Table 1.

Factors Contributing to Bottlenecks in the Gene-Discovery Pipeline

Clinical data	•non-specific clinical presentations (e.g., developmental delay and hypotonia) •ultra-rare and unrecognized genetic diseases •lack of ontology encompassing the complete spectrum of human phenotypes •insufficient utilization of ontologies or 3D facial-gestalt analysis in phenotyping •inconsistent multidisciplinary approaches to patient evaluation •inability to account for and compare age-specific disease presentations
Genomic data	•technical limitations of WES (e.g., copy-number variants and structural variation are not captured well) •lack of standardized technical and informatics approaches •incompleteness of population-specific control datasets
Data discovery and sharing	•lack of a widely adopted data-sharing framework •lack of common data-sharing standards •lack of a systematic way to record data-use conditions •lack of a privacy-preserving linkage system for each research participant
Genetic evidence	•siloed datasets •lack of and use of data-sharing infrastructure
Functional evidence	•lack of standardized and moderate-throughput analyses of variant impact •lack of biological insight into the function of most human genes
Novel disease mechanisms	•lack of expertise in the analysis of non-coding variants •other mechanisms including tissue-specific mosaicism, methylation, and di- or oligogenic inheritance