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

de Vries, PS; Sabater-Lleal, M; Chasman, DI; Trompet, S; Ahluwalia, TS; Teumer, A; Kleber, ME; Chen, M-H; Wang, JJ; Attia, JR; et al. de Vries, PS; Sabater-Lleal, M; Chasman, DI; Trompet, S; Ahluwalia, TS; Teumer, A; Kleber, ME; Chen, M-H; Wang, JJ; Attia, JR; Marioni, RE; Steri, M; Weng, L-C; 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, AJM; Grotevendt, A; Taylor, KD; Delgado, GE; Kifley, A; Lopez, LM; Berentzen, TL; Mangino, M; Bandinelli, S; Morrison, AC; Hamsten, A; Tofler, G; de Maat, MPM; Draisma, HHM; 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, EJC; 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 (2017) Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study. PLoS One, 12 (1). e0167742. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0167742
SGUL Authors: Strachan, David Peter

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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.

Item Type: Article
Additional Information: 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.
Keywords: General Science & Technology, MD Multidisciplinary
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: PLoS One
ISSN: 1932-6203
Language: eng
Dates:
DateEvent
20 January 2017Published
19 November 2016Accepted
Publisher License: Creative Commons: Public Domain Dedication
Projects:
Project IDFunderFunder ID
HHSN268201100012CNHLBI NIH HHSUNSPECIFIED
RC2 MH089951NIMH NIH HHSUNSPECIFIED
R01 HL103612NHLBI NIH HHSUNSPECIFIED
HHSN268201100009INHLBI NIH HHSUNSPECIFIED
R01 NS017950NINDS NIH HHSUNSPECIFIED
R01 MH081802NIMH NIH HHSUNSPECIFIED
R01 HL120393NHLBI NIH HHSUNSPECIFIED
HHSN268201100010CNHLBI NIH HHSUNSPECIFIED
UL1 RR025005NCRR NIH HHSUNSPECIFIED
HHSN268201100008CNHLBI NIH HHSUNSPECIFIED
U01 HL080295NHLBI NIH HHSUNSPECIFIED
HHSN268201100005GNHLBI NIH HHSUNSPECIFIED
HHSN268201100008INHLBI NIH HHSUNSPECIFIED
R01 HL043851NHLBI NIH HHSUNSPECIFIED
R01 HL059367NHLBI NIH HHSUNSPECIFIED
HHSN268201100007CNHLBI NIH HHSUNSPECIFIED
R01 MD009164NIMHD NIH HHSUNSPECIFIED
HHSN268200800007CNHLBI NIH HHSUNSPECIFIED
HHSN268201100011INHLBI NIH HHSUNSPECIFIED
HHSN268201100011CNHLBI NIH HHSUNSPECIFIED
R01 HL086694NHLBI NIH HHSUNSPECIFIED
R01 HL087652NHLBI NIH HHSUNSPECIFIED
U01 HG004402NHGRI NIH HHSUNSPECIFIED
UL1 TR000124NCATS NIH HHSUNSPECIFIED
N01HC55222NHLBI NIH HHSUNSPECIFIED
N01HC85086NHLBI NIH HHSUNSPECIFIED
R01 HL105756NHLBI NIH HHSUNSPECIFIED
U01 DK062418NIDDK NIH HHSUNSPECIFIED
P30 DK063491NIDDK NIH HHSUNSPECIFIED
HHSN268201100006CNHLBI NIH HHSUNSPECIFIED
HHSN268201200036CNHLBI NIH HHSUNSPECIFIED
R01 AG033193NIA NIH HHSUNSPECIFIED
HHSN268201100005INHLBI NIH HHSUNSPECIFIED
K24 DK080140NIDDK NIH HHSUNSPECIFIED
U24 MH068457NIMH NIH HHSUNSPECIFIED
R01 CA047988NCI NIH HHSUNSPECIFIED
R01 HL080467NHLBI NIH HHSUNSPECIFIED
N01HC85082NHLBI NIH HHSUNSPECIFIED
HHSN268201100009CNHLBI NIH HHSUNSPECIFIED
N01HC85083NHLBI NIH HHSUNSPECIFIED
HHSN268201100005CNHLBI NIH HHSUNSPECIFIED
N01HC25195NHLBI NIH HHSUNSPECIFIED
HHSN268201100007INHLBI NIH HHSUNSPECIFIED
N01HC85079NHLBI NIH HHSUNSPECIFIED
R01 AG023629NIA NIH HHSUNSPECIFIED
R01 HL087641NHLBI NIH HHSUNSPECIFIED
N01HC85080NHLBI NIH HHSUNSPECIFIED
N01HC85081NHLBI NIH HHSUNSPECIFIED
PubMed ID: 28107422
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/108530
Publisher's version: https://doi.org/10.1371/journal.pone.0167742

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