Nordic Red Cattle Research Articles

Novel haplotypes responsible for prenatal death in Nordic Red and Danish Jersey cattle

Haplotypes that are common in a population but not observed as homotypes in living animals may harbor lethal alleles that compromise embryo survival. In this study, we searched for homozygous-deficient haplotypes in the genomes of 19,309 Nordic Red Dairy (RDC) and 4,291 Danish Jersey (JER) cattle genotyped using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For statistically significant deficient haplotypes, we evaluated the effect on nonreturn rate in at-risk matings (mating between carrier bull and daughter of carrier sire) versus not-at-risk matings (mating between noncarrier bull and daughter of noncarrier sire). Next, we analyzed whole-genome sequence variants from the 1000 Bull Genomes Project to identify putative causal variants underlying these haplotypes. In RDC, we identified 3 homozygous-deficient regions (HDR) that overlapped with known recessive lethal mutations: a 662-kb deletion on chromosome 12 in RDC [Online Mendelian Inheritance in Animals (OMIA) 001901-9913), a missense mutation in TUBD1, g.11063520T>C, in Braunvieh cattle (OMIA 001939-9913), and a 525-kb deletion on chromosome 23 in RDC (OMIA 001991-9913)]. In addition, we identified 15 novel HDR and their tag haplotypes for the underlying causative variants. The tag haplotype located between 39.2 and 40.3 Mbp on chromosome 18 had a negative effect on nonreturn rate in at-risk mating, confirming embryonic lethality. In Danish Jersey, we identified 12 novel HDR and their tag haplotypes for underlying causative variants. For 3 of these 12 tag haplotypes, insemination records of at-risk mating showed a negative effect on nonreturn rate, confirming the association with early embryonic mortality. Cattle that had both genotype and whole-genome sequence data were analyzed to detect the causative variants underlying each tag haplotype. However, none of the functional variants or deletions showed concordance with carrier status of the novel tag haplotypes. Carrier status of these detected haplotypes can be used to select bulls to reduce the frequencies of lethal alleles in the population and to avoid at-risk matings.

Genetic parameters for cow-specific digestibility predicted by near infrared reflectance spectroscopy

Digestibility traits included in this study were dry matter digestibility (DMD, g/kg), which was calculated based on the indigestible neutral detergent fibre (iNDF, g/kg of dry matter) content in faeces (iNDFf) and in diet (iNDFd), and iNDFf predicted directly from faecal samples by near infrared reflectance spectroscopy (NIRS). The data set was collected at three research herds in Finland and one in Norway including in total 931 records from 328 lactating Nordic Red Cattle and Holstein cows. Observations were associated with different accuracy, due to the differences in sampling protocols used for collecting faecal samples. Heritability estimates varied between different sampling protocols and ranged from 0.14 ± 0.06 to 0.51 ± 0.24 for DMD and from 0.13 ± 0.05 to 0.48 ± 0.18 for iNDFf. Estimated genetic standard deviations were 10.5 g/kg and 6.2 g/kg dry matter for DMD and iNDFf, respectively. Results of our study indicated that recording only the iNDF content in the faeces is sufficient to determine genetic variation in cows’ ability to digest feed. The coefficient of genetic variation for DMD was rather small (1.7%), but could be utilized if it is supported by a positive analysis of benefits over costs.

The effect of CAPN1 and CAST gene variations on meat quality traits in Finnish Aberdeen Angus and Nordic Red Cattle populations

High meat quality and specifically meat tenderness are desired traits by the consumers, however the environmental impact of meat production is becoming a relevant factor in the industry. Therefore, breeding of dual purpose cattle breeds may answer the high demand of meat production in the future. In this study we identified statistical differences between genotypes of CAST and CAPN1 gene variants with meat quality traits in a dairy breed (Nordic Red Cattle) and compared the results with beef breed (Aberdeen Angus). Our results show that the favorable alleles have not been selected in the studied dairy breed and thus could be used as a tool for improvement of meat quality. The genes were associated with specific meat quality traits (i.e. sensory juiciness, marbling score and meat color) also in the dairy breed. This supports the utility of known meat quality associated genetic variants to improve meat quality in dairy breeds.

MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle

MicroRNAs (miRNA) are key modulators of gene expression and so act as putative fine-tuners of complex phenotypes. Here, we hypothesized that causal variants of complex traits are enriched in miRNAs and miRNA-target networks. First, we conducted a genome-wide association study (GWAS) for seven functional and milk production traits using imputed sequence variants (13~15 million) and >10,000 animals from three dairy cattle breeds, i.e., Holstein (HOL), Nordic red cattle (RDC) and Jersey (JER). Second, we analyzed for enrichments of association signals in miRNAs and their miRNA-target networks. Our results demonstrated that genomic regions harboring miRNA genes were significantly (P < 0.05) enriched with GWAS signals for milk production traits and mastitis, and that enrichments within miRNA-target gene networks were significantly higher than in random gene-sets for the majority of traits. Furthermore, most between-trait and across-breed correlations of enrichments with miRNA-target networks were significantly greater than with random gene-sets, suggesting pleiotropic effects of miRNAs. Intriguingly, genes that were differentially expressed in response to mammary gland infections were significantly enriched in the miRNA-target networks associated with mastitis. All these findings were consistent across three breeds. Collectively, our observations demonstrate the importance of miRNAs and their targets for the expression of complex traits.

Genome-wide mapping of large deletions and their population-genetic properties in dairy cattle.

Large genomic deletions are potential candidate for loss-of-function, which could be lethal as homozygote. Analysing whole genome data of 175 cattle, we report 8,480 large deletions (199 bp–773 KB) with an overall false discovery rate of 8.8%; 82% of which are novel compared with deletions in the dbVar database. Breakpoint sequence analyses revealed that majority (24 of 29 tested) of the deletions contain microhomology/homology at breakpoint, and therefore, most likely generated by microhomology-mediated end joining. We observed higher differentiation among breeds for deletions in some genic-regions, such as ABCA12, TTC1, VWA3B, TSHR, DST/BPAG1, and CD1D. The genes overlapping deletions are on average evolutionarily less conserved compared with known mouse lethal genes (P-value = 2.3 × 10−6). We report 167 natural gene knockouts in cattle that are apparently nonessential as live homozygote individuals are observed. These genes are functionally enriched for immunoglobulin domains, olfactory receptors, and MHC classes (FDR = 2.06 × 10−22, 2.06 × 10−22, 7.01 × 10−6, respectively). We also demonstrate that deletions are enriched for health and fertility related quantitative trait loci (2-and 1.5-fold enrichment, Fisher’s P-value = 8.91 × 10−10 and 7.4 × 10−11, respectively). Finally, we identified and confirmed the breakpoint of a ∼525 KB deletion on Chr23:12,291,761-12,817,087 (overlapping BTBD9, GLO1 and DNAH8), causing stillbirth in Nordic Red Cattle.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits.

A 0.5-Mbp deletion on bovine chromosome 23 is a strong candidate for stillbirth in Nordic Red cattle.

BackgroundA whole-genome association study of 4631 progeny-tested Nordic Red dairy cattle bulls using imputed next-generation sequencing data revealed a major quantitative trait locus (QTL) that affects birth index (BI) on Bos taurus autosome (BTA) 23. We analyzed this QTL to identify which of the component traits of BI are affected and understand its molecular basis.ResultsA genome-wide scan of BI in Nordic Red dairy cattle detected major QTL on BTA6, 14 and 23. The strongest associated single nucleotide polymorphism (SNP) on BTA23 was located at 13,313,896 bp with - log_{10} ({text{p}}) = 50.63. Analyses of component traits showed that the QTL had a large effect on stillbirth. Based on the 10 most strongly associated SNPs with stillbirth, we constructed a haplotype. Among this haplotype’s alleles, HAPQTL had a large negative effect on stillbirth. No animals were found to be homozygous for HAPQTL. Analysis of stillbirth records that were categorized by carrier status for HAPQTL of the sire and maternal grandsire suggested that this haplotype had a recessive mode of inheritance. Illumina BovineHD BeadChip genotypes and genotype intensity data indicated a chromosomal deletion between 12.28 and 12.81 Mbp on BTA23. An independent set of Illumina Bovine50k BeadChip genotypes identified a recessive lethal haplotype that spanned the deleted region.ConclusionsA deleted region of approximately 500 kb that spans three genes on BTA23 was identified and is a strong candidate QTL with a large effect on BI by increasing stillbirth.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0215-z) contains supplementary material, which is available to authorized users.

Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants.

BackgroundThe Nordic Red Cattle consisting of three different populations from Finland, Sweden and Denmark are under a joint breeding value estimation system. The long history of recording of production and health traits offers a great opportunity to study production traits and identify causal variants behind them. In this study, we used whole genome sequence level data from 4280 progeny tested Nordic Red Cattle bulls to scan the genome for loci affecting milk, fat and protein yields.ResultsUsing a genome-wise significance threshold, regions on Bos taurus chromosomes 5, 14, 23, 25 and 26 were associated with fat yield. Regions on chromosomes 5, 14, 16, 19, 20 and 25 were associated with milk yield and chromosomes 5, 14 and 25 had regions associated with protein yield. Significantly associated variations were found in 227 genes for fat yield, 72 genes for milk yield and 30 genes for protein yield. Ingenuity Pathway Analysis was used to identify networks connecting these genes displaying significant hits. When compared to previously mapped genomic regions associated with fertility, significantly associated variations were found in 5 genes common for fat yield and fertility, thus linking these two traits via biological networks.ConclusionThis is the first time when whole genome sequence data is utilized to study genomic regions affecting milk production in the Nordic Red Cattle population. Sequence level data offers the possibility to study quantitative traits in detail but still cannot unambiguously reveal which of the associated variations is causative. Linkage disequilibrium creates difficulties to pinpoint the causative genes and variations. One solution to overcome these difficulties is the identification of the functional gene networks and pathways to reveal important interacting genes as candidates for the observed effects. This information on target genomic regions may be exploited to improve genomic prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0363-8) contains supplementary material, which is available to authorized users.

Genome-wide association study for female fertility in Nordic Red cattle.

BackgroundThe 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.ResultsA 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).ConclusionThis 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 materialThe online version of this article (doi:10.1186/s12863-015-0269-x) contains supplementary material, which is available to authorized users.

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds

This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed.

Loci associated with adult stature also affect calf birth survival in cattle.

BackgroundUnderstanding 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.ResultsA 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.ConclusionsResults 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 materialThe online version of this article (doi:10.1186/s12863-015-0202-3) contains supplementary material, which is available to authorized users.

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait’s economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.

Across-country test-day model evaluations for Holstein, Nordic Red Cattle, and Jersey

Three random regression models were developed for routine genetic evaluation of Danish, Finnish, and Swedish dairy cattle. Data included over 169 million test-day records with milk, protein, and fat yield observations from over 8.7 million dairy cows of all breeds. Variance component analyses showed significant differences in estimates between Holstein, Nordic Red Cattle, and Jersey, but only small to moderate differences within a breed across countries. The obtained variance component estimates were used to build, for each breed, their own set of covariance functions. The covariance functions describe the animal effects on milk, protein, and fat yields of the first 3 lactations as 9 different traits, assuming the same heritabilities and a genetic correlation of unity across countries. Only 15, 27, and 7 eigenfunctions with the largest eigenvalues were used to describe additive genetic animal effects and nonhereditary animal effects across lactations and within later lactations, respectively. These reduced-rank covariance functions explained 99.0 to 99.9% of the original variances but reduced the number of animal equations to be solved by 44%. Moderate rank reduction for nonhereditary animal effects and use of one-third-smaller measurement error correlations than obtained from variance component estimation made the models more robust against extreme observations. Estimation of the genetic levels of the countries’ subpopulations within a breed was found sensitive to the way the breed effects were modeled, especially for the genetically heterogeneous Nordic Red Cattle. Means to ensure that only additive genetic effects entered the estimated breeding values were to describe the crossbreeding effects by fixed and random cofactors and the calving age effect by an age × breed proportion interaction, and to model phantom parent groups as random effects. To ensure that genetic variances were the same across the 3 countries in breeding value estimation, as suggested by the variance component estimates, the applied multiplicative heterogeneous variance adjustment method had to be tailored using country-specific reference measurement error variances. Results showed the feasibility of across-country genetic evaluation of cows and sires based on original test-day phenotypes. Nevertheless, applying a thorough model validation procedure is essential throughout the model building process to obtain reliable breeding values.

A comparison of Nordic Red, Holstein-Friesian and Finnish native cattle bulls for beef production and carcass traits

The objective of this study was to determine beef production traits of purebred Nordic Red (NR), Holstein-Friesian (Hol), Eastern Finncattle (EF), Northern Finncattle (NF) and Western Finncattle (WF) bulls. The data collected from slaughterhouses included observations of 164 862 NR, 87 323 Hol, 283 EF, 523 NF and 1 728 WF bulls. For estimating valuable cuttings also a separate dataset was collected including observations of 16 036 NR, 8 806 Hol, 18 EF, 126 NF, and 133 WF bulls. NR and Hol had the highest carcass gain (532 and 542 g d-1, respectively). Overall, the data showed poorer gain of the Finnish native breeds compared to NR and Hol. Among the native breeds, WF (427 g d-1) showed better growth compared to EF (356 g d-1) and NF (385 g d-1). Less differences were observed in valuable cuttings as might be inferred based on the differences in carcass conformation scores.

Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle

Mastitis is a mammary disease that frequently affects dairy cattle. Despite considerable research on the development of effective prevention and treatment strategies, mastitis continues to be a significant issue in bovine veterinary medicine. To identify major genes that affect mastitis in dairy cattle, 6 chromosomal regions on Bos taurus autosome (BTA) 6, 13, 16, 19, and 20 were selected from a genome scan for 9 mastitis phenotypes using imputed high-density single nucleotide polymorphism arrays. Association analyses using sequence-level variants for the 6 targeted regions were carried out to map causal variants using whole-genome sequence data from 3 breeds. The quantitative trait loci (QTL) discovery population comprised 4,992 progeny-tested Holstein bulls, and QTL were confirmed in 4,442 Nordic Red and 1,126 Jersey cattle. The targeted regions were imputed to the sequence level. The highest association signal for clinical mastitis was observed on BTA 6 at 88.97 Mb in Holstein cattle and was confirmed in Nordic Red cattle. The peak association region on BTA 6 contained 2 genes: vitamin D-binding protein precursor (GC) and neuropeptide FF receptor 2 (NPFFR2), which, based on known biological functions, are good candidates for affecting mastitis. However, strong linkage disequilibrium in this region prevented conclusive determination of the causal gene. A different QTL on BTA 6 located at 88.32 Mb in Holstein cattle affected mastitis. In addition, QTL on BTA 13 and 19 were confirmed to segregate in Nordic Red cattle and QTL on BTA 16 and 20 were confirmed in Jersey cattle. Although several candidate genes were identified in these targeted regions, it was not possible to identify a gene or polymorphism as the causal factor for any of these regions.

