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Do you want to know the details of genomics?  How do they exactly take the analysis and turn it into numbers and indexes for all the traits?  I will show you two quick Technical Abstract from the USDA-ARS website (and there are hundreds more!) and you can choose for your self if you believe these government Geneticist!

"Genomic Geneticist"

Geneticist Tad Sonstegard analyzes BovineSNP50 BeadChips for genotypic data that decodes each animal’s genome at more than 50,000 locations. This type of data is used in cattle research ranging from genome selection to mapping of congenital defects.

Development and Characterization of a High Density SNP Genotyping Assay for Cattle

The success of genomewide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. We describe the development of a highly parallel custom genotyping assay interrogating 54,001 SNP loci to support these applications in cattle. This assay is made available to the community by Illumina as the BovineSNP50 BeadChip. The utility of the assay is substantially enhanced because of the use of high quality SNP from a SNP discovery method that simultaneously estimates minor allele frequencies (MAF) and by the implementation of a novel SNP selection algorithm to ensure genomewide coverage, compact gap distribution, and high average MAF. A panel of 576 animals from 21 cattle breeds and six outgroup species was genotyped to establish that from 39,765 to 46,492 SNP are polymorphic within individual breeds, with an average MAF ranging from 0.24 to 0.27. Genotype data were analyzed to discover 79 putative copy number variants in cattle, some of which were independently confirmed by arrayCGH and Q-PCR. The utility of the assay for GWA analysis was demonstrated by precisely localizing the QTL region for coat color in the Angus breed (Melanocortin 1 Receptor on chromosome 18) and the QTL region harboring the POLL locus in Hereford and Limousin to the centromere of chromosome 1.

Distribution and Location of Genetic Effects for Dairy Traits

Genetic effects for many dairy traits and for total economic merit are fairly evenly distributed across all chromosomes. A high-density scan using 38,416 SNP markers for 5,285 bulls confirmed two previously-known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared to those two individual models. Differences between models were small (1 to 2%) for traits with no major genes, and larger for heavy tails with traits having known QTL (6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual SNP were not tracking causative mutations with the exception of DGAT. Distributions of BTA14-specific EBV showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Chromosomal EBV may also be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appears to be related to calf size or birth weight, and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.

Genome differences between breeds

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Look how confusing and so close the gene pool is between breeds!  There is no way that they can get a correct number for PL which is the “game changer” in all the indexes!  Not to mention that PL does not positively correlate with longer lasting cows!

My conclusion

Sorry to bore you!  Genomics is new and is going to change our industry.  It has been very good to us putting our young sire on the same level playing field as the big A.I. companies.  It has really grown our sales significantly!!!  Thank you to all the companies promoting genomics!!!  But the bottom line is do these bulls actually make great cattle?  My confidence has never been high on a bull until I have seen lots and lots of daughters with my own eyes!

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