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

Research output: Contribution to journalArticle

Standard

Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray. / Coleman, Jonathan; Euesden, Jack; Patel, Hamel; Folarin, Amos A.; Newhouse, Stephen; Breen, Gerome.

In: Briefings In Functional Genomics, Vol. 15, No. 4, 07.2016, p. 298-304.

Research output: Contribution to journalArticle

Harvard

Coleman, J, Euesden, J, Patel, H, Folarin, AA, Newhouse, S & Breen, G 2016, 'Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray', Briefings In Functional Genomics, vol. 15, no. 4, pp. 298-304. https://doi.org/10.1093/bfgp/elv037

APA

Coleman, J., Euesden, J., Patel, H., Folarin, A. A., Newhouse, S., & Breen, G. (2016). Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray. Briefings In Functional Genomics, 15(4), 298-304. https://doi.org/10.1093/bfgp/elv037

Vancouver

Coleman J, Euesden J, Patel H, Folarin AA, Newhouse S, Breen G. Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray. Briefings In Functional Genomics. 2016 Jul;15(4):298-304. https://doi.org/10.1093/bfgp/elv037

Author

Coleman, Jonathan ; Euesden, Jack ; Patel, Hamel ; Folarin, Amos A. ; Newhouse, Stephen ; Breen, Gerome. / Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray. In: Briefings In Functional Genomics. 2016 ; Vol. 15, No. 4. pp. 298-304.

Bibtex Download

@article{6d35e3ac2b424444b09d03431bad0265,
title = "Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray",
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.",
keywords = "GWAS, Methods, Low-coverage microarray, Imputation, Analysis",
author = "Jonathan Coleman and Jack Euesden and Hamel Patel and Folarin, {Amos A.} and Stephen Newhouse and Gerome Breen",
year = "2016",
month = "7",
doi = "10.1093/bfgp/elv037",
language = "English",
volume = "15",
pages = "298--304",
journal = "Briefings In Functional Genomics",
issn = "2041-2649",
publisher = "Oxford University Press",
number = "4",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray

AU - Coleman, Jonathan

AU - Euesden, Jack

AU - Patel, Hamel

AU - Folarin, Amos A.

AU - Newhouse, Stephen

AU - Breen, Gerome

PY - 2016/7

Y1 - 2016/7

N2 - 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.

AB - 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.

KW - GWAS

KW - Methods

KW - Low-coverage microarray

KW - Imputation

KW - Analysis

U2 - 10.1093/bfgp/elv037

DO - 10.1093/bfgp/elv037

M3 - Article

VL - 15

SP - 298

EP - 304

JO - Briefings In Functional Genomics

JF - Briefings In Functional Genomics

SN - 2041-2649

IS - 4

ER -

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