Syndromic male subfertility: A network view of genome–phenome associations. (15th March 2022)

• Record Type: Journal Article
• Authors: Mikec, Špela; Kolenc, Živa; Peterlin, Borut; Horvat, Simon; Pogorevc, Neža; Kunej, Tanja
• Is Part Of: Andrology. Volume 10:Number 4(2022)
• Page Start: 720
• Page End: 732
• Publication Date: 2022-03-15
• Subjects: genome -- male infertility -- network medicine -- phenome -- syndrome -- systems biology; Andrology -- Periodicals; 616.65
• DOI: 10.1111/andr.13167
• Languages: English
• ISSNs: 2047-2919

Abstract

Abstract: Background: Male infertility is a disorder of the reproductive system with a highly complex genetic landscape. In most cases, the reason for male infertility remains unknown; however, the importance of genetic abnormalities in the diagnosis of subfertility/infertility is becoming increasingly recognized. Several syndromes include impaired male fertility in the clinical picture, although a comprehensive analysis of genetic causes of the syndromology perspective of male reproduction is not yet available. Objectives: (1) To develop a catalog of syndromes and corresponding genes associated with impaired male fertility and (2) to visualize an up‐to‐date genome–phenome network of syndromic male subfertility. Materials and methods: Published literature was retrieved from the Online Mendelian Inheritance in Man, Orphanet, Human Phenotype Ontology and PubMed databases using keywords "male infertility, " "syndrome, " "gene, " and "case report"; time period from 1980 to September, 2021. Retrieved data were organized as a catalog and complemented with identification numbers of syndromes (MIM ID) and genes (Gene ID). The genome–phenome network and the phenome network were visualized using Cytoscape and Gephi software platforms. Protein–protein interaction analysis was performed using STRING tool. Results: Retrieved syndromes were presented as (1) a catalog containing 63 syndromes and 93 associated genes, (2) a genome–phenome network including CHD7 and WT1 genes and Noonan and Kartagener syndromes, and (3) a phenome network including 63 syndromes, and 25 categories of clinical features. Discussion: The developed catalog will contribute to the advances and translational impact toward understanding the factors of syndromic male infertility. Visualized networks provide simple, flexible tools for clinicians and researchers to quickly generate hypotheses and gain a deeper understanding of underlying mechanisms affecting male reproduction. Conclusion: Recognition of the significance of genome–phenome visualization as part of network medicine can help expedite efforts toward unravelling molecular mechanisms and enable advances personal/precision medicine of male reproduction and other complex traits.