32 research outputs found

    Health and population effects of rare gene knockouts in adult humans with related parents.

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    Examining complete gene knockouts within a viable organism can inform on gene function. We sequenced the exomes of 3222 British adults of Pakistani heritage with high parental relatedness, discovering 1111 rare-variant homozygous genotypes with predicted loss of function (knockouts) in 781 genes. We observed 13.7% fewer homozygous knockout genotypes than we expected, implying an average load of 1.6 recessive-lethal-equivalent loss-of-function (LOF) variants per adult. When genetic data were linked to the individuals' lifelong health records, we observed no significant relationship between gene knockouts and clinical consultation or prescription rate. In this data set, we identified a healthy PRDM9-knockout mother and performed phased genome sequencing on her, her child, and control individuals. Our results show that meiotic recombination sites are localized away from PRDM9-dependent hotspots. Thus, natural LOF variants inform on essential genetic loci and demonstrate PRDM9 redundancy in humans.The study was funded by the Wellcome Trust (WT102627 and WT098051), Barts Charity (845/1796), Medical Research Council (MR/M009017/1). This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Collaboration for Applied Health Research and Care (CLAHRC) for Yorkshire and Humber. Core support for Born in Bradford is also provided by the Wellcome Trust (WT101597). V.N. was supported by the Wellcome Trust PhD Studentship (WT099769). D.G.M. and K.K. were supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM104371. E.R.M. is funded by NIHR Cambridge Biomedical Research Centre. H.H. is supported by awards to establish the Farr Institute of Health Informatics Research, London, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, NIHR, National Institute for Social Care and Health Research, and Wellcome Trust.This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via https://doi.org/10.1126/science.aac862

    Analysis of protein-coding genetic variation in 60,706 humans

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    Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

    The landscape of tolerated genetic variation in humans and primates.

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    Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases

    A gene-to-patient approach uplifts novel disease gene discovery and identifies 18 putative novel disease genes

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    PurposeExome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck.MethodsWe apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries.ResultsResults from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction–constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had &gt;1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries.ConclusionUsing large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.</p

    Neurogenetic fetal akinesia and arthrogryposis : genetics, expanding genotype-phenotypes and functional genomics

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    Background Fetal akinesia and arthrogryposis are clinically and genetically heterogeneous and have traditionally been refractive to genetic diagnosis. The widespread availability of affordable genome-wide sequencing has facilitated accurate genetic diagnosis and gene discovery in these conditions. Methods We performed next generation sequencing (NGS) in 190 probands with a diagnosis of arthrogryposis multiplex congenita, distal arthrogryposis, fetal akinesia deformation sequence or multiple pterygium syndrome. This sequencing was a combination of bespoke neurogenetic disease gene panels and whole exome sequencing. Only class 4 and 5 variants were reported, except for two cases where the identified variants of unknown significance (VUS) are most likely to be causative for the observed phenotype. Co-segregation studies and confirmation of variants identified by NGS were performed where possible. Functional genomics was performed as required. Results Of the 190 probands, 81 received an accurate genetic diagnosis. All except two of these cases harboured class 4 and/or 5 variants based on the American College of Medical Genetics and Genomics guidelines. We identified phenotypic expansions associated with CACNA1S, CHRNB1, GMPPB and STAC3. We describe a total of 50 novel variants, including a novel missense variant in the recently identified gene for arthrogryposis with brain malformations - SMPD4. Conclusions Comprehensive gene panels give a diagnosis for a substantial proportion (42%) of fetal akinesia and arthrogryposis cases, even in an unselected cohort. Recently identified genes account for a relatively large proportion, 32%, of the diagnoses. Diagnostic-research collaboration was critical to the diagnosis and variant interpretation in many cases, facilitated genotype-phenotype expansions and reclassified VUS through functional genomics
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