Adapting simultaneous analysis phylogenomic techniques to study complex disease gene relationships

Abstract

The characterization of complex diseases remains a great challenge for biomedical researchers due to the myriad interactions of genetic and environmental factors. Network medicine approaches strive to accommodate these factors holistically. Phylogenomic techniques that can leverage available genomic data may provide an evolutionary perspective that may elucidate knowledge for gene networks of complex diseases and provide another source of information for network medicine approaches. Here, an automated method is presented that leverages publicly available genomic data and phylogenomic techniques, resulting in a gene network. The potential of approach is demonstrated based on a case study of nine genes associated with Alzheimer Disease, a complex neurodegenerative syndrome. The developed technique, which is incorporated into an update to a previously described Perl script called "ASAP," was implemented through a suite of Ruby scripts entitled "ASAP2," first compiles a list of sequence-similarity based orthologues using PSI-BLAST and a recursive NCBI BLAST+ search strategy, then constructs maximum parsimony phylogenetic trees for each set of nucleotide and protein sequences, and calculates phylogenetic metrics (Incongruence Length Difference between orthologue sets, partitioned Bremer support values, combined branch scores, and Robinson-Foulds distance) to provide an empirical assessment of evolutionary conservation within a given genetic network. In addition to the individual phylogenetic metrics, ASAP2 provides results in a way that can be used to generate a gene network that represents evolutionary similarity based on topological similarity (the Robinson-Foulds distance). The results of this study demonstrate the potential for using phylogenomic approaches that enable the study of multiple genes simultaneously to provide insights about potential gene relationships that can be studied within a network medicine framework that may not have been apparent using traditional, single-gene methods. Furthermore, the results provide an initial integrated evolutionary history of an Alzheimer Disease gene network and identify potentially important co-evolutionary clustering that may warrant further investigation.

Targeted Sequencing and Meta-Analysis of Preterm Birth

“…As shown here, prior genetic analysis and prior filtering of both patients and gene targets improves the likelihood of identifying otherwise difficult-to-find rare variants [ 55 – 60 ]. Replication in another cohort of patients, comparison with genes associated evolutionarily with preterm birth and the addition of phylogenomic analyses are needed to validate and add veracity to these candidate genes [ 61 ].…”

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

“…Metabolomics data provides clues as to NPs' original functions in their source organisms, which can often be extended to their effects when applied to humans (Xie et al, 2008; Yan et al, 2015; Zhang et al, 2016). Phylogenomics can highlight similarities between the genetic epidemiologies of complex diseases in humans vs. model organisms, possibly suggesting species from which to mine compounds that can treat these diseases (Romano et al, 2015). Even the predator/prey adaptations of NP-producing species can suggest the biological function of NPs (de la Vega and Possani, 2005; Miller et al, 2016); the discovery that the cone snail Conus geographus hunts fish by releasing insulin into the surrounding water (resulting in rapid hypoglycemic shock in the prey) led to the identification of a powerful insulin-receptor-binding motif that has shown considerable promise for future treatments of diabetes (Menting et al, 2016).…”