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.

Genetic parameters of pregnancy loss in dairy cows estimated from pregnancy-associated glycoproteins in milk.

This study examined the feasibility of using pregnancy-associated glycoproteins (PAG) in milk within breeding for pregnancy maintenance and assessed the genetic variation in pregnancy loss traits. A total of 374,206 PAG samples from 41,889 Swedish Red (SR) and 82,187 Swedish Holstein (SH) cows were collected at monthly test-day milkings in 1,119 Swedish herds. Pregnancy status was defined based on PAG levels and confirmed by data on artificial insemination (AI), calving, and culling from d 1 postinsemination to calving. Pregnancy loss traits were defined as embryonic loss (diagnosed 28 d to 41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss. Least squares means (± standard error, %) and genetic parameters were estimated using mixed linear models. Heritability was estimated to be 0.02, 0.02, and 0.03 for embryonic loss, fetal loss, and total pregnancy loss, respectively. Cows with pregnancy loss had lower PAG concentrations than cows which successfully maintained pregnancy and calved. PAG recording was limited to monthly test-day milking, resulting in low estimated embryonic loss (17.5 ± 0.4 and 18.7 ± 0.4 in SR and SH, respectively) and higher fetal loss (32.8 ± 0.5 and 35.1 ± 0.5 in SR and SH, respectively). Pregnancy loss might have occurred earlier but remained undetected until the next test-day milking, when it was recorded as fetal loss rather than embryonic loss. Estimated genetic correlation between embryonic and fetal pregnancy loss traits and classical fertility traits were in general high. Identification of novel genetic traits from PAG data can be highly specific, as PAG are only secreted by the placenta. Thus, PAG could be useful indicators in selection to genetically improve pregnancy maintenance and reduce reproductive losses in milk production. Further studies are needed to clarify how these results could be applied in breeding programs concurrent with selection for classical fertility traits.

Performance of different methods for testing polymyxin B: comparison of broth microdilution, agar dilution and MIC test strip in mcr-1 positive and negative Escherichia coli.

Antimicrobial susceptibility testing with the last-resort antibiotics polymyxins (polymyxin B and colistin) is associated with several methodological issues. Currently, broth microdilution (BMD) is recommended for colistin and polymyxin B. BMD is laborious and the utility of alternative methods needs to be evaluated for polymyxin B susceptibility testing. In this study, using BMD as a reference method, the performance of agar dilution (AD) and MIC test strips (MTS) were evaluated in polymyxin B susceptibility testing. BMD, AD and MTS were used to determine MICs of 193 clinical isolates of Escherichia coli. Seventy-nine were positive for the polymyxin resistance gene mcr-1. Method performances were evaluated based on pair-wise agreements with the reference method (BMD) and statistical testing. AD and MTS showed an unacceptable number of very major errors (VMEs) compared with BMD, 9·3 and 10·7%, respectively. The essential agreement (EA) was low for AD (49·7%), but high for MTS (97·8%). However, statistical testing showed that MTS tended to yield a one-step lower MIC (P < 0·01) compared with BMD. The discordances observed with MTS and AD in comparison with BMD for polymyxin B susceptibility testing for E. coli suggest their inapplicability in routine testing. A large number of isolates clustered around the susceptibility breakpoint (2-4 mg l-1 ) and several mcr-1 positive isolates (17%) were determined as susceptible with BMD. A screening breakpoint for mcr-1 of 2 mg l-1 should also be considered.

Genetic parameters for reproductive losses estimated from in-line milk progesterone profiles in Swedish dairy cattle.

