We report our results of 1000 diagnostic WES cases based on 2819 sequenced samples from 54 countries with a wide phenotypic spectrum. Clinical information given by the requesting physicians was translated to HPO terms. WES processes were performed according to standardized settings. We identified the underlying pathogenic or likely pathogenic variants in 307 families (30.7%). In further 253 families (25.3%) a variant of unknown significance, possibly explaining the clinical symptoms of the index patient was identified. WES enabled timely diagnosing of genetic diseases, validation of causality of specific genetic disorders of PTPN23, KCTD3, SCN3A, PPOX, FRMPD4, and SCN1B, and setting dual diagnoses by detecting two causative variants in distinct genes in the same patient. We observed a better diagnostic yield in consanguineous families, in severe and in syndromic phenotypes. Our results suggest that WES has a better yield in patients that present with several symptoms, rather than an isolated abnormality. We also validate the clinical benefit of WES as an effective diagnostic tool, particularly in nonspecific or heterogeneous phenotypes. We recommend WES as a first-line diagnostic in all cases without a clear differential diagnosis, to facilitate personal medical care.

BackgroundChromosomal rearrangements involving 17q23 have been described rarely. Deletions at 17q23.1q23.2 have been reported in individuals with developmental delay and growth retardation, whereas duplications at 17q23.1q23.2 appear to segregate with clubfoot. Dosage alterations in the TBX2 and TBX4 genes, located in 17q23.2, have been proposed to be responsible for the phenotypes observed in individuals with 17q23.1q23.2 deletions and duplications. In this report, we present the clinical phenotype of a child with a previously unreported de novo duplication at 17q23.2q23.3 located distal to the TBX2 and TBX4 region.Case presentationWe report a 7.5-year-old boy with speech and language disorder, learning difficulties, incoordination, fine motor skill impairment, infrequent seizures with abnormal EEG, and behavior disturbances (mild self-inflicted injuries, hyperactivity-inattention, and stereotyped hand movements). Chromosomal microarray revealed a 2-Mb duplication of chromosome 17q23.2q23.3. Both parents did not have the duplication indicating that this duplication is de novo in the child.ConclusionsThe duplicated region encompasses 16 genes. It is possible that increased dosage of one or more genes in this region is responsible for the observed phenotype. The TANC2 gene is one of the genes in the duplicated region.It encodes a member of the TANC (tetratricopeptide repeat, ankyrin repeat and coiled-coil containing) family which includes TANC1 and TANC2. These proteins are highly expressed in brain and play major roles in synapsis regulation. Hence, it is suggestive that TANC2 is the likely candidate gene responsible for the observed phenotype as an increased TANC2 dosage can potentially alter synapsis, resulting in neuronal dysfunction and the neurobehavioral phenotype observed in this child with 17q23.2q23.3 duplication.

Background The quantitative level of pathogens present in a host is a major driver of infectious disease (ID) state and outcome. However, the majority of ID diagnostics are qualitative. Next-generation sequencing (NGS) is an emerging ID diagnostics and research tool to provide insights, including tracking transmission, evolution, and identifying novel strains. Methods We built a novel likelihood-based computational method to leverage pathogen-specific genome-wide NGS data to detect SARS-CoV-2, profile genetic variants, and furthermore quantify levels of these pathogens. We used de-identified clinical specimens tested for SARS-CoV-2 using RT-PCR, SARS-CoV-2 NGS Assay (hybrid capture, Twist Bioscience), or ARTIC (amplicon-based) platform, and COVID-DX software. A training (n=87) and validation (n=22) set was selected to establish the strength of our quantification model. We fit non-uniform probabilistic error profiles to a deterministic sigmoidal equation that more realistically represents observed data and used likelihood maximized over several different read depths to improve accuracy over a wide range of values of viral load. Given the proportion of the genome covered at varying depths for a single sample as input data, our model estimated the Ct of that sample as the value that produces the maximum likelihood of generating the observed genome coverage data. Results The model fit on 87 SARS-CoV-2 NGS Assay training samples produced a good fit to the 22 validation samples, with a coefficient of correlation (r2) of ~0.8. The accuracy of the model was high (mean absolute % error of ~10%, meaning our model is able to predict the Ct value of each sample within a margin of ±10% on average). Because of the nature of the commonly used ARTIC protocol, we found that all quantitative signals in this data were lost during PCR amplification and the model is not applicable for quantification of samples captured this way. The ability to model quantification is a major advantage of the SARS-CoV-2 NGS assay protocol. The likelihood-based model to estimate SARS-CoV-2 viral titer Left Observed genome coverage (y-axis) plotted against Ct value (x-axis). The best-fitting logistic curve is demonstrated with a red line with shaded areas above and below representing the fitted error profile. RIGHT: Model-estimated Ct values (y-axis) compared to laboratory Ct values (x-axis) with grey bars representing estimated confidence intervals. The 1:1 diagonal is shown as a dotted line. Conclusion To our knowledge, this is the first model to incorporate sequence data mapped across the genome of a pathogen to quantify the level of that pathogen in a clinical specimen. This has implications in ID diagnostics, research, and metagenomics. Disclosures Heather L. Wells, MPH, Biotia, Inc. (Consultant) Joseph Barrows, MS, Biotia (Employee) Mara Couto-Rodriguez, MS, Biotia (Employee) Xavier O. Jirau Serrano, B.S., Biotia (Employee) Marilyne Debieu, PhD, Biotia (Employee) Karen Wessel, PhD, Labor Zotz/Klimas (Employee) Christopher Mason, PhD, Biotia (Board Member, Advisor or Review Panel member, Shareholder) Dorottya Nagy-Szakal, MD PhD, Biotia Inc (Employee, Shareholder) Niamh B. O’Hara, PhD, Biotia (Board Member, Employee, Shareholder)