NGS and Male Infertility: Biomarkers Wanted

Male factor abnormalities account for 30 to 50% of all infertility cases [1]. Nowadays it is clear that genetic abnormalities are a significant cause of male infertility, with about 20% of patients with sperm defects presenting gene mutations [2]. Next-generation sequencing (NGS) technologies allowed the exponential increase in knowledge of new genes involved in male infertility, offering an enormous amount of data in a fast way [3]. Currently, whole exome sequencing (WES) is the most used approach to study the genetics of infertility. Although WES covers only the coding regions of the genome, it is cost-effective and the data are easy to interpret. Also, there is a high likelihood of identifying significant variants, since approximately 85% of disease-causing mutations are thought to occur in gene coding regions [4]. On the other hand, whole genome sequencing (WGS) analysis provides the most complete information of the genetic variants in an individual, but there are still regions (repetitive or satellite) that cannot be sequenced by WGS. Also, WGS is too pricey (too time and labour consuming) for most research and clinical laboratories, the data produced are technically demanding, and their functional interpretation is challenging, with most of the information gathered having an unknown meaning [5]. Nevertheless, it is becoming clear that non-coding regions of the genome also play crucial roles in normal physiology and development, namely in testis and epididymis development and in spermatogenesis [6,7]. Non-coding regions of the genome were already found to be useful for disease diagnosis (e.g. expression profiles of micro RNA are able to accurately identify the origin of some tumours, enabling their classification), prognosis (in some tumours), and therapies (RNA-based and RNA-targeted therapies) [8]. Thus, non-coding regions of the genome should not be forgotten in the biomedical research and, consequently, WES is not enough: There is an urgent need that WGS becomes the standard approach. For that to happen, the inherent costs should decrease, and extremely high-performance computing and intensive bioinformatics support have to be developed.