|O'Connell, Jeff - UNIV OF MD SCHOOL OF MED|
|Van Tassell, Curtis|
|Schnabel, R - UNIV OF MISSOURI|
|Taylor, J - UNIV OF MISSOURI|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 1, 2009
Publication Date: June 1, 2009
Repository URL: http://hdl.handle.net/10113/31765
Citation: Cole, J.B., Van Raden, P.M., O'Connell, J.R., Van Tassell, C.P., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Wiggans, G.R. 2009. Distribution and Location of Genetic Effects for Dairy Traits. Journal of Dairy Science. 92(6):2931-2946. Interpretive Summary: Genetic effects for many dairy traits and for total economic merit are fairly evenly distributed across all chromosomes. A high-density genome scan 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, and may be related to calf birthweight. Prediction accuracy was highest when each marker was assumed to have an effect on each trait, but with a heavy-tailed distribution. Genomic evaluations may be used to find the approximate location of causative mutations in the genome. Results validate quantitative genetic assumptions.
Technical Abstract: 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 birthweight, 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.