Identification of de novo variants from parent-proband duos via long-read sequencing
Boukas, L.; Delot, E. C.; Pitsava, G.; Lambert, C.; Fanslow, C.; Baybayan, P.; Belhadj, S.; Losic, B.; Harting, J.; Bluske, K.; LoTempio, J.; Al-Kouatly, H.; Karam, R.; Rowell, W.; Xiao, C.; Vilain, E.; Berger, S. I.
Show abstract
While de novo variants cause many Mendelian disorders, their detection currently requires sequencing of the proband and both biological parents. This is not feasible when only one parent is available, a limitation for millions of families. Here, we develop duoNovo, which identifies de novo variants from parent-proband duos using long-read sequencing followed by haplotype reconstruction and detection of identical-by-descent haplotype blocks. We sequenced 117 trios with PacBio HiFi sequencing and applied duoNovo to each of the 234 duos constructed by masking one parent, classifying over 60 million variants according to their de novo status. We evaluated duoNovos performance against classifications obtained using the full trios (which included over 10,000 de novo variants), demonstrating very high precision ([~] 98% among variants absent from gnomAD) and low error rate. In a cohort of 74 duos undergoing diagnostic evaluation, duoNovo prioritized a de novo intronic variant likely to disrupt splicing, while also uncovering the de novo status of two variants already classified as pathogenic. In summary, duoNovo has the potential to significantly increase the diagnostic yield of single-parent genetic testing, and represents an example where long-read sequencing provides clear benefit over short-read sequencing even for single nucleotide variants. It is freely available as an R package (https://github.com/sbergercnmc/duonovo).
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
"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.