Literature DB >> 26443613

Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray.

Jonathan R I Coleman, Jack Euesden, Hamel Patel, Amos A Folarin, Stephen Newhouse, Gerome Breen.   

Abstract

The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies would increase comparability and the potential for effective meta-analysis. This article provides a discussion of important aspects of quality control, imputation and analysis of genome-wide data from a low-coverage microarray, as well as a straight-forward guide to performing a genome-wide association study. A detailed protocol is provided online, with example scripts available at https://github.com/JoniColeman/gwas_scripts.
© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  GWAS; analysis; imputation; low-coverage microarray; methods

Mesh:

Year:  2015        PMID: 26443613      PMCID: PMC5863770          DOI: 10.1093/bfgp/elv037

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  31 in total

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Authors:  Michael E Weale
Journal:  Methods Mol Biol       Date:  2010

2.  A haplotype map of the human genome.

Authors: 
Journal:  Nature       Date:  2005-10-27       Impact factor: 49.962

Review 3.  Whole-genome genotyping.

Authors:  Kevin L Gunderson; Frank J Steemers; Hongi Ren; Pauline Ng; Lixin Zhou; Chan Tsan; Weihua Chang; Dave Bullis; Joe Musmacker; Christine King; Lori L Lebruska; David Barker; Arnold Oliphant; Kenneth M Kuhn; Richard Shen
Journal:  Methods Enzymol       Date:  2006       Impact factor: 1.600

4.  GenABEL: an R library for genome-wide association analysis.

Authors:  Yurii S Aulchenko; Stephan Ripke; Aaron Isaacs; Cornelia M van Duijn
Journal:  Bioinformatics       Date:  2007-03-23       Impact factor: 6.937

5.  Genome-wide association studies of quantitative traits with related individuals: little (power) lost but much to be gained.

Authors:  Peter M Visscher; Toby Andrew; Dale R Nyholt
Journal:  Eur J Hum Genet       Date:  2008-01-09       Impact factor: 4.246

6.  FaST linear mixed models for genome-wide association studies.

Authors:  Christoph Lippert; Jennifer Listgarten; Ying Liu; Carl M Kadie; Robert I Davidson; David Heckerman
Journal:  Nat Methods       Date:  2011-09-04       Impact factor: 28.547

7.  Data quality control in genetic case-control association studies.

Authors:  Carl A Anderson; Fredrik H Pettersson; Geraldine M Clarke; Lon R Cardon; Andrew P Morris; Krina T Zondervan
Journal:  Nat Protoc       Date:  2010-08-26       Impact factor: 13.491

8.  Advantages and pitfalls in the application of mixed-model association methods.

Authors:  Jian Yang; Noah A Zaitlen; Michael E Goddard; Peter M Visscher; Alkes L Price
Journal:  Nat Genet       Date:  2014-02       Impact factor: 38.330

9.  Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.

Authors:  Bryan Howie; Christian Fuchsberger; Matthew Stephens; Jonathan Marchini; Gonçalo R Abecasis
Journal:  Nat Genet       Date:  2012-07-22       Impact factor: 38.330

10.  An integrated map of genetic variation from 1,092 human genomes.

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

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Authors:  Amanda K Tilot; Arianna Vino; Katerina S Kucera; Duncan A Carmichael; Loes van den Heuvel; Joery den Hoed; Anton V Sidoroff-Dorso; Archie Campbell; David J Porteous; Beate St Pourcain; Tessa M van Leeuwen; Jamie Ward; Romke Rouw; Julia Simner; Simon E Fisher
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-10-21       Impact factor: 6.237

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Journal:  F1000Res       Date:  2021-07-14

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Review 4.  Tutorial: a guide to performing polygenic risk score analyses.

Authors:  Shing Wan Choi; Timothy Shin-Heng Mak; Paul F O'Reilly
Journal:  Nat Protoc       Date:  2020-07-24       Impact factor: 13.491

Review 5.  Quality Control Measures and Validation in Gene Association Studies: Lessons for Acute Illness.

Authors:  Maria Cohen; Ashley J Lamparello; Lukas Schimunek; Fayten El-Dehaibi; Rami A Namas; Yan Xu; A Murat Kaynar; Timothy R Billiar; Yoram Vodovotz
Journal:  Shock       Date:  2020-03       Impact factor: 3.533

6.  The polygenic risk for bipolar disorder influences brain regional function relating to visual and default state processing of emotional information.

Authors:  Danai Dima; Simone de Jong; Gerome Breen; Sophia Frangou
Journal:  Neuroimage Clin       Date:  2016-11-01       Impact factor: 4.881

7.  Genome-Wide Association of Heroin Dependence in Han Chinese.

Authors:  Gursharan Kalsi; Jack Euesden; Jonathan R I Coleman; Francesca Ducci; Fazil Aliev; Stephen J Newhouse; Xiehe Liu; Xiaohong Ma; Yingcheng Wang; David A Collier; Philip Asherson; Tao Li; Gerome Breen
Journal:  PLoS One       Date:  2016-12-09       Impact factor: 3.240

8.  Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene.

Authors:  Xenia Gonda; Nora Eszlari; Dora Torok; Zsofia Gal; Janos Bokor; Andras Millinghoffer; Daniel Baksa; Peter Petschner; Peter Antal; Gerome Breen; Gabriella Juhasz; Gyorgy Bagdy
Journal:  Transl Psychiatry       Date:  2021-06-01       Impact factor: 6.222

9.  Imputation and Reanalysis of ExomeChip Data Identifies Novel, Conditional and Joint Genetic Effects on Parkinson's Disease Risk.

Authors:  Linduni M Rodrigo; Dale R Nyholt
Journal:  Genes (Basel)       Date:  2021-05-04       Impact factor: 4.096

10.  Association between ALDH2 and ADH1B Polymorphisms and the Risk for Colorectal Cancer in Koreans.

Authors:  Chang Kyun Choi; Min-Ho Shin; Sang-Hee Cho; Hye-Yeon Kim; Wei Zheng; Jirong Long; Sun-Seog Kweon
Journal:  Cancer Res Treat       Date:  2020-12-24       Impact factor: 4.679

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