Multimodal Genomewide Analysis Improves Diagnosis of Severe Developmental Disorders in Children

Article

Investigators identify factors that may affect the probability of diagnosis in an evaluation of over 13,500 families with probands with severe developmental disorders.

Caroline Wright, PhD

Credit: University of Exeter Medical School

Caroline Wright, PhD

Credit: University of Exeter Medical School

Data from a new study indicate multimodal analysis of genomewide data had good diagnostic power for severe, probably monogenic, difficult-to-diagnose developmental disorders, even after previous attempts at diagnosis.1

Investigators identified several factors that may affect the probability of diagnosis, including recruitment in a parent-offspring trio, prematurity, in-utero exposure to antiepileptic medications, maternal diabetes, and African ancestry.

The implications of these findings are pivotal for the diagnosis and management of pediatric disorders and may help guide future research in this area, the study read.

Diagnosing Difficult Pediatric Disorders

Pediatric disorders encompass a group of genetically heterogeneous conditions that can be highly penetrant and amenable to genomewide diagnostic approaches.

However, finding a molecular diagnosis for these disorders can be difficult, but it can have lifelong benefits for affected individuals and their families.

Caroline Wright, PhD, Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon and Exeter Hospital, and a team of investigators conducted a large-scale sequencing study in the UK and Ireland to evaluate novel genetic causes of severe, probably monogenic, developmental disorders in children.

More than 13,500 families with probands (the first affected individuals in a family) with severe, monogenic, difficult-to-diagnose developmental disorders from 24 regional genetics services in were included in the evaluation. Standardized phenotypic data were collected, and exome sequencing and microarray analyses were performed to investigate novel genetic causes.

For validation and diagnostic interpretation to inform communication with families, investigators developed an iterative variant analysis pipeline and reported candidate variants to clinical teams. Multiple regression analyses were performed to evaluate factors affecting the probability of diagnosis.

The Results

The analyses consisted of 13,449 probands. On average, results reported 1.0 candidate variant per parent-offspring trio and 2.5 variants per singleton proband. A diagnosis was made in approximately 41% of probands (n = 5502). Within that subpopulation, 76% had a pathogenic de novo variant.

An additional 22% of probands (n = 2997) exhibited variants of uncertain significance in genes that were strongly linked to monogenic developmental disorders. Investigators observed recruitment in a parent-offspring trio had the largest effect on the probability of diagnosis (OR, 4.70; 95% confidence interval [CI], 4.16 -5.31).

Probands were less likely to receive a diagnosis if they were born extremely prematurely (22-27 weeks’ gestation; OR, 0.39; 95% CI, 0.22-0.68). They also had in-utero exposure to antiepileptic medications (OR, 0.44; 95% CI, 0.29-0.67), mothers with diabetes (OR, 0.52; 95% CI, 0.41-0.67), or were of African ancestry (OR, 0.51; 95% CI, 0.31-0.78).

“Despite the provision of clinical genetic and genomic testing services across the United Kingdom and Ireland, these probands show how a genome-driven approach in combination with detailed phenotyping can improve diagnostic yield over the previous standard of care,” investigators wrote.

References:

  1. Wright C, Campbell P, Eberhardt R, et al. Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland. The New England Journal of Medicine. 2023. doi: 10.1056/NEJMoa2209046
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