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

By Daniel J. Schaid, Wenan Chen, N. Larson. Nature reviews genetics, 2018

790 citations recorded by the source corpus.

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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/321d136b50d0db07b584bf627d89af52bbbcb71a

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Citations in the corpus

A simple new approach to variable selection in regression, with application to genetic fine mapping (2018, methodology): “This idea was introduced in Servin and Stephens (2007) to fine-map genetic associations, and consequently it has been adopted and extended by others, including Veyrieras et al.”
Comparative genetic architectures of schizophrenia in East Asian and European populations (2018, neutral): “Causal variants in complex genetic disorders are defined as those that mechanistically contribute to the disorders, but this does not imply that the variant in isolation is likely to result in the disorder 41,42 .”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, background): “Fine-mapping is the process of narrowing down genetic association signals to a small number of potential causal variants [1–4], and it plays an important part in the effort to understand the genetic causes of diseases [5,6].”