Genetic markers for lactation persistency in primiparous Australian dairy cows

Pryce, J. E., Haile-Mariam, M., Verbyla, K., Bowman, P. J., Goddard, M. E. and Hayes, B. J. (2010) Genetic markers for lactation persistency in primiparous Australian dairy cows. Journal of Dairy Science, 93 5: 2202-2214. doi:10.3168/jds.2009-2666

Author Pryce, J. E.
Haile-Mariam, M.
Verbyla, K.
Bowman, P. J.
Goddard, M. E.
Hayes, B. J.
Title Genetic markers for lactation persistency in primiparous Australian dairy cows
Journal name Journal of Dairy Science   Check publisher's open access policy
ISSN 0022-0302
Publication date 2010-05
Sub-type Article (original research)
DOI 10.3168/jds.2009-2666
Open Access Status Not yet assessed
Volume 93
Issue 5
Start page 2202
End page 2214
Total pages 13
Place of publication New York, NY, United States
Publisher Elsevier
Language eng
Abstract Good performance in extended lactations of dairy cattle may have a beneficial effect on food costs, health, and fertility. Because data for extended lactation performance is scarce, lactation persistency has been suggested as a suitable selection criterion. Persistency phenotypes were calculated in several ways: P1 was yield relative to an approximate peak, P2 was the slope after peak production, and P3 was a measure derived to be phenotypically uncorrelated to yield and calculated as a function of linear regressions on test-day deviations of days in milk. Phenotypes P1, P2, and P3 were calculated for sires as solutions estimated from a random regression model fitted to milk yield. Because total milk yield, calculated as the sum of daily sire solutions, was correlated to P1 and P2 (r=0.30 and 0.35 for P1 and P2, respectively), P1 and P2 were also adjusted for milk yield (P1adj and P2adj, respectively). To find genomic regions associated with the persistency phenotypes, we used a discovery population of 743 Holstein bulls proven before 2005 and 2 validation data sets of 357 Holstein bulls proven after 2005 and 294 Jersey sires. Two strategies were used to search for genomic regions associated with persistency: 1) persistency solutions were regressed on each single nucleotide polymorphism (SNP) in turn and 2) a genomic selection method (BayesA) was used where all SNP were fitted simultaneously. False discovery rates in the validation data were high (>66% in Holsteins and >77% in Jerseys). However, there were 2 genomic regions on chromosome 6 that validated in both breeds, including a cluster of 6 SNP at around 13.5 to 23.7 Mbp and another cluster of 5 SNP (70.4 to 75.6 Mbp). A third cluster validated in both breeds on chromosome 26 (0.33 to 1.46 Mbp). Validating SNP effects across 2 breeds is unlikely to happen by chance even when false discovery rates within each breed are high. However, marker-assisted selection on these selected SNP may not be the best way to improve this trait because the average variation explained by validated SNP was only 1 to 2%. Genomic selection could be a better alternative. Correlations between genomic breeding values predicted using all SNP simultaneously and estimated breeding values based on progeny test were twice as high as the equivalent correlations between estimated breeding values and parent average. Persistency is a good candidate for genomic selection because the trait is expressed late in lactation.
Keyword Genome-wide association study
Genomic selection
Lactation persistency
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
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