92 results on '"Bijma, Piter"'
Search Results
2. Towards genetic improvement of social behaviours in livestock using large-scale sensor data: data simulation and genetic analysis
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Wang, Zhuoshi, Doekes, Harmen, and Bijma, Piter
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- 2023
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3. Predicting the impact of genotype-by-genotype interaction on the purebred–crossbred genetic correlation from phenotype and genotype marker data of parental lines
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Duenk, Pascal, Wientjes, Yvonne C. J., Bijma, Piter, Iversen, Maja W., Lopes, Marcos S., and Calus, Mario P. L.
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- 2023
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4. On the origin of the genetic variation in infectious disease prevalence: Genetic analysis of disease status versus infections for Digital Dermatitis in Dutch dairy cattle.
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Kulkarni, Pranav, Biemans, Floor, de Jong, Mart, and Bijma, Piter
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Mortellaro ,disease transmission ,heritability ,recovery ,susceptibility ,Animals ,Cattle ,Cattle Diseases ,Communicable Diseases ,Digital Dermatitis ,Genetic Variation ,Phenotype - Abstract
The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status (0,1; indicating being free versus infected), whereas for infections, we analysed binary data on disease transmission events (1,0; indicating becoming infected or not). The analyses of the two traits were compared using cross-validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious.
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- 2021
5. Socially Affected Traits, Inheritance and Genetic Improvement
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Bijma, Piter, Spangler, Matthew L., Section editor, Meyers, Robert A., Editor-in-Chief, and Spangler, Matthew L., editor
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- 2023
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6. Enhanced camera-based individual pig detection and tracking for smart pig farms
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Guo, Qinghua, Sun, Yue, Orsini, Clémence, Bolhuis, J. Elizabeth, de Vlieg, Jakob, Bijma, Piter, and de With, Peter H.N.
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- 2023
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7. Comparison of linkage disequilibrium estimated from genotypes versus haplotypes for crossbred populations
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Alemu, Setegn Worku, Bijma, Piter, Calus, Mario P. L., Liu, Huiming, Fernando, Rohan L., and Dekkers, Jack C. M.
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- 2022
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8. The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
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- 2022
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9. Can breeders prevent pathogen adaptation when selecting for increased resistance to infectious diseases?
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Hulst, Andries D., Bijma, Piter, and De Jong, Mart C. M.
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- 2022
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10. Predictions of the accuracy of genomic prediction: connecting R2, selection index theory, and Fisher information
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Bijma, Piter and Dekkers, Jack C. M.
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- 2022
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11. Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead.
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Niehoff, Tobias A. M., ten Napel, Jan, Bijma, Piter, Pook, Torsten, Wientjes, Yvonne C. J., Hegedűs, Bernadett, and Calus, Mario P. L.
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LOCUS (Genetics) ,MATE selection ,GENE mapping ,VARIANCES - Abstract
Background: Breeding programs are judged by the genetic level of animals that are used to disseminate genetic progress. These animals are typically the best ones of the population. To maximise the genetic level of very good animals in the next generation, parents that are more likely to produce top performing offspring need to be selected. The ability of individuals to produce high-performing progeny differs because of differences in their breeding values and gametic variances. Differences in gametic variances among individuals are caused by differences in heterozygosity and linkage. The use of the gametic Mendelian sampling variance has been proposed before, for use in the usefulness criterion or Index5, and in this work, we extend existing approaches by not only considering the gametic Mendelian sampling variance of individuals, but also of their potential offspring. Thus, the criteria developed in this study plan one additional generation ahead. For simplicity, we assumed that the true quantitative trait loci (QTL) effects, genetic map and the haplotypes of all animals are known. Results: In this study, we propose a new selection criterion, ExpBVSelGrOff, which describes the genetic level of selected grand-offspring that are produced by selected offspring of a particular mating. We compare our criterion with other published criteria in a stochastic simulation of an ongoing breeding program for 21 generations for proof of concept. ExpBVSelGrOff performed better than all other tested criteria, like the usefulness criterion or Index5 which have been proposed in the literature, without compromising short-term gains. After only five generations, when selection is strong (1%), selection based on ExpBVSelGrOff achieved 5.8% more commercial genetic gain and retained 25% more genetic variance without compromising inbreeding rate compared to selection based only on breeding values. Conclusions: Our proposed selection criterion offers a new tool to accelerate genetic progress for contemporary genomic breeding programs. It retains more genetic variance than previously published criteria that plan less far ahead. Considering future gametic Mendelian sampling variances in the selection process also seems promising for maintaining more genetic variance. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines
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Duenk, Pascal, Bijma, Piter, Wientjes, Yvonne C. J., and Calus, Mario P. L.
