Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study
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Author(s)
Type
Journal Article
Abstract
An increasing number of genome-wide association (GWA) studies are now using the higher
resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation
that 1000G imputation will lead to the discovery of additional associated loci when compared
to HapMap imputation. In order to assess the improvement of 1000G over HapMap
imputation in identifying associated loci, we compared the results of GWA studies of circulating
fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation
we performed a meta-analysis of 22 studies comprising the same 91,953 individuals.
We identified six additional signals using 1000G imputation, while 29 loci were associated
using both HapMap and 1000G imputation. One locus identified using HapMap imputation
was not significant using 1000G imputation. The genome-wide significance threshold of
5×10−8 is based on the number of independent statistical tests using HapMap imputation,
and 1000G imputation may lead to further independent tests that should be corrected for.
When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10−8
),
the number of loci significant only using HapMap imputation increased to 4 while the number
of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation
enabled the identification of 20% more loci than HapMap imputation, although the advantage
of 1000G imputation became less clear when a stricter Bonferroni correction was used.
More generally, our results provide insights that are applicable to the implementation of
other dense reference panels that are under development.
resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation
that 1000G imputation will lead to the discovery of additional associated loci when compared
to HapMap imputation. In order to assess the improvement of 1000G over HapMap
imputation in identifying associated loci, we compared the results of GWA studies of circulating
fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation
we performed a meta-analysis of 22 studies comprising the same 91,953 individuals.
We identified six additional signals using 1000G imputation, while 29 loci were associated
using both HapMap and 1000G imputation. One locus identified using HapMap imputation
was not significant using 1000G imputation. The genome-wide significance threshold of
5×10−8 is based on the number of independent statistical tests using HapMap imputation,
and 1000G imputation may lead to further independent tests that should be corrected for.
When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10−8
),
the number of loci significant only using HapMap imputation increased to 4 while the number
of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation
enabled the identification of 20% more loci than HapMap imputation, although the advantage
of 1000G imputation became less clear when a stricter Bonferroni correction was used.
More generally, our results provide insights that are applicable to the implementation of
other dense reference panels that are under development.
Date Issued
2017-01-20
Date Acceptance
2016-11-19
Citation
PLoS ONE, 2017, 12 (1)
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS ONE
Volume
12
Issue
1
Copyright Statement
This is an open access article, free of all
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
copyright, and may be freely reproduced,
distributed, transmitted, modified, built upon, or
otherwise used by anyone for any lawful purpose.
The work is made available under the Creative
Commons CC0 public domain dedication.
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Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
CARDIOVASCULAR-DISEASE
SUSCEPTIBILITY LOCI
GENETIC-VARIANTS
COMMON VARIANTS
METAANALYSIS
CONSORTIUM
IMPUTATION
PRESSURE
INSIGHTS
BIOLOGY
Publication Status
Published
Article Number
e0167742