ArticleBioinformatics (Oxford, England)2014
Fast and accurate imputation of summary statistics enhances evidence of functional enrichment.
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Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 9 of them syntheses that pooled it.
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131citing papers in PubMed, 9 pooled it
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131 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Fine-mapping a genome-wide meta-analysis of 98,374 migraine cases identifies 181 sets of candidate causal variants.Nature communications · 2026Pooled it
- Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status.Breast cancer research : BCR · 2024Pooled it
- A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes.American journal of human genetics · 2023Pooled it
- Reconstructing SNP allele and genotype frequencies from GWAS summary statistics.Scientific reports · 2022Pooled it
- Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations.Nature communications · 2019Pooled it
- Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes.Nature communications · 2018Pooled it
- Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.PLoS genetics · 2018Pooled it
- Immunochip Meta-Analysis of Inflammatory Bowel Disease Identifies Three Novel Loci and Four Novel Associations in Previously Reported Loci.Journal of Crohn's & colitis · 2018Pooled it
- Pooled it
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- Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.Nature genetics · 2026Article
- Multi-omics integration provides biological insight and prioritizes potential drug targets in multiple sclerosis progression.Journal of neuroinflammation · 2026Article
- Proteomic Risk Score for Prediction of Incident Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Article
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Integrating human plasma proteomes with genome-wide association data implicates novel proteins and drug targets for rheumatoid arthritis.Clinical proteomics · 2026Article
- FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.Bioinformatics (Oxford, England) · 2025Article
- Fast Probabilistic Whitening Transformation for Ultra-High Dimensional Genetic Data.bioRxiv : the preprint server for biology · 2025Article
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
- European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites.Nature communications · 2025Article
- Proteomic Signatures for Risk Prediction of Atrial Fibrillation.Circulation · 2025Article
71 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Bogdan PasaniucDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Noah ZaitlenDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Huwenbo ShiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Gaurav BhatiaDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har
Alexander GusevDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har
Joseph PickrellDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Joel HirschhornDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
David P StrachanDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Nick PattersonDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK.
Alkes L PriceDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, 02115, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA, 02115, Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, Department of Genetics Harvard Medical School, Boston, MA, 02115 and Division of Population Health Sciences and Education, St George's, University of London, UK Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, 90024, Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, 90024, Department of Medicine, Lung Biology Center, University of California San Francisco, San Francisco, 94143, Program in Genetic Epidemiology and Statistical Genetics, Har
Funding
GRADUATE TRAINING IN BIOSTATISTICST32ES007142 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Brent Andrew Coull · 1985 to 2026
$11.9MSTATISTICAL METHODS FOR GENE MAPPINGR01GM053275 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANGE, KENNETH L · 1995 to 2019
$9.3MMethods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3MFine-mapping heritability at known disease loci with correlated markersF32GM106584 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI GUSEV, ALEXANDER · 2013 to 2015
$159kMetrics and methods for cross-population fine mappingR03CA162200 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PASANIUC, BOGDAN · 2012 to 2013
$149kMedical Research Council G0000934Medical Research Council G1001799NCI NIH HHS R03 CA162200NHGRI NIH HHS R01 HG006399NIEHS NIH HHS T32 ES007142NIGMS NIH HHS F32 GM106584NIGMS NIH HHS R01 GM053275Wellcome Trust 068545/Z/02
Abstract
motivationImputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available.
resultsIn simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses. AVAILABILITY AND IMPLEMENTATION: Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/. CONTACT: bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.edu SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.
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AlgorithmsBiostatisticsCase-Control StudiesCohort StudiesGenome-Wide Association StudyGenotypeHumansLinkage DisequilibriumPhenotypePolymorphism, Single NucleotideSoftwareTime Factors
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