The QTL MAS 2011 simulated dataset was analysed to predict breeding values of individuals with known (2000 observations) and unknown (1000 observations) phenotypes. Genotype data were selected according to three criteria. Markers with Minor Allele Frequency (MAF) lower than 5% were excluded from the dataset. Then, LD between markers was measured using r^{2}. SNPs in complete LD with at least one other SNP were picked out for further analysis. Basing on subsets of closely linked markers (MAF>5%, r^{2}=1), haplotypes were constructed. Bayesian algorithm implemented in PHASE was used for haplotypes construction and for their frequencies estimation [4]. Haplotypes with population frequency lower than 1% were omitted in further analysis [5]. Inferred haplotype effects were estimated using statistical models for breeding values prediction. Four statistical models were considered. Fixed model (FM) handled haplotypes effects as fixed. The fitted model was the following: y = 1_{
n
}*μ*_{1}+*Xg*_{1}+*e*_{1}, where *y* is a vector of phenotypes, 1_{
n
} is a vector of ones, *n* is number of known phenotypes, *μ*_{1} is an overall mean, *X* is a design matrix of haplotype effects, *g*_{1} is a vector of fixed haplotype effects, *e*_{1} is a vector of random residual effects and ${e}_{1}~N\left(0,{\sigma}_{e1}^{2}\right)$. Two random models (RM1 and RM2) treated haplotype effects as random. RM1 was the following: *y*=1_{
n
}*μ*_{2}+*Xg*_{2}+*e*_{2}, where *y*,1_{
n
}, *n*, *μ*_{2}, *X* are defined analogically as above, *g*_{2} is a vector of random haplotype effects and ${g}_{2}~N\left(0,\frac{{\sigma}_{g2}^{2}}{\#haplotypes}\right)$, *e*_{2} is a vector of random residual effects and ${e}_{2}~N\left(0,{\sigma}_{e2}^{2}\right)$. RM2 was the following: *y*=1_{
n
}*μ*_{3}+*Xg*_{3}+*e*_{3}, where *y*,1_{
n
}*,n*, *μ*_{3}, *X* are defined analogically as above, *g*_{3} is a vector of random haplotype effects and ${g}_{3}~N\left(0,{\sigma}_{g3}^{2}\frac{haplotypelength}{\#alleles}\right)$, *e*_{3} is a vector of random residual effects and ${e}_{3}~N\left(0,{\sigma}_{e3}^{2}\right)$. In RM1 the homogeneous variance whatever haplotype length, and in RM2 the heterogeneous variance depending of the haplotype length was assumed. Animal model (AM) was also fitted to the data to predict breeding values and to compare results obtained with previous models. AM was defined as follows: *y*=1_{
n
}*μ*+*Zg*+*e*, where *y*,1_{
n
}, *n,μ* are defined as in previous models, *Z* is a design matrix of random additive polygenic effects, *g* is a vector of random additive polygenic effects and $g~N\left(0,A{\sigma}_{g}^{2}\right)$, *A* is the numerator relationship matrix,*e* is a vector of random residual effects and $e~N\left(0,{\sigma}_{e}^{2}\right)$. The breeding values for individual *j* estimated using FM, RM1 and RM2 were defined as a sum of haplotype effects of the individual. The results of considered models were compared using the Pearson's correlation coefficients. All computations were performed using R-package.