ArticleGenetic epidemiology2017
PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables.
Article in Genetic epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Korea Biobank Array: Design and Identification of Coding Variants Associated with Blood Biochemical Traits.Scientific reports · 2019Article
- New Common and Rare Variants Influencing Metabolic Syndrome and Its Individual Components in a Korean Population.Scientific reports · 2018Article
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5 authors.
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Abstract
Imputation is widely used for obtaining information about rare variants. However, one issue concerning imputation is the low accuracy of imputed rare variants as the inaccurate imputed rare variants may distort the results of region-based association tests. Therefore, we developed a pre-collapsing imputation method (PreCimp) to improve the accuracy of imputation by using collapsed variables. Briefly, collapsed variables are generated using rare variants in the reference panel, and a new reference panel is constructed by inserting pre-collapsed variables into the original reference panel. Following imputation analysis provides the imputed genotypes of the collapsed variables. We demonstrated the performance of PreCimp on 5,349 genotyped samples using a Korean population specific reference panel including 848 samples of exome sequencing, Affymetrix 5.0, and exome chip. PreCimp outperformed a traditional post-collapsing method that collapses imputed variants after single rare variant imputation analysis. Compared with the results of post-collapsing method, PreCimp approach was shown to relatively increase imputation accuracy about 3.4-6.3% when dosage r
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