Estimates of Genetic Parameters for Canadian Holstein Female Reproduction Traits

The objective of this study was to describe the genetic and phenotypic relationship between milk urea nitrogen (MUN) and reproductive traits in Iranian Holstein dairy cows. Test-day MUN data obtained from 57 301 dairy cows on 20 large dairy herds in Iran between January 2005 and June 2009. Genetic parameters for MUN and reproductive traits were estimated with a five-trait model using ASREML program. Random regression test-day models were used to estimate heritabilities separately for MUN from first, second and third lactations. Regression curves were modeled using Legendre polynomials of order 3. Herd-year-season along with age at calving was included as fixed effects in all models for reproductive traits. Heritabilities for MUN and reproductive traits were estimated separately for first lactation, second lactation and third lactation. The estimated heritabilities for MUN varied from 0.18 to 0.22. The heritability estimate was low for reproductive traits, which ranged from 0.02 to 0.06 for different traits and across parities. Except for days open, phenotypic and genetic correlations of MUN with reproductive performance traits were close to zero. Genetic correlations between MUN and days open were 0.23, 0.35 and 0.45 in first, second and third lactation, respectively. However, the phenotypic correlation between MUN at different parities was moderate (0.28 to 0.35), but the genetic correlation between MUN at different parities was high and ranged from 0.84 to 0.97. This study shows a limited application of MUN for use in selection programs to improve reproductive performance.

Section: Resultssupporting

confidence: 91%

Genetic relationship between milk urea nitrogen and reproductive performance in Holstein dairy cows

Hossein-Zadeh

Ardalan

2011

“…In cattle, Jamrozik et al (2005) have estimated the proportions of maternal genetic variance of the total variance for fertility traits such as the interval from first service to conception, the non-return rate at 56 days, and stillbirth, using linear models. Even though these are not quite compatible with the foaling (or calving) rate, they offer some comparison to our results.…”

Section: Pedigrees and Inbreedingmentioning

confidence: 99%

“…Threshold models have been used in similar studies on bovine fertility (Kadarmideen et al, 2000;Guerra et al, 2006) and in a twinning study for horses (Wolc et al, 2006), but linear models are used as well (Jamrozik et al, 2005). For a dichotomous variable that is based on an underlying normally distributed trait such as the foaling rate, a threshold model would be theoretically correct.…”

Section: Pedigrees and Inbreedingmentioning

confidence: 99%

Effects of inbreeding and other genetic components on equine fertility

Sairanen

Nivola

Katila

et al. 2009

The Finnish mating records of Standardbred trotters (SB; n 5 33 679) and Finnhorses (FH; n 5 32 731) were analysed to study the effect of the level of inbreeding on foaling rates and to estimate the heritability of foaling rate. A linear mixed model was assumed, with the outcome of the foaling (foal or no foal) as the trait of the study. A restricted maximum likelihood-based method was used to calculate the estimates of the variance components. Predictions of breeding values and estimates of fixed effects were also calculated. The average level of inbreeding was 9.9% in the SB and 3.6% in the FH. The average foaling rates were better in the SB (72.6%) than in the FH (66.3%), but within each breed intense inbreeding had a statistically significant negative effect on foaling rate ( P , 0.05). Also, the mating type, the age and breeding type of the mare, and the age of the stallion had statistically significant effects on foaling rate ( P , 0.001). The heritability of foaling rate was between 3.4% and 3.7% in SBs and between 5.5% and 9.8% in FHs, when the outcome of the foaling was considered to be a trait of the expected foal. With the same model, the estimates of maternal genetic effect were 4.7% for SBs and 3.2% for FHs, and the estimates of the permanent environmental effects of the stallion were between 1.3% and 1.7%. Avoiding matings with very high inbreeding coefficients would improve foaling rates. It would also be possible to devise a breeding program for better equine fertility, but because the heritability is low, improvement of environmental factors deserves special attention.

“…Focussing on increasing genetic merit primarily for yield reduces genetic merit for fertility (Veerkamp et al, 2003), but conjoint improvement for reproductive performance and milk yield, say maintaining 70% to 80% of the yearly increase in yield (Veerkamp et al, 2000), is possible (Andersen-Ranberg et al, 2005;Jamrozik et al, 2005). In line with this, most leading dairy cattle breeding programmes incorporate fertility, as derived from calving dates and insemination dates, in their selection indices (Miglior et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Expression profiles of genes regulating dairy cow fertility: recent findings, ongoing activities and future possibilities

Beerda

Wyszyńska-Koko

Pas

et al. 2008

Subfertility has negative effects for dairy farm profitability, animal welfare and sustainability of animal production. Increasing herd sizes and economic pressures restrict the amount of time that farmers can spend on counteractive management. Genetic improvement will become increasingly important to restore reproductive performance. Complementary to traditional breeding value estimation procedures, genomic selection based on genome-wide information will become more widely applied. Functional genomics, including transcriptomics (gene expression profiling), produces the information to understand the consequences of selection as it helps to unravel physiological mechanisms underlying female fertility traits. Insight into the latter is needed to develop new effective management strategies to combat subfertility. Here, the importance of functional genomics for dairy cow reproduction so far and in the near future is evaluated. Recent gene profiling studies in the field of dairy cow fertility are reviewed and new data are presented on genes that are expressed in the brains of dairy cows and that are involved in dairy cow oestrus (behaviour). Fast-developing new research areas in the field of functional genomics, such as epigenetics, RNA interference, variable copy numbers and nutrigenomics, are discussed including their promising future value for dairy cow fertility.