Aetiological coding sequence variants in non-syndromic premature ovarian failure: From genetic linkage analysis to next generation sequencing (2015)
Premature ovarian failure (POF) is a frequent pathology affecting 1-1.5% of women under 40 years old. Despite advances in diagnosing and treating human infertility, POF is still classified as being idiopathic in 50-80% of cases, strongly suggesting a genetic origin for the disease. Different types of autosomal and X-linked genetic anomalies can originate the phenotype in syndromic and non-syndromic POF cases. Particular interest has been focused on research into non-syndromic POF causative coding variants during the past two decades. This has been based on the assumption that amino acid substitutions might modify the intrinsic physicochemical properties of functional proteins, thereby inducing pathological phenotypes. In this case, a restricted number of mutations might originate the disease. However, like other complex pathologies, POF might result from synergistic/compensatory effects caused by several low-to-mildly drastic mutations which have frequently been classified as non-functional SNPs. Indeed, reproductive phenotypes can be considered as quantitative traits resulting from the subtle interaction of many genes. Although numerous sequencing projects have involved candidate genes, only a few coding mutations explaining a low percentage of cases have been described. Such apparent failure to identify aetiological coding sequence variations might have been due to the inherent molecular complexity of mammalian reproduction and to the difficulty of simultaneously analysing large genomic regions by Sanger sequencing.The purpose of this review is to present the molecular and cellular effects caused by non-synonymous mutations which have been formally associated, by functional tests, with the aetiology of hypergonadotropic non-syndromic POF. Considerations have also been included regarding the polygenic nature of reproduction and POF, as well as future approaches for identifying novel aetiological genes based on next generation sequencing (NGS). © 2015 The Author.