This study assessed the extent of reproductive losses and associated genetic parameters in dairy cattle, using in-line milk progesterone records for 14 Swedish herds collected by DeLaval's Herd Navigator. A total of 330,071 progesterone samples were linked to 10,219 inseminations (AI) from 5,238 lactations in 1,457 Swedish Red and 1,847 Swedish Holstein cows. Pregnancy loss traits were defined as early embryonic loss (1-24 d after AI), late embryonic loss (25-41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss (from d 1 after AI until calving). The following classical fertility traits were also analyzed: interval from calving to first service, interval from calving to last service, interval between first and last service, calving interval, and number of inseminations per service period. Least squares means with standard error (LSM ± SE), heritabilities, and genetic correlations were estimated in a mixed linear model. Fixed effects included breed, parity (1, 2, ≥3), estrus cycle number when the AI took place, and a linear regression on 305-d milk yield. Herd by year and season of AI, cow, and permanent environmental effect were considered random effects. Extensive (approximately 45%) early embryonic loss was found, but with no difference between the breeds. Swedish Red was superior to Swedish Holstein in the remaining pregnancy loss traits with, respectively: late embryonic loss of 6.1 ± 1.2% compared with 13.3 ± 1.1%, fetal loss of 7.0 ± 1.2% compared with 12.3 ± 1.2%, and total pregnancy loss of 54.4 ± 1.4% compared with 60.6 ± 1.4%. Swedish Red also had shorter calving to first service and calving to last service than Swedish Holstein. Estimated heritability was 0.03, 0.06, and 0.02 for early embryonic, late embryonic, and total pregnancy loss, respectively. Milk yield was moderately genetically correlated with both early and late embryonic loss (0.52 and 0.39, respectively). The pregnancy loss traits were also correlated with several classical fertility traits (-0.46 to 0.92). In conclusion, Swedish Red cows had lower reproductive loss during late embryonic stage, fetal stage, and in total, and better fertility than Swedish Holstein cows. The heritability estimates for pregnancy loss traits were of the same order of magnitude as previously reported for classical fertility traits. These findings could be valuable in work to determine genetic variation in reproductive loss and its potential usefulness as an alternative fertility trait to be considered in genetic or genomic evaluations.

Genome-wide association study of normal and atypical progesterone profiles in Holstein-Friesian dairy cows.

Female fertility has a major role in dairy production and affects the profitability of dairy cattle. The genetic progress obtained by traditional selection can be slow because of the low heritability of classical fertility traits. Endocrine fertility traits based on progesterone concentration in milk have higher heritability and more directly reflect the cow's own reproductive physiology. The aim of our study was to identify genomic regions for 7 endocrine fertility traits in dairy cows by performing a genome-wide association study with 54,000 SNP. The next step was to fine-map targeted genomic regions with significant SNP using imputed sequences to identify potential candidate genes associated with the normal and atypical progesterone profiles. The association between a SNP and a phenotype was assessed by a single SNP analysis, using a linear mixed model that included a random polygenic effect. Phenotypes and genotypes were available for 1,126 primiparous and multiparous Holstein-Friesian cows from research herds in Ireland, the Netherlands, Sweden, and the United Kingdom. In total, 44 significant SNP associated with 7 endocrine fertility traits were identified on Bos taurus autosome (BTA) 1-4, 6, 8-9, 11-12, 14-17, 19, 21-24, and 29. Three chromosomes, BTA8, BTA17, and BTA23, were imputed from 54,000 SNP genotypes to the whole-genome sequence level with Beagle version 4.1. The fine-mapping identified several significant associations with delayed cyclicity, cessation of cyclicity, commencement of luteal activity, and inter-ovulatory interval. These associations may contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes reported to affect reproduction were also identified in the targeted genomic regions. However, due to high linkage disequilibrium, it was not possible to identify putative causal genes or polymorphisms for any of the regions.

Genetic parameters of endocrine fertility traits based on in-line milk progesterone profiles in Swedish Red and Holstein dairy cows.

