DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways.

MOTIVATION: Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases. RESULTS: We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes. AVAILABILITY: DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.