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 clinical phenotypes of MCT8 deficiency with genotype-phenotype relationships present across survival and 24 out of 32 disease features; (ii) demonstrate a mild phenocopy in ~400,000 individuals with common genetic variants in MCT8; (iii) assess therapeutic effectiveness, which did not differ among LoF-categories; (iv) advance structural insights in normal and mutated MCT8 by delineating seven critical functional domains; (v) create a pathogenicity-severity MCT8 variant classifier that accurately predicted pathogenicity (AUC:0.91) and severity (AUC:0.86) for 8151 variants. Our information-dense mapping provides a generalizable approach to advance multiple dimensions of rare genetic disorders.

• MeSH
• Deep Learning * MeSH
• Child MeSH
• Adult MeSH
• Phenotype * MeSH
• Genetic Variation MeSH
• Genetic Association Studies MeSH
• Genomics methods   MeSH
• Thyroid Hormones metabolism   genetics   MeSH
• Humans MeSH
• X-Linked Intellectual Disability genetics   metabolism   MeSH
• Adolescent MeSH
• Loss of Function Mutation MeSH
• Child, Preschool MeSH
• Monocarboxylic Acid Transporters * genetics   metabolism   MeSH
• Severity of Illness Index MeSH
• Muscular Atrophy genetics   metabolism   pathology   MeSH
• Muscle Hypotonia genetics   metabolism   MeSH
• Symporters * genetics   metabolism   MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH