Bogdan Pasaniuc1, Noah Zaitlen2, Huwenbo Shi2, Gaurav Bhatia3, Alexander Gusev3, Joseph Pickrell1, Joel Hirschhorn2, David P Strachan2, Nick Patterson2, Alkes L Price3. 1. 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, 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. 2. 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. 3. 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, 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
Abstract
MOTIVATION: Imputation 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. RESULTS: In 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.
MOTIVATION: Imputation 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. RESULTS: In 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.
Authors: Bryan Howie; Christian Fuchsberger; Matthew Stephens; Jonathan Marchini; Gonçalo R Abecasis Journal: Nat Genet Date: 2012-07-22 Impact factor: 38.330
Authors: Goncalo R Abecasis; Adam Auton; Lisa D Brooks; Mark A DePristo; Richard M Durbin; Robert E Handsaker; Hyun Min Kang; Gabor T Marth; Gil A McVean Journal: Nature Date: 2012-11-01 Impact factor: 49.962
Authors: Andrew J Schork; Wesley K Thompson; Phillip Pham; Ali Torkamani; J Cooper Roddey; Patrick F Sullivan; John R Kelsoe; Michael C O'Donovan; Helena Furberg; Nicholas J Schork; Ole A Andreassen; Anders M Dale Journal: PLoS Genet Date: 2013-04-25 Impact factor: 5.917
Authors: Alexander Gusev; S Hong Lee; Gosia Trynka; Hilary Finucane; Bjarni J Vilhjálmsson; Han Xu; Chongzhi Zang; Stephan Ripke; Brendan Bulik-Sullivan; Eli Stahl; Anna K Kähler; Christina M Hultman; Shaun M Purcell; Steven A McCarroll; Mark Daly; Bogdan Pasaniuc; Patrick F Sullivan; Benjamin M Neale; Naomi R Wray; Soumya Raychaudhuri; Alkes L Price Journal: Am J Hum Genet Date: 2014-11-06 Impact factor: 11.025
Authors: Diptavo Dutta; Peter VandeHaar; Lars G Fritsche; Sebastian Zöllner; Michael Boehnke; Laura J Scott; Seunggeun Lee Journal: Am J Hum Genet Date: 2021-03-16 Impact factor: 11.025
Authors: Kyle Gettler; Mamta Giri; Ephraim Kenigsberg; Jerome Martin; Ling-Shiang Chuang; Nai-Yun Hsu; Lee A Denson; Jeffrey S Hyams; Anne Griffiths; Joshua D Noe; Wallace V Crandall; David R Mack; Richard Kellermayer; Clara Abraham; Gabriel Hoffman; Subra Kugathasan; Judy H Cho Journal: Genes Immun Date: 2019-01-29 Impact factor: 2.676