Hanna Julienne1, Huwenbo Shi2, Bogdan Pasaniuc2, Hugues Aschard1. 1. Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France. 2. Departments of Pathology and Lab Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Abstract
MOTIVATION: Multi-trait analyses using public summary statistics from genome-wide association studies (GWASs) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. Although methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses. RESULTS: We fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for variants of all effect sizes on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel. AVAILABILITY AND IMPLEMENTATION: The python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Multi-trait analyses using public summary statistics from genome-wide association studies (GWASs) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. Although methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses. RESULTS: We fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for variants of all effect sizes on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel. AVAILABILITY AND IMPLEMENTATION: The python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Bogdan Pasaniuc; Noah Zaitlen; Huwenbo Shi; Gaurav Bhatia; Alexander Gusev; Joseph Pickrell; Joel Hirschhorn; David P Strachan; Nick Patterson; Alkes L Price Journal: Bioinformatics Date: 2014-07-01 Impact factor: 6.937
Authors: Patrick Turley; Raymond K Walters; Omeed Maghzian; Aysu Okbay; James J Lee; Mark Alan Fontana; Tuan Anh Nguyen-Viet; Robbee Wedow; Meghan Zacher; Nicholas A Furlotte; Patrik Magnusson; Sven Oskarsson; Magnus Johannesson; Peter M Visscher; David Laibson; David Cesarini; Benjamin M Neale; Daniel J Benjamin Journal: Nat Genet Date: 2018-01-01 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 R Wood; Tonu Esko; Jian Yang; Sailaja Vedantam; Tune H Pers; Stefan Gustafsson; Audrey Y Chu; Karol Estrada; Jian'an Luan; Zoltán Kutalik; Najaf Amin; Martin L Buchkovich; Damien C Croteau-Chonka; Felix R Day; Yanan Duan; Tove Fall; Rudolf Fehrmann; Teresa Ferreira; Anne U Jackson; Juha Karjalainen; Ken Sin Lo; Adam E Locke; Reedik Mägi; Evelin Mihailov; Eleonora Porcu; Joshua C Randall; André Scherag; Anna A E Vinkhuyzen; Harm-Jan Westra; Thomas W Winkler; Tsegaselassie Workalemahu; Jing Hua Zhao; Devin Absher; Eva Albrecht; Denise Anderson; Jeffrey Baron; Marian Beekman; Ayse Demirkan; Georg B Ehret; Bjarke Feenstra; Mary F Feitosa; Krista Fischer; Ross M Fraser; Anuj Goel; Jian Gong; Anne E Justice; Stavroula Kanoni; Marcus E Kleber; Kati Kristiansson; Unhee Lim; Vaneet Lotay; Julian C Lui; Massimo Mangino; Irene Mateo Leach; Carolina Medina-Gomez; Michael A Nalls; Dale R Nyholt; Cameron D Palmer; Dorota Pasko; Sonali Pechlivanis; Inga Prokopenko; Janina S Ried; Stephan Ripke; 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David P Strachan; Jacques S Beckmann; Sonja I Berndt; Michael Boehnke; Ingrid B Borecki; Mark I McCarthy; Andres Metspalu; Kari Stefansson; André G Uitterlinden; Cornelia M van Duijn; Lude Franke; Cristen J Willer; Alkes L Price; Guillaume Lettre; Ruth J F Loos; Michael N Weedon; Erik Ingelsson; Jeffrey R O'Connell; Goncalo R Abecasis; Daniel I Chasman; Michael E Goddard; Peter M Visscher; Joel N Hirschhorn; Timothy M Frayling Journal: Nat Genet Date: 2014-10-05 Impact factor: 38.330