Investigation of genetic correlation among traits using massive SNP genotype in German Holstein population
Abstract: The aims of this study were to demonstrate the genomic architecture of quantitative traits using Illumina BovineSNP50 Beadchip as well as the identification of chromosomes or SNPs affecting multiple traits in German Holstein population. For this research, a total of 2333 German Holstein bulls were genotyped for 54,001 SNPs. Only SNPs with less than 5% missing genotypes and minor allele frequency greater than 3% were used. Finally, among 45181 SNPs distributed on 29 autosome and XY pseudo-autosomal chromosomes, 43,838 known position SNPs were selected. Total additive genomic variance were calculated by sums of chromosomal variances and covariances between them or SNP variance and covariances between SNPs for milk, fat, protein yield and somatic cell score traits. Chromosomal genetic correlations were estimated for six categories of traits: production (3 traits), udder health (1 trait), milkability (4 traits), fertility (6 traits), calving (4 traits) and body type (2 traits). SNP genetic correlations were calculated for fat and milk yield on BTA14 and BTA20 as well. All bovine chromosomes contribute to construct the total additive genetic variance. Sums of the chromosomal additive genetic variances and covariance between chromosomes were equal with total additive genetic variance as well as sums of SNP variances and covariance between SNPs along the genome. Chromosomal additive genetic variance explain 54.49 to 69.9% of total additive genetic variance with higher additive genetic variance on BTA14 for milk and fat yields and BTA6 for protein yield and somatic cell score traits. Sum of SNPs variance explain 6.3 to 9.6% of total additive genetic variance with higher SNPs additive genetic variance on XY pseudo-autosomal. Results of chromosomal genetic correlations between analyzed traits showed negative and positive correlations between traits across chromosomes. e.g. BTA14 has strong negative correlation between fat with milk and protein yields. Higher positive correlations between milk, fat and protein yields with SCS have been seen on BTA26. In the other hand, correlations between traits across SNPs can exhibit chromosomal regions having positive or negative correlations for interested traits. It can help to design low density chip with high correlated SNPs for economical traits in genomic selection.
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