Revisiting benchmark study for response to methodological critiques of 'Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases'

Simulation of undiagnosed patients with novel genetic conditions

Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.

Evaluation of a whole-exome sequencing pipeline and benchmarking of causal germline variant prioritizers

Most causal variants of Mendelian diseases are exonic. Whole-exome sequencing (WES) has become the diagnostic gold standard, but causative variant prioritization constitutes a bottleneck. Here we assessed an in-house sample-to-sequence pipeline and benchmarked free prioritization tools for germline causal variants from WES data. WES of 61 unselected patients with a known genetic disease cause was obtained. Variant prioritizations were performed by diverse tools and recorded to obtain a diagnostic yield when the causal variant was present in the first, fifth, and 10th top rankings. A fraction of causal variants was not captured by WES (8.2%) or did not pass the quality control criteria (13.1%). Most of the applications inspected were unavailable or had technical limitations, leaving nine tools for complete evaluation. Exomiser performed best in the top first rankings, while LIRICAL led in the top fifth rankings. Based on the more conservative top 10th rankings, Xrare had the highest diagnostic yield, followed by a three-way tie among Exomiser, LIRICAL, and PhenIX, then followed by AMELIE, TAPES, Phen-Gen,  AIVar, and VarNote-PAT. Xrare, Exomiser, LIRICAL, and PhenIX are the most efficient options for variant prioritization in real patient WES data.