Akawi, N., McRae, J., Ansari, M. et al. (40 more authors) (2015) Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families. Nature Genetics, 47 (11). pp. 1363-1369. ISSN 1061-4036
Abstract
Discovery of most autosomal recessive disease-associated genes has involved analysis of large, often consanguineous multiplex families or small cohorts of unrelated individuals with a well-defined clinical condition. Discovery of new dominant causes of rare, genetically heterogeneous developmental disorders has been revolutionized by exome analysis of large cohorts of phenotypically diverse parent-offspring trios1,2. Here we analyzed 4,125 families with diverse, rare and genetically heterogeneous developmental disorders and identified four new autosomal recessive disorders. These four disorders were identified by integrating Mendelian filtering (selecting probands with rare, biallelic and putatively damaging variants in the same gene) with statistical assessments of (i) the likelihood of sampling the observed genotypes from the general population and (ii) the phenotypic similarity of patients with recessive variants in the same candidate gene. This new paradigm promises to catalyze the discovery of novel recessive disorders, especially those with less consistent or nonspecific clinical presentations and those caused predominantly by compound heterozygous genotypes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Nature America, Inc. This is an author produced version of a paper subsequently published in Nature Genetics. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Aug 2018 10:16 |
Last Modified: | 24 Aug 2018 10:16 |
Published Version: | https://doi.org/10.1038/ng.3410 |
Status: | Published |
Publisher: | Nature Publishing Group |
Refereed: | Yes |
Identification Number: | 10.1038/ng.3410 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134793 |