Nakouzi, G. | Search bioRxiv

bioRxiv2023

Poison exon annotations improve the yield of clinically relevant variants in genomic diagnostic testing

PurposeNeurodevelopmental disorders (NDDs) often result from rare genetic variation, but genomic testing yield for NDDs remains around 50%, suggesting some clinically relevant rare variants may be missed by standard analyses. Here we analyze "poison exons" (PEs) which, while often absent from standard gene annotations, are alternative exons whose inclusion results in a premature termination codon. Variants that alter PE inclusion can lead to loss-of-function and may be highly penetrant contributors to disease. MethodsWe curated published RNA-seq data from developing mouse cortex to define 1,937 PE regions conserved between humans and mice and potentially relevant to NDDs. We then analyzed variants found by genome sequencing in multiple NDD cohorts. ResultsAcross 2,999 probands, we found six clinically relevant variants in PE regions that were previously overlooked. Five of these variants are in genes that are part of the sodium voltage-gated channel alpha subunit family (SCN1A, SCN2A, and SCN8A), associated with epilepsies. One variant is in SNRPB, associated with Cerebrocostomandibular Syndrome. These variants have moderate to high computational impact assessments, are absent from population variant databases, and were observed in probands with features consistent with those reported for the associated gene. ConclusionWith only a minimal increase in variant analysis burden (most probands had zero or one candidate PE variants in a known NDD gene, with an average of 0.77 per proband), annotation of PEs can improve diagnostic yield for NDDs and likely other congenital conditions.

genetics↗

bioRxiv2020

Reducing Sanger Confirmation Testing through False Positive Prediction Algorithms

PurposeClinical genome sequencing (cGS) followed by orthogonal confirmatory testing is standard practice. While orthogonal testing significantly improves specificity it also results in increased turn-around-time and cost of testing. The purpose of this study is to evaluate machine learning models trained to identify false positive variants in cGS data to reduce the need for orthogonal testing. MethodsWe sequenced five reference human genome samples characterized by the Genome in a Bottle Consortium (GIAB) and compared the results to an established set of variants for each genome referred to as a truth-set. We then trained machine learning models to identify variants that were labeled as false positives. ResultsAfter training, the models identified 99.5% of the false positive heterozygous single nucleotide variants (SNVs) and heterozygous insertions/deletions variants (indels) while reducing confirmatory testing of true positive SNVs to 1.67% and indels to 20.29%. Employing the algorithm in clinical practice reduced orthogonal testing using dideoxynucleotide (Sanger) sequencing by 78.22%. ConclusionOur results indicate that a low false positive call rate can be maintained while significantly reducing the need for confirmatory testing. The framework that generated our models and results is publicly available at https://github.com/HudsonAlpha/STEVE.

bioinformatics↗