Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies

Abstract

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 cooccurring 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. K E Y W O R D S congenital contractural arachnodactyly, digenic variants, genome sequencing, Marfan syndrome, variant interpretation

Added Value of Clinical Sequencing: WGS-Based Profiling of Pharmacogenes

“…To determine and compare relative allele frequencies of pharmacogenetically-relevant variants in gnomAD exomes v2.1.1, genomes v3, and our in-house cohort, we applied multiple filtering criteria. Using VarSeq's automated filter functions, we filtered for the 45 SNVs and indels implemented in the PREPARE trial [9] (i.e., the most well described/known PGx variants with DPWG guidelines) as well as for variants potentially affecting gene function: (i) Loss-of-function (LOF) variants, defined as premature termination codons (PTCs) caused by nonsense and frameshift mutations with or without expected nonsense-mediated mRNA decay, as well as canonical splice site variants (intronic ±1-2 bp) caused by single nucleotide changes and in silico predicted to alter splicing; (ii) missense variants classified as "damaging" or "deleterious" by all six corresponding in silico prediction tools (FATHMM, FATHMM-MKL, MutationAssessor, MutationTaster, Polyphen2, SIFT) as previously described [43]. The resulting lists of LOF and missense variants were subsequently filtered for variants listed in ClinVar (v2019.11; ncbi.nlm.nih.gov/clinvar) as "Drug Response" [44] and/or in the Human Gene Mutation Database (HGMD) professional (v2019.4; portal.biobase-international.com) as "FP", "DFP", "DM?…”

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

“…Nevertheless, additional criteria are usually needed to refine the number of possible candidate variants. On the other hand, although selecting variants that are extremely rare or absent in population-based reference datasets may be helpful in a first step of the analysis, using very low frequency cutoffs may remove clinically relevant variants and may lead to false negative findings, especially in hard to solve cases [5]. More complex statistical frameworks that account for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets have been recently suggested for a more effective frequency-based variant filtering [5,6].…”

Potential predictors of severe cardiovascular involvement in Marfan syndrome: the emphasized role of genotype–phenotype correlations in improving risk stratification—a literature review

“…Results have been conflicting in the field of genotype–phenotype correlations, which could be down to small sample sizes and differences in study designs or due to co-occurring genetic modifier(s) [ 1 ] or differences in blood pressure load damaging the aortic wall. However, certain trends have emerged that could give base for larger studies with the aim of establishing well-defined correlations that could contribute to the improvement of risk stratification of aortic complications in MFS patients.…”

“…Marfan syndrome (MFS) is a systemic connective tissue disorder, affecting approximately 1 in 3000–5000 people [ 1 ]. The main clinical features are presented in the cardiovascular, musculoskeletal and ocular systems [ 2 , 3 ].…”