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    ABSTRACT: Training horses improves athletic capabilities by inducing skeletal muscle-specific and systemic adaptations. However, rest is required to recover from exercise or else overtraining may occur and affect performance and welfare. Biomarkers would be useful to identify early chronic overtraining in animals. The objective of the current study was to investigate skeletal muscle gene expression patterns and underlying biological mechanisms related to training of different intensities and detraining. Untrained 20month-old Standardbred geldings were exercised at varying intensities (endurance and sprint) followed by detraining (n=5 per phase). The results indicated that training mainly affected skeletal muscle-specific protein metabolism and increased CO2 export from the tissues. Intensive training increased energy metabolism and affected heart and adipose tissues, while having an adverse effect on stress, apoptosis and immune capacity. The intensity of the training could be related to decreased expression of extra cellular matrix proteins (ECM), cell-cell contacts and intracellular signalling pathways. During detraining, most mechanisms were reversed, but heart tissue-related changes and increased expression of skeletal muscle-specific proteins were still evident. The study suggested that changes to ECM expression and cell-cell contact mechanisms may be long-lasting and related to multifactorial aspects of training and detraining. These biomarkers may be useful to identify horses in the early stages of chronic overloading or early overtraining.
    The Veterinary Journal 05/2013;
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    ABSTRACT: Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.Heredity advance online publication, 20 March 2013; doi:10.1038/hdy.2013.15.
    Heredity 03/2013;
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    ABSTRACT: Inbreeding has long been recognized as a primary cause of fitness reduction in both wild and domesticated populations. Consanguineous matings cause inheritance of haplotypes that are identical by descent (IBD) and result in homozygous stretches along the genome of the offspring. Size and position of regions of homozygosity (ROHs) are expected to correlate with genomic features such as GC content and recombination rate, but also direction of selection. Thus, ROHs should be non-randomly distributed across the genome. Therefore, demographic history may not fully predict the effects of inbreeding. The porcine genome has a relatively heterogeneous distribution of recombination rate, making Sus scrofa an excellent model to study the influence of both recombination landscape and demography on genomic variation. This study utilizes next-generation sequencing data for the analysis of genomic ROH patterns, using a comparative sliding window approach. We present an in-depth study of genomic variation based on three different parameters: nucleotide diversity outside ROHs, the number of ROHs in the genome, and the average ROH size. We identified an abundance of ROHs in all genomes of multiple pigs from commercial breeds and wild populations from Eurasia. Size and number of ROHs are in agreement with known demography of the populations, with population bottlenecks highly increasing ROH occurrence. Nucleotide diversity outside ROHs is high in populations derived from a large ancient population, regardless of current population size. In addition, we show an unequal genomic ROH distribution, with strong correlations of ROH size and abundance with recombination rate and GC content. Global gene content does not correlate with ROH frequency, but some ROH hotspots do contain positive selected genes in commercial lines and wild populations. This study highlights the importance of the influence of demography and recombination on homozygosity in the genome to understand the effects of inbreeding.
    PLoS Genetics 11/2012; 8(11):e1003100.
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    ABSTRACT: Over the last ten years, genomic selection has developed enormously. Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone. However, to reach high accuracies, large reference populations are needed. In many livestock populations or even species, such populations cannot be established when traits are difficult or expensive to record, or when the population size is small. The value of genomic selection is then questionable. In this study, we compare traditional breeding schemes based on own performance or progeny information to genomic selection schemes, for which the number of phenotypic records is limiting. Deterministic simulations were performed using selection index theory. Our focus was on the equilibrium response obtained after a few generations of selection. Therefore, we first investigated the magnitude of the Bulmer effect with genomic selection. Results showed that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP estimated breeding values, and is independent of the accuracy of selection. The reduction in response with genomic selection is greater than with selection based directly on phenotypes without the use of pedigree information, such as mass selection. To maximize the accuracy of genomic estimated breeding values when the number of phenotypic records is limiting, the same individuals should be phenotyped and genotyped, rather than genotyping parents and phenotyping their progeny. When the generation interval cannot be reduced with genomic selection, large reference populations are required to obtain a similar response to that with selection based on BLUP estimated breeding values based on own performance or progeny information. However, when a genomic selection scheme has a moderate decrease in generation interval, relatively small reference population sizes are needed to obtain a similar response to that with selection on traditional BLUP estimated breeding values. When the trait of interest cannot be recorded on the selection candidate, genomic selection schemes are very attractive even when the number of phenotypic records is limited, because traditional breeding requires progeny testing schemes with long generation intervals in those cases.
    Genetics Selection Evolution 08/2012; 44:26.
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    ABSTRACT: Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. Estimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect (p ^i) was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91. Bayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high.
    BMC proceedings 05/2012; 6 Suppl 2:S8.
  • Heredity 03/2012; 109(1):1-3.
