ArticleThe annals of applied statistics2010
USING LINEAR PREDICTORS TO IMPUTE ALLELE FREQUENCIES FROM SUMMARY OR POOLED GENOTYPE DATA.
Article in The annals of applied statistics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.Nature communications · 2025Pooled it
- Meta-analysis indicates that common variants at the DISC1 locus are not associated with schizophrenia.Molecular psychiatry · 2012Pooled it
- Spatial Collinearity Constrains Multivariate Molecular-Enriched Network Estimation.Human brain mapping · 2026Article
- Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.Nature genetics · 2026Article
- Joint modeling of whole-genome sequencing data for human height via approximate message passing.Cell genomics · 2026Article
- A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India.HGG advances · 2026Article
- Posttranscriptional control of hepatic CEACAM1 3'UTR by human antigen R (HuR) mitigates sterile liver inflammation.JCI insight · 2025Article
- Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms.American journal of human genetics · 2025Article
- Inferring disease architecture and predictive ability with LDpred2-auto.American journal of human genetics · 2023Article
- Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix.Nature communications · 2023Article
- Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease.NeuroImage · 2023Article
- Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies.Nature genetics · 2023Article
- Fast and accurate Bayesian polygenic risk modeling with variational inference.American journal of human genetics · 2023Article
- SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.Nature communications · 2022Article
- Leveraging the local genetic structure for trans-ancestry association mapping.American journal of human genetics · 2022Article
- Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling.Biometrics · 2022Article
- RápidoPGS: a rapid polygenic score calculator for summary GWAS data without a test dataset.Bioinformatics (Oxford, England) · 2021Article
- Scalable bias-corrected linkage disequilibrium estimation under genotype uncertainty.Heredity · 2021Article
- Scalable probabilistic PCA for large-scale genetic variation data.PLoS genetics · 2020Article
- A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recently-developed genotype imputation methods are a powerful tool for detecting untyped genetic variants that affect disease susceptibility in genetic association studies. However, existing imputation methods require individual-level genotype data, whereas in practice it is often the case that only summary data are available. For example this may occur because, for reasons of privacy or politics, only summary data are made available to the research community at large; or because only summary data are collected, as in DNA pooling experiments. In this article, we introduce a new statistical method that can accurately infer the frequencies of untyped genetic variants in these settings, and indeed substantially improve frequency estimates at typed variants in pooling experiments where observations are noisy. Our approach, which predicts each allele frequency using a linear combination of observed frequencies, is statistically straight-forward, and related to a long history of the use of linear methods for estimating missing values (e.g. Kriging). The main statistical novelty is our approach to regularizing the covariance matrix estimates, and the resulting linear predictors, which is based on methods from population genetics. We find that, besides being both fast and flexible - allowing new problems to be tackled that cannot be handled by existing imputation approaches purpose-built for the genetic context - these linear methods are also very accurate. Indeed, imputation accuracy using this approach is similar to that obtained by state-of-the art imputation methods that use individual-level data, but at a fraction of the computational cost.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.