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- 2021
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13. Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters.
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Kistler, Tristan, Brascamp, Evert W., Basso, Benjamin, Bijma, Piter, and Phocas, Florence
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HONEYBEES ,GENETIC models ,DAM failures ,STATISTICAL models ,PHENOTYPES ,FIXED effects model ,DAMS ,GENEALOGY - Abstract
Background: Breeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population. Methods: Simulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates. Results: The most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates. Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates. Conclusions: Knowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model. [ABSTRACT FROM AUTHOR]
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- 2024
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14. Double-Camera Fusion System for Animal-Position Awareness in Farming Pens
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Huo, Shoujun, Sun, Yue, Guo, Qinghua, Tan, Tao, Bolhuis, J. Elizabeth, Bijma, Piter, de With, Peter H.N., Huo, Shoujun, Sun, Yue, Guo, Qinghua, Tan, Tao, Bolhuis, J. Elizabeth, Bijma, Piter, and de With, Peter H.N.
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In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to the large scale of breeding and expensive labour. Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance. However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive. We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues. This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered. Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training. The learned parameters are then used in a semi-supervised network and fine-tuned with a small number of manually annotated landmarks. The actual pixel displacement error is introduced as a complement to an image similarity measure. The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods. This approach also reduces the registration time of an unseen image pair to less than 0.5 s. The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis.
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- 2023
15. The long-term effects of genomic selection : 2. Changes in allele frequencies of causal loci and new mutations
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Wientjes, Yvonne C.J., Bijma, Piter, Van Den Heuvel, Joost, Zwaan, Bas J., Vitezica, Zulma G., Calus, Mario P.L., Wientjes, Yvonne C.J., Bijma, Piter, Van Den Heuvel, Joost, Zwaan, Bas J., Vitezica, Zulma G., and Calus, Mario P.L.
- Abstract
Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.
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- 2023
16. The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations.
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Wientjes, Yvonne C. J., Bijma, Piter, van den Heuvel, Joost, Zwaan, Bas J., Vitezica, Zulma G., and Calus, Mario P. L.
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CHROMOSOMES , *GENETIC mutation , *GENETICS , *ALLELES , *GENETIC polymorphisms , *GENETIC variation , *COMPARATIVE studies , *GENOMICS , *GENOMES , *GENETIC markers , *RESEARCH funding , *GENETIC techniques , *PREDICTION models , *PHENOTYPES , *GENEALOGY , *POISSON distribution - Abstract
Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (~15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles. [ABSTRACT FROM AUTHOR]
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- 2023
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17. Investigating the potential of incorporating indirect genetic effects into genetic evaluations of dairy calf disease traits.
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Lynch, Colin, Bijma, Piter, de Jong, Mart, Hulst, Dries, Alcantara, Lucas, Schenkel, Flávio S., Miglior, Filippo, Kelton, David, and Baes, Christine F.
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INFECTIOUS disease transmission , *QUANTITATIVE genetics , *PARTURITION , *GENETIC models , *GENETIC variation - Abstract
The goal of infectious disease control is often local eradication, but this is theoretically impossible to achieve based on classical quantitative genetic theory. Current methods focus on the susceptibility of individuals to disease and assume that exposure to a pathogen is 1) constant over time, 2) equal among individuals, and (3) due entirely to the environment. For this reason, it is likely that conventional genetic methods are capturing only a fraction of genetic variation in disease occurrence. The incorporation of epidemiological theory into quantitative genetics provides an opportunity to better determine the level of genetic variation in infectious disease traits. This stems from the ability to include the positive feed-back dynamics of infectious disease transmission. From an epidemiolocal perspective, both susceptibility and infectivity also have indirect genetic effects (IGE), because the genotype of one individual impacts the risk of infection of other individuals, and this can drastically affect the rate and direction of response to selection. Epidemiological models unravel the genetic heterogeneity in both susceptibility and infectivity traits, and account for the impact each animal has on its contemporaries compared with conventional quantitative genetic approaches. From a calf health perspective, there is a shift towards group housing dairy calves, primarily due to welfare and social acceptability. In turn, this will lead to increased animal interactions from a young age. Therefore, the aim of this study was to investigate the potential of incorporating IGE into current quantitative approaches to determine its impact on selection