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, Frånberg 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, Völker 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 André 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, März 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
Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands.
University of Texas Health Science Center at Houston, Houston, TX, United States of America.
PLoS One. 2017 Jan 20;12(1):e0167742. doi: 10.1371/journal.pone.0167742. eCollection 2017.
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.
现在，越来越多的全基因组关联（GWA）研究使用分辨率更高的千人基因组计划参考面板（1000G）进行基因填充，期望与HapMap基因填充相比，1000G基因填充将发现更多相关位点。为了评估1000G相对于HapMap基因填充在识别相关位点方面的改进，我们比较了基于这两个参考面板的循环纤维蛋白原GWA研究结果。使用HapMap和1000G基因填充，我们对包含相同91,953名个体的22项研究进行了荟萃分析。我们使用1000G基因填充识别出另外6个信号，而使用HapMap和1000G基因填充均有29个位点相关。使用HapMap基因填充识别出的一个位点在使用1000G基因填充时不显著。全基因组显著性阈值5×10-8是基于使用HapMap基因填充的独立统计检验数量，而1000G基因填充可能会导致应进行校正的更多独立检验。当对1000G GWA研究使用更严格的Bonferroni校正（P值<2.5×10-8）时，仅使用HapMap基因填充显著的位点数量增加到4个，而仅使用1000G显著的位点数量减少到5个。总之，1000G基因填充比HapMap基因填充能够多识别出20%的位点，尽管在使用更严格的Bonferroni校正时，1000G基因填充的优势变得不那么明显。更普遍地说，我们的结果为正在开发的其他密集参考面板的实施提供了适用的见解。
