Literature DB >> 20517342

Genotype imputation for genome-wide association studies.

Jonathan Marchini1, Bryan Howie.   

Abstract

In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases. Genotype imputation has been used widely in the analysis of GWA studies to boost power, fine-map associations and facilitate the combination of results across studies using meta-analysis. This Review describes the details of several different statistical methods for imputing genotypes, illustrates and discusses the factors that influence imputation performance, and reviews methods that can be used to assess imputation performance and test association at imputed SNPs.

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Year:  2010        PMID： 20517342     DOI： 10.1038/nrg2796

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN： 1471-0056            Impact factor:   53.242


  49 in total

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Journal:  Nat Genet       Date:  2008-09       Impact factor: 38.330

7.  Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

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Journal:  PLoS Genet       Date:  2009-06-26       Impact factor: 5.917

8.  Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci.

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9.  Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.

Authors:  Eleftheria Zeggini; Michael N Weedon; Cecilia M Lindgren; Timothy M Frayling; Katherine S Elliott; Hana Lango; Nicholas J Timpson; John R B Perry; Nigel W Rayner; Rachel M Freathy; Jeffrey C Barrett; Beverley Shields; Andrew P Morris; Sian Ellard; Christopher J Groves; Lorna W Harries; Jonathan L Marchini; Katharine R Owen; Beatrice Knight; Lon R Cardon; Mark Walker; Graham A Hitman; Andrew D Morris; Alex S F Doney; Mark I McCarthy; Andrew T Hattersley
Journal:  Science       Date:  2007-04-26       Impact factor: 47.728

10.  Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip.

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  770 in total

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2.  The effect of reference panels and software tools on genotype imputation.

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3.  Performance of genotype imputations using data from the 1000 Genomes Project.

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4.  A linear complexity phasing method for thousands of genomes.

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5.  Family-based association tests using genotype data with uncertainty.

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6.  Rheumatoid arthritis: RA risk variants in the groove.

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7.  SNP-skimming: A fast approach to map loci generating quantitative variation in natural populations.

Authors:  Carolyn A Wessinger; John K Kelly; Peng Jiang; Mark D Rausher; Lena C Hileman
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8.  Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels.

Authors:  Wei Zhou; Lars G Fritsche; Sayantan Das; He Zhang; Jonas B Nielsen; Oddgeir L Holmen; Jin Chen; Maoxuan Lin; Maiken B Elvestad; Kristian Hveem; Goncalo R Abecasis; Hyun Min Kang; Cristen J Willer
Journal:  Genet Epidemiol       Date:  2017-09-01       Impact factor: 2.135

Review 9.  Determining causality and consequence of expression quantitative trait loci.

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