ArticleScientific reports2019
Revisit Population-based and Family-based Genotype Imputation.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- Adjustment for Genotype Imputation Uncertainty Corrects for Inflated Type I Error in Family-Based Association Testing.Genetic epidemiology · 2025Article
- Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance.BMC genomics · 2025Article
- Large-scale genome sequencing redefines the genetic footprints of high-altitude adaptation in Tibetans.Genome biology · 2023Article
- 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
- Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species.Frontiers in genetics · 2023Article
- Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa.Tropical animal health and production · 2022Article
- Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools.Methods in molecular biology (Clifton, N.J.) · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
Genome-Wide Association (GWA) with population-based imputation (PBI) has been successful in identifying common variants associated with complex diseases; however, much heritability remains to be explained and low frequency variants (LFV) may contribute. To identify LFV, a study of unrelated individuals may no longer be as efficient as a family study, where rare population variants can be frequent in families. Family-based imputation (FBI) provides an opportunity to evaluate LFV. To compare the performance of PBI and FBI, we conducted extensive simulations, generating genotypes using SeqSIMLA from various reference panels for families. We masked genotype information for variants unavailable in Framingham 550 K GWA genotype data in less informative subjects selected by GIGI-Pick. We implemented IMPUTE2 with duoHMM in SHAPEIT (Impute2_duoHMM) for PBI, MERLIN and GIGI for FBI and PedBLIMP for a hybrid approach. In general, FBI in both MERLIN and GIGI outperformed other approaches with imputation accuracy greater than 0.99 for the squared correlation and imputation quality scores (IQS) especially for LFV, although imputation accuracy from MERLIN depends on pedigree splitting for larger families. PBI performed worst with the exception of good imputation accuracy for common variants when a closely ancestry matched reference is used. In summary, linkage disequilibrium (LD) information from large available genotype resources provides good imputation for common variants with well-selected reference panels without requiring densely sequenced data in family members, while imputation of LFV with FBI benefits more from information on inheritance patterns within families yielding better imputation.
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