This project aims to investigate the genetic architecture and phenotypic variability of treatable inborn errors of metabolism (IMDs) using data from the UK Biobank. IMDs are rare genetic disorders that can cause significant morbidity and mortality but are often treatable if diagnosed early. Despite advances in genetic testing, there is limited understanding of the population-level prevalence of pathogenic variants for IMDs, and their relationship to clinical phenotypes remains underexplored.
The research will address the following questions:
1. What is the allele frequency of pathogenic and likely pathogenic variants associated with treatable IMDs in the UK Biobank?
2. Can statistical phasing be used to identify individuals putatively affected by treatable IMDs?
3. How do metabolic profiles and clinical data predict outcomes in individuals carrying risk variants or identified as putatively affected by IMDs?
The objectives are:
1. To estimate the population-level frequency of genetic variants associated with treatable IMDs.
2. To use computational phasing to identify individuals likely to have IMDs.
3. To develop quantitative severity scores by mapping disease-specific symptoms to structured clinical and laboratory data.
The rationale is that understanding the genetic and phenotypic spectrum of treatable IMDs will enhance diagnostic precision and inform early interventions. By integrating genomic data with clinical features, this research will improve public health outcomes and provide new insights into the prevention and management of treatable IMDs.