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    ABSTRACT: Genomic imprinting is an important epigenetic phenomenon, which on the phenotypic level can be detected by the difference between the two heterozygote classes of a gene. Imprinted genes are important in both the development of the placenta and the embryo, and we hypothesized that imprinted genes might be involved in female fertility traits. We therefore performed an association study for imprinted genes related to female fertility traits in two commercial pig populations. For this purpose, 309 SNPs in fifteen evolutionary conserved imprinted regions were genotyped on 689 and 1050 pigs from the two pig populations. A single SNP association study was used to detect additive, dominant and imprinting effects related to four reproduction traits; total number of piglets born, the number of piglets born alive, the total weight of the piglets born and the total weight of the piglets born alive. Several SNPs showed significant (q-value < 0.10) additive and dominant effects and one SNP showed a significant imprinting effect. The SNP with a significant imprinting effect is closely linked to DIO3, a gene involved in thyroid metabolism. The imprinting effect of this SNP explained approximately 1.6% of the phenotypic variance, which corresponded to approximately 15.5% of the additive genetic variance. In the other population, the imprinting effect of this QTL was not significant (q-value > 0.10), but had a similar effect as in the first population. The results of this study indicate a possible association between the imprinted gene DIO3 and female fertility traits in pigs.
    PLoS ONE 01/2012; 7(2):e31825.
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    ABSTRACT: The objectives of this study were to develop breed-specific single nucleotide polymorphisms (SNPs) in five pig breeds sequenced with Illumina's Genome Analyzer and to investigate their usefulness for breed assignment purposes. DNA pools were prepared for Duroc, Landrace, Large White, Pietrain and Wild Boar. The total number of animals used for sequencing was 153. SNP discovery was performed by aligning the filtered reads against Build 7 of the pig genome. A total of 313,964 high confidence SNPs were identified and analysed for the presence of breed-specific SNPs (defined in this context as SNPs for which one of the alleles was detected in only one breed). There were 29,146 putative breed-specific SNPs identified, of which 4441 were included in the PorcineSNP60 beadchip. Upon re-examining the genotypes obtained using the beadchip, 193 SNPs were confirmed as being breed specific. These 193 SNPs were subsequently used to assign an additional 490 individuals from the same breeds, using the sequenced individuals as reference populations. In total, four breed assignment tests were performed. Results showed that for all methods tested 99% of the animals were correctly assigned, with an average probability of assignment of at least 99.2%, indicating the high utility of breed-specific markers for breed assignment and traceability. This study provides a blueprint for the way next-generation sequencing technologies can be used for the identification of breed-specific SNPs, as well as evidence that these SNPs may be a powerful tool for breed assignment and traceability of animal products to their breeds of origin.
    Animal Genetics 12/2011; 42(6):613-20.
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    ABSTRACT: Genetic selection is a major force shaping life on earth. In classical genetic theory, response to selection is the product of the strength of selection and the additive genetic variance in a trait. The additive genetic variance reflects a population's intrinsic potential to respond to selection. The ordinary additive genetic variance, however, ignores the social organization of life. With social interactions among individuals, individual trait values may depend on genes in others, a phenomenon known as indirect genetic effects. Models accounting for indirect genetic effects, however, lack a general definition of heritable variation. Here I propose a general definition of the heritable variation that determines the potential of a population to respond to selection. This generalizes the concept of heritable variance to any inheritance model and level of organization. The result shows that heritable variance determining potential response to selection is the variance among individuals in the heritable quantity that determines the population mean trait value, rather than the usual additive genetic component of phenotypic variance. It follows, therefore, that heritable variance may exceed phenotypic variance among individuals, which is impossible in classical theory. This work also provides a measure of the utilization of heritable variation for response to selection and integrates two well-known models of maternal genetic effects. The result shows that relatedness between the focal individual and the individuals affecting its fitness is a key determinant of the utilization of heritable variance for response to selection.
    Genetics 09/2011; 189(4):1347-59.
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    ABSTRACT: The sow provides a specific environment to her offspring during gestation and lactation. Certain features in the early life of the sow (sow history features) may affect her ability to deliver and feed a healthy litter. In genetic analyses of grow-finish traits, these effects are estimated as common litter or permanent sow effects. The objective of this research was to identify sow history features that affect the growth rate (GR) and feed intake (FI) of her offspring during the grow-finish stage. Data from 17,743 grow-finish pigs, coming from 604 sires and 681 crossbred sows, were recorded between May 2001 and February 2010 at the experimental farm of the Institute for Pig Genetics (Beilen, the Netherlands). The grow-finish stage was divided into 2 phases (phase 1: 26 to 75 kg; phase 2: 75 to 115 kg). The sow history features were birth litter size, birth year and season, birth farm, weaning age, age of transfer to the experimental farm, and age at first insemination. The sow features were added to the basic model one at a time to study their effect on the grow-finish traits of the pigs. Subsequently, significant sow features (P < 0.1) were fitted simultaneously in an animal model. With every extra piglet in the birth litter of the sow, the GR of her offspring decreased by 1 g/d and the FI decreased by 4 g/d. Every extra day to the first insemination increased the GR of grow-finish pigs by 0.1 g/d. The heritability estimates for GR and FI (only in phase 2 of the grow-finish stage) decreased after adding the sow features to the model. No differences were found in estimates of the common litter effects between the basic model and the model with all significant sow features. The estimates of the permanent sow effect changed for FI from 0.03 (basic model) to 0.00 (model with sow features), and for FI in phase 1, the permanent sow effect decreased from 0.03 (basic model) to 0.01 (model with sow features). In conclusion, selected sow features do affect the grow-finish traits of the pigs, but their estimates are small and explain only a small proportion of the differences in the GR and FI of grow-finish pigs. The sow features partially explained the permanent sow effect of FI-related traits and did not explain the common litter effect. Although the sow early life features can affect piglet traits, they do not predict which sows produce better performing offspring in the grow-finish stage.
    Journal of Animal Science 08/2011; 90(1):116-26.
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