Evaluating fertility traits based on endocrine progesterone profiles is becoming a promising option to improve dairy cow fertility. Several studies have been conducted on endocrine fertility traits, mainly in the Holstein breed. In this study, focusing also on the Swedish Red (SR) breed, genetic parameters were estimated for classical and endocrine fertility traits, the latter based on in-line milk progesterone records obtained for 14 Swedish herds using DeLaval Herd Navigator (DeLaval International, Tumba, Sweden). A total of 210,403 observations from 3,437 lactations of 1,107 SR and 1,538 Holstein cows were used. Mixed linear animal models were used for estimation of genetic parameters. Least squares means analysis showed that Holstein cows had a 2.5-d-shorter interval from calving to commencement of luteal activity (C-LA) and longer length of first inter-ovulatory interval (IOI) than SR cows. The highest mean interval for C-LA, IOI, and first luteal phase length (LPL) was observed in the fourth parity. The incidence of short (<18 d), normal, (18-24 d), and long (>24 d) IOI was 29.3, 40.7, and 30%, respectively. Genetic analysis indicated moderate heritability (h2) for C-LA (h2 = 0.24), luteal activity during the first 60 d in milk (LA60, h2 = 0.15), proportion of samples with luteal activity (PLA, h2 = 0.13), and calving to first heat (CFH, h2 = 0.18), and low heritability estimates for LPL (h2 = 0.08) and IOI (h2 = 0.03) in the combined data set for both breeds. Similar heritability estimates were obtained for each breed separately except for IOI and LPL in SR cows, for which heritability was estimated to be zero. Swedish Red cows had 0.01 to 0.06 higher heritability estimates for C-LA, LA60, and PLA than did Holstein cows. Calving interval had moderate heritability among the classical traits for Holstein and the combined data set, but h2 was zero for SR. Commencement of luteal activity had a strong genetic correlation with LA60 (mean ± SE; -0.88 ± 0.06), PLA (-0.72 ± 0.11), and CFH (0.90 ± 0.04). Similarly, CFH had a strong genetic correlation with IOI (0.98 ± 0.20). Number of inseminations per series showed a weak genetic correlation with all endocrine traits except IOI. Overall, endocrine traits had higher heritability estimates than classical traits in both breeds, and may have a better potential to explain the actual reproductive status of dairy cows than classical traits. This might favor inclusion of some endocrine fertility traits-especially those related to commencement of luteal activity-as selection criteria and breeding goal traits if recording becomes more common in herds. Further studies on genetic and genomic evaluations for endocrine fertility traits may help to provide firm conclusions. A prerequisite is that the data from automatic devices be made available to recording and breeding organizations in the future and included in a central database.

Improving accuracy of bulls' predicted genomic breeding values for fertility using daughters' milk progesterone profiles.

The main objective of this study was to investigate the benefit of accuracy of genomic prediction when combining records for an intermediate physiological phenotype in a training population with records for a traditional phenotype. Fertility was used as a case study, where commencement of luteal activity (C-LA) was the physiological phenotype, whereas the interval from calving to first service and calving interval were the traditional phenotypes. The potential accuracy of across-country genomic prediction and optimal recording strategies of C-LA were also investigated in terms of the number of farms and number of repeated records for C-LA. Predicted accuracy was obtained by estimating population parameters for the traits in a data set of 3,136 Holstein Friesian cows with 8,080 lactations and using a deterministic prediction equation. The effect of genetic correlation, heritability, and reliability of C-LA on the accuracy of genomic prediction were investigated. When the existing training population was 10,000 bulls with reliable estimated breeding value for the traditional trait, predicted accuracy for the physiological trait increased from 0.22 to 0.57 when 15,000 cows with C-LA records were added to the bull training population; but, when the interest was in predicting the traditional trait, we found no benefit from the additional recording. When the genetic correlation was higher between the physiological and traditional traits (0.7 instead of 0.3), accuracy increased less when adding the 15.000 cows with C-LA (from 0.51 to 0.63). In across-country predictions, we observed little to no increase in accuracy of the intermediate physiological phenotype when the training population from Sweden was large, but when accuracy increased the training population was small (200 cows), from 0.19 to 0.31 when 15,000 cows were added from the Netherlands (genetic correlation of 0.5 between countries), and from 0.19 to 0.48 for genetic correlation of 0.9. The predicted accuracy initially increased substantially when recording on the same farm was extended and multiple C-LA records per cow were used in prediction compared with single records; that is, accuracy increased from 0.33 with single records to 0.38 with multiple records (on average 1.6 records per cow) from 2 yr of recording C-LA. But, when the number C-LA per cow increased beyond 2 yr of recording, we noted no substantial benefit in accuracy from multiple records. For example, for 5 yr of recording (on average 2.5 records per cow), accuracy was 0.47; on doubling the recording period to 10 yr (on average 3.1 records per cow), accuracy increased by 0.07 units, whereas when C-LA was recorded for 15 yr (on average 3.3 records per cow) accuracy increased only by 0.05 units. Therefore, for genomic prediction using expensive equipment to record traits for training populations, it is important to optimize the recording strategy. The focus should be on recording more cows rather than continuous recording on the same cows.

Genetic heterogeneity of feed intake, energy-corrected milk, and body weight across lactation in primiparous Holstein, Nordic Red, and Jersey cows.

In this study, we aimed to estimate and compare the genetic parameters of dry matter intake (DMI), energy-corrected milk (ECM), and body weight (BW) as 3 feed efficiency-related traits across lactation in 3 dairy cattle breeds (Holstein, Nordic Red, and Jersey). The analyses were based on weekly records of DMI, ECM, and BW per cow across lactation for 842 primiparous Holstein cows, 746 primiparous Nordic Red cows, and 378 primiparous Jersey cows. A random regression model was applied to estimate variance components and genetic parameters for DMI, ECM, and BW in each lactation week within each breed. Phenotypic means of DMI, ECM, and BW observations across lactation showed to be in very similar patterns between breeds, whereas breed differences lay in the average level of DMI, ECM, and BW. Generally, for all studied breeds, the heritability for DMI ranged from 0.2 to 0.4 across lactation and was in a range similar to the heritability for ECM. The heritability for BW ranged from 0.4 to 0.6 across lactation, higher than the heritability for DMI or ECM. Among the studied breeds, the heritability estimates for DMI shared a very similar range between breeds, whereas the heritability estimates for ECM tended to be different between breeds. For BW, the heritability estimates also tended to follow a similar range between breeds. Among the studied traits, the genetic variance and heritability for DMI varied across lactation, and the genetic correlations between DMI at different lactation stages were less than unity, indicating a genetic heterogeneity of feed intake across lactation in dairy cattle. In contrast, BW was the most genetically consistent trait across lactation, where BW among all lactation weeks was highly correlated. Genetic correlations between DMI, ECM, and BW changed across lactation, especially in early lactation. Energy-corrected milk had a low genetic correlation with both DMI and BW at the beginning of lactation, whereas ECM was highly correlated with DMI in mid and late lactation. Based on our results, genetic heterogeneity of DMI, ECM, and BW across lactation generally was observed in all studied dairy breeds, especially for DMI, which should be carefully considered for the recording strategy of these traits. The genetic correlations between DMI, ECM, and BW changed across lactation and followed similar patterns between breeds.

Neglect of lactation stage leads to naive assessment of residual feed intake in dairy cattle.

Residual feed intake (RFI) is a candidate trait for feed efficiency in dairy cattle. We investigated the influence of lactation stage on the effect of energy sinks in defining RFI and the genetic parameters for RFI across lactation stages for primiparous dairy cattle. Our analysis included 747 primiparous Holstein cows, each with recordings on dry matter intake (DMI), milk yield, milk composition, and body weight (BW) over 44 lactation weeks. For each individual cow, energy-corrected milk (ECM), metabolic BW (MBW), and change in BW (ΔBW) were calculated in each week of lactation and were taken as energy sinks when defining RFI. Two RFI models were considered in the analyses; RFI model [1] was a 1-step RFI model with constant partial regression coefficients of DMI on energy sinks (ECM, MBW, and ΔBW) over lactation. In RFI model [2], data from 44 lactation weeks were divided into 11 consecutive lactation periods of 4 wk in length. The RFI model [2] was identical to model [1] except that period-specific partial regressions of DMI on ECM, MBW, and ΔBW in each lactation period were allowed across lactation. We estimated genetic parameters for RFI across lactation by both models using a random regression method. Using RFI model [2], we estimated the period-specific effects of ECM, MBW, and ΔBW on DMI in all lactation periods. Based on results from RFI model [2], the partial regression coefficients of DMI on ECM, MBW, and ΔBW differed across lactation in RFI. Constant partial regression coefficients of DMI on energy sinks over lactation was not always sufficient to account for the effects across lactation and tended to give roughly average information from all period-specific effects. Heritability for RFI over 44 lactation weeks ranged from 0.10 to 0.29 in model [1] and from 0.10 to 0.23 in model [2]. Genetic variance and heritability estimates for RFI from model [2] tended to be slightly lower and more stable across lactation than those from model [1]. In both models, RFI was genetically different over lactation, especially between early and later lactation stages. Genetic correlation estimates for RFI between early and later lactation tended to be higher when using model [2] compared with model [1]. In conclusion, partial regression coefficients of DMI on energy sinks differed across lactation when modeling RFI. Neglect of lactation stage when defining RFI could affect the assessment of RFI and the estimation of genetic parameters for RFI across lactation.

Genetic parameters for dry matter intake in primiparous Holstein, Nordic Red, and Jersey cows in the first half of lactation.

