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<p>Substantial challenges persist in defining the genetic etiology of female infertility, as new gene–disease associations frequently lack robust validation. This review analyzes the application of recent evidence-based frameworks to female infertility and compares three commercial gene panels. Our analysis revealed minimal overlap, with only 26 genes shared across all panels, indicating a lack of consensus driven more by proprietary criteria than standardized evidence. This discordance may have direct clinical consequences. The inclusion of not-validated or low-evidence genes elevates the risk of incorrect molecular diagnosis, whereas the omission of validated genes can lead to false-negative diagnoses. We conclude that the field requires standardized guidelines and international collaboration to establish a dynamically updated, evidence-based gene list. This is essential for improving diagnostic accuracy, clarifying clinical interpretation, and enabling personalized reproductive medicine.</p>

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Current gene panels for female infertility: gaps between evidence and clinical application

  • 期刊论文

  • 发表日期：

    2026年4月18日

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  • Seyed Mohammad-Hossein Nemati,
  • Majid Zaki-Dizaji

摘要

Substantial challenges persist in defining the genetic etiology of female infertility, as new gene–disease associations frequently lack robust validation. This review analyzes the application of recent evidence-based frameworks to female infertility and compares three commercial gene panels. Our analysis revealed minimal overlap, with only 26 genes shared across all panels, indicating a lack of consensus driven more by proprietary criteria than standardized evidence. This discordance may have direct clinical consequences. The inclusion of not-validated or low-evidence genes elevates the risk of incorrect molecular diagnosis, whereas the omission of validated genes can lead to false-negative diagnoses. We conclude that the field requires standardized guidelines and international collaboration to establish a dynamically updated, evidence-based gene list. This is essential for improving diagnostic accuracy, clarifying clinical interpretation, and enabling personalized reproductive medicine.