potential. Our study looked at two common infectious diseases in calves on dairy farms: respiratory problems (RESP) and diarrhea (DIAR). Producer-recorded data comprised of 19,445 records collected on 34 herds that group housed calves between 2007 and 2020. Calves were allocated into pen groups based on birth dates and herd specific housing practices. Original phenotypes were split into 10 records, each representing a week of life, to determine when exactly calves became sick and infective. Several scenarios were investigated with respect to the time an animal was infective for following a disease case (1 or 2 wk), and the maximum difference in weeks between animal birth dates to be included within the same pen (2 to 5 wk). Variance components were estimated using a generalized linear mixed model fitting a complementary-log-log link function with offset for exposure. Initial heritability estimates for susceptibility on the observed scale for DIAR ranged from 0.03 to 0.07, and from 0.02 to 0.03 for RESP across scenarios. While heritability estimates for infectivity are currently being investigated, the results for susceptibility highlight the potential for incorporating IGE into genetic evaluation of disease traits and provide an introduction towards incorporating epidemiological theory into classical quantitative approaches to better respect the transmission dynamics of infections. [ABSTRACT FROM AUTHOR]
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- 2024
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18. Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms
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AISS Animal Welfare, Guo, Qinghua, Sun, Yue, Min, Lan, Putten, Arjen van, Knol, Egbert Frank, Visser, Bram, Rodenburg, T. Bas, Bolhuis, J. Elizabeth, Bijma, Piter, de With, Peter H.N., AISS Animal Welfare, Guo, Qinghua, Sun, Yue, Min, Lan, Putten, Arjen van, Knol, Egbert Frank, Visser, Bram, Rodenburg, T. Bas, Bolhuis, J. Elizabeth, Bijma, Piter, and de With, Peter H.N.
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- 2022
19. Heritability of daily activity over time in broilers
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AISS Animal Welfare, Ellen, Esther, Sluis, Malou van der, Klerk, B. de, Henshall, John, Haas, Yvette de, Rodenburg, Bas, Bijma, Piter, AISS Animal Welfare, Ellen, Esther, Sluis, Malou van der, Klerk, B. de, Henshall, John, Haas, Yvette de, Rodenburg, Bas, and Bijma, Piter
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- 2022
20. The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C.J., Bijma, Piter, Calus, Mario P.L., Zwaan, Bas J., Vitezica, Zulma G., van den Heuvel, Joost, Wientjes, Yvonne C.J., Bijma, Piter, Calus, Mario P.L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
- Abstract
Background Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data. Results Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive effects acro, Background Genomic selection has revolutionized genetic improvement in animals and plants, but little is known about its long-term effects. Here, we investigated the long-term effects of genomic selection on response to selection, genetic variance, and the genetic architecture of traits using stochastic simulations. We defined the genetic architecture as the set of causal loci underlying each trait, their allele frequencies, and their statistical additive effects. We simulated a livestock population under 50 generations of phenotypic, pedigree, or genomic selection for a single trait, controlled by either only additive, additive and dominance, or additive, dominance, and epistatic effects. The simulated epistasis was based on yeast data. Results Short-term response was always greatest with genomic selection, while response after 50 generations was greater with phenotypic selection than with genomic selection when epistasis was present, and was always greater than with pedigree selection. This was mainly because loss of genetic variance and of segregating loci was much greater with genomic and pedigree selection than with phenotypic selection. Compared to pedigree selection, selection response was always greater with genomic selection. Pedigree and genomic selection lost a similar amount of genetic variance after 50 generations of selection, but genomic selection maintained more segregating loci, which on average had lower minor allele frequencies than with pedigree selection. Based on this result, genomic selection is expected to better maintain genetic gain after 50 generations than pedigree selection. The amount of change in the genetic architecture of traits was considerable across generations and was similar for genomic and pedigree selection, but slightly less for phenotypic selection. Presence of epistasis resulted in smaller changes in allele frequencies and less fixation of causal loci, but resulted in substantial changes in statistical additive eff
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- 2022
21. Capturing indirect genetic effects on phenotypic variability : Competition meets canalization
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Marjanovic, Jovana, Mulder, Han A., Rönnegård, Lars, de Koning, Dirk Jan, Bijma, Piter, Marjanovic, Jovana, Mulder, Han A., Rönnegård, Lars, de Koning, Dirk Jan, and Bijma, Piter
- Abstract
Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is pos
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- 2022
22. Additional file 5 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
- Abstract
Additional file 5: Figure S1. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S2. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S3. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S7. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S8. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).
