RGnet: Recessive Genotype Network in a Large Mendelian Disease Cohort

Abstract

Recessive genotypes, including compound heterozygotes and homozygotes formed by rare variants that impact gene function, affect both alleles and were linked to numerous diseases and traits. However, the underlying patterns and interconnections of these recessive genotypes in large cohorts have rarely been studied. To address this gap, the Recessive Genotype Network (RGnet) was developed. This network model maps variant and genotype features to visualize and analyze recessive genotype patterns within large cohorts. Additionally, it uses permutation-based analyses to assess the enrichment of these genotypes in relation to specific phenotypes. Demonstrated through its application to the genetic deafness gene SLC26A4 in 22,125 cases affected by hearing loss, RGnet successfully identified pathogenic variants with high connectivity, providing a reliable method for exploring the pathogenic mechanisms underlying recessive disorders or traits.