ArticlePLoS genetics2016
Bias Characterization in Probabilistic Genotype Data and Improved Signal Detection with Multiple Imputation.
Article in PLoS genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- Removing array-specific batch effects in GWAS mega-analyses by applying a two-step imputation workflow.Bioinformatics advances · 2026Article
- Convergent evolution of complex adaptive traits modulates angiogenesis in high-altitude Andean and Himalayan human populations.Communications biology · 2025Article
- TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.Nature methods · 2024Article
- TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.bioRxiv : the preprint server for biology · 2023Article
- Comparison of multiple imputation and other methods for the analysis of imputed genotypes.BMC genomics · 2023Article
- Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores.HGG advances · 2022Article
- Dietary, Cultural, and Pathogens-Related Selective Pressures Shaped Differential Adaptive Evolution among Native Mexican Populations.Molecular biology and evolution · 2022Article
- Accurate Imputation of Untyped Variants from Deep Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations.Journal of animal science and biotechnology · 2020Article
- Making the Most of Clumping and Thresholding for Polygenic Scores.American journal of human genetics · 2019Article
- Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations.Journal of animal science and biotechnology · 2019Article
- Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr.Bioinformatics (Oxford, England) · 2018Article
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2 authors.
Funding
Abstract
Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data.
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