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- 2022
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23. Additional file 4 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
- Abstract
Additional file 4: Figure S1. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under RANDOM selection. Figure S2. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under MASS selection. Figure S3. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S7. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S8. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S11. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S12. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S13. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S14. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S15. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).
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- 2022
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24. Additional file 2 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
- Abstract
Additional file 2: Figure S1. Allele frequency distribution of segregating causal loci over 50 generations without selection. Figure S2. Extent of LD (r2) in the simulated population before selection as a function of distance in one random replicate. Figure S3. Trend in the variation in minor allele frequency (MAF) of segregating causal loci for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S4. The difference between additive genic and additive genetic variance which represents a transient loss in genetic variance for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S5. Phenotypic trend for the GBLUP model with own performance records and with and without a dominance effect for the genetic models with non-additive effects. The phenotypic trend is scaled by the additive genetic standard deviation in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S6. Trend in additive genetic (A, B) and additive genic (C, D) variance for the GBLUP model with own performance and with and without a dominance effect for the genetic models with non-additive effects. The trend is scaled by the additive genetic or additive genic variance in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error.
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- 2022
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25. Additional file 3 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
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ComputingMethodologies_SIMULATIONANDMODELING ,Computer Science::Neural and Evolutionary Computation ,Data_FILES ,ComputingMethodologies_GENERAL ,Quantitative Biology::Genomics ,Computer Science::Operating Systems - Abstract
Additional file 3. Decomposition of additive genetic variance. This file provides a theoretical decomposition of the additive genetic variance.
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- 2022
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26. Additional file 6 of The long-term effects of genomic selection: 1. Response to selection, additive genetic variance, and genetic architecture
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Wientjes, Yvonne C. J., Bijma, Piter, Calus, Mario P. L., Zwaan, Bas J., Vitezica, Zulma G., and van den Heuvel, Joost
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Additional file 6: Table S1. Maximum genetic gain1 that is still possible after 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1The maximum genetic gain in generation 50 is estimated as the genetic gain when all loci would be fixed for the favourable allele, using the statistical additive effects of generation 50 and neglecting mutations. 2Results are shown as averages across the 20 replicates with their corresponding standard errors of the mean between brackets. Table S2. Percentual change in the components of the genetic variance after 10 and 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Increases in the value of a component are represented in bold. Table S3. Average pedigree inbreeding coefficient after 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Table S4. Average and variance of change in allele frequency of causal loci1 across 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Causal loci included only the causal loci segregating in generation 0. 2Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets.
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27. Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms
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Guo, Qinghua, Sun, Yue, Min, Lan, van Putten, Arjen, Knol, Egbert, Visser, Bram, Rodenburg, T., Bolhuis, J., Bijma, Piter, de With, Peter H.N., Farinella, Giovanni Maria, Radeva, Petia, Bouatouch, Kadi, Video Coding & Architectures, Eindhoven MedTech Innovation Center, Center for Care & Cure Technology Eindhoven, and EAISI Health
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Animal Detection ,Multi-Object Tracking Models ,WIAS ,Life Science ,Adaptation Physiology ,Fokkerij en Genomica ,Animal Tracking ,Animal Breeding and Genomics ,Adaptatiefysiologie - Abstract
It is important to detect negative behavior of animals for breeding in order to improve their health and welfare. In this work, AI is employed to assist individual animal detection and tracking, which enables the future analysis of behavior for individual animals. The study involves animal groups of pigs and laying hens. First, two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, namely Joint Detection and Embedding (JDE) and FairMOT. Both models detect and track individual animals automatically and continuously. Second, a weighted association algorithm is proposed, which is feasible for both MOT methods to optimize the object re-identification (re-ID), thereby improving the tracking performance. The proposed methods are evaluated on manually annotated datasets. The best tracking performance on pigs is obtained by FairMOT with the weighted association, resulting in an IDF1 of 90.3%, MOTA of 90.8%, MOTP of 83.7%, number of identity switches of 14, and an execution rate of 20.48 fps. For the laying hens, FairMOT with the weighted association also achieves the best tracking performance, with an IDF1 of 88.8%, MOTA of 86.8%, MOTP of 72.8%, number of identity switches of 2, and an execution rate of 21.01 fps. These results show a promising high accuracy and robustness for the individual animal tracking.
