Back

Copy Number Variant analysis by exome sequencing is an effective approach to optimize diagnostic yield for developmental disorders, the DDD-Africa study

Louw, N.; Makay, P.; Mpangase, P.; Naicker, T.; Yates, L.; Honey, E.; Mbungu, G.; Van Den Bogaert, K.; Firth, H.; Hurles, M.; Lukusa, P.; Devriendt, K.; Krause, A.; Carstens, N.; Lumaka, A.; Lombard, Z.

2026-02-07 genetic and genomic medicine
10.64898/2026.02.06.26345639 medRxiv
Show abstract

Copy number variants (CNV) contribute significantly to the pathogenic variation associated with developmental disorders. CNV detection is often not included in standard exome sequencing (ES) analysis. Complementary methods such as chromosomal microarray are typically offered in diagnostic laboratories to diagnose pathogenic CNV. In this study, we aimed to develop an optimal approach for incorporating CNV detection within our ES analysis process for the Deciphering Developmental Disorders in Africa (DDD-Africa) cohort. We analyzed ES data from 505 probands with a developmental disorder, applying a CNV detection approach that assessed data generated using the tools CANOES and XHMM. When available, parental ES data was used to assess inheritance patterns. We confirmed a diagnosis in 42/505 (8,3%) patients with 44 pathogenic CNV identified in the probands. There were 31 deletions and 13 duplications. Among the 27 probands with parental data, all identified CNV were de novo. The addition of CNV analysis to our ES analysis pipeline resulted in an 8.3% increase in diagnostic yield in the DDD-Africa cohort without additional laboratory cost. This approach offers a feasible approach which is likely to reduce analytical cost and is suitable for low- and middle-income countries where funding and resources for genomic medicine initiatives are limited.

Published in European Journal of Human Genetics (predicted rank #1) · training set

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.