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RESEARCH PRODUCT
Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study
Graciela E. DelgadoAlanna C. MorrisonJie HuangToshiko TanakaKarl J. LacknerTorben HansenMagdalena ZoledziewskaJan W. JukemaAntonella MulasGordon D.o. LowePhilipp S. WildMaria Sabater-llealWeihua GuanDavid P. StrachanJohanna MazurPaul MitchellStefania BandinelliCristina VenturiniCristina VenturiniAlbert HofmanAlbert HofmanDaniel I. ChasmanPaul M. RidkerPirro G. HysiFernando RivadeneiraKent D. TaylorP. Eline SlagboomMassimo ManginoMassimo ManginoJouke J. HottengaVera GrossmannMaristella SteriIan J. DearyPaul S. De VriesPaul S. De VriesAnn RumleyMark McevoyMarcus E. KleberTarunveer S. AhluwaliaTarunveer S. AhluwaliaChristopher OldmeadowRiccardo E. MarioniRiccardo E. MarioniLynda M. RoseHarald BinderNaveed SattarAnton J. M. De CraenRené PoolFrancesco CuccaSaonli BasuJie Jin WangOscar H. FrancoElizabeth G. HollidayStella TrompetRodney J. ScottMoniek P.m. De MaatWinfried MärzWinfried MärzWinfried MärzAnnette KifleyWendy L. McardleAlexander TeumerNicholas L. SmithNicholas L. SmithEco J. C. De GeusJohn M. StarrChristopher J. O'donnellChristopher J. O'donnellJohn AttiaBruce M. PsatyBruce M. PsatyMattias FrånbergMattias FrånbergUwe VölkerTim D. SpectorHarmen H.m. DraismaTanja ZellerSymen LigthartDorret I. BoomsmaAnders HamstenQiong YangQiong YangBarbara McknightAndré G. UitterlindenLu-chen WengWeihong TangGeoffrey H. ToflerHugh WatkinsTina L. BerentzenDavid J. StottJerome I. RotterAnne GrotevendtJennifer A. BrodyLorna M. LopezLorna M. LopezLorna M. LopezAbbas DehghanAbbas DehghanLuigi FerrucciAndreas GreinacherDena G. HernandezMing-huei ChenMing-huei Chensubject
Netherlands Twin Register (NTR)0301 basic medicineGlycobiologySocial Scienceslcsh:MedicineGenome-wide association study030105 genetics & heredityBiochemistryMathematical and Statistical TechniquesSociologyCell SignalingConsortiaGENETIC-VARIANTSMedicine and Health SciencesIMPUTATIONInternational HapMap Projectlcsh:ScienceGeneticsMultidisciplinaryCOMMON VARIANTSGenomicsMultidisciplinary SciencesINSIGHTSCARDIOVASCULAR-DISEASEPhysical SciencessymbolsScience & Technology - Other TopicsHealth Services ResearchGenomic Signal ProcessingStatistics (Mathematics)Research ArticleSignal TransductionGenotypingSUSCEPTIBILITY LOCIGeneral Science & TechnologyBIOLOGYSingle-nucleotide polymorphismGenomicsHapMap ProjectComputational biologyPRESSUREBiologyResearch and Analysis Methods03 medical and health sciencessymbols.namesakeMD MultidisciplinaryGenome-Wide Association StudiesGeneticsJournal Article/dk/atira/pure/keywords/cohort_studies/netherlands_twin_register_ntr_HumansStatistical Methods1000 Genomes ProjectMolecular Biology TechniquesMolecular BiologyMETAANALYSISGlycoproteinsScience & Technologylcsh:RHuman GenomeCONSORTIUMBiology and Life SciencesComputational BiologyFibrinogenHuman GeneticsCell BiologyComparative GenomicsGenome AnalysisHealth Care030104 developmental biologyBonferroni correctionlcsh:QHaplotype estimationMathematicsImputation (genetics)Meta-AnalysisGenome-Wide Association Studydescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-20 |