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- 2022
28. Breeding Values in Honey Bees
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Uzunov, Aleksandar, Brascamp, Evert W., Du, Manuel, Bijma, Piter, and Büchler, Ralph
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- 2023
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29. Predictions of the accuracy of genomic prediction: connecting R2, selection index theory, and Fisher information.
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Bijma, Piter and Dekkers, Jack C. M.
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INFORMATION resources ,FISHER information ,FORECASTING - Abstract
Background: Deterministic predictions of the accuracy of genomic estimated breeding values (GEBV) when combining information sources have been developed based on selection index theory (SIT) and on Fisher information (FI). These two approaches have resulted in slightly different results when considering the combination of pedigree and genomic information. Here, we clarify this apparent contradiction, both for the combination of pedigree and genomic information and for the combination of subpopulations into a joint reference population. Results: First, we show that existing expressions for the squared accuracy of GEBV can be understood as a proportion of the variance explained. Next, we show that the apparent discrepancy that has been observed between accuracies based on SIT vs. FI originated from two sources. First, the FI referred to the genetic component that is captured by the marker genotypes, rather than the full genetic component. Second, the common SIT-based derivations did not account for the increase in the accuracy of GEBV due to a reduction of the residual variance when combining information sources. The SIT and FI approaches are equivalent when these sources are accounted for. Conclusions: The squared accuracy of GEBV can be understood as a proportion of the variance explained. The SIT and FI approaches for combining information for GEBV are equivalent and provide identical accuracies when the underlying assumptions are equivalent. [ABSTRACT FROM AUTHOR]
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- 2022
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30. The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection.
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Bijma, Piter, Hulst, Andries D., and de Jong, Mart C. M.
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- *
COMMUNICABLE disease epidemiology , *BIOLOGICAL models , *COMMUNICABLE diseases , *GENETICS , *GENETIC variation , *INFECTIOUS disease transmission , *SYMPTOMS , *BASIC reproduction number , *HUMAN reproductive technology , *DISEASE risk factors - Abstract
Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants. [ABSTRACT FROM AUTHOR]
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- 2022
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31. Breeding Beyond Monoculture: Putting the "Intercrop" Into Crops.
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Bourke, Peter M., Evers, Jochem B., Bijma, Piter, van Apeldoorn, Dirk F., Smulders, Marinus J. M., Kuyper, Thomas W., Mommer, Liesje, and Bonnema, Guusje
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CATCH crops ,PLANT breeding ,INTERCROPPING ,CROPS ,CROPPING systems ,MONOCULTURE agriculture - Abstract
Intercropping is both a well-established and yet novel agricultural practice, depending on one's perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., "trait-based" breeding) or quantitative genetics-driven (i.e., "product-based" breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future. [ABSTRACT FROM AUTHOR]
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- 2021
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32. 58 Integrating Quantitative Genetics and Epidemiology: Why Selection Against Infectious Diseases Is More Promising Than We Think
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Bijma, Piter and Bijma, Piter
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Pathogens have profound effects on livestock. The low heritabilities of individual binary disease status suggest limited prospects for genetic improvement. However, a proper quantitative genetic theory for infectious diseases, including transmission dynamics, is currently lacking. Here we present a quantitative genetic theory for endemic infectious diseases, focussing on the genetic factors that determine the prevalence (P; the mean fraction of the population that is infected). We present simple expressions for breeding values and genetic parameters for the prevalence. Without genetic variation in infectiousness, breeding values for prevalence are a factor 1/P greater than the ordinary breeding values for individual binary disease status (0/1). Hence, even though prevalence is the simple average of individual binary disease status, breeding values for prevalence show much greater variation than our ordinary breeding values. This implies that the genetic variance that determines the potential response of prevalence to selection is largely due to indirect genetic effects (IGE), and thus hidden to ordinary genetic analysis and selection. Hence, the genetic variance that determines the potential of livestock populations to respond to selection must be much greater than currently believed, particularly at low prevalence. We evaluated this implication using simulation of endemics following standard methods in epidemiology. Results show that response of prevalence to selection increases very strongly when prevalence decreases, and is much greater than predicted by our ordinary breeding values. These results supports our theoretical findings, and show that selection against infectious diseases is much more promising than currently believed.
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- 2021
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33. Integrating Quantitative Genetics and Epidemiology: Why Selection Against Infectious Diseases Is More Promising Than We Think.
