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Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study.

Authors :
de Vries PS
Sabater-Lleal M
Chasman DI
Trompet S
Ahluwalia TS
Teumer A
Kleber ME
Chen MH
Wang JJ
Attia JR
Marioni RE
Steri M
Weng LC
Pool R
Grossmann V
Brody JA
Venturini C
Tanaka T
Rose LM
Oldmeadow C
Mazur J
Basu S
Frånberg M
Yang Q
Ligthart S
Hottenga JJ
Rumley A
Mulas A
de Craen AJ
Grotevendt A
Taylor KD
Delgado GE
Kifley A
Lopez LM
Berentzen TL
Mangino M
Bandinelli S
Morrison AC
Hamsten A
Tofler G
de Maat MP
Draisma HH
Lowe GD
Zoledziewska M
Sattar N
Lackner KJ
Völker U
McKnight B
Huang J
Holliday EG
McEvoy MA
Starr JM
Hysi PG
Hernandez DG
Guan W
Rivadeneira F
McArdle WL
Slagboom PE
Zeller T
Psaty BM
Uitterlinden AG
de Geus EJ
Stott DJ
Binder H
Hofman A
Franco OH
Rotter JI
Ferrucci L
Spector TD
Deary IJ
März W
Greinacher A
Wild PS
Cucca F
Boomsma DI
Watkins H
Tang W
Ridker PM
Jukema JW
Scott RJ
Mitchell P
Hansen T
O'Donnell CJ
Smith NL
Strachan DP
Dehghan A
Source :
PloS one [PLoS One] 2017 Jan 20; Vol. 12 (1), pp. e0167742. Date of Electronic Publication: 2017 Jan 20 (Print Publication: 2017).
Publication Year :
2017

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.<br />Competing Interests: Dr. BM Psaty serves on the DSMB for a clinical trial of a device funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Steno Diabetes Center and Synlab Holding Deutschland GmbH provided support in the form of salaries for authors T.S.A. and W.M. respectively, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Details

Language :
English
ISSN :
1932-6203
Volume :
12
Issue :
1
Database :
MEDLINE
Journal :
PloS one
Publication Type :
Academic Journal
Accession number :
28107422
Full Text :
https://doi.org/10.1371/journal.pone.0167742