Genomic sequencing has made remarkable progress in identifying the underlying molecular causes of rare monogenic disorders. This cutting-edge technology is becoming increasingly available in diagnostic clinics worldwide, and it has mainly benefited the field of pediatrics. The high clinical need and potential for lifelong benefit with diagnosis and treatment make genomic sequencing an attractive option for early diagnosis of severe diseases in children. Moreover, early presentation of a patient with severe disease makes genetic diagnosis more feasible because causal variants are mainly absent from control data sets.
The progress in the genomic study of rare pediatric diseases has been spearheaded by numerous diagnostic research groups worldwide. One such study is the Deciphering Developmental Disorders (DDD), which recruited more than 13,500 families across the United Kingdom and Ireland. This study generated exome sequencing and microarray data, complemented by rich clinical phenotypes recorded by more than 200 clinicians. The DDD study is one of the first to combine large-scale genomic research with individual patient feedback. In this article, we describe the analytic strategies developed over a decade in the DDD study to identify and classify thousands of new molecular diagnoses and report the factors affecting the probability of receiving a diagnosis.
In a groundbreaking study published in the New England Journal Of Medicine, the Deciphering Developmental Disorders (DDD) project has identified and communicated molecular diagnoses to thousands of families in the United Kingdom and Ireland affected by severe, previously undiagnosed developmental disorders. Despite the provision of clinical genetic and genomic testing services, the genome-driven approach, in combination with detailed phenotyping, has improved diagnostic yield over the previous standard of care. The study highlights the value of using diverse and agnostic variant-detection methods combined with stringent variant-filtering rules and repeated, iterative variant analysis and classification to enable new diagnoses from existing data.
The DDD study found a high burden of pathogenic de novo variants, consistent with similar studies, and a current diagnostic yield of approximately 41%. The analysis supports clinical intuition about the likelihood of establishing a molecular diagnosis in patients with developmental disorders and moves toward quantifying the expectation of making such a diagnosis. The work also highlights groups with lower diagnostic yield in the cohort and reinforces the imperative to increase participation in research involving underrepresented groups.
Proband’s of African ancestry had a meager diagnostic yield, owing in part to the lack of ancestry-matched controls to estimate allele frequency and the lower likelihood of being recruited in a family trio. However, excluding cohort-specific factors, the multivariable logistic-regression model predicted a diagnostic yield of 52% among probands in the top decile of the probability of receiving a diagnosis, compared with a yield of 16% among probands in the bottom decile.
The study hypothesizes that the lower diagnostic yield observed among probands with certain prenatal risk factors reflects a more significant role of environmental influences that affect them. Further exploration is needed to better understand this cohort’s relative contributions and interplay of genetic and environmental influences.
Overall, the DDD study marks a significant milestone in using genomic sequencing and detailed phenotyping to improve diagnostic yield for severe, previously undiagnosed developmental disorders. The study’s findings can potentially revolutionize the standard of care for patients with developmental disorders, and further research is needed to refine and improve this approach. The article discusses the genetic architecture of developmental disorders, which is complex and heterogeneous.
While highly penetrant de novo variants can facilitate diagnosis and gene-disease discovery, many probands likely have multiple contributing factors, including rare and common incompletely penetrant genetic variants and nongenetic causes. The liability-threshold model of disease suggests that environmental contributions play a substantial role in some cases. The article also highlights the need for further research to identify more diagnoses in protein-coding genes and to evaluate incompletely penetrant variants.
The article also describes the methods used in the DDD study, which involved exome sequencing and microarray analyses to detect sequence and structural variants among genes in the DDG2P database. The study recruited infants and children and took a conservative approach to individual variant feedback, focusing primarily on the diagnosis.
The study also integrated ethics at a high level and involved an extensive network of expert clinicians and researchers. However, the study had some limitations. It did not capture most noncoding variants, complex structural variants, or tissue-specific mosaicism. The study also needed to be funded to capture longitudinal phenotype data or to assess social or environmental contributions to developmental disorders comprehensively.