Genetic markers for lactation persistency in primiparous Australian dairy cows

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.