Table 2.

Challenges and possible improvements in variant interpretation

Key points for variant interpretation	Challenges	Possible improvements
Deep phenotyping	Identification of clinical gene-related hallmarks	International natural history studies on large cohorts of patients; a large consensus on the diagnostic and prognostic value of each test/hallmark
Population data: allele frequency threshold	Phenotypic divergence (1 gene = several diseases)	Large epidemiological studies
Phasing/segregation	Time-consuming and cost-ineffective PCR-based analysis	Novel sequencing technologies, TRIO or multi-sample sequencing
Elusive variants	Repetitive regions, low covered areas, CNV-prone sequences, cryptic splice-causing variants	Improved computational tools, novel sequencing technologies, second-tier tests
Variant annotation/functional validation:
In silico tools	Conflicting predictions; uncertain accuracy	Improved (more accurate) computational tools
In vitro experiments	Large proteins to be dissected in more manageable fragments	Benchmark assays
In vivo or ex vivo experiments	High cost, non-scalability	International multidisciplinary consortia
Public disease-databases	Not standardized interpretation; limited number of shared variants	Sharing data; gene/disease-tailored guidelines for an improved variant interpretation