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Objective: Holsteins are known as the world's highest-milk producing dairy cattle. The purpose of this study was to identify genetic regions strongly associated with milk traits (milk production, fat, and protein) using Korean Holstein data. Methods: This study was performed using single nucleotide polymorphism (SNP) chip data (Illumina BovineSNP50 Beadchip) of 911 Korean Holstein individuals. We inferred each genomic estimated breeding values based on best linear unbiased prediction (BLUP) and ridge regression using BLUPF90 and R. We then performed a genome-wide association study and identified genetic regions related to milk traits. Results: We identified 9, 6, and 17 significant genetic regions related to milk production, fat and protein, respectively. These genes are newly reported in the genetic association with milk traits of Holstein. Conclusion: This study complements a recent Holstein genome-wide association studies that identified other SNPs and genes as the most significant variants. These results will help to expand the knowledge of the polygenic nature of milk production in Holsteins. [ABSTRACT FROM AUTHOR]

Milk-related traits (milk yield, fat and protein) have been crucial to selection of Holstein. It is essential to find the current selection trends of Holstein. Despite this, uncovering the current trends of selection have been ignored in previous studies. We suggest a new formula to detect the current selection trends based on single nucleotide polymorphisms (SNP). This suggestion is based on the best linear unbiased prediction (BLUP) and the Fisher's fundamental theorem of natural selection both of which are trait-dependent. Fisher's theorem links the additive genetic variance to the selection coefficient. For Holstein milk production traits, we estimated the additive genetic variance using SNP effect from BLUP and selection coefficients based on genetic variance to search highly selective SNPs. Through these processes, we identified significantly selective SNPs. The number of genes containing highly selective SNPs with p-value <0.01 (nearly top 1% SNPs) in all traits and p-value <0.001 (nearly top 0.1%) in any traits was 14. They are phosphodiesterase 4B (PDE4B), serine/threonine kinase 40 (STK40), collagen, type XI, alpha 1 (COL11A1), ephrin-A1 (EFNA1), netrin 4 (NTN4), neuron specific gene family member 1 (NSG1), estrogen receptor 1 (ESR1), neurexin 3 (NRXN3), spectrin, beta, non-erythrocytic 1 (SPTBN1), ADP-ribosylation factor interacting protein 1 (ARFIP1), mutL homolog 1 (MLH1), transmembrane channel-like 7 (TMC7), carboxypeptidase X, member 2 (CPXM2) and ADAM metallopeptidase domain 12 (ADAM12). These genes may be important for future artificial selection trends. Also, we found that the SNP effect predicted from BLUP was the key factor to determine the expected current selection coefficient of SNP. Under Hardy-Weinberg equilibrium of SNP markers in current generation, the selection coefficient is equivalent to 2*SNP effect. [ABSTRACT FROM AUTHOR]

Linkage disequilibrium between markers or genetic variants underlying interesting traits affects many genomic methodologies. In many genomic methodologies, the effective population size (Ne) is important to assess the genetic diversity of animal populations. In this study, dairy cattle were genotyped using the Illumina BovineHD Genotyping BeadChips for over 777,000 SNPs located across all autosomes, mitochondria and sex chromosomes, and 70,000 autosomal SNPs were selected randomly for the final analysis. We characterized more accurate linkage disequilibrium in a sample of 96 dairy cattle producing milk in Korea. Estimated linkage disequilibrium was relatively high between closely linked markers (>0.6 at 10 kb) and decreased with increasing distance. Using formulae that related the expected linkage disequilibrium to Ne, and assuming a constant actual population size, Ne was estimated to be approximately 122 in this population. Historical Ne, calculated assuming linear population growth, was suggestive of a rapid increase Ne over the past 10 generations, and increased slowly thereafter. Additionally, we corrected the genomic relationship structure per chromosome in calculating r2 and estimated Ne. The observed Ne based on r2 corrected by genomics relationship structure can be rationalized using current knowledge of the history of the dairy cattle breeds producing milk in Korea. [ABSTRACT FROM AUTHOR]