ArticleBMC genetics2015
Impact of genetic similarity on imputation accuracy.
Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Imputef: imputation of polyploid genotype classes and allele frequencies.BMC genomics · 2025Article
- Machine learning solutions for integrating partially overlapping genetic datasets and modelling host-endophyte effects in ryegrass (Frontiers in plant science · 2025Article
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- A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome.NPJ genomic medicine · 2024Article
- A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software.Life (Basel, Switzerland) · 2022Review
- Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods.Environmental health perspectives · 2022Article
- Expression quantitative trait loci for ETV4 and MEOX1 are associated with adult asthma in Japanese populations.Scientific reports · 2021Article
- Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.Genetic epidemiology · 2020Article
- Easing US restrictions on mitochondrial replacement therapy would protect research interests but grease the slippery slope.Journal of assisted reproduction and genetics · 2019Article
- Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population.BMC genomics · 2019Article
- Genotype imputation performance of three reference panels using African ancestry individuals.Human genetics · 2018Article
- Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels.Genetic epidemiology · 2017Article
- Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Comparing performance of modern genotype imputation methods in different ethnicities.Scientific reports · 2016Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenotype imputation is a common technique in genetic research. Genetic similarity between target population and reference dataset is crucial for high-quality results. Although several reference panels are available, it is often not clear which is the most optimal for a particular target dataset to be imputed. Maximizing genetic similarity between study sample and intended reference panels may be the straight forward method for selecting the genetically best-matched reference. However, the impact of genetic similarity on imputation accuracy has not yet been studied in detail.
resultsWe performed a simulation study in 20 ethnic groups obtained from POPRES. High-quality SNPs were masked and re-imputed with MaCH, MaCH-minimac and IMPUTE2 using four different HapMap reference panels (CEU, CHB-JPT, MEX and YRI). Imputation accuracy was assessed by different statistics. Genetic similarity between ethnic groups and reference populations were measured by F -statistics (F(ST)) originally proposed by Wright and G -statistics (G(ST)) introduced by Nei and others. To assess the predictive power of these measures regarding imputation accuracy, we analysed relations between them and corresponding imputation accuracy scores. We found that population genetic distances between homogeneous reference and target populations were strongly linearly correlated with resulting imputation accuracies irrespective of considered distance measure, imputation accuracy measure, missingness and imputation software used. Possible exception was African population.
conclusionUsage of G(ST) or F(ST)-related measures for predicting the optimal reference panel for imputation frameworks relying on a specific reference is highly recommended. A cut-off of G(ST) < 0.01 is recommended to achieve good imputation results for high-frequency variants and small data sets. The linear relationship is less pronounced for low-frequency variants for which we also observed a dependence of imputation accuracy on the number of polymorphic sites in the reference. We also show that the software specific measures MaCH-Rsq and IMPUTE-info must be interpreted with caution if the genetic distance of target and reference population is high.
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