Increased diagnostic yield from negative whole genome‐slice panels using automated reanalysis

We evaluated the diagnostic yield using genome‐slice panel reanalysis in the clinical setting using an automated phenotype/gene ranking system. We analyzed whole genome sequencing (WGS) data produced from clinically ordered panels built as bioinformatic slices for 16 clinically diverse, undiagnosed cases referred to the Pediatric Mendelian Genomics Research Center, an NHGRI‐funded GREGoR Consortium site. Genome‐wide reanalysis was performed using Moon™, a machine‐learning‐based tool for variant prioritization. In five out of 16 cases, we discovered a potentially clinically significant variant. In four of these cases, the variant was found in a gene not included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome‐wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting.

included in the original panel due to phenotypic expansion of a disorder or incomplete initial phenotyping of the patient. In the fifth case, the gene containing the variant was included in the original panel, but being a complex structural rearrangement with intronic breakpoints outside the clinically analyzed regions, it was not initially identified. Automated genome-wide reanalysis of clinical WGS data generated during targeted panels testing yielded a 25% increase in diagnostic findings and a possibly clinically relevant finding in one additional case, underscoring the added value of analyses versus those routinely performed in the clinical setting. Many panels are built as bioinformatic slices of larger exome-or genome-based assays. Benchwork is test-agnostic, but analysis focuses on specific genes. Sequencing data remain available for reanalysis or evaluation of additional genes. 2 We demonstrate the potential of automated reanalysis with variant calling for both small and structural variants coupled with sophisticated machine-learning-based variant annotation and prioritization for patients with complex phenotypes undiagnosed after clinical genome-slice panels.  Table S1) were referred to the Pediatric Mendelian Genomics Research Center (PMGRC) ( Table 1). Phenotypic data were summarized using Human Phenotype Ontology (HPO) terms.

| Variant prioritization and analysis with Moon™
WGS bam files were processed to fastq files and re-aligned to hg19 using bwa-mem2. Variants were called using octopus. 3 Variant files, HPO terms, ages of onset and sex were uploaded to Moon™ (Invitae, San Francisco, CA). 4,5 Variants were classified according to ACMG guidelines. 6 Variant validation by high-coverage RNA sequencing was performed for Case 3 (methods in Data S1).

| RESULTS
Automated reanalysis of WGS using MOON™ after negative clinical panels yielded potentially diagnostic findings in five cases (11 remaining cases are described in Data S1).

| Case 1
A 5-year-old male presented with dermatitis. Oral steroids, not topical steroids or antihistamines, slowed progression and prevented new eruptions. He was otherwise healthy, without exposures or acute illnesses around the onset of symptoms.
Testing for two genes related to erythrokeratodermia variabilis was negative. Reanalysis of WGS data revealed two pathogenic lossof-function (LoF) variants in the FLG gene associated with autosomal recessive ichthyosis vulgaris, explaining the xerosis and hyperlinear palms ( Table 1). This gene was not included on the original panel as a narrower phenotype was considered but was the highest-ranked candidate by Moon™ due to the HPO term for dermatitis. This gene is often excluded from panel testing due to highly homologous regions that confound exome-based variant calling. Variants were confirmed by Sanger sequencing in a CLIA lab. NARS2 encodes an aminoacyl-tRNA-synthetase; biallelic variants cause combined oxidative phosphorylation deficiency 24 (COXPD24; OMIM #616239). NARS2 was not evaluated by previous genetic testing as the panels targeted specific phenotypes, which presented as components of the complex presentation. Moon ranked it high due to phenotypic terms generalized hypotonia, laryngomalacia, supraventricular tachycardiac, respiratory distress, and status epilepticus, and the variant was considered consistent with the clinical presentation.    Figure S1). to the patient's liver failure, a risk factor exacerbating another process, or an unrelated finding. It was paternally inherited from an asymptomatic father, but paternal urine and enzyme testing could not be performed.

| DISCUSSION
We reanalyzed WGS data generated during clinical gene panel testing of 16 patients with suspected genetic disorders using an automated, machine-learning-based variant-prioritization tool. 5,10 In five of the 16 patients (31%), reanalysis yielded additional reportable findings.
Our study serves as proof-of-concept that genome analysis after negative panels, supported by sophisticated variant annotation tools, can increase diagnostic yield in clinical settings and argues for increased use of WGS as a backbone test for genetic panels.
In four of the five cases where our reanalysis uncovered reportable variants, variants were present in genes not included in the origi-  We also identified a treatable diagnosis and important phenotypic expansion in Case 3. CCDS1 is a known cause of autism and neurodevelopmental delay, 12 but this patient also developed TdP, of which QT prolongation is a known risk factor. 13 At the time of analysis,

ACKNOWLEDGEMENTS
We thank the participants and referring physicians for their roles in this study.

FUNDING INFORMATION
The study was supported in part by the National Human Genome

DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in GREGoR data set at https://gregorconsortium.org/data.

ETHICS STATEMENT
Informed consent was obtained from every individual (or legal representative) whose data are included.