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Improving feed efficiency of dairy cows through breeding is expected to reduce enteric methane production per unit of milk produced. This study examined the effect of 2 forage-to-concentrate ratios on methane production, rumen fermentation, and nutrient digestibility in Holstein and Jersey dairy cows divergent in residual feed intake (RFI). Before experimental onset, RFI was estimated using a random regression model on phenotypic herd data. Ten lactating Holstein and 10 lactating Jersey cows were extracted from the herd and allocated to a high or low pre-experimental RFI group of 5 animals each within breed. Cows were fed ad libitum with total mixed rations either low (LC) or high (HC) in concentrates during 3 periods in a crossover design with a back-cross and staggered approach. Forage-to-concentrate ratio was 68:32 for LC and 39:61 for HC. Cows adapted to the diets in 12 to 24 d and feces were subsequently collected on 2 d. Afterward, gas exchange was measured in respiration chambers and rumen liquid was collected once after cows exited the chambers. Pre-experimental RFI was included in the statistical analysis as a class (low and high RFI) or continuous variable. Methane per kilogram of dry matter intake (DMI) was lower for Holsteins than Jerseys and the response to increased concentrate level was more pronounced for Holsteins than Jerseys (27.2 vs.13.8%); a similar pattern was found for the acetate:propionate ratio. However, methane production per kilogram of energy-corrected milk (ECM) was unaffected by breed. Further, total-tract digestibility of neutral detergent fiber was higher for Jerseys than Holsteins. For RFI as a class variable, DMI, methane production regardless of the expression, and digestibility were unaffected by RFI. For RFI as a continuous variable, DMI was lower and methane per kilogram of DMI was higher for cows with negative (efficient) than positive (inefficient) RFI values, and neutral detergent fiber digestibility was higher for Holsteins with negative than positive RFI values, but not for Jerseys. Daily methane production and methane per kilogram of ECM were unaffected by RFI. In conclusion, methane per kilogram of DMI of Jerseys was lowered to a smaller extent in response to the HC diet than of Holsteins. When pre-experimental RFI was used as a continuous variable, higher methane per kilogram of DMI was found for cows with negative RFI than positive RFI values, but not for methane per kilogram of ECM. These findings call for validation in larger studies.

Conventional and organic production systems mainly differ in feeding strategies, outdoor and pasture access, and the use of antibiotic treatments. These environmental differences could lead to a genotype by environment interaction (G × E) and a requirement for including G × E in breeding decisions. The objectives of this study were to estimate variance components and heritabilities for conventional and organic production systems and investigate G × E under these 2 production systems for female fertility traits in Danish Holsteins. The analyzed traits included the interval from calving to first insemination (ICF), the interval from first to last insemination, number of inseminations per conception (NINS), and non-return rate within 56 d after the first insemination. Records of female fertility in heifers and the first 3 lactations in cows as well as grass ratio of feed at herd level were collected during the period from 2011 to 2016. The performances of a trait in heifers and cows (lactation 1 to 3) were considered as different traits. The (co)variance components and the resulting heritabilities and genetic correlations were estimated using 2 models. One was a bivariate model treating performances of a trait under organic and conventional production systems as 2 different traits using a reduced data set, and the other was a reaction norm model with random regression on the production system and the grass ratio of feed using a full data set. The full data set comprised records of 37,836 females from 112 organic herds and 513,599 females from 1,224 conventional herds, whereas the reduced data set comprised records from all these 112 organic herds and 92,696 females from 185 convention herds extracted from the full data set with grass ratio of feed lower than 0.20. All female fertility performances of the organic production system were superior to those of the conventional production system. Besides, heterogeneities in additive genetic variances and heritabilities were observed between conventional and organic production systems for all traits. Furthermore, genetic correlations between these 2 production systems ranged from 0.607 to 1.000 estimated from bivariate models and from 0.848 to 0.999 estimated from reaction norm models. Statistically significant G × E were observed for NINS in heifers, non-return rate within 56 d after the first insemination in heifers, and ICF from the bivariate model, and for ICF and NINS in cows from the reaction norm model.

Background The selective breeding of cattle with high-feed efficiencies (FE) is an important goal of beef and dairy cattle producers. Global gene expression patterns in relevant tissues can be used to study the functions of genes that are potentially involved in regulating FE. In the present study, high-throughput RNA sequencing data of liver biopsies from 19 dairy cows were used to identify differentially expressed genes (DEGs) between high- and low-FE groups of cows (based on Residual Feed Intake or RFI). Subsequently, a profile of the pathways connecting the DEGs to FE was generated, and a list of candidate genes and biomarkers was derived for their potential inclusion in breeding programmes to improve FE. Results The bovine RNA-Seq gene expression data from the liver was analysed to identify DEGs and, subsequently, identify the molecular mechanisms, pathways and possible candidate biomarkers of feed efficiency. On average, 57 million reads (short reads or short mRNA sequences

The aim of this study was to gain a better understanding of the variability in shape and features of all progesterone profiles during estrus cycles in cows and to create templates for cycle shapes and features as a base for further research. Milk progesterone data from 1418 estrus cycles, coming from 1009 lactations, was obtained from the Danish Cattle Research Centre in Foulum, Denmark. Milk samples were analyzed daily using a Ridgeway ELISA-kit. Estrus cycles with less than 10 data points or shorter than 4 days were discarded, after which 1006 cycles remained in the analysis. A median kernel of three data points was used to smooth the progesterone time series. The time between start of progesterone rise and end of progesterone decline was identified by fitting a simple model consisting of base length and a quadratic curve to progesterone data, and this luteal-like phase (LLP) was used for further analysis. The data set of 1006 LLP's was divided into five quantiles based on length. Within quantiles, a cluster analysis was performed on the basis of shape distance. Height, upward and downward slope, and progesterone level on Day 5 were compared between quantiles. Also, the ratio of typical versus atypical shapes was described, using a reference curve on the basis of data in Q1–Q4. The main results of this article were that (1) most of the progesterone profiles showed a typical profile, including the ones that exceeded the optimum cycle length of 24 days; (2) cycles in Q2 and Q3 had steeper slopes and higher peak progesterone levels than cycles in Q1 and Q4 but, when normalized, had a similar shape. Results were used to define differences between quantiles that can be used as templates. Compared to Q1, LLP's in Q2 had a shape that is 1.068 times steeper and 1.048 times higher. Luteal-like phases in Q3 were 1.053 times steeper and 1.018 times higher. Luteal-like phases in Q4 were 0.977 times steeper and 0.973 times higher than LLP's in Q1. This article adds to our knowledge about the variability of progesterone profiles and their shape differences. The profile clustering procedure described in this article can be used as a means to classify progesterone profiles without recourse to an a priori set of rules, which arbitrarily segment the natural variability in these profiles. Using data-derived profile shapes may allow a more accurate assessment of the effects of, e.g., nutritional management or breeding system on progesterone profiles.

Background The Nordic Red Cattle (NRC) consists of animls belonging to the Danish Red, Finnish Ayrshire, and Swedish Red breeds. Compared to the Holstein breed, NRC animals are smaller, have a shorter calving interval, lower mastitis incidence and lower rates of stillborn calves, however they produce less milk, fat and protein. Female fertility is an important trait for the dairy cattle farmer. Selection decisions in female fertilty in NRC are based on the female fertility index (FTI). FTI is a composite index including a number of sub-indices describing aspects of female fertility in dairy cattle. The sub-traits of FTI are: number of inseminations per conception (AIS) in cows (C) and heifers (H), the length in days of the interval from calving to first insemination (ICF) in cows, days from first to last insemination (IFL) in cows and heifers, and 56-day non-return rate (NRR) in cows and heifers. The aim of this study was first to identify QTL for FTI by conducting a genome scan for variants associated with fertility index using imputed whole genome sequence data based on 4207 Nordic Red sires, and subsequently analyzing which of the sub-traits were affected by each FTI QTL by associating them with the sub-traits. Results A total 17,388 significant SNP markers (−log10(P) > 8.25) were detected for FTI distributed over 25 chromosomes. The chromosomes with the most significant markers were tested for associations with the underlying sub-traits: BTA1 (822 SNP), BTA2 (220 SNP), BTA3 (83 SNP), BTA5 (195 SNP), two regions on BTA6 (503 SNP), BTA13 (980 SNP), BTA15 (23 SNP), BTA20 (345 SNP), and BTA24 (104 SNP). The fertility traits underlying the FTI peak area were: BTA1 (IFLC, IFLH), BTA2 (AISH, IFLH, NRRH), BTA3 (AISH, NRRH), BTA5 (AISC, AISH, IFLH), BTA6 (region 1: AISH, NRRH; region 2: AISH, IFLH), BTA13 (IFLH, IFLC), BTA15 (IFLC, NRRH), and BTA24 (AISH, IFLH). For BTA20 all sub-traits had SNP markers with a –log10(P) > 10. Furthermore the genes assigned to the most significant SNP for FTI were located on BTA6 (GPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521). Conclusion This study 1) shows that many markers within FTI QTL regions were significantly associated with both AISH and IFLH, and 2) identified candidate genes for FTI located on BTA6 (GPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521). It is not known how the genes/variants identified in this study regulate female fertility, however the majority of these genes were involved in protein binding, 3) a SNP in a QTL region for FTI on BTA20 was previously validated in three cattle breeds. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0269-x) contains supplementary material, which is available to authorized users.

Background Female fertility is an important trait in cattle breeding programs. In the Nordic countries selection is based on a fertility index (FTI). The fertility index is a weighted combination of four female fertility traits estimated breeding values for number of inseminations per conception (AIS), 56-day non-return rate (NRR), number of days from first to last insemination (IFL), and number of days between calving and first insemination (ICF). The objective of this study was to identify associations between sequence variants and fertility traits in Jersey cattle based on 1,225 Jersey sires from Denmark with official breeding values for female fertility traits. The association analyses were carried out in two steps: first the cattle genome was scanned for quantitative trait loci using a sire model for FTI using imputed whole genome sequence variants; second the significant quantitative trait locus regions were re-analyzed using a linear mixed model (animal model) for both FTI and its component traits AIS, NRR, IFL and ICF. The underlying traits were analyzed separately for heifers (first parity cows) and cows (later parity cows) for AIS, NRR, and IFL. Results In the first step 6 QTL were detected for FTI: one QTL on each of BTA7, BTA20, BTA23, BTA25, and two QTL on BTA9 (QTL9–1 and QTL9–2). In the second step, ICF showed association with the QTL regions on BTA7, QTL9–2 QTL2 on BTA9, and BTA25, AIS for cows on BTA20 and BTA23, AIS for heifers on QTL9–2 on BTA9, IFL for cows on BTA20, BTA23 and BTA25, IFL for heifers on BTA7 and QTL9-2 on BTA9, NRR for heifers on BTA7 and BTA23, and NRR for cows on BTA23. Conclusion The genome wide association study presented here revealed 6 genomic regions associated with FTI. Screening these 6 QTL regions for the underlying female fertility traits revealed that different female fertility traits showed associations with different subsets of the individual FTI QTL peaks. The result of this study contributed to a better insight into the genetic control of FTI in the Danish Jersey. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0210-3) contains supplementary material, which is available to authorized users.

Background There is often a pronounced disagreement between results obtained from different genome-wide association studies in cattle. There are multiple reasons for this disagreement. Particularly the presence of false positives leads to a need to validate detected QTL before they are optimally incorporated or weighted in selection decisions or further studied for causal gene. In dairy cattle progeny testing scheme new data is routinely accumulated which can be used to validate previously discovered associations. However, the data is not an independent sample and the sample size may not be sufficient to have enough power to validate previous discoveries. Here we compared two strategies to validate previously detected QTL when new data is added from the same study population. We compare analyzing a combined dataset (COMB) including all data presently available to only analyzing a validation dataset (VAL) i.e. a new dataset not previously analyzed as an independent replication. Secondly, we confirm SNP detected in the Reference population (REF) (i.e. previously analyzed dataset consists of older bulls) in the VAL dataset. Results Clearly the results from the combined (COMB) dataset which had nearly twice the sample size of other two subsets allowed the detection of far more significant associations than the two smaller subsets. The number of significant SNPs in REF (older bulls) was about four times higher compare to VAL (younger bulls) though both had similar sample sizes, 2,219 and 2,039 respectively. A total of 424 SNP-trait combinations on 22 chromosomes showed genome-wide significant association involving 284 unique SNPs in the COMB dataset. In the REF data set 101 associations (73 unique SNPs) and in the VAL 24 associations (18 unique SNPs) were found genome-wide significant. Sixty-eight percent of the SNPs in the REF dataset could be confirmed in the VAL dataset. Out of 469 unique SNPs showing chromosome-wide significant association with calving traits in the REF dataset 321 could be confirmed in the VAL dataset at P Conclusions The follow-up study for GWAS in cattle will depend on the aim of the study. If the aim is to discover novel QTL, analyses of the COMB dataset is recommended, while in case of identification of the causal mutation underlying a QTL, confirmation of the discovered SNPs are necessary to avoid following a false positive.

Before implementing selection based on quantitative trait loci (QTL) for fertility, it is important to determine the existence of correlated effects between the fertility QTL and QTL with effects on production traits. When a QTL is detected for a trait that is a composite of subtraits, it is of interest to validate which of the subtraits are affected by the QTL. Phenotypic and marker data were collected from 34 grandsire families from the Danish Holstein population. First, the trait data for "fertility treatments" were separated into their underlying subtraits: uterine infections, antibiotics placed in the placenta, and abortions. In addition, retained placenta was selected for analysis because it is related to uterine infections. A genome scan was performed using 416 microsatellite markers for the fertility treatment subtraits and retained placenta, and an additional genome scan for milk production traits conditional on the QTL regions for the subtraits and retained placenta was conducted. Second, we selected 24 genomic regions harboring QTL for fertility traits from a previous study. A QTL scan for milk production traits conditional on the selected regions was conducted. We found that 16 selected genomic regions containing a QTL for fertility (including the fertility treatment subtraits and retained placenta) also harbored QTL for milk yield or milk composition traits. Furthermore, 12 QTL regions corresponding to 9 different fertility traits (including the fertility treatment subtraits) did not harbor a QTL for milk production or milk composition traits; that is, the region was specific for the fertility trait. The genome scan for the fertility treatment subtraits did not correspond to the QTL found for fertility treatments. No QTL were detected for the subtrait abortion, however genome scans for retained placenta revealed 4 different QTL Before implementing selection based on quantitative trait loci (QTL) for fertility, it is important to determine the existence of correlated effects between the fertility QTL and QTL with effects on production traits. When a QTL is detected for a trait that is a composite of subtraits, it is of interest to validate which of the subtraits are affected by the QTL. Phenotypic and marker data were collected from 34 grandsire families from the Danish Holstein population. First, the trait data for "fertility treatments" were separated into their underlying subtraits: uterine infections, antibiotics placed in the placenta, and abortions. In addition, retained placenta was selected for analysis because it is related to uterine infections. A genome scan was performed using 416 microsatellite markers for the fertility treatment subtraits and retained placenta, and an additional genome scan for milk production traits conditional on the QTL regions for the subtraits and retained placenta was conducted. Second, we selected 24 genomic regions harboring QTL for fertility traits from a previous study. A QTL scan for milk production traits conditional on the selected regions was conducted. We found that 16 selected genomic regions containing a QTL for fertility (including the fertility treatment subtraits and retained placenta) also harbored QTL for milk yield or milk composition traits. Furthermore, 12 QTL regions corresponding to 9 different fertility traits (including the fertility treatment subtraits) did not harbor a QTL for milk production or milk composition traits; that is, the region was specific for the fertility trait. The genome scan for the fertility treatment subtraits did not correspond to the QTL found for fertility treatments. No QTL were detected for the subtrait abortion, however genome scans for retained placenta revealed 4 different QTL

Data from the joint Nordic breeding value prediction for Danish and Swedish Holstein grandsire families were used to locate quantitative trait loci (QTL) for female fertility traits in Danish and Swedish Holstein cattle. Up to 36 Holstein grandsires with over 2,000 sons were genotyped for 416 microsatellite markers. Single trait breeding values were used for 12 traits relating to female fertility and female reproductive disorders. Data were analyzed by least squares regression analysis within and across families. Twenty-six QTL were detected on 17 different chromosomes. The best evidence was found for QTL segregating on Bos taurus chromosome (BTA)1, BTA7, BTA10, and BTA26. On each of these chromosomes, several QTL were detected affecting more than one of the fertility traits investigated in this study. Evidence for segregation of additional QTL on BTA2, BTA9, and BTA24 was found Data from the joint Nordic breeding value prediction for Danish and Swedish Holstein grandsire families were used to locate quantitative trait loci (QTL) for female fertility traits in Danish and Swedish Holstein cattle. Up to 36 Holstein grandsires with over 2,000 sons were genotyped for 416 microsatellite markers. Single trait breeding values were used for 12 traits relating to female fertility and female reproductive disorders. Data were analyzed by least squares regression analysis within and across families. Twenty-six QTL were detected on 17 different chromosomes. The best evidence was found for QTL segregating on Bos taurus chromosome (BTA)1, BTA7, BTA10, and BTA26. On each of these chromosomes, several QTL were detected affecting more than one of the fertility traits investigated in this study. Evidence for segregation of additional QTL on BTA2, BTA9, and BTA24 was found

Background Female fertility is important for the maintenance of the production in a dairy cattle herd. Two QTL regions on BTA04 and on BTA13 previously detected in Nordic Holstein (NH) and validated in the Danish Jersey (DJ) and Nordic Red (NR) were investigated further in the present study to further refine the QTL locations. Refined QTL regions were imputed to the full sequence data. The genes in the regions were then studied to ascertain their possible effect on fertility traits. Results BTA04 was screened for number of inseminations (AIS), 56-day non-return rate (NRR), days from first to last insemination (IFL), and the interval from calving to first insemination (ICF) in the range of 38,257,758 to 40,890,784 bp, whereas BTA13 was screened for ICF only in the range from 21,236,959 to 46,150,079 with the HD bovine SNP array for NH, DJ and NR. No markers in the DJ and NR breeds reached significance. By analyzing imputed sequence data the QTL position on BTA04 was narrowed down to two regions in the NH. In these two regions a total of 9 genes were identified. BTA13 was analyzed using sequence data for the NH breed. The highest –log10(P-value) was 19.41 at 33,903,159 bp. Two regions were identified: Region 1: 33,900,143-33,908,994 bp and Region 2: 34,051,815-34,056,728 bp. SNPs within and between these two regions were annotated as intergenic. Conclusion Screening BTA04 and BTA13 for female fertility traits in NH, NR and DJ suggested that the QTL for female fertility were specific for NH. A missense mutation in CD36 showed the strongest association with fertility traits on BTA04. The annotated SNPs on BTA13 were all intergenic variants. It is possible that BTA13 at this stage is poorly annotated such that the associated polymorphisms are located in as-yet undiscovered genes. Fertility traits are complex traits as many different biological and physiological factors determine whether a cow is fertile. Therefore it is not expected that there is a simple explanation with an obvious candidate gene but it is more likely a network of genes and intragenic variants that explain the variation of these traits. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-790) contains supplementary material, which is available to authorized users.

Background Understanding the underlying pleiotropic relationships among quantitative traits is necessary in order to predict correlated responses to artificial selection. The availability of large-scale next-generation sequence data in cattle has provided an opportunity to examine whether pleiotropy is responsible for overlapping QTL in multiple economic traits. In the present study, we examined QTL affecting cattle stillbirth, calf size, and adult stature located in the same genomic region. Results A genome scan using imputed whole genome sequence variants revealed one QTL with large effects on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39 Mb) on chromosome 6 in Nordic Red cattle. The targeted region was analyzed for SCI and BCI component traits. The QTL peak included LCORL and NCAPG genes, which had been reported to influence fetal growth and adult stature in several species. The QTL exhibited large effects on calf size and stature in Nordic Red cattle. Two deviant haplotypes (HAP1 and HAP2) were resolved which increased calf size at birth, and affected adult body conformation. However, the haplotypes also resulted in increased calving difficulties and calf mortality due to increased calf size at birth. Haplotype locations overlapped, however linkage disequilibrium (LD) between the sites was low, suggesting that two independent mutations were responsible for similar effects. The difference in prevalence between the two haplotypes in Nordic Red subpopulations suggested independent origins in different populations. Conclusions Results of our study identified QTL with large effects on body conformation and service sire calving traits on chromosome 6 in cattle. We present robust evidence that variation at the LCORL and NCAPG locus affects calf size at birth and adult stature. We suggest the two deviant haplotypes within the QTL were due to two independent mutations. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0202-3) contains supplementary material, which is available to authorized users.

Background The results obtained from genome-wide association studies (GWAS) often show pronounced disagreements. Validation of association studies is therefore desired before marker information is incorporated in selection decisions. A reliable way to confirm a discovered association between genetic markers and phenotypes is to validate the results in different populations. Therefore, the objective of this study was to validate single nucleotide polymorphism (SNP) marker associations to female fertility traits identified in the Nordic Holstein (NH) cattle population in the Nordic Red (NR) and Jersey (JER) cattle breeds. In the present study, we used data from 3,475 NH sires which were genotyped with the BovineSNP50 Beadchip to discover associations between SNP markers and eight female fertility-related traits. The significant SNP markers were then tested in NR and JER cattle. Results A total of 4,474 significant associations between SNP markers and eight female fertility traits were detected in NH cattle. These significant associations were then validated in the NR (4,998 sires) and JER (1,225 sires) dairy cattle populations. We were able to validate 836 of the SNPs discovered in NH cattle in the NR population, as well as 686 SNPs in the JER population. 152 SNPs could be confirmed in both the NR and JER populations. Conclusions The present study presents strong evidence for association of SNPs with fertility traits across three cattle breeds. We provide strong evidence that SNPs for many fertility traits are concentrated at certain areas on the genome (BTA1, BTA4, BTA7, BTA9, BTA11 and BTA13), and these areas would be highly suitable for further study in order to identify candidate genes for female fertility traits in dairy cattle.