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The objective of the paper was to classify 50 SNPs (from 17 chromosomes) according to their contribution to the meatness of 293 boars of two breeds (Polish Landrace and Polish Large White) using entropy analysis and standard association analysis. The collected data were classified into two groups (according to the official EUROP procedure) and used for entropy analysis. Associations of single genotypes versus their groups (located at single chromosomes) with the trait studied were estimated by the use of the Generalized Linear Model (GLM). Thus meatness was included as a continuous variable. The most important contributions have been estimated by both approaches for the following SNPs: SULT1A1:g.76G>A (SSC3), PKLR:g.384C>T (SSC4), MYOD1:c.566G>C (SSC2), TNNT3:g.153T>C (SSC2), GAA:g.38T>C (SSC12), LDLRR1:c.459A>G (SSC8), MYF6:g.255T>C (SSC5), CAS:g.499A>C (SSC2), PPARGC:c.678T>A (SSC15). Moreover, interactions among some studied loci are suggested, especially for the loci at chromosome 1.

In human and animal genetics dense single nucleotide polymorphism (SNP) panels are widely used to describe genetic variation. In particular genomic selection in dairy cattle has become a routinely applied tool for prediction of additive genetic values of animals, especially of young selection candidates. The aim of the study was to investigate how well an additive genetic value can be predicted using various sets of approximately 3000 SNPs selected out of the 54 001 SNPs in an Illumina BovineSNP50 BeadChip high density panel. Effects of SNPs from the nine subsets of the 54 001 panel were estimated using a model with a random uncorrelated SNPs effect based on a training data set of 1216 Polish Holstein-Friesian bulls whose phenotypic records were approximated by deregressed estimated breeding values for milk, protein, and fat yields. Predictive ability of the low density panels was assessed using a validation data set of 622 bulls. Correlations between direct and conventional breeding values routinely estimated for the Polish population were similar across traits and clearly across sets of SNPs. For the training data set correlations varied between 0.94 and 0.98, for the validation data set between 0.25 and 0.46. The corresponding correlations estimated using the 54 001 panel were: 0.98 for the three traits (training), 0.98 (milk and fat yields, validation), and 0.97 (protein yield, validation). The optimal subset consisted of SNPs selected based on their highest effects for milk yield obtained from the evaluation of all 54 001 SNPs. A low density SNP panel allows for reasonably good prediction of future breeding values. Even though correlations between direct and conventional breeding values were moderate, for young selection candidates a low density panel is a better predictor than a commonly used average of parental breeding values.