Gene set analysis for interpreting genetic studies

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Gene set analysis for interpreting genetic studies. / Pers, Tune H.

In: Human Molecular Genetics, Vol. 25, No. R2, 01.10.2016, p. R133-R140.

Research output: Contribution to journal › Review › Research › peer-review

Harvard

Pers, TH 2016, 'Gene set analysis for interpreting genetic studies', Human Molecular Genetics, vol. 25, no. R2, pp. R133-R140. https://doi.org/10.1093/hmg/ddw249

APA

Pers, T. H. (2016). Gene set analysis for interpreting genetic studies. Human Molecular Genetics, 25(R2), R133-R140. https://doi.org/10.1093/hmg/ddw249

Vancouver

Pers TH. Gene set analysis for interpreting genetic studies. Human Molecular Genetics. 2016 Oct 1;25(R2):R133-R140. https://doi.org/10.1093/hmg/ddw249

Author

Pers, Tune H. / Gene set analysis for interpreting genetic studies. In: Human Molecular Genetics. 2016 ; Vol. 25, No. R2. pp. R133-R140.

Bibtex

@article{80450617fdb9491abe0d13f84effea23,
title = "Gene set analysis for interpreting genetic studies",
abstract = "Interpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways and functional annotations and may hence point towards novel biological insights. However, despite the growing availability of GSA tools, the sizeable amount of variants identified for a vast number of complex traits, and many irrefutably trait-associated gene sets, the gap between discovery and interpretation remains. More efficient interpretation requires more complete and consistent gene set representations of biological pathways, phenotypes and functional annotations. In this review, I examine different types of gene sets, discuss how inconsistencies in gene set definitions impact GSA, describe how GSA has helped to elucidate biology and outline potential future directions.",
author = "Pers, {Tune H}",
note = "{\textcopyright} The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.",
year = "2016",
month = oct,
day = "1",
doi = "10.1093/hmg/ddw249",
language = "English",
volume = "25",
pages = "R133--R140",
journal = "Human Molecular Genetics",
issn = "0964-6906",
publisher = "Oxford University Press",
number = "R2",

}

RIS

TY - JOUR

T1 - Gene set analysis for interpreting genetic studies

AU - Pers, Tune H

N1 - © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Interpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways and functional annotations and may hence point towards novel biological insights. However, despite the growing availability of GSA tools, the sizeable amount of variants identified for a vast number of complex traits, and many irrefutably trait-associated gene sets, the gap between discovery and interpretation remains. More efficient interpretation requires more complete and consistent gene set representations of biological pathways, phenotypes and functional annotations. In this review, I examine different types of gene sets, discuss how inconsistencies in gene set definitions impact GSA, describe how GSA has helped to elucidate biology and outline potential future directions.

AB - Interpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways and functional annotations and may hence point towards novel biological insights. However, despite the growing availability of GSA tools, the sizeable amount of variants identified for a vast number of complex traits, and many irrefutably trait-associated gene sets, the gap between discovery and interpretation remains. More efficient interpretation requires more complete and consistent gene set representations of biological pathways, phenotypes and functional annotations. In this review, I examine different types of gene sets, discuss how inconsistencies in gene set definitions impact GSA, describe how GSA has helped to elucidate biology and outline potential future directions.

U2 - 10.1093/hmg/ddw249

DO - 10.1093/hmg/ddw249

M3 - Review

C2 - 27511725

VL - 25

SP - R133-R140

JO - Human Molecular Genetics

JF - Human Molecular Genetics

SN - 0964-6906

IS - R2

ER -

ID: 172817465