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Bijma, Piter
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- *
COMMUNICABLE diseases , *QUANTITATIVE genetics , *INFECTIOUS disease transmission , *GENETIC variation , *HERITABILITY - Abstract
Pathogens have profound effects on livestock. The low heritabilities of individual binary disease status suggest limited prospects for genetic improvement. However, a proper quantitative genetic theory for infectious diseases, including transmission dynamics, is currently lacking. Here we present a quantitative genetic theory for endemic infectious diseases, focussing on the genetic factors that determine the prevalence (P; the mean fraction of the population that is infected). We present simple expressions for breeding values and genetic parameters for the prevalence. Without genetic variation in infectiousness, breeding values for prevalence are a factor 1/P greater than the ordinary breeding values for individual binary disease status (0/1). Hence, even though prevalence is the simple average of individual binary disease status, breeding values for prevalence show much greater variation than our ordinary breeding values. This implies that the genetic variance that determines the potential response of prevalence to selection is largely due to indirect genetic effects (IGE), and thus hidden to ordinary genetic analysis and selection. Hence, the genetic variance that determines the potential of livestock populations to respond to selection must be much greater than currently believed, particularly at low prevalence. We evaluated this implication using simulation of endemics following standard methods in epidemiology. Results show that response of prevalence to selection increases very strongly when prevalence decreases, and is much greater than predicted by our ordinary breeding values. These results supports our theoretical findings, and show that selection against infectious diseases is much more promising than currently believed. [ABSTRACT FROM AUTHOR]
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- 2021
- Full Text
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34. Double-Camera Fusion System for Animal-Position Awareness in Farming Pens.
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Huo S, Sun Y, Guo Q, Tan T, Bolhuis JE, Bijma P, and de With PHN
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In livestock breeding, continuous and objective monitoring of animals is manually unfeasible due to the large scale of breeding and expensive labour. Computer vision technology can generate accurate and real-time individual animal or animal group information from video surveillance. However, the frequent occlusion between animals and changes in appearance features caused by varying lighting conditions makes single-camera systems less attractive. We propose a double-camera system and image registration algorithms to spatially fuse the information from different viewpoints to solve these issues. This paper presents a deformable learning-based registration framework, where the input image pairs are initially linearly pre-registered. Then, an unsupervised convolutional neural network is employed to fit the mapping from one view to another, using a large number of unlabelled samples for training. The learned parameters are then used in a semi-supervised network and fine-tuned with a small number of manually annotated landmarks. The actual pixel displacement error is introduced as a complement to an image similarity measure. The performance of the proposed fine-tuned method is evaluated on real farming datasets and demonstrates significant improvement in lowering the registration errors than commonly used feature-based and intensity-based methods. This approach also reduces the registration time of an unseen image pair to less than 0.5 s. The proposed method provides a high-quality reference processing step for improving subsequent tasks such as multi-object tracking and behaviour recognition of animals for further analysis.
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- 2022
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35. Capturing indirect genetic effects on phenotypic variability: Competition meets canalization.
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Marjanovic J, Mulder HA, Rönnegård L, de Koning DJ, and Bijma P
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Phenotypic variability of a genotype is relevant both in natural and domestic populations. In the past two decades, variability has been studied as a heritable quantitative genetic trait in its own right, often referred to as inherited variability or environmental canalization. So far, studies on inherited variability have only considered genetic effects of the focal individual, that is, direct genetic effects on inherited variability. Observations from aquaculture populations and some plants, however, suggest that an additional source of genetic variation in inherited variability may be generated through competition. Social interactions, such as competition, are often a source of Indirect Genetic Effects (IGE). An IGE is a heritable effect of an individual on the trait value of another individual. IGEs may substantially affect heritable variation underlying the trait, and the direction and magnitude of response to selection. To understand the contribution of IGEs to evolution of environmental canalization in natural populations, and to exploit such inherited variability in animal and plant breeding, we need statistical models to capture this effect. To our knowledge, it is unknown to what extent the current statistical models commonly used for IGE and inherited variability capture the effect of competition on inherited variability. Here, we investigate the potential of current statistical models for inherited variability and trait values, to capture the direct and indirect genetic effects of competition on variability. Our results show that a direct model of inherited variability almost entirely captures the genetic sensitivity of individuals to competition, whereas an indirect model of inherited variability captures the cooperative genetic effects of individuals on their partners. Models for trait levels, however, capture only a small part of the genetic effects of competition. The estimation of direct and indirect genetic effects of competition, therefore, is possible with models for inherited variability but may require a two-step analysis., Competing Interests: The authors declare that they have no competing interests., (© 2022 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.)
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- 2022
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