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Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study.
Monday,2017-05-01 16:24 America/Los_Angeles — ecterry
| Title | Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study. |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Authors | de Vries, PS, Sabater-Lleal, M, Chasman, DI, Trompet, S, Ahluwalia, TS, Teumer, A, Kleber, ME, Chen, M-H, Wang, JJin, 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, P Slagboom, E, 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 |
| Journal | PLoS One |
| Volume | 12 |
| Issue | 1 |
| Pagination | e0167742 |
| Date Published | 2017 |
| ISSN | 1932-6203 |
| Abstract | <p>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.</p> |
| DOI | 10.1371/journal.pone.0167742 |
| Alternate Journal | PLoS ONE |
| PubMed ID | 28107422 |
| PubMed Central ID | PMC5249120 |
| Grant List | HHSN268201100012C / HL / NHLBI NIH HHS / United States RC2 MH089951 / MH / NIMH NIH HHS / United States R01 HL103612 / HL / NHLBI NIH HHS / United States HHSN268201100009I / HL / NHLBI NIH HHS / United States R01 NS017950 / NS / NINDS NIH HHS / United States R01 MH081802 / MH / NIMH NIH HHS / United States R01 HL120393 / HL / NHLBI NIH HHS / United States HHSN268201100010C / HL / NHLBI NIH HHS / United States UL1 RR025005 / RR / NCRR NIH HHS / United States HHSN268201100008C / HL / NHLBI NIH HHS / United States U01 HL080295 / HL / NHLBI NIH HHS / United States HHSN268201100005G / HL / NHLBI NIH HHS / United States HHSN268201100008I / HL / NHLBI NIH HHS / United States R01 HL043851 / HL / NHLBI NIH HHS / United States R01 HL059367 / HL / NHLBI NIH HHS / United States HHSN268201100007C / HL / NHLBI NIH HHS / United States R01 MD009164 / MD / NIMHD NIH HHS / United States HHSN268200800007C / HL / NHLBI NIH HHS / United States HHSN268201100011I / HL / NHLBI NIH HHS / United States HHSN268201100011C / HL / NHLBI NIH HHS / United States R01 HL086694 / HL / NHLBI NIH HHS / United States R01 HL087652 / HL / NHLBI NIH HHS / United States U01 HG004402 / HG / NHGRI NIH HHS / United States UL1 TR000124 / TR / NCATS NIH HHS / United States N01HC55222 / HL / NHLBI NIH HHS / United States MR/K026992/1 / / Medical Research Council / United Kingdom N01HC85086 / HL / NHLBI NIH HHS / United States R01 HL105756 / HL / NHLBI NIH HHS / United States U01 DK062418 / DK / NIDDK NIH HHS / United States P30 DK063491 / DK / NIDDK NIH HHS / United States HHSN268201100006C / HL / NHLBI NIH HHS / United States HHSN268201200036C / HL / NHLBI NIH HHS / United States R01 AG033193 / AG / NIA NIH HHS / United States HHSN268201100005I / HL / NHLBI NIH HHS / United States K24 DK080140 / DK / NIDDK NIH HHS / United States U24 MH068457 / MH / NIMH NIH HHS / United States R01 CA047988 / CA / NCI NIH HHS / United States R01 HL080467 / HL / NHLBI NIH HHS / United States N01HC85082 / HL / NHLBI NIH HHS / United States HHSN268201100009C / HL / NHLBI NIH HHS / United States N01HC85083 / HL / NHLBI NIH HHS / United States HHSN268201100005C / HL / NHLBI NIH HHS / United States N01HC25195 / HL / NHLBI NIH HHS / United States HHSN268201100007I / HL / NHLBI NIH HHS / United States N01HC85079 / HL / NHLBI NIH HHS / United States R01 AG023629 / AG / NIA NIH HHS / United States R01 HL087641 / HL / NHLBI NIH HHS / United States N01HC85080 / HL / NHLBI NIH HHS / United States N01HC85081 / HL / NHLBI NIH HHS / United States |
ePub date:
17/01