Dataset Information

Revisit Population-based and Family-based Genotype Imputation.

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.

SUBMITTER: Liu CT

PROVIDER: S-EPMC6372660 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Revisit Population-based and Family-based Genotype Imputation.

Liu Ching-Ti CT Deng Xuan X Fisher Virginia V Heard-Costa Nancy N Xu Hanfei H Zhou Yanhua Y Vasan Ramachandran S RS Cupples L Adrienne LA

Scientific reports 20190212 1

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 p ...[more]

PMID: 30755687

Similar Datasets

Project description:BackgroundImputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE.ResultsWe considered three scenarios: imputation of partially missing genotypes with usage of an external reference panel, without usage of an external reference panel, as well as imputation of completely un-typed SNPs using an external reference panel. We first created various datasets applying different SNP quality filters and masking certain percentages of randomly selected high-quality SNPs. We imputed these SNPs and compared the results between the different filtering scenarios by using established and newly proposed measures of imputation quality. While the established measures assess certainty of imputation results, our newly proposed measures focus on the agreement with true genotypes. These measures showed that pre-imputation SNP-filtering might be detrimental regarding imputation quality. Moreover, the strongest drivers of imputation quality were in general the burden of missingness and the number of SNPs used for imputation. We also found that using a reference panel always improves imputation quality of partially missing genotypes. MaCH performed slightly better than IMPUTE2 in most of our scenarios. Again, these results were more pronounced when using our newly defined measures of imputation quality.ConclusionEven a moderate filtering has a detrimental effect on the imputation quality. Therefore little or no SNP filtering prior to imputation appears to be the best strategy for imputing small to moderately sized datasets. Our results also showed that for these datasets, MaCH performs slightly better than IMPUTE2 in most scenarios at the cost of increased computing time.

Dataset Information

Revisit Population-based and Family-based Genotype Imputation.

Publications

Revisit Population-based and Family-based Genotype Imputation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure