The human gene damage index as a gene-level approach to prioritizing exome variants

Significance The protein-coding exome of a patient with a monogenic disease contains about 20,000 variations, of which only one or two are disease causing. When attempting to select disease-causing candidate mutation(s), a challenge is to filter out as many false-positive (FP) variants as possible. In this study, we describe the gene damage index (GDI), a metric for the nonsynonymous mutational load in each protein-coding gene in the general population. We show that the GDI is an efficient gene-level method for filtering out FP variants in genes that are highly damaged in the general population. The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.