Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration

Groeneweg, Stefan; van Geest, Ferdy S; Martín, Mariano; Dias, Mafalda; Frazer, Jonathan; Medina-Gomez, Carolina; Sterenborg, Rosalie B T M; Wang, Hao; Dolcetta-Capuzzo, Anna; de Rooij, Linda J; Teumer, Alexander; Abaci, Ayhan; van den Akker, Erica L T; Ambegaonkar, Gautam P; Armour, Christine M; Bacos, Iiuliu; Bakhtiani, Priyanka; Barca, Diana; Bauer, Andrew J; van den Berg, Sjoerd A A

doi:10.5167/uzh-278163

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Groeneweg, S., van Geest, F. S., Martín, M., Dias, M., Frazer, J., Medina-Gomez, C., Sterenborg, R. B. T. M., Wang, H., Dolcetta-Capuzzo, A., de Rooij, L. J., Teumer, A., Abaci, A., van den Akker, E. L. T., Ambegaonkar, G. P., Armour, C. M., Bacos, I., Bakhtiani, P., Barca, D., Bauer, A. J., … et al. (2025). Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration. Nature Communications, 16, 2479. https://doi.org/10.1038/s41467-025-56628-w

Abstract

Predicting and quantifying phenotypic consequences of genetic variants in rare disorders is a major challenge, particularly pertinent for ‘actionable’ genes such as thyroid hormone transporter MCT8 (encoded by the X-linked SLC16A2 gene), where loss-of-function (LoF) variants cause a rare neurodevelopmental and (treatable) metabolic disorder in males. The combination of deep phenotyping data with functional and computational tests and with outcomes in population cohorts, enabled us to: (i) identify the genetic aetiology of divergent cl