From genome-wide associations to candidate causal variants by statistical fine-mapping

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues. Fine-mapping is the process by which a trait-associated region from a genome-wide association study (GWAS) is analysed to identify the particular genetic variants that are likely to causally influence the examined trait. This Review discusses the diverse statistical approaches to fine-mapping and their foundations, strengths and limitations, including integration of trans-ethnic human population data and functional annotations.