ArticleBMC genetics2011
An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations.
Article in BMC genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.Human molecular genetics · 2021Pooled it
- An Advanced Entropy Approach for Minimizing False Discoveries in Imputation-Based Association Analyses.Research square · 2025Article
- Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study.Animals : an open access journal from MDPI · 2023Article
- Ascertainment bias from imputation methods evaluation in wheat.BMC genomics · 2016Article
- Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.Human genetics · 2013Article
- Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.BMC medical genomics · 2012Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundGenome wide association studies (GWAS) are becoming the approach of choice to identify genetic determinants of complex phenotypes and common diseases. The astonishing amount of generated data and the use of distinct genotyping platforms with variable genomic coverage are still analytical challenges. Imputation algorithms combine directly genotyped markers information with haplotypic structure for the population of interest for the inference of a badly genotyped or missing marker and are considered a near zero cost approach to allow the comparison and combination of data generated in different studies. Several reports stated that imputed markers have an overall acceptable accuracy but no published report has performed a pair wise comparison of imputed and empiric association statistics of a complete set of GWAS markers.
resultsIn this report we identified a total of 73 imputed markers that yielded a nominally statistically significant association at P < 10 -5 for type 2 Diabetes Mellitus and compared them with results obtained based on empirical allelic frequencies. Interestingly, despite their overall high correlation, association statistics based on imputed frequencies were discordant in 35 of the 73 (47%) associated markers, considerably inflating the type I error rate of imputed markers. We comprehensively tested several quality thresholds, the haplotypic structure underlying imputed markers and the use of flanking markers as predictors of inaccurate association statistics derived from imputed markers.
conclusionsOur results suggest that association statistics from imputed markers showing specific MAF (Minor Allele Frequencies) range, located in weak linkage disequilibrium blocks or strongly deviating from local patterns of association are prone to have inflated false positive association signals. The present study highlights the potential of imputation procedures and proposes simple procedures for selecting the best imputed markers for follow-up genotyping studies.
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