Efficiency of haplotype-based methods to fine-map QTLs and embryonic lethal variants affecting fertility: Illustration with a deletion segregating in Nordic Red cattle

Despite its importance, fertility has been declining in many cattle populations. In dairy cattle, this decline is often attributed to the negative correlation between fertility and productions traits. Recent studies showed that embryonic lethal variants might also account for a non-negligible fraction of the fertility decline. Therefore identification of such embryonic lethal variants is essential to improve fertility. We herein illustrate, with an example of a large recessive lethal deletion recently identified in Nordic Red cattle, that haplotype-based method are particularly efficient to identify such embryonic lethal variants. We first show that haplotypes can be used in traditional QTL mapping approaches and that they present very high linkage disequilibrium with underlying variants. Haplotypes can also be used in scan for lack of homozygosity. Indeed, if a haplotype is associated to a recessive lethal variant, significantly fewer living individuals will be homozygote for that haplotype than expected. For both approaches, haplotype-based methods were particularly efficient. The lack of homozygosity approach achieved higher significance than the QTL approach. Only frequent variants can be detected with both approaches unless huge genotyped cohorts are available. An alternative approach would rely on identifying potential harmful variants in next-generation sequencing data followed by the genotyping of a larger population for these variants.

Genomic predictions based on a joint reference population for the Nordic Red cattle breeds

The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K–DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.

A 660-Kb Deletion with Antagonistic Effects on Fertility and Milk Production Segregates at High Frequency in Nordic Red Cattle: Additional Evidence for the Common Occurrence of Balancing Selection in Livestock

In dairy cattle, the widespread use of artificial insemination has resulted in increased selection intensity, which has led to spectacular increase in productivity. However, cow fertility has concomitantly severely declined. It is generally assumed that this reduction is primarily due to the negative energy balance of high-producing cows at the peak of lactation. We herein describe the fine-mapping of a major fertility QTL in Nordic Red cattle, and identify a 660-kb deletion encompassing four genes as the causative variant. We show that the deletion is a recessive embryonically lethal mutation. This probably results from the loss of RNASEH2B, which is known to cause embryonic death in mice. Despite its dramatic effect on fertility, 13%, 23% and 32% of the animals carry the deletion in Danish, Swedish and Finnish Red Cattle, respectively. To explain this, we searched for favorable effects on other traits and found that the deletion has strong positive effects on milk yield. This study demonstrates that embryonic lethal mutations account for a non-negligible fraction of the decline in fertility of domestic cattle, and that associated positive effects on milk yield may account for part of the negative genetic correlation. Our study adds to the evidence that structural variants contribute to animal phenotypic variation, and that balancing selection might be more common in livestock species than previously appreciated.

Validation of associations for female fertility traits in Nordic Holstein, Nordic Red and Jersey dairy cattle

BackgroundThe 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.ResultsA 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.ConclusionsThe 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.

Accuracy of genomic prediction using different models and response variables in theNordicRed cattle population

Breeding animals can be accurately evaluated using appropriate genomic prediction models, based on marker data and phenotype information. In this study, direct genomic values (DGV) were estimated for 16 traits of Nordic Total Merit (NTM) Index in Nordic Red cattle population using three models and two different response variables. The three models were as follows: a linear mixed model (GBLUP), a Bayesian variable selection model similar to BayesA (BayesA*) and a Bayesian least absolute shrinkage and selection operator model (Bayesian Lasso). The response variables were deregressed proofs (DRP) and conventional estimated breeding values (EBV). The reliability of genomic predictions was measured on bulls in the validation data set as the squared correlation between DGV and DRP divided by the reliability of DRP. Using DRP as response variable, the reliabilities of DGV among the 16 traits ranged from 0.151 to 0.569 (average 0.317) for GBLUP, from 0.152 to 0.576 (average 0.318) for BayesA* and from 0.150 to 0.570 (average 0.320) for Bayesian Lasso. Using EBV as response variable, the reliabilities ranged from 0.159 to 0.580 (average 0.322) for GBLUP, from 0.157 to 0.578 (average 0.319) for BayesA* and from 0.159 to 0.582 (average 0.325) for Bayesian Lasso. In summary, Bayesian Lasso performed slightly better than the other two models, and EBV performed slightly better than DRP as response variable, with regard to prediction reliability of DGV. However, these differences were not statistically significant. Moreover, using EBV as response variable would result in problems with the scale of the resulting DGV and potential problem due to double counting.