The effect of genetic defects on pregnancy loss in Swedish dairy cattle

The effect of carrier status of 10 lethal recessive genetic defects on pregnancy maintenance in Swedish dairy cattle was examined. The genetic defects were Ayrshire Haplotype 1, Ayrshire Haplotype 2, BTA12, BTA23, and Brown Swiss Haplotype 2 in Red Dairy Cattle (RDC), and Holstein Haplotype 1, 3, 4, 6, and 7 (HH1–HH7) in Holstein. Effects of carrier status of BTA12 and HH3 on conception rate (CR), interval from first to last service (FLS), and milk production were also examined. Data were obtained for 1,429 herds in the Swedish milk recording system, while information on carrier status of genetic defects was obtained from the Nordic Cattle Genetic Evaluation. In total, data on 158,795 inseminations in 28,432 RDC and 22,018 Holstein females were available. Data permitted separate analyses of BTA12 and HH3, but carrier frequencies of other defects were too low to enable further analysis. Pregnancy loss was defined as failure to maintain pregnancy, where pregnancy status was confirmed with manual and chemical pregnancy diagnosis, insemination, calving, sales and culling data. Odds ratios (OR) and probabilities of pregnancy loss and CR were estimated using generalized linear mixed models, while pregnancy loss, CR, FLS, milk, protein, and fat yields were analyzed using linear mixed models. Pregnancy losses were reported on average within the first month post-AI. At-risk matings were more prone to suffer pregnancy loss in BTA12 (OR = 1.79) and HH3 carriers (OR = 1.77) than not-at-risk matings. At-risk matings also had lower CR (OR = 0.62 and 0.63 for BTA12 and HH3, respectively) than not-at-risk matings. Carrier females of BTA12 had longer FLS and higher milk production than noncarriers. Conception rate and pregnancy maintenance could be improved by avoiding at-risk matings. This finding could help reduce pregnancy loss due to genetic defects in the breeding program for improved fertility

Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations

AbstractThis study investigated the possibility of increasing the reliability of direct genomic values (DGV) by combining reference populations. The data were from 3,735 bulls from Danish, Swedish, and Finnish Red dairy cattle populations. Single nucleotide polymorphism markers were fitted as random variables in a Bayesian model, using published estimated breeding values as response variables. In total, 17 index traits were analyzed. Reliabilities were estimated using a 5-fold cross validation, and calculated as the within-year squared correlation between estimated breeding values and DGV. Marker effects were estimated using reference populations from individual countries, as well as using a combined reference population from all 3 countries. Single-country reference populations gave mean reliabilities across 17 traits of 0.19 to 0.23, whereas the combined reference gave mean reliabilities of 0.26 for all populations. Using marker effects from 1 population to predict the other 2 gave a loss in mean reliability of 0.14 to 0.21 when predicting Swedish or Finnish animals with Danish marker effects, or vice versa. Using Swedish or Finnish marker effects to predict each other only showed a loss in mean reliability of 0.03 to 0.05. A combined Swedish-Finnish reference population led to an average reliability as high as that from the 3-country reference population, but somewhat different for individual traits. The results from this study show that it is possible to increase the reliability of DGV by combining reference populations from related populations

Prediction of weight and percentage of salable meat from Brazilian market lambs by subjective conformation and fatness scores

ABSTRACT This study assessed the use of conformation and fatness scores of the EUROP sheep carcass grading system to predict weight and percentage of salable meat from Brazilian market lambs. Data were collected from in vivo, carcass, and retail production from 252 uncastrated lambs. Evaluated models included single regressions, two multivariate models, and one determined by the stepwise procedure. Conformation was moderately correlated with weight of salable meat. Fatness scores were correlated with rump perimeter, carcass width, and thoracic depth with coefficients of −0.33, −0.32, and −0.23, respectively. Body weight was the best single predictor for weight of salable meat and cold carcass yield for percentage of salable meat. All multivariate models for weight of salable meat prediction were significant. Stepwise regression with body weight, leg perimeter, thoracic depth, rump perimeter, and fatness scores predicted 98% of weight of salable meat variation. For percentage of salable meat prediction, stepwise regression with cold carcass yield, leg perimeter, and conformation score was significant. The EUROP conformation and fatness scores can be used in Brazil for the prediction of lamb meat production

