Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study

De Vries, Paul S., Sabater-Lleal, Maria, Chasman, Daniel I., Trompet, Stella, Ahluwalia, Tarunveer S., Teumer, Alexander, Kleber, Marcus E., Chen, Ming-Huei, Wang, Jie Jin, Attia, John R., Marioni, Riccardo E., Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A., Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M., Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Franberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J., Rumley, Ann, Mulas, Antonella, De Craen, Anton J. M., Grotevendt, Anne, Taylor, Kent D., Delgado, Graciela E., Kifley, Annette, Lopez, Lorna M., Berentzen, Tina L., Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C., Hamsten, Anders, Tofler, Geoffrey, De Maat, Moniek P. M., Draisma, Harmen H. M., Lowe, Gordon D., Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J., Volker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G., McEvoy, Mark A., Starr, John M., Hysi, Pirro G., Hernandez, Dena G., Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L., Slagboom, P. Eline, Zeller, Tanja, Psaty, Bruce M., Uitterlinden, Andre G., De Geus, Eco J. C., Stott, David J., Binder, Harald, Hofman, Albert, Franco, Oscar H., Rotter, Jerome I., Ferrucci, Luigi, Spector, Tim D., Deary, Ian J., Marz, Winfried, Greinacher, Andreas, Wild, Philipp S., Cucca, Francesco, Boomsma, Dorret I., Watkins, Hugh, Tang, Weihong, Ridker, Paul M., Jukema, Jan W., Scott, Rodney J., Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J., Smith, Nicholas L., Strachan, David P. and Dehghan, Abbas (2017) Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study. PLoS One, 12 1: e0167742-e0167742. doi:10.1371/journal.pone.0167742


Author De Vries, Paul S.
Sabater-Lleal, Maria
Chasman, Daniel I.
Trompet, Stella
Ahluwalia, Tarunveer S.
Teumer, Alexander
Kleber, Marcus E.
Chen, Ming-Huei
Wang, Jie Jin
Attia, John R.
Marioni, Riccardo E.
Steri, Maristella
Weng, Lu-Chen
Pool, Rene
Grossmann, Vera
Brody, Jennifer A.
Venturini, Cristina
Tanaka, Toshiko
Rose, Lynda M.
Oldmeadow, Christopher
Mazur, Johanna
Basu, Saonli
Franberg, Mattias
Yang, Qiong
Ligthart, Symen
Hottenga, Jouke J.
Rumley, Ann
Mulas, Antonella
De Craen, Anton J. M.
Grotevendt, Anne
Taylor, Kent D.
Delgado, Graciela E.
Kifley, Annette
Lopez, Lorna M.
Berentzen, Tina L.
Mangino, Massimo
Bandinelli, Stefania
Morrison, Alanna C.
Hamsten, Anders
Tofler, Geoffrey
De Maat, Moniek P. M.
Draisma, Harmen H. M.
Lowe, Gordon D.
Zoledziewska, Magdalena
Sattar, Naveed
Lackner, Karl J.
Volker, Uwe
McKnight, Barbara
Huang, Jie
Holliday, Elizabeth G.
McEvoy, Mark A.
Starr, John M.
Hysi, Pirro G.
Hernandez, Dena G.
Guan, Weihua
Rivadeneira, Fernando
McArdle, Wendy L.
Slagboom, P. Eline
Zeller, Tanja
Psaty, Bruce M.
Uitterlinden, Andre G.
De Geus, Eco J. C.
Stott, David J.
Binder, Harald
Hofman, Albert
Franco, Oscar H.
Rotter, Jerome I.
Ferrucci, Luigi
Spector, Tim D.
Deary, Ian J.
Marz, Winfried
Greinacher, Andreas
Wild, Philipp S.
Cucca, Francesco
Boomsma, Dorret I.
Watkins, Hugh
Tang, Weihong
Ridker, Paul M.
Jukema, Jan W.
Scott, Rodney J.
Mitchell, Paul
Hansen, Torben
O'Donnell, Christopher J.
Smith, Nicholas L.
Strachan, David P.
Dehghan, Abbas
Title Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2017-01-20
Sub-type Article (original research)
DOI 10.1371/journal.pone.0167742
Open Access Status DOI
Volume 12
Issue 1
Start page e0167742
End page e0167742
Total pages 22
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Subject 1300 Biochemistry, Genetics and Molecular Biology
1100 Agricultural and Biological Sciences
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.
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID HHSN268201100012C
RC2 MH089951
R01 HL103612
HHSN268201100009I
R01 NS017950
R01 MH081802
R01 HL120393
HHSN268201100010C
UL1 RR025005
HHSN268201100008C
U01 HL080295
HHSN268201100005G
HHSN268201100008I
R01 HL043851
R01 HL059367
HHSN268201100007C
R01 MD009164
HHSN268200800007C
HHSN268201100011I
HHSN268201100011C
R01 HL086694
R01 HL087652
U01 HG004402
UL1 TR000124
N01HC55222
MR/K026992/1
N01HC85086
R01 HL105756
U01 DK062418
P30 DK063491
HHSN268201100006C
HHSN268201200036C
R01 AG033193
HHSN268201100005I
K24 DK080140
U24 MH068457
R01 CA047988
R01 HL080467
N01HC85082
HHSN268201100009C
N01HC85083
HHSN268201100005C
N01HC25195
HHSN268201100007I
N01HC85079
R01 AG023629
R01 HL087641
N01HC85080
N01HC85081
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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Queensland Brain Institute Publications
 
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