UK-Ireland study aims to improve genomic diagnosis of rare pediatric diseases


In a current observation posted in The New England Journal of Medicine, researchers carried out a large-scale sequencing observe using data collected over more than a decade from a previous study, the Deciphering Developmental Disorders (DDD) study.

It was undertaken to describe the analytical techniques developed in DDD research to identify and classify thousands of new molecular diagnoses and to report factors influencing the probability of a rare diagnosis.

The background

The DDD study was among the pioneering work in the United Kingdom (UK) and Ireland to mix large-scale genomic research with character-affected person responses.

This generated extensive exome sequencing and microarray data, further enriched by clinical phenotype data, enrolled by >200 UK and Irish clinicians over the same period. The hybrid clinical-research approach employed in this study is now standard practice in genomic medicine.

DDD studies have led to thousands of new diagnoses, identified ~60 new developmental disorders, and enabled over 350 genotype- or phenotype-specific projects.

Another key thing of the DDD has a look at becoming a stay online platform, Decipher, which enables the re-evaluation of reported genomic variants with current data, facilitating new opportunities for new molecular diagnoses.  

Overall, this takes a look at confirming the significance of national recruitment, systematic phenotyping, man or woman response, variation interpretation, and records sharing.

Molecular diagnosis of the pediatric disease has apparent advantages; For example, it increases the likelihood of treating them with profound lifelong benefits in the early years of life.  

Thus, next-era genomewide sequencing technology ought to significantly advantage the area of pediatrics, in which the genetic architecture of most disorders is heterogeneous and many harbor highly penetrant, pathogenic de novo genetic variants.

Furthermore, the number of partial diagnoses suggests that multiple factors, including environmental factors, contribute to the diagnosis, Thus, hampering the analysis and large-scale gene-disorder discovery in lots of probands with not unusual place incompletely penetrant genetic variants.

About study

The current study explores novel genetic causes for rare, difficult-to-diagnose developmental (or monogenic) pediatric disorders.

To this end, they first describe the analytical techniques developed in DDD studies, which may help identify and classify new molecular diagnoses.

Next, they document factors that may influence the likelihood of receiving a diagnosis for a rare pediatric disease.

They set up a multicentre research collaboration across 24 regional genetics services across the UK and Ireland and created a guidance committee comprising scientists, a bioethicist, and clinicians.  

In addition, they employed a sociologist to conduct ethics research.

A group of representative medical geneticists, studies nurses, and genetic counselors recruited greater than 13,500 households with 13,610probands, 88% of whom were from a family, that is, parent-child trios, between April 2011 and April 2015.

Eligibility criteria for participation in this study included neurodevelopmental disorders or congenital abnormalities, including abnormal growth measurements, dysmorphic features, atypical behavioral phenotypes, and genetic disorders.  

These situations had giant implications, however, their molecular foundation become unclear.

The group carried out 3 unbiased genomic assays of the usage of deoxyribonucleic acid (DNA) from the proband, as follows:

  1. Exome Sequencing
  2. Exon-Focused Array Comparative Genomic Hybridization (aCGH); And   
  3. Single-Nucleotide Polymorphism (SNP) Genotyping

The result

Study analysis covers a set of 13,449 probands. On average, researchers identified 1.0 and 2.5 candidate variants for each parent-offspring trio and singleton proband, respectively.

Clinical and computational tactics towards the variation category caused a prognosis in ~41% (5,502 of 13,449)of the probands, and surprisingly, 76% of them had a highly penetrant pathogenic de novo variant. 

Another 22% (2,997 of 13,449) of probands had genetic variants of uncertain significance strongly associated with monogenic developmental disorders in children. 

Surprisingly, the assignment of parent-child trios had the greatest effect on the probability of a diagnosis, with an odds ratio (OR) of 4.70 and a 95% self-belief interval [CI].

As predicted by multivariable logistic regression models, those in the top decile showed a higher diagnostic yield (52% vs. 16%) compared with probands in the bottom decile. 

However, these predictions are not attributable to cohort-specific factors. For example, probands with African ancestry showed a lower likelihood of receiving a diagnosis, OR=0.51.

Similarly, intrauterine exposure to antiepileptic drugs and preterm birth between 22 and 27 weeks of gestation reduced the probability of diagnosis with respective ORs of 0.44 and 0.39.

Overall, the authors found 4425 concordant variants between the clinical and predicted classifications of variant pathogenicity, Suggesting this hybrid method had an excessive sensitivity of 99.5% and a specificity of 85%.


The present study highlights that standard, phenotype-driven diagnostic methods have high failure rates and low diagnostic yield, particularly for families in which parental genotype data and ancestry-matched controls are missing, eg, probands of African ancestry. 

Thus, increasing the participation of underserved groups in research is essential to increase the current diagnostic yield of ~41%.

In the future, emphasis should be placed on improving this project by capturing longitudinal phenotype data and evaluating environmental factors that contribute to developmental disorders.

Nevertheless, this research significantly demonstrated how clinicians, genomic scientists, and bioinformaticians can work together to make molecular diagnoses of developmental disorders. 

About the author

Marta Lopez

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By Marta Lopez

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