual property, and conflicts of interest on the part of service providers, remain issues of concern. Finally, most respondents stressed the need to strengthen international cooperation in terms of data sharing, clinical research, and diagnostic expertise for URD patients in low and medium income countries. Discussion: The survey highlighted that many countries experienced a discrepancy between the growing expertise and scientific value, the level of awareness and commitment on the part of relevant parties, and funding bodies. Country-tailored public health actions, including general syllabus of medical schools and of the education of other health professionals, are needed to reduce such gaps.VSh is supported by Health Systems Research Institute of Thailand (65-040). SJ is supported by National Medical Research Council, Singapore (Grants ID CSAINV21jun-0003 and CIRG22jul-0003).S

Loss-of-function mutations in UDP-Glucose 6-Dehydrogenase cause recessive developmental epileptic encephalopathy

AbstractDevelopmental epileptic encephalopathies are devastating disorders characterized by intractable epileptic seizures and developmental delay. Here, we report an allelic series of germline recessive mutations in UGDH in 36 cases from 25 families presenting with epileptic encephalopathy with developmental delay and hypotonia. UGDH encodes an oxidoreductase that converts UDP-glucose to UDP-glucuronic acid, a key component of specific proteoglycans and glycolipids. Consistent with being loss-of-function alleles, we show using patients’ primary fibroblasts and biochemical assays, that these mutations either impair UGDH stability, oligomerization, or enzymatic activity. In vitro, patient-derived cerebral organoids are smaller with a reduced number of proliferating neuronal progenitors while mutant ugdh zebrafish do not phenocopy the human disease. Our study defines UGDH as a key player for the production of extracellular matrix components that are essential for human brain development. Based on the incidence of variants observed, UGDH mutations are likely to be a frequent cause of recessive epileptic encephalopathy.</jats:p

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits

KIAA1109 Variants Are Associated with a Severe Disorder of Brain Development and Arthrogryposis.

Whole-exome and targeted sequencing of 13 individuals from 10 unrelated families with overlapping clinical manifestations identified loss-of-function and missense variants in KIAA1109 allowing delineation of an autosomal-recessive multi-system syndrome, which we suggest to name Alkuraya-Kučinskas syndrome (MIM 617822). Shared phenotypic features representing the cardinal characteristics of this syndrome combine brain atrophy with clubfoot and arthrogryposis. Affected individuals present with cerebral parenchymal underdevelopment, ranging from major cerebral parenchymal thinning with lissencephalic aspect to moderate parenchymal rarefaction, severe to mild ventriculomegaly, cerebellar hypoplasia with brainstem dysgenesis, and cardiac and ophthalmologic anomalies, such as microphthalmia and cataract. Severe loss-of-function cases were incompatible with life, whereas those individuals with milder missense variants presented with severe global developmental delay, syndactyly of 2nd and 3rd toes, and severe muscle hypotonia resulting in incapacity to stand without support. Consistent with a causative role for KIAA1109 loss-of-function/hypomorphic variants in this syndrome, knockdowns of the zebrafish orthologous gene resulted in embryos with hydrocephaly and abnormally curved notochords and overall body shape, whereas published knockouts of the fruit fly and mouse orthologous genes resulted in lethality or severe neurological defects reminiscent of the probands' features.This article is freely available via Open Access. Click on the Additional Link above to access the full-text via the publisher's site

Family history assessment significantly enhances delivery of precision medicine in the genomics era

10.1186/s13073-020-00819-1Genome Medicine131

DCC mutation update: Congenital mirror movements, isolated agenesis of the corpus callosum, and developmental split brain syndrome

The deleted in colorectal cancer (DCC) gene encodes the netrin-1 (NTN1) receptor DCC, a transmembrane protein required for the guidance of commissural axons. Germline DCC mutations disrupt the development of predominantly commissural tracts in the central nervous system (CNS) and cause a spectrum of neurological disorders. Monoallelic, missense, and predicted loss-of-function DCC mutations cause congenital mirror movements, isolated agenesis of the corpus callosum (ACC), or both. Biallelic, predicted loss-of-function DCC mutations cause developmental split brain syndrome (DSBS). Although the underlying molecular mechanisms leading to disease remain poorly understood, they are thought to stem from reduced or perturbed NTN1 signaling. Here, we review the 26 reported DCC mutations associated with abnormal CNS development in humans, including 14 missense and 12 predicted loss-of-function mutations, and discuss their associated clinical characteristics and diagnostic features. We provide an update on the observed genotype-phenotype relationships of congenital mirror movements, isolated ACC and DSBS, and correlate this to our current understanding of the biological function of DCC in the development of the CNS. All mutations and their associated phenotypes were deposited into a locus-specific LOVD (https://databases.lovd.nl/shared/genes/DCC)