1,720,975 research outputs found
Genomic evaluation considering the mosaic genome of the crossbred pig
Full text linkIn pigs, the breeding goal is to improve performance of crossbred (CB) animals in commercial farms. The best purebred (PB) animals to produce CB animals can be selected based on their genomic estimated breeