Showing papers by "Roel F. Veerkamp published in 2010"
TL;DR: It is demonstrated that genomic selection could be used to select for EB, confirming its genetic background, and the study suggests that it may be possible to selected for minimally recorded traits; for instance, those measured on experimental farms, using genomic selection.
55 citations
TL;DR: Possible applications of systems biology approaches in the field of female fertility and estrous behavior are discussed, and mathematical models can be helpful to improve the understanding of complex biological networks, like estrus regulation.
Abstract: Selection in dairy cattle for a higher milk yield has coincided with declined fertility One of the factors is reduced expression of estrous behavior Changes in systems that regulate the estrous behavior could be manifested by altered gene expression This literature review describes the current knowledge on mechanisms and genes involved in the regulation of estrous behavior The endocrinological regulation of the estrous cycle in dairy cows is well described Estradiol (E2) is assumed to be the key regulator that synchronizes endocrine and behavioral events Other pivotal hormones are, for example, progesterone, gonadotropin releasing hormone and insulin-like growth factor-1 Interactions between the latter and E2 may play a role in the unfavorable effects of milk yield-related metabolic stress on fertility in high milk-producing dairy cows However, a clear understanding of how endocrine mechanisms are tied to estrous behavior in cows is only starting to emerge Recent studies on gene expression and signaling pathways in rodents and other animals contribute to our understanding of genes and mechanisms involved in estrous behavior Studies in rodents, for example, show that estrogen-induced gene expression in specific brain areas such as the hypothalamus play an important role Through these estrogen-induced gene expressions, E2 alters the functioning of neuronal networks that underlie estrous behavior, by affecting dendritic connections between cells, receptor populations and neurotransmitter releases To improve the understanding of complex biological networks, like estrus regulation, and to deal with the increasing amount of genomic information that becomes available, mathematical models can be helpful Systems biology combines physiological and genomic data with mathematical modeling Possible applications of systems biology approaches in the field of female fertility and estrous behavior are discussed
43 citations
TL;DR: Which combination of alternative SCC traits can be used best to reduce both CM and SCM and whether direct information on CM is useful in this respect is examined.
36 citations
TL;DR: It can be concluded that 2STEP can be used to estimate genetic parameters for direct and associative effects on survival time in laying hens, and selection based on both direct and Associative genetic effects, using either 2STEP or LAM gave the best prediction of survival time.
Abstract: Background
Mortality due to cannibalism in laying hens is a difficult trait to improve genetically, because censoring is high (animals still alive at the end of the testing period) and it may depend on both the individual itself and the behaviour of its group members, so-called associative effects (social interactions). To analyse survival data, survival analysis can be used. However, it is not possible to include associative effects in the current software for survival analysis. A solution could be to combine survival analysis and a linear animal model including associative effects. This paper presents a two-step approach (2STEP), combining survival analysis and a linear animal model including associative effects (LAM).
20 citations
TL;DR: Studying these genes will help to improve the understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines.
Abstract: Intensive selection for high milk yield in dairy cows has raised production levels substantially but at the cost of reduced fertility, which manifests in different ways including reduced expression of oestrous behaviour. The genomic regulation of oestrous behaviour in bovines remains largely unknown. Here, we aimed to identify and study those genes that were associated with oestrous behaviour among genes expressed in the bovine anterior pituitary either at the start of oestrous cycle or at the mid-cycle (around day 12 of cycle), or regardless of the phase of cycle. Oestrous behaviour was recorded in each of 28 primiparous cows from 30 days in milk onwards till the day of their sacrifice (between 77 and 139 days in milk) and quantified as heat scores. An average heat score value was calculated for each cow from heat scores observed during consecutive oestrous cycles excluding the cycle on the day of sacrifice. A microarray experiment was designed to measure gene expression in the anterior pituitary of these cows, 14 of which were sacrificed at the start of oestrous cycle (day 0) and 14 around day 12 of cycle (day 12). Gene expression was modelled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model on data from day 0 cows alone (analysis 1), day 12 cows alone (analysis 2) and the combined data from day 0 and day 12 cows (analysis 3). Genes whose expression patterns showed significant linear or non-linear relationships with average heat scores were identified in all three analyses (177, 142 and 118 genes, respectively). Gene ontology terms enriched among genes identified in analysis 1 revealed processes associated with expression of oestrous behaviour whereas the terms enriched among genes identified in analysis 2 and 3 were general processes which may facilitate proper expression of oestrous behaviour at the subsequent oestrus. Studying these genes will help to improve our understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines.
15 citations
TL;DR: The proposed method to predict haplotypes of animals that are not genotyped using mixed model equations is computationally very efficient and suitable for marker-assisted breeding value estimation in large livestock populations including effects of a number of known QTL.
Abstract: Background
In livestock populations, missing genotypes on a large proportion of animals are a major problem to implement the estimation of marker-assisted breeding values using haplotypes. The objective of this article is to develop a method to predict haplotypes of animals that are not genotyped using mixed model equations and to investigate the effect of using these predicted haplotypes on the accuracy of marker-assisted breeding value estimation.
15 citations
TL;DR: For large livestock populations it can be concluded that gene-assisted breeding value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation.
Abstract: In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing gene-assisted breeding value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted breeding value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD-LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted breeding value estimation increased accuracies of total estimated breeding value (EBV) with 0% to 22% for genotyped animals in comparison to conventional breeding value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD-LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD-LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted breeding value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.
12 citations
TL;DR: Correlations between estimated breeding values were high between models, except when the model was used that assumed that all SNP effects came from one distribution, and the model that used the selected 14 SNP found associated with QTL, gave close to unity correlations with the full parameterisations.
