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Nat Rev Genet. 2018 Aug;19(8):491-504. doi: 10.1038/s41576-018-0016-z.

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

Author information

1
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA. schaid@mayo.edu.
2
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
3
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.

Abstract

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.

PMID:
29844615
PMCID:
PMC6050137
[Available on 2019-08-01]
DOI:
10.1038/s41576-018-0016-z

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