Year: 2022
Type: article
Abstract: ABSTRACT Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies (GWAS) represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here we present an analysis framework for the “Prioritisation of candidate causal Genes at Molecular QTLs” (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 213 previously reported and extensively curated metabolite QTLs. For 98% of these loci (n=209), the expert-curated gene was one of the candidate causal genes prioritised by ProGeM. Systematic benchmarking analyses revealed that 70% (n=150) of the candidate causal genes were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of true positive causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. Finally, we provide evidence that these conclusions may also apply to cis-protein QTLs and loci associated with complex diseases. ProGeM is freely available via GitHub.
Source: Clinical Medicine
Authors: Anna Daunt, Geraldine O'Hara
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4297914153