02696naa a2200277 a 450000100080000000500110000800800410001902200140006002400350007410000130010924501140012226000090023650001180024552018200036365000130218365000090219665000140220565000130221965000260223265000340225870000160229270000160230870000160232470000150234077300630235510507062019-10-15       2012    bl uuuu     u00u1 u    #d  a0016-67237 a10.1017/S00166723120002742DOI1 aWANG, H.  aGenome-wide association mapping including phenotypes from relatives without genotypes.h[electronic resource]  c2012  aArticle history: Received 19 September 2011 / Revised 8 December 2011 and 9 March 2012. / Accepted 13 March 2012.  aABSTRACT.  A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 05. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 089 was obtained by ssGBLUP after one iteration, which was 001 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 082 and 074 for ssGBLUP and CGWAS with m=8, and 083 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.  © Cambridge University Press 2012.  aANIMALES  aCRIA  aFENOTIPOS  aGENOTIPO  aMARCADORES GENÉTICOS  aMEJORAMIENTO GENÉTICO ANIMAL1 aMISZTAL, I.1 aAGUILAR, I.1 aLEGARRA, A.1 aMUIR, W.M.  tGenetics Research, 2012gv.94, no.2, p.73-83. OPEN ACCESS.