Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data

By C. Wright, Tomas W. Fitzgerald, W. Jones, Stephen Clayton, J. McRae, M. Kogelenberg, D. King, K. Ambridge, Daniel M. Barrett, Tanya Bayzetinova and 22 more. The Lancet, 2015

1,792 citations recorded by the source corpus.

Summary Background Human genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount. Methods The Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team. Findings Around 80 000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation. Interpretation Implementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene–phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial. Funding Health Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/67977a7d69ab4c00c17f5caeaa1ebb4278fb0f5a

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Citations in the corpus

• CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions (2024, neutral): “1 ( 54 ), ClinVar ( 37 ), and the Deciphering Developmental Disorder (DDD) study ( 55 ), resulting in a total of 2088 benign and 1655 pathogenic variants (see also Supplementary Notes S1 and S6).”
• Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays (2021, neutral): “To demonstrate this, we took the 2,429 disease list (including 2,149 genes) from the developmental disorders genotype-to-phenotype database (DDG2P) (Wright et al., 2015), filtered them to select the top 1,959 genes based on the expression threshold in our dataset (Data S1), and then projected these g”
• The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species (2016, positive): “A phenotypic profile is the subject of non-exact matching within and across species us-ing ontology integration and semantic similarity algorithms (10,11) in software applications such as Exomiser (12) and Genomiser (13), and this approach has been shown to assist disease diagnosis (14–16).”
• Regional Anesthesia in the Patient Receiving Antithrombotic or Thrombolytic Therapy: American Society of Regional Anesthesia and Pain Medicine Evidence-Based Guidelines (Fourth Edition) (2018, background): “For example, the efficacy of thrombolytics therapy following a cerebral embolic clot markedly decreases after 6 to 8 hours.(22) This implies that anticoagulants will have a hard time lysing a clot after 8 hours.”
• Next-generation diagnostics and disease-gene discovery with the Exomiser (2015, neutral): “The HPO is widely used in the rare disease community, having been adopted by the Sanger Institute’s DECIPHER and DDD projects 74 , the rare disease section of the UK 100,000 Genomes Project, the NIH Undiagnosed Diseases Program and Network, the Matchmaker Exchange and many others.”