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Genet Epidemiol. 2019 Sep;43(6):717-726. doi: 10.1002/gepi.22215. Epub 2019 May 30.

A comparison of two workflows for regulome and transcriptome-based prioritization of genetic variants associated with myocardial mass.

Author information

1
Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
2
Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
3
Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
4
Department of Population and Quantitative Health Sciences, Case Western Reserve University, Ohio.
5
Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
6
Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
7
Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom.

Abstract

A typical task arising from main effect analyses in a Genome Wide Association Study (GWAS) is to identify single nucleotide polymorphisms (SNPs), in linkage disequilibrium with the observed signals, that are likely causal variants and the affected genes. The affected genes may not be those closest to associating SNPs. Functional genomics data from relevant tissues are believed to be helpful in selecting likely causal SNPs and interpreting implicated biological mechanisms, ultimately facilitating prevention and treatment in the case of a disease trait. These data are typically used post GWAS analyses to fine-map the statistically significant signals identified agnostically by testing all SNPs and applying a multiple testing correction. The number of tested SNPs is typically in the millions, so the multiple testing burden is high. Motivated by this, in this study we investigated an alternative workflow, which consists in utilizing the available functional genomics data as a first step to reduce the number of SNPs tested for association. We analyzed GWAS on electrocardiographic QRS duration using these two workflows. The alternative workflow identified more SNPs, including some residing in loci not discovered with the typical workflow. Moreover, the latter are corroborated by other reports on QRS duration. This indicates the potential value of incorporating functional genomics information at the onset in GWAS analyses.

KEYWORDS:

GWAS; SNP preselection; functional genomics; left ventricular mass

PMID:
31145509
PMCID:
PMC6687530
[Available on 2020-09-01]
DOI:
10.1002/gepi.22215

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