Development of a genetic indicator of biodiversity for farm animals

In 2002, Parties to the UN Convention on Biological Diversity made a commitment to "achieve by 2010 a significant reduction of the current rate of biodiversity loss at the global, regional and national level". In order to assess progress towards the 2010 target a limited number of indicators of biodiversity need to be developed using existing data. The objectives of this study were i) to produce an indicator of genetic diversity for livestock species; and ii) to evaluate the proposed indicator in UK sheep and cattle. The indicator proposed is the species average effective population size (Ne) for the lower tail of the distribution of Ne across breeds within the species. It is sensitive to i) genetic variation within breeds, as it is based on the Ne of individual breeds, and ii) what is happening to breeds most at risk of disappearing. A total of 31 sheep and 20 cattle UK breed societies provided the information required for estimating Ne. This represents 53% of sheep and 58% of cattle breeds native to the UK. For breeds with pedigrees available, Ne was estimated from rates of change in inbreeding. For breeds with no pedigree information, Ne was estimated from predictive equations assuming mass selection on a trait with a heritability of 0.4. For each species, the indicator was calculated by i) estimating Ne for each breed; ii) calculating the distribution of Ne; iii) finding the average Ne for the lower 20% tail of the distribution. In step iii), 20% was chosen because, given the number of breeds with available information, it provides a good compromise between giving high weight to the breeds most at risk, without being too sensitive to events surrounding a single breed. For sheep, the indicator values were 36 and 41 animals for years 2001 and 2007, respectively. Equivalent values for cattle were 26 and 34. Bootstrapping was used to assess if these changes over time would have been observed with data from all native breeds. There was no evidence that the small increase in the value of the sheep indicator, predicted from the available sample of breed data, would have been observed with all breeds. However, there was evidence (P < 0.05) to support that an increase would have been observed with data from all cattle breeds. © 2010 Elsevier B.V. All rights reserved

Single-step genome-wide association study uncovers known and novel candidate genomic regions for endocrine and classical fertility traits in Swedish Red and Holstein dairy cows

In a study aiming to identify candidate genomic regions associated with endocrine and classical fertility traits in Swedish Red (SR) and Holstein cows, data on 3955 lactations in 1164 SR and 1672 Holstein cows were examined. The dataset comprised milk progesterone (P4) levels (n = 341,212) in 14 Swedish herds, automatically collected and analyzed in-line using the DeLaval Herd NavigatorTM. Endocrine traits studied were: days from calving to commencement of luteal activity (C-LA), first luteal phase length (LPL), length of first inter-luteal interval, length of first inter-ovulatory interval (IOI), luteal activity during the first 60 DIM, and proportion of samples with luteal activity during the first 60 DIM. Classical fertility traits based on insemination data were also investigated, such as days from calving to last insemination and calving interval. A total of 180 SR and 312 Holstein cows were genotyped with a low-density SNP chip and imputed to 50 K. Single-step genome-wide association (ssGWAS) was used to explore candidate genomic regions associated with fertility traits. A mixed linear single-trait animal model was fitted, considering season and parity as fixed effects and animal and permanent environment as random effects. The results revealed 990 and 415 SNPs above the threshold (-log (p-value) >= 4) for SR and Holstein cows, respectively. The breeds shared only eight SNPs significantly associated with fertility traits. Annotation analysis revealed 281 SNPs located in 241 genes. Functional enrichment analysis using DAVID tools reduced the number to 80 genes, which were mediated in various biological processes and KEGG pathways in multiple functions, including folliculogenesis, embryogenesis, uterine growth and development, immune response, and ovarian cysts. Of the 80 genes, 67 were associated with fertility traits in SR cows and 13 in Holstein. Most genes were associated with LPL and IOI in SR cows, but in Holstein the only association with an endocrine trait was with C-LA. Twenty QTL regions that embedded 40 genes were associated with fertility traits in both breeds. All the QTLs detected, except at BTA2 and BTA19 are novel QTL regions that were not reported previously. These novel QTL regions embedded the candidate genes that include ARHGAP20, PHLDB1, CACNA1D, ATG7, CCNE1, GPI, CDH13, ECT2, PLD1, FBN2, KIF3A, FGF12, KCNMB2, GJA1, MAN1A1, KCNN2, SMAD6, MAPK8IP1, PHF21A, LPXN, MMRN1, KCNIP4, NID2, PCDHGA8, GRIA1, PCDHGB4, PHLDB2, STXBP5L, PPP3CA, PTPRR, SRGAP1, SNX27, SPTA1, S100A10, TBC1D20 and ITCH. The candidate regions may help to improve genetic progress in female fertility if used in selection decisions. A challenge for future research is to determine why different regions seem relevant for different traits and breeds, and the practical implications for genomic selection