ArticleAmerican journal of human genetics2009
The relationship between imputation error and statistical power in genetic association studies in diverse populations.
Article in American journal of human genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus.Communications biology · 2021Pooled it
- A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci.Nature communications · 2018Pooled it
- Reflections on the Human Genome Diversity Project: a conversation with Marcus W. Feldman, Henry T. Greely, and Mary-Claire King.Genetics · 2026Article
- Molecular QTL are enriched for structural variants in a cattle long-read cohort.Communications biology · 2026Article
- A multiethnic genome-wide analysis of 19,420 individuals identifies novel loci associated with axial length and shared genetic influences with refractive error and myopia.Frontiers in genetics · 2023Article
- Implementation of CYP2D6 copy-number imputation panel and frequency of key pharmacogenetic variants in Finnish individuals with a psychotic disorder.The pharmacogenomics journal · 2022Article
- Why are rare variants hard to impute? Coalescent models reveal theoretical limits in existing algorithms.Genetics · 2021Article
- Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.Frontiers in genetics · 2021Article
- A multiethnic genome-wide analysis of 44,039 individuals identifies 41 new loci associated with central corneal thickness.Communications biology · 2020Article
- Functional validity, role, and implications of heavy alcohol consumption genetic loci.Science advances · 2020Article
- Generating High Density, Low Cost Genotype Data in Soybean [G3 (Bethesda, Md.) · 2019Article
- Genetic variation in theProceedings of the National Academy of Sciences of the United States of America · 2018Article
- Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies.Bioinformatics (Oxford, England) · 2018Article
- A large multi-ethnic genome-wide association study identifies novel genetic loci for intraocular pressure.Nature communications · 2017Article
- Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation.Nature genetics · 2017Article
- PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables.Genetic epidemiology · 2017Article
- A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health Records.PLoS genetics · 2016Article
- Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants.PloS one · 2016Article
- Strategies for Imputing and Analyzing Rare Variants in Association Studies.Trends in genetics : TIG · 2015Review
- Whole-exome imputation of sequence variants identified two novel alleles associated with adult body height in African Americans.Human molecular genetics · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Genotype-imputation methods provide an essential technique for high-resolution genome-wide association (GWA) studies with millions of single-nucleotide polymorphisms. For optimal design and interpretation of imputation-based GWA studies, it is important to understand the connection between imputation error and power to detect associations at imputed markers. Here, using a 2x3 chi-square test, we describe a relationship between genotype-imputation error rates and the sample-size inflation required for achieving statistical power at an imputed marker equal to that obtained if genotypes at the marker were known with certainty. Surprisingly, typical imputation error rates (approximately 2%-6%) lead to a large increase in the required sample size (approximately 10%-60%), and in some African populations whose genotypes are particularly difficult to impute, the required sample-size increase is as high as approximately 30%-150%. In most populations, each 1% increase in imputation error leads to an increase of approximately 5%-13% in the sample size required for maintaining power. These results imply that in GWA sample-size calculations investigators will need to account for a potentially considerable loss of power from even low levels of imputation error and that development of additional genomic resources that decrease imputation error will translate into substantial reduction in the sample sizes needed for imputation-based detection of the variants that underlie complex human diseases.
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