Biology subjects

Seo, G. H.

Publications and source records attributed to Seo, G. H..

4 recordsLinked to original sources

bioRxiv2022

Disease-causing variant recommendation system for clinical genome interpretation with adjusted scores for artefactual variants

BackgroundIn the process of finding the causative variant of rare diseases (RD), accurate assessment and prioritization of genetic variants is essential. Although quality control (QC) of genetic variants is strictly performed, the presence of artefactual variants in the remaining set of variants can deteriorate the process. Variant QC and prioritization have been treated as separate processes, leading to limited efficiency and risk of misdiagnosis. ResultsWe developed a disease-causing variant recommendation system that integrates quality control into variant prioritization by adjusting scores for artefactual variants. We confirmed that the QC-related features of the variants contribute to a significant performance improvement. For genomic data from 2,878 patients with rare disorders, the recall rate of finding causative variants was 0.961 for the top 5 ranked variants. We also found that our system recognized the anomaly of QC-related features, so that the scores of artifactual variants to be disease-causing were assessed relatively low. ConclusionsIntegration of variant QC and prioritization help reduce the risk of misdiagnosis based on artefactual variants and increase the effectiveness of clinical genome interpretation.

genetics↗

bioRxiv2020

Quality threshold evaluation of Sanger confirmation for results of whole exome sequencing in clinically diagnostic setting

BackgroundWith the ability to simultaneously sequence more than 5,000 disease-associated genes, next-generation sequencing (NGS) has replaced Sanger sequencing as the preferred method in the diagnostic field at the laboratory level. However, Sanger sequencing has been used routinely to confirm identified variants prior to reporting results. This validation process causes a turnaround time delay and cost increase. Thus, this study aimed to set a quality threshold that does not require Sanger confirmation by analyzing the characteristics of identified variants from whole exome sequencing (WES). MethodsOur study analyzed data on a total of 694 disease-causing variants from 578 WES samples that had been diagnosed with suspected genetic disease. These samples were sequenced by Novaseq6000 and Exome Research Panel v2. All 694 variants (513 single-nucleotide variants (SNVs) and 181 indels) were validated by Sanger sequencing. ResultsA total of 693 variants included 512 SNVs and 181 indels from 578 patients and 367 genes. Five hundred seven heterozygous SNVs with at > 250 quality score and > 0.3 allele fraction were 100% confirmed by Sanger sequencing. Five heterozygous variants and one homozygous variant were not confirmed by Sanger sequencing, which showed 98.8% accuracy. There were 146 heterozygous variants and 35 homozygous variants among 181 indels, of which 11 heterozygous variants were not confirmed by Sanger sequencing (93.9% accuracy). Five non-confirmed variants with high quality were not identified on the ram .bam file. ConclusionOur results indicate that Sanger confirmation is not necessary for exome-derived SNVs with > 250 quality score and 0.3 > allele fraction set to an appropriate quality threshold. Indels or SNVs that do not meet the quality threshold should be reviewed by raw .bam file and Sanger confirmation should be performed to ensure accurate reporting.

genetics↗

bioRxiv2020

A novel PS4 criterion approach based on symptoms of rare diseases and in-house frequency data in a Bayesian framework.

The American College of Medical Genetics (ACMG) and Genomics/Association for Molecular Pathology (AMP) previously reported standardized guidance for the assessment of genetic variants. One of the criteria regarding the prevalence in a case-control study, PS4, is important due to its evidence of pathogenicity. Despite recent studies approaching gene- and disease-specific probands, interpretation of a variant to PS4 still has certain limitations for rare variants. Here, we suggest a generalized method, Bayesian odds ratio (BayesianOR), applicable to PS4 via decomposing a disease to its symptoms and applying a Bayesian framework. Using this approach, we demonstrate reproducibility of the calculation of the original odds ratio from well-studied epilepsy data and verify the applicability to in-house frequencies for various rare diseases. In addition, BayesianOR showed a significant difference in tendency with different ClinVar pathogenicity, using in-house data. Thus, the novel method described here should provide an improved interpretation of sequence variants. Furthermore, we anticipate that it will enhance the diagnosis of patients with rare diseases.

genetics↗

bioRxiv2019

High diagnostic yield and clinical utility of WES for patients with undiagnosed genetic disorder by automating variant interpretation

PurposeEVIDENCE, an automated interpretation system, has been developed to facilitate the entire process of whole exome sequencing (WES) analyses. This study investigated the diagnostic yield of EVIDENCE in patients suspected genetic disorders. MethodsDNA from 330 probands (age range, 0-68 years) with suspected genetic disorders were subjected to WES. Candidate variants were identified by EVIDENCE and confirmed by testing family members and/or clinical reassessments. ResultsThe average number of overlapping organ categories per patient was 4.5 {+/-} 5.0. EVIDENCE reported a total 244 variants in 215 (65.1%) of the 330 probands. After clinical reassessment and/or family member testing, 196 variants were identified in 171 probands (51.8%), including 115 novel variants. These variants were confirmed as being responsible for 146 genetic disorders. One hundred-seven (54.6%) of the 196 variants were categorized as pathogenic or likely pathogenic before, and 146 (74.6%) after, clinical assessment and/or family member testing. Factors associated with a variant being confirmed as causative include rules, such as PVS1, PS1, PM1, PM5, and PP5, and similar symptom scores between that variant and a patients phenotype. ConclusionThis new, automated variant interpretation system facilitated the diagnosis of various genetic diseases with a 51% improvement in diagnostic yield.

genetics↗