Cohort-wide automated identification of morbid alleles with the human gene connectome (TECH1P.835)

Abstract

Abstract To determine the disease-causing allele(s) underlying primary human inborn errors, high-throughput genomic methods are applied and provide thousands of gene variants per patient. We recently reported a novel approach, the “human gene connectome” (HGC) - the set of all in silico-predicted biologically plausible routes and distances between all pairs of human genes. We demonstrated that the HGC is the most powerful approach for prioritizing high-throughput genetic variants in Mendelian disease studies. However, there is currently no available method for automating the selection of candidate disease-causing mutant alleles in the absence of a known morbid gene in at least one patient with the disease of interest, posing a major bottleneck in the field in high-throughput clinical genomics. We hypothesized that within a cohort of patients with the same Mendelian (or nearly Mendelian) disease, the cluster that contains the key disease-causing gene for each patient is the HGC-predicted biologically smallest cluster. We then developed and applied a Mendelian clustering algorithm, which estimates the biologically smallest HGC-predicted cluster that contains one allele per patient. By that we estimated and statistically validated a set of disease-causing alleles in a whole exome sequencing cohort herpes simplex encephalitis patients. This new unbiased approach should facilitate the discovery of morbid alleles in patients with primary inborn errors that lack a genetic etiology.