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.

Mapping quantitative trait loci (QTL) in sheep. IV. Analysis of lactation persistency and extended lactation traits in sheep

“…Two genome-wide and two partial bovine QTL studies [10,12,28,29] were summarised in the meta-assembly; results from two genome-wide association studies were not loaded into the ovine genome database [39,40]. Until now, no QTL study for lactation persistency in sheep has been reported.…”

The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction

“…Compared to within-breed genomic prediction, reliability of across-breed predictions may be lower due to differences in allele frequencies, LD pattern, and haplotypes among breeds (e.g., De Roos et al 2008;Pryce et al 2010;Goddard 2012) and because family relationships among full-bred individuals of different breeds are absent . In addition, breed-specific allele effects might exist (Spelman et al 2002;Thaller et al 2003), which further reduces the reliability of genomic prediction for multibreed populations.…”

Genome-wide association study for lactation persistency, female fertility, longevity, and lifetime profit index traits in Holstein dairy cattle

“…Genomic regions explaining variation in female fertility traits in cattle have been identified in several genome-wide association studies (GWAS) within a variety of breeds (Höglund et al, 2009;Pryce et al, 2010;Schulman et al, 2011;Sahana et al, 2011;Hawken et al, 2012;Peñagaricano et al, 2012;Minozzi et al, 2013;Höglund et al, 2015). Significant associations have been identified on several chromosomes for age at puberty (Hawken et al, 2012), cow nonreturn rate (Holmberg and Andersson-Eklund, 2006), pregnancy rate (Ashwell et al, 2004), and calving performance (Holmberg and Andersson-Eklund, 2006).…”