A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis

Abstract

BackgroundGenetic colocalization analysis is a statistical method that evaluates whether genetic association signals (i.e., loci identified in genome-wide association studies [GWAS]) between two traits (e.g., microRNA [miRNA] expression levels and osteoarthritis [OA]) are shared or independent. This method is useful for providing insights into the biological relevance of genetic association signals, particularly in intergenic regions, which can help to elucidate disease mechanisms in OA and other complex traits. ObjectivesTo review the existing literature on genetic colocalization methods, assess their suitability for studying OA, and investigate their capacity to integrate miRNA data, while bearing in view their statistical assumptions. DesignWe followed scoping review methodology and used Covidence software for data management. Search terms for colocalization, GWAS, and genetic or statistical models were used in the databases MEDLINE and EMBASE, searched till March 4, 2024. ResultsOur search returned 546 peer-reviewed papers, of which 96 were included following title/abstract and full-text screening. Based on both cumulative and annual publication counts, the most cited method for colocalization analysis was coloc. Four papers examined OA-related phenotypes, and none examined miRNA. Two approaches to colocalization analysis using miRNA were postulated based on further hand-searching. ConclusionsColocalization analysis is a largely unexplored method in OA. Many of the approaches to colocalization analysis identified in this review, including the integration of GWAS and miRNA data, may help to elucidate genetic and epigenetic factors implicated in OA and other complex traits.