01803nam a2200313 a 450000100080000000500110000800800410001910000160006024500470007626001480012352009540027165300090122565300170123465300120125170000130126370000160127670000160129270000160130870000170132470000210134170000140136270000160137670000170139270000160140970000130142570000180143870000160145670000170147210619302021-03-31       2014    bl uuuu     u01u1 u    #d1 aMISZTAL, I.  aGWAS using ssGBLUP.h[electronic resource]  aIn: Proceedings of the World Congress on Genetics Applied to Livestock Production, 10., Vancouver, BC, Canada, August 17-22, 2014. p.325.c2014  aABSTRACT. This study aimed to compare results of genome-wide associations obtained from various methodologies for GWAS when applied to two lines of broiler chicken. Each line contained >250k birds with 3 traits and 5k SNP60k genotypes. Methods included single-step GWAS, single marker model and BayesB. Mannhattan plots were based on variances of 20-SNP segments, as shorter segments produced noisy plots. Only a few segments explained >1 % of the additive variance. One segment explained >20% variance in BayesB but 3% with ssGWAS and <1% with a single marker model. In two lines, no major segment overlapped for any trait. When analyses used slices of generations (1-3,2-4,3-5,1-5), variances for the same segment varied greatly.  The plots were more distinct with a new data set that included >16k genotypes, but no segment explained >1% of the variance.  Strength of associations strongly depends on methodologies and details of implementations.  aGWAS  aSNP variance  aSsGBLUP1 aWANG, H.1 aAGUILAR, I.1 aLEGARRA, A.1 aTSURUTA, S.1 aLOURENCO, D.1 aFRAGOMENI, B. O.1 aZHANG, X.1 aMUIR, W. M.1 aCHENG, H. H.1 aOKIMOTO, R.1 aWING, T.1 aHAWKEN, R. R.1 aZUMBACH, B.1 aFERNANDO, R.