Abstract: Background
The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling
8 citations
01 Jan 2010
TL;DR: In this paper, the authors quantify genetic variation for tail length at birth for Clun Forest and Hampshire Down sheep in The Netherlands as a starting point for a breeding program and demonstrate that better management i.e. cleaning tails and (preventive) medical treatment, myiasis can be prevented.
Abstract: Tail docking was a regular practice on sheep farms in the Netherlands. The main purpose was to reduce the accumulation of faeces and urine stain in order to prevent myiasis (Scobie and O' Connell, (2002)). As such, it is an example of a management practice which causes a temporary decrease in animal welfare in order to avoid larger problems later in life. Behavioural and endocrinological studies demonstrate pain caused by docking (Mellor and Murray, (1989a and 1989b), Kent et al. (1993); Graham et al. (1997)), and it is considered a unwanted mutilation by policymakers in the Netherlands. Therefore, there is an active policy to promote alternatives for such management practices. With better management i.e. cleaning tails and (preventive) medical treatment, myiasis can be prevented. Consequently tail docking is prohibited in The Netherlands since January 1 2008. Three English breeds (Suffolk, Clun Forest and Hampshire Down) have too long tails for efficient prevention of myiasis and are temporary exempted from this prohibition on the provision that a breeding programme is started to breed for shorter tails. Moderate to high heritabilities for tail length have been found for the Finnish Landrace with h2 = 0.77 (Branford Oltenacu and Boylan (1974)), for the Rambouillet h2 = 0.39 (Shelton (1977)) and for the Suffolk h2 = 0.41 (De Haas and Veerkamp (2004)). The objective of this study was to quantify genetic variation for tail length at birth for Clun Forest and Hampshire Down sheep in The Netherlands as a starting point for a breeding programme.
6 citations
01 Jan 2010
TL;DR: The uptake of genomic selection in dairy cattle in recent years has been very high because thousands of bulls that have been progeny tested in the last decades are available as a reference population with very reliable phenotypes, leading to genomic EBVs with high reliabilities.
Abstract: Genomic selection may result in higher rates of genetic gain over traditional selection because genomic EBVs have higher reliabilities than BLUP EBVs, especially for young animals, and secondly because young animals with high genomic EBVs become attractive to be selected as parents, which reduces the generation interval (Meuwissen et al. (2001)). The advantages of genomic selection may be highest for dairy cattle breeding programs because the generation interval in traditional progeny testing schemes is large and selection of young bulls for progeny testing is inaccurate (Schaeffer (2006)) Furthermore, thousands of bulls that have been progeny tested in the last decades are available as a reference population with very reliable phenotypes, leading to genomic EBVs with high reliabilities (VanRaden et al. (2009)). For these reasons, the uptake of genomic selection in dairy cattle in recent years has been very high.
6 citations
01 Jan 2010
TL;DR: High-throughput genotyping has made it possible to move from linkage studies to whole genome association studies, expected to result in higher power to detect QTL with smaller effects as well as more accurate estimates of QTL location.
Abstract: High-throughput genotyping has made it possible to move from linkage studies to whole genome association studies. This is expected to result in higher power to detect QTL with smaller effects as well as more accurate estimates of QTL location. So far a limited number of Genome Wide Association Studies (GWAS) have been published in dairy cattle. In general, cattle GWAS have been done with genotypes and estimated breeding values or daughter yield deviations on progeny tested bulls. GWAS based on EBVs of bulls are very powerful but they are limited to routinely recorded traits. More detailed phenotypic recording takes place on experimental farms, however, the number of records on one farm is usually small. The cost of genotyping a large number of samples continues to go down rapidly, and collection of accurate phenotypes are now the limiting factor for GWAS.
Journal Article•
TL;DR: In some countries where direct genomic values (DGV) are currently used in the national evaluation, DGV are calculated by replacing a pedigree based relationship matrix by a genomic relationship matrix (G) (e.g. Berry et al., 2009; VanRaden, 2008).
Abstract: In some countries where direct genomic values (DGV) are currently used in the national evaluation, DGV are calculated by replacing a pedigree based relationship matrix by a genomic relationship matrix (G) (e.g. Berry et al., 2009; VanRaden, 2008). In other countries, methods are used where SNP effects are individually explicitly estimated and summed across the genome for each animal (e.g. De Roos et al., 2009).
TL;DR: The results indicate that genetic selection for milk yield will not have a correlated response on the lactoferrin content of milk, however, more data and a larger number of animals are required to obtain more precise estimates of the genetic correlations.
01 Aug 2010
TL;DR: In this article, the authors proposed to disentangle the different fatty acids from total fat percentage to improve the accuracy of energy balance prediction and reduce the number of cumulated error terms.
Abstract: Energy balance (EB) has long been considered an important indicator of cow health status (Heuer et al., 1999). Cows in negative EB (energy output exceeds energy intake) tend to have poorer reproductive performance and health (Heuer et al., 1999). However, monitoring EB and estimating breeding values require accurate and routinely available phenotypes for difficult and expensive to measure traits such as cow dry matter intake (DMI). Several methods, have been proposed to predict energy balance from routinely recorded traits (Friggens et al., 2007), including indexes such as milk fat to protein ratio. As the components of some such indexes are themselves predicted from milk, there is an accumulation of errors in the prediction process, thereby biasing downwards the correlation between true and predicted EB. Cows in negative EB mobilise body fat altering the fatty acid composition of the milk produced (Stoop et al., 2009). It has been shown that the fatty acid composition of milk can be predicted using mid infrared (MIR) spectrometry of milk (Soyeurt et al., 2006) and also that these predicted values are heritable. Being able to disentangle the different fatty acids from total fat percentage may aid in more accurately predicting EB. Better still, reducing the number of cumulated error terms by attempting to predict EB directly from MIR, may further improve the accuracy of prediction.