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. 2015 Apr 4;385(9975):1305-14.
doi: 10.1016/S0140-6736(14)61705-0. Epub 2014 Dec 17.

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

Caroline F Wright  1 , Tomas W Fitzgerald  2 , Wendy D Jones  2 , Stephen Clayton  2 , Jeremy F McRae  2 , Margriet van Kogelenberg  2 , Daniel A King  2 , Kirsty Ambridge  2 , Daniel M Barrett  2 , Tanya Bayzetinova  2 , A Paul Bevan  2 , Eugene Bragin  2 , Eleni A Chatzimichali  2 , Susan Gribble  2 , Philip Jones  2 , Netravathi Krishnappa  2 , Laura E Mason  2 , Ray Miller  2 , Katherine I Morley  3 , Vijaya Parthiban  2 , Elena Prigmore  2 , Diana Rajan  2 , Alejandro Sifrim  2 , G Jawahar Swaminathan  2 , Adrian R Tivey  2 , Anna Middleton  2 , Michael Parker  4 , Nigel P Carter  2 , Jeffrey C Barrett  2 , Matthew E Hurles  2 , David R FitzPatrick  5 , Helen V Firth  6 ; DDD study
Collaborators, Affiliations

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

Caroline F Wright et al. Lancet. .

Abstract

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.

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Figures

Figure 1
Figure 1
Study workflow SNV=single nucleotide variant. Indel=insertion or deletion. CNV=copy number variant. UPD=uniparental disomy.
Figure 2
Figure 2
Variant filtering logic for clinical reporting within the study Genomic variants were filtered on the basis of six factors, of which the first five were automated and the final one was done manually: (1) frequency, prevalence of the variant in the general population (MAF ≤1%); (2) function, most severe predicted functional consequence, such as LOF, defined by specific sequence ontology terms (transcript ablation, splice donor variant, splice acceptor variant, stop-gained, frameshift variant, stop-lost, initiator codon variant, in-frame insertion, in-frame deletion, missense variant, transcript amplification, and coding sequence variant); (3) location, genomic location compared with DDG2P of published genes; (4) variant type, genotype (eg, heterozygous or homozygous) and loss or gain for small CNVs (which were only considered when they contained entire genes in which LOF or dominant negative mutations had been previously reported, and gains were only considered when they overlapped genes in which increased gene dosage mutations had been previously reported); (5) inheritance, aspects of the pipeline that are dependent on inheritance information derived from parental data are shaded; and (6) phenotype, patient phenotype was manually compared against published phenotypes for a particular gene. MAF=minor allele frequency. CNV=copy number variant. LOF=loss of function. DDG2P=Developmental Disorders Genotype-to-Phenotype database.
Figure 3
Figure 3
Representation of phenotypic diversity in cohort Our patient cohort represents children with a wide range of severe undiagnosed developmental disorders ascertained clinically across the UK.
Figure 4
Figure 4
Analysis of flagged variants in all 1133 children excluding (red) and including (blue) filtering on the basis of parental genotypes and affected status (using the November 2013 version of DDG2P) (A) Histogram of the number of flagged single nucleotide variants and insertion-deletions in 1133 children with and without parental data. (B) Mean number of flagged variants per child with and without parental data for families where neither, one, or both parents are affected by a developmental phenotype, subdivided by DDG2P genetic mechanism. Note that compound heterozygous variants are counted once. Red=proband-only analysis. Blue=family-trio analysis with parental genotype data. Filled=autosomal dominant DDG2P genes. Vertical stripes=autosomal recessive DDG2P genes. Horizontal stripes=X-linked DDG2P genes. DDG2P=Developmental Disorders Genotype-to-Phenotype database.
Figure 5
Figure 5
Genetic diagnoses associated with broad phenotype categories Circos-style plot representing the genetic heterogeneity within developmental disorders, showing individual diagnoses in known Developmental Disorders Genotype-to-Phenotype database genes, which links the genomic location of each gene with some key phenotypes in each child. Phenotypes are listed outside the widest arc of the circle, chromosome numbers are indicated outside the smaller arc, and individual gene names are listed inside. Links are coloured by phenotype group. See appendix 2 for details of the diagnoses. ID=intellectual disability. CHD=congenital heart defect. ASD=autism spectrum disorders. Deaf=hearing impairment. Cleft=oral cleft. VI=visual impairment. MC=microcephalic dwarfism. PD=polydactyly.
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References

    1. Lupski JR, Reid JG, Gonzaga-Jauregui C. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N Engl J Med. 2010;362:1181–1191. - PMC - PubMed
    1. Bamshad MJ, Ng SB, Bigham AW. Exome sequencing as a tool for mendelian disease gene discovery. Nat Rev Genet. 2011;12:745–755. - PubMed
    1. Yang Y, Muzny DM, Reid JG. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med. 2013;369:1502–1511. - PMC - PubMed
    1. Worthey EA, Mayer AN, Syverson GD. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet Med. 2011;13:255–262. - PubMed
    1. de Ligt J, Willemsen MH, van Bon BWM. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367:1921–1929. - PubMed

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