Dry matter intake (DMI) is a key component of feed efficiency in dairy cattle. In this study, we estimated genetic parameters of DMI over the first 24 lactation weeks in 3 dairy cattle breeds: Holstein, Nordic Red, and Jersey. In total, 1,656 primiparous cows (717 Holstein, 663 Nordic Red, and 276 Jersey) from Denmark, Finland, and Sweden were studied. For each breed, variance components, heritability, and repeatability for weekly DMI were estimated in 6 consecutive periods of the first 24 lactation weeks based on a repeatability animal model. Genetic correlations for DMI between different lactation periods were estimated using bivariate models. Based on our results, Holstein and Nordic Red cows had similar DMI at the beginning of lactation, but later in lactation Holstein cows had a slightly higher DMI than Nordic Red cows. In comparison, Jersey cows had a significantly lower DMI than the other 2 breeds within the first 24 lactation weeks. Heritability estimates for DMI ranged from 0.20 to 0.40 in Holsteins, 0.25 to 0.41 in Nordic Red, and 0.17 to 0.42 in Jerseys within the first 24 lactation weeks. Genetic and phenotypic variances for DMI varied along lactation within each breed and tended to be higher in the middle of lactation than at the beginning of the lactation. High genetic correlations were noted for DMI in lactation wk 5 to 24 in all 3 breeds, whereas DMI at early lactation (lactation wk 1 to 4) tended to be genetically different from DMI in the middle of lactation. The 3 breeds in this study might differ in their genetic variances for DMI, but the differences were not statistically significant in most of the studied periods. Breed differences for the genetic variance tended to be more obvious than for heritability. The potential breed differences in genetic variation for DMI should be considered in a future study using feed intake information from multiple breeds.

Genome-wide association study for endocrine fertility traits using single nucleotide polymorphism arrays and sequence variants in dairy cattle.

Endocrine fertility traits, which are defined from progesterone concentration levels in milk, are interesting indicators of dairy cow fertility because they more directly reflect the cows own reproductive physiology than classical fertility traits, which are more biased by farm management decisions. The aim of this study was to detect quantitative trait loci (QTL) for 7 endocrine fertility traits in dairy cows by performing a genome-wide association study with 85k single nucleotide polymorphisms (SNP), and then fine-map targeted QTL regions, using imputed sequence variants. Two classical fertility traits were also analyzed for QTL with 85k SNP. The association between a SNP and a phenotype was assessed by single-locus regression for each SNP, using a linear mixed model that included a random polygenic effect. A total of 2,447 Holstein Friesian cows with 5,339 lactations with both phenotypes and genotypes were used for association analysis. Heritability estimates ranged from 0.09 to 0.15 for endocrine fertility traits and 0.03 to 0.10 for classical fertility traits. The genome-wide association study identified 17 QTL regions for endocrine fertility traits on Bos taurus autosomes (BTA) 2, 3, 8, 12, 15, 17, 23, and 25. The highest number (5) of QTL regions from the genome-wide association study was identified for the endocrine trait "proportion of samples with luteal activity." Overlapping QTL regions were found between endocrine traits on BTA 2, 3, and 17. For the classical trait calving to first service, 3 QTL regions were identified on BTA 3, 15, and 23, and an overlapping region was identified on BTA 23 with endocrine traits. Fine-mapping target regions for the endocrine traits on BTA 2 and 3 using imputed sequence variants confirmed the QTL from the genome-wide association study, and identified several associated variants that can contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes underlying endocrine fertility traits were also identified in the target regions and are discussed. However, due to high linkage disequilibrium, it was not possible to specify genes or polymorphisms as causal factors for any of the regions.

Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles.

The aim of this study was to define endocrine fertility traits from in-line milk progesterone (P4) records and to estimate genetic parameters for these traits. Correlations of classical fertility (calving interval and calving to first service) and milk production traits with endocrine fertility traits were also estimated. In-line milk P4 records (n=160,952) collected from June 2009 through November 2013 for 2,273 lactations of 1,561 Holstein-Friesian cows in 12 commercial herds in the Netherlands were analyzed for (the log of) the number of days from calving till commencement of luteal activity (lnC-LA), proportion of samples between 25 and 60 d in milk with luteal activity (PLA), presence or absence of luteal activity for a cow between 25 and 60 d in milk, interval from commencement of luteal activity to first service (CLAFS), first luteal phase length, length of first interluteal interval, and length of first interovulatory interval. Milk P4 records were sampled, on average, every 2 d. Genetic parameters were estimated using a mixed linear animal model. Heritability estimates (±SE) of endocrine fertility traits were 0.12±0.05 for lnC-LA, 0.12±0.05 for PLA, and 0.11±0.06 for CLAFS, and their repeatability estimates were 0.29±0.04, 0.21±0.04, and 0.15±0.06, respectively. The genetic correlation of lnC-LA with PLA was -0.91±0.06 and with CLAFS was -0.56±0.25. The genetic correlations of lnC-LA were 0.26±0.33 with calving interval and 0.37±0.21 with calving to first service. Genetic correlations of the milk production traits with lnC-LA ranged from 0.04 to 0.18 and 0.07 to 0.65 with classical fertility traits. The phenotypic correlations of all endocrine fertility traits with milk production traits were close to zero (0.01 to 0.07). This study shows that in-line P4 records can be used to define and explore several heritable endocrine fertility traits in dairy cows and might help in selection for improved fertility.

