A novel homology-based algorithm for the identification of physically linked clusters of paralogous genes

Abstract

Highly diverse phenotypic traits are often encoded by clusters of gene paralogs that are physically linked on chromosomes. Examples include olfactory receptor gene clusters involved in the recognition of diverse odors, defensin and phospholipase gene clusters involved in snake venoms, and Hox gene clusters involved in morphological diversity. Historically, gene clusters have been identified subjectively as genomic neighborhoods containing several paralogs, however, their genomic arrangements are often highly variable with respect to gene number, intergenic distance, and synteny. For example, the prolactin gene cluster shows variation in paralogous gene number, order and intergenic distance across mammals, whereas animal Hox gene clusters are often broken into sub-clusters of different sizes. A lack of formal definition for clusters of gene paralogs does not only hamper the study of their evolutionary dynamics, but also the discovery of novel ones in the exponentially growing body of genomic data. To address this gap, we developed a novel homology-based algorithm, CGPFinder, which formalizes and automates the identification of clusters of gene paralogs (CGPs) by examining the physical distribution of individual gene members of families of paralogous genes across chromosomes. Application of CGPFinder to diverse mammalian genomes accurately identified CGPs for many well-known gene clusters in the human and mouse genomes (e.g., Hox, protocadherin, Siglec, and beta-globin gene clusters) as well as for 20 other mammalian genomes. Differences were due to the exclusion of non-homologous genes that have historically been considered parts of specific gene clusters, the inclusion or absence of one or more genes between the CGPs and their corresponding gene clusters, and the splitting of certain gene clusters into distinct CGPs. Finally, examination of human genes showing tissue-specific enhancement of their expression by CGPFinder identified members of several well-known gene clusters (e.g., cytochrome P450, aquaporins, and olfactory receptors) and revealed that they were unequally distributed across tissues. By formalizing and automating the identification of CGPs and of genes that are members of CGPs, CGPFinder will facilitate furthering our understanding of the evolutionary dynamics of genomic neighborhoods containing CGPs, their functional implications, and how they are associated with phenotypic diversity.