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Although Genome Reference Consortium Human Build 38 (GRCh38) was released with improvement over GRCh37, it has not been widely adopted. Several liftover tools have been developed as a convenient approach for GRCh38 implementation. This study aimed to investigate the accuracy of liftover tools for genome conversion. Two Variant Call Format (VCF) files aligned to GRCh37 and GRCh38 were downloaded from ClinVar (clinvar_20221217.vcf.gz). Liftover tools such as CrossMap, NCBI Remap, and UCSC liftOver were used to convert genome coordinates from GRCh37 to GRCh38. The accuracy of CrossMap, NCBI Remap, and UCSC liftOver were 99.81% (1,567,838/1,570,748), 99.69% (1,565,953/1,570,748), and 99.99% (1,570,550/1,570,748), respectively. Variants that failed conversion via all three liftover tools were all indels/duplications: a pathogenic/likely pathogenic variant (n = 1) and benign/likely benign variants (n = 7). The eight variants that failed conversion were identified in the ALMS, TTN, CFTR, SLCO, LDLR, PCNT, MID1, and GRIA3 genes, and all the variants were not in the VCF files aligned to GRCh37. This study demonstrated that three liftover tools could successfully convert reference genomes from GRCh37 to GRCh38 in more than 99% of ClinVar variants. This study takes the first step to clinically implement GRCh38 using liftover tools. Further clinical studies are warranted to compare the performance of liftover tools and to validate re-alignment approaches in routine clinical settings. [ABSTRACT FROM AUTHOR]

Background Accurate nomenclature of variants is an essential element for genetic diagnosis and patient care. Objective To investigate annotation differences of clinical variants between annotation tools. Methods We analyzed 218,156 clinical variants from the Human Gene Mutation Database. Multiple nomenclatures based on RefSeq transcripts were provided using ANNOVAR and snpEff. Results The concordance rate between ANNOVAR and snpEff was approximately 85%. Based on the Human Genome Variation Society (HGVS) nomenclature, snpEff was more accurate than ANNOVAR (coding variants, 99.3% vs 84.9%; protein variants, 94.3% vs 79.8%). When annotating each variant with ANNOVAR and snpEff, the accuracy of nomenclature was 99.5%. Conclusions There were substantial differences between ANNOVAR and snpEff annotations. The findings of this study suggest that simultaneous use of multiple annotation tools could decrease nomenclature errors and contribute to providing standardized clinical reporting. [ABSTRACT FROM AUTHOR]

Objective Discordant variant classifications among public databases is one of the well-documented limitations when interpreting the pathogenicity of variants. The aim of this study is to investigate the level of germline variant misannotation from the Human Gene Mutation Database (HGMD) and the annotation concordance between databases. Methods We used a total of 188,106 classified variants (disease-causing mutations [n = 179,454] and polymorphisms [n = 8652]) in 6466 genes from the HGMD. All variants were reanalyzed based on the American College of Medical Genetics and Genomics (ACMG) guidelines and compared to ClinVar database variants. Results When variants were classified based on the ACMG guidelines, misclassification was observed in 3.47% (2289/65,896) of variants. The overall concordance between HGMD and ClinVar was 97.62% (52,499/53,780) of variants studied. Conclusion Variants in databases must be used with caution when variant pathogenicity is interpreted. This study reveals the frequency of misannotation of the HGMD variants and annotation concordance between databases in depth. [ABSTRACT FROM AUTHOR]