Genetic analysis of atypical progesterone profiles in Holstein-Friesian cows from experimental research herds.

The objective of this study was to quantify the genetic variation in normal and atypical progesterone profiles and investigate if this information could be useful in an improved genetic evaluation for fertility for dairy cows. The phenotypes derived from normal profiles included cycle length traits, including commencement of luteal activity (C-LA), interluteal interval, luteal phase length. and interovulatory interval. In total, 44,977 progesterone test-day records were available from 1,612 lactations on 1,122 primiparous and multiparous Holstein-Friesian cows from Ireland, the Netherlands, Sweden, and the United Kingdom. The atypical progesterone profiles studied were delayed cyclicity, prolonged luteal phase, and cessation of cyclicity. Variance components for the atypical progesterone profiles were estimated using a sire linear mixed model, whereas an animal linear mixed model was used to estimate variance components for the cycle length traits. Heritability was moderate for delayed cyclicity (0.24 ± 0.05) and C-LA (0.18 ± 0.04) but low for prolonged luteal phase (0.02 ± 0.04), luteal phase length (0.08 ± 0.05), interluteal interval (0.08 ± 0.14), and interovulatory interval (0.03 ± 0.04). No genetic variation was detected for cessation of cyclicity. Commencement of luteal activity, luteal phase length, and interovulatory interval were moderately to strongly genetically correlated with days from calving to first service (0.35 ± 0.12, 0.25 ± 0.14, and 0.76 ± 0.24, respectively). Delayed cyclicity and C-LA are traits that can be important in both genetic evaluations and management of fertility to detect (earlier) cows at risk of compromised fertility. Delayed cyclicity and C-LA were both strongly genetically correlated with milk yield in early lactation (0.57 ± 0.14 and 0.45 ± 0.09, respectively), which may imply deterioration in these traits with selection for greater milk yield without cognizance of other traits.

Inhaling salbutamol may decrease time to exhaustion in some contexts of heavy endurance performances.

PURPOSE: To study the effect of inhaling a beta-agonist (salbutamol) compared to placebo on skiing and cycling performance in well-trained elite athletes. METHODS: Three different exercise protocols were used, all with a cross-over double blind placebo-controlled design. Participants inhaled 800 µg salbutamol or a placebo prior to the test, which was repeated on a following day with the participants inhaling the other substance. Fifteen junior elite skiers performed four free-style high intensity sprints (1100 m/work time 3.5-4.5 min). Twelve elite cyclists carried out a short cycling protocol, starting with two 5 min submaximal workloads followed by a maximal intermittent performance test to exhaustion. Another 12 elite cyclists performed the maximal intermittent performance test to exhaustion after a 150 min long submaximal cycling protocol. RESULTS: Group mean time for the ski sprints increased, with no difference between treatment groups. In the short cycling protocol time to exhaustion was 9.1% (95% CI 52-161) lower after inhaling salbutamol compared to placebo and in the long cycling protocol time to exhaustion was 9.1% (95% CI - 121-267) lower after inhaling salbutamol compared to placebo. Blood lactate, heart rate and ventilation increased during submaximal exercise with salbutamol compared to placebo in the short cycling protocol (p < .05). CONCLUSION: This study could not confirm any positive performance effects from inhaling 800 µg salbutamol compared to placebo in skiing and high-intensity intermittent cycling performance. Instead, time to exhaustion in the maximal intermittent performance test was lower in both cycling protocols.HighlightsThere was no difference in performance time between salbutamol and placebo treatment in real-life applicable repeated ski sprints.Time to exhaustion in the maximal intermittent performance test was 9.1% lower after inhaling salbutamol compared to placebo, both when performed after 10 and 150 min of submaximal cycling.

