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Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies
Author(s) -
Najafi Arash,
Caspar Sylvan M.,
Meienberg Janine,
Rohrbach Marianne,
Steinmann Beat,
Matyas Gabor
Publication year - 2020
Publication title -
clinical genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.543
H-Index - 102
eISSN - 1399-0004
pISSN - 0009-9163
DOI - 10.1111/cge.13640
Subject(s) - genetics , in silico , biology , mutation , genome , population , gene , phenotype , medical genetics , genetic heterogeneity , computational biology , medicine , environmental health
Genome‐scale high‐throughput sequencing enables the detection of unprecedented numbers of sequence variants. Variant filtering and interpretation are facilitated by mutation databases, in silico tools, and population‐based reference datasets such as ExAC/gnomAD, while variants are classified using the ACMG/AMP guidelines. These methods, however, pose clinically relevant challenges. We queried the gnomAD dataset for (likely) pathogenic variants in genes causing autosomal‐dominant disorders. Furthermore, focusing on the fibrillinopathies Marfan syndrome (MFS) and congenital contractural arachnodactyly (CCA), we screened 500 genomes of our patients for co‐occurring variants in FBN1 and FBN2 . In gnomAD, we detected 2653 (likely) pathogenic variants in 253 genes associated with autosomal‐dominant disorders, enabling the estimation of variant‐filtering thresholds and disease predisposition/prevalence rates. In our database, we discovered two families with hitherto unreported co‐occurrence of FBN1 / FBN2 variants causing phenotypes with mixed or modified MFS/CCA clinical features. We show that (likely) pathogenic gnomAD variants may be more frequent than expected and are challenging to classify according to the ACMG/AMP guidelines as well as that fibrillinopathies are likely underdiagnosed and may co‐occur. Consequently, selection of appropriate frequency cutoffs, recognition of digenic variants, and variant classification represent considerable challenges in variant interpretation. Neglecting these challenges may lead to incomplete or missed diagnoses.