Dataset Information

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

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.

SUBMITTER: de Vries PS

PROVIDER: S-EPMC5249120 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

de Vries Paul S PS Sabater-Lleal Maria M Chasman Daniel I DI Trompet Stella S Ahluwalia Tarunveer S TS Teumer Alexander A Kleber Marcus E ME Chen Ming-Huei MH Wang Jie Jin JJ Attia John R JR Marioni Riccardo E RE Steri Maristella M Weng Lu-Chen LC Pool Rene R Grossmann Vera V Brody Jennifer A JA Venturini Cristina C Tanaka Toshiko T Rose Lynda M LM Oldmeadow Christopher C Mazur Johanna J Basu Saonli S Frånberg Mattias M Yang Qiong Q Ligthart Symen S Hottenga Jouke J JJ Rumley Ann A Mulas Antonella A de Craen Anton J M AJ Grotevendt Anne A Taylor Kent D KD Delgado Graciela E GE Kifley Annette A Lopez Lorna M LM Berentzen Tina L TL Mangino Massimo M Bandinelli Stefania S Morrison Alanna C AC Hamsten Anders A Tofler Geoffrey G de Maat Moniek P M MP Draisma Harmen H M HH Lowe Gordon D GD Zoledziewska Magdalena M Sattar Naveed N Lackner Karl J KJ Völker Uwe U McKnight Barbara B Huang Jie J Holliday Elizabeth G EG McEvoy Mark A MA Starr John M JM Hysi Pirro G PG Hernandez Dena G DG Guan Weihua W Rivadeneira Fernando F McArdle Wendy L WL Slagboom P Eline PE Zeller Tanja T Psaty Bruce M BM Uitterlinden André G AG de Geus Eco J C EJ Stott David J DJ Binder Harald H Hofman Albert A Franco Oscar H OH Rotter Jerome I JI Ferrucci Luigi L Spector Tim D TD Deary Ian J IJ März Winfried W Greinacher Andreas A Wild Philipp S PS Cucca Francesco F Boomsma Dorret I DI Watkins Hugh H Tang Weihong W Ridker Paul M PM Jukema Jan W JW Scott Rodney J RJ Mitchell Paul P Hansen Torben T O'Donnell Christopher J CJ Smith Nicholas L NL Strachan David P DP Dehghan Abbas A

PloS one 20170120 1

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 ...[more]

PMID: 28107422

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Project description:Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.

Dataset Information

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

Publications

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

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