Adipositas athletica: a group of neglected conditions associated with medical risks.

At the 2008 Olympics, the body weight of the athletes varied from 28 to 181 kg and many Olympic athletes therefore today have a stature far from the ancient Greek athletic ideals. Athletes and sports associated with leanness and their medical problems have been researched extensively. However, there has been less focus on those athletes, who may gain a competitive advantage by having excess body fat, being large or oversized. The present review introduces for the first time the concept of Adipositas athletica and gives a description and classification of these athletes. Adipositas athletica is defined as having a higher than "athletic normal" (Greek Olympic ideals) fat mass and being an elite athlete. The condition is divided into subgroups based on the intent or non-intent to increase body fat per se. Another factor is the intent to increase body mass as well as increasing physiological factors such as strength or endurance. It is concluded that most of the sports-medicine community-physicians, researchers and nutritionists - has neglected these oversized and obese athletes, their long- and short-term morbidity and mortality, their eating problems and the use of performance-enhancing drugs. Although some data on overweight and obese athletes exist, the most remarkable finding when reviewing the literature is the paucity of data. Many unanswered questions remain and great strides remain to be made.

Culling reasons in organic and conventional dairy herds and genotype by environment interaction for longevity.

Dairy cow longevity combines all functional traits and is thought to be especially important in organic production, which is an established, increasing part of Swedish dairy production, representing approximately 6% of the market. The aim of this study was to compare dynamics in culling reasons between organic and conventional production and to analyze genotype by environment interactions for longevity. The data contained information from all organic herds with information available from official recording (n=402) and from approximately half of the conventional herds (n=5,335). Records from Swedish Holsteins (n=155,379) and Swedish Red cows (n=160,794) that had their first calf between January 1998 and September 2003 were included. The opportunity period for longevity was at least 6 yr. Six longevity traits were defined: length of productive life; survival through first, second, and third lactations; fertility-determined survival; and udder health-determined survival. Twenty codes were used to describe the cause of culling, and these were divided into 8 groups: udder health, low fertility, low production, leg problems, metabolic diseases, other diseases, other specified causes, and unspecified cause. The main reason for culling cows in organic herds was poor udder health, whereas for cows in conventional herds it was low fertility. Furthermore, the shift in main culling reason from fertility, which was most common in first lactation regardless of production system, to udder health occurred at a lower age in organic production. Heritabilities and genetic correlations for the longevity traits expressed in organic and conventional herds were estimated from a bivariate animal model. The genetic correlations were close to unity (>0.88), except for fertility-determined survival in the Swedish Red breed (0.80). Heritabilities were low to moderate, and no clear pattern was identified for production system or breed. In general, the results indicate that farmers' culling criteria differ between organic and conventional production. Different preferences may influence the need for alternative selection indexes for organic production, with different weightings of traits, or a separate breeding program. However, no genotype by environment interaction of importance was found between the production systems.

Short communication: Quantifying bias in a single-trait international model ignoring covariances from multiple-trait national models.

The current method in use for international genetic evaluations, called single-trait multiple across-country evaluation (ST-MACE), does not consider residual covariances among traits, making possible only the inclusion of one trait per country in an analysis. The aim of this study was to quantify the effect of bias resulting from treating traits from the same country as nationally independent in an international genetic evaluation. Data from the September 2007 Interbull test evaluation for Holstein female fertility traits were used. Data included were 1 trait from Belgium, Switzerland, Spain, and the United States of America, and 2 traits from Canada, Germany-Austria, and Denmark-Finland-Sweden. The biased results were obtained from a 10-variate ST-MACE analysis including all country traits. The unbiased results were obtained from 8 different 7-variate ST-MACE analyses, each including only 1 trait per country. Average absolute bias in the genetic correlations among 2-trait countries (0.11) was higher than for between 1-trait countries and 2-trait countries (0.07) and for among 1-trait countries (0.03). The results of the biased and the unbiased analyses were different, not only due to bias, but also because of different number of traits involved in the analyses. Differences were considerable (on average, 0.08 to 6.91) for reliabilities, which were higher for traits with lower heritability. Average differences were minor (-0.04 to 0.03 standard deviations) for predicted genetic merits. However, for the top 100 bulls in each country trait, these differences were important (on average, -0.26 to 0.11 standard deviation of predicted genetic merit), which caused considerable changes in bull rankings. The results of this study showed that the effect of bias, caused by ignoring covariances from multiple-trait national models in an ST-MACE analysis, is of such a magnitude that necessitates the use of another method such as multiple-trait multiple across-country evaluation.

