Gene set analysis for interpreting genetic studies

Tune H Pers

doi:10.1093/hmg/ddw249

Gene set analysis for interpreting genetic studies

9 Citations (Scopus)

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.

Original language	English
Journal	Human Molecular Genetics
Volume	25
Issue number	R2
Pages (from-to)	R133-R140
Number of pages	8
ISSN	0964-6906
DOIs	https://doi.org/10.1093/hmg/ddw249
Publication status	Published - 1 Oct 2016

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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.",

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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.

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Gene set analysis for interpreting genetic studies

Abstract

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