A literature review at genome scale: improving clinical variant assessment

PURPOSE: Over 150,000 variants have been reported to cause Mendelian disease in the medical literature. It is still difficult to leverage this knowledge base in clinical practice as many reports lack strong statistical evidence or may include false associations. Clinical laboratories assess whether these variants (along with newly observed variants that are adjacent to these published ones) underlie clinical disorders. METHODS: We measured whether citation data—including journal impact factor and the number of cited variants (NCV) in each gene with published disease associations—can be used to improve variant assessment. RESULTS: Surprisingly, we find that impact factor is not predictive of pathogenicity, but the NCV score for each gene can provide statistical support of pathogenicity. When combining this gene-level citation metric with variant-level evolutionary conservation and structural features, classification accuracy reaches 89.5%. Further, variants identified in clinical exome sequencing cases have higher NCV scores than simulated rare variants from ExAC in matched genes and consequences (p<2.22×10(−16)). CONCLUSION: Aggregate citation data can complement existing variant-based predictive algorithms, and can boost their performance without accessing and reviewing large numbers of manuscripts. The NCV is a slow-growing metric of scientific knowledge about each gene’s association with disease. FUNDING: This research was supported by NIH NHGRI grant HG007229 (C.C.) and NIGMS grant GM078598 (I.A., D.J., and S.S.).