Application of a multiple-trait, multiple-country genetic evaluation model for female fertility traits.

The need to implement a method that can handle multiple traits per country in international genetic evaluations is evident. Today, many countries have implemented multiple-trait national genetic evaluations and they may expect to have their traits simultaneously analyzed in international genetic evaluations. Traits from the same country are residually correlated and the method currently in use, single-trait multiple across-country evaluation (ST-MACE), cannot handle nonzero residual correlations. Therefore, multiple-trait, multiple across-country evaluation (MT-MACE) was proposed to handle several traits from the same country simultaneously. To test the robustness of MT-MACE on real data, female fertility was chosen as a complex trait with low heritability. Data from 7 Holstein populations, 3 with 2 traits and 4 with 1 trait, were used. The differences in the estimated genetic correlations by MT-MACE and the single ST-MACE analysis (average absolute deviation of 0.064) were due to the bias of considering several traits from the same country in the ST-MACE analysis. However, the differences between the estimated genetic correlations by MT-MACE and multiple ST-MACE analyses avoiding more than one trait per country in each analysis (average absolute deviation of 0.066) were due to the lack of analysis of the correlated traits from the same country together and using the reported within-country genetic correlations. Applying MT-MACE resulted in reliability gain in international genetic evaluations, which was different from trait to trait and from bull to bull. The average reliability gain by MT-MACE over ST-MACE was 3.0 points for domestic bulls and 6.3 points for foreign bulls. Even countries with 1 trait benefited from the joint analysis of traits from the 2-trait countries. Another superiority of MT-MACE over ST-MACE is that the bulls that do not have national genetic evaluation for some traits from multiple trait countries will receive international genetic evaluations for those traits. Rank correlations were high between ST-MACE and MT-MACE when considering all bulls. However, the situation was different for the top 100 bulls. Simultaneous analysis of traits from the same country affected bull ranks, especially for top 100 bulls. Multi-trait MACE is a recommendable and robust method for international genetic evaluations and is appropriate for handling multiple traits per country, which can increase the reliability of international genetic evaluations.

Relationship between somatic cell count and milk yield in different stages of lactation.

The association between somatic cell count (SCC) and daily milk yield in different stages of lactation was investigated in cows free of clinical mastitis (CM). Data were recorded between 1989 and 2004 in a research herd, and consisted of weekly test-day (TD) records from 1,155 lactations of Swedish Holstein and Swedish Red cows. The main data set (data set A) containing 36,117 records excluded TD affected by CM. In this data set, the geometric mean SCC was 55,000 and 95,000 cells/mL in primiparous and multiparous cows, respectively. A subset of data set A (data set B), containing 27,753 records excluding all TD sampled in lactations affected by CM, was created to investigate the effect of subclinical mastitis (SCM) in lactations free of CM. Daily milk yields were analyzed using a mixed linear model with lactation stage; linear, quadratic and cubic regressions of log(2)-transformed and centered SCC nested within lactation stage; weeks in lactation; TD season; parity; breed; pregnancy status; year-season of calving; calving, reproductive, metabolic and claw disorders; and housing system as fixed effects. A random regression was included to further improve the modeling of the lactation curve. Primiparous and multiparous cows were analyzed separately. The magnitude of daily milk loss associated with increased SCC depended on stage of lactation and parity, and was most extensive in late lactation irrespective of parity. In data set A, daily milk loss at an SCC of 500,000 cells/mL ranged from 0.7 to 2.0 kg (3 to 9%) in primiparous cows, depending on stage of lactation. In multiparous cows, corresponding loss was 1.1 to 3.7 kg (4 to 18%). Regression coefficients of primiparous cows estimated from data set B were consistent with those obtained from data set A, whereas data set B generated more negative regression coefficients of multiparous cows suggesting a higher milk loss associated with increased SCC in lactations in which the cow did not develop CM. The 305-d milk loss in the average lactation affected with SCM was 155 kg of milk (2%) in primiparous cows and 445 kg of milk (5%) in multiparous cows. It was concluded that multiparous cows in late lactation can be expected to be responsible for the majority of the herd-level production loss caused by SCM, and that preventive measures need to focus on reducing the incidence of SCM in such cows.