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Nat Genet. 2016 Oct;48(10):1284-1287. doi: 10.1038/ng.3656. Epub 2016 Aug 29.

Next-generation genotype imputation service and methods.

Author information

1
Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.
2
Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria.
3
Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Cagliari, Italy.
4
Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy.
5
Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado, USA.
6
Clinical Trials Branch, Division of Epidemiology and Clinical Applications, National Eye Institute, US National Institutes of Health, Bethesda, Maryland, USA.
7
HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.
8
Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA.
9
Laboratory of Genetics, National Institute on Aging, US National Institutes of Health, Baltimore, Maryland, USA.
10
Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
11
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
12
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
13
Neurobiology-Neurodegeneration and Repair Laboratory, National Eye Institute, US National Institutes of Health, Bethesda, Maryland, USA.
14
Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), affiliated with the University of Lübeck, Bolzano, Italy.
#
Contributed equally

Abstract

Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.

PMID:
27571263
PMCID:
PMC5157836
DOI:
10.1038/ng.3656
[Indexed for MEDLINE]
Free PMC Article

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