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Genotype Imputation in Genome-Wide Association Studies
Adam C Naj1,2
1Department of Biostatistics, Epidemiology, and Informatics and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Current Protocols in Human Genetics
|June 20, 2019
Summary
Genotype imputation enhances genome-wide association studies by filling in missing genetic data using reference panels. This improves statistical power and data harmonization for genetic research.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genotype imputation is crucial for inferring missing genotypes in genetic studies.
- Missing genotypes can limit the statistical power and data harmonization capabilities of genome-wide association studies (GWAS).
Purpose of the Study:
- To review the concepts, methods, and resources for genotype imputation.
- To provide a practical guide for implementing genotype imputation in genetic research.
- To highlight recent advancements in haplotype reference panels and imputation servers.
Main Methods:
- Review of imputation concepts and methodologies for diverse genotype data types.
- Description of a two-step imputation process: phasing study genotypes and imputing reference panel genotypes.
- Identification of web-based resources and online imputation servers.
Main Results:
- Genotype imputation improves statistical power in GWAS by reducing missing data.
- Imputation simplifies data harmonization for meta-analyses by increasing variant overlap.
- It enhances the number and density of genomic variants for association testing.
Conclusions:
- Genotype imputation is a valuable tool for advancing genetic association studies.
- The review provides a comprehensive overview and practical guidance for imputation implementation.
- Recent developments in reference panels and servers facilitate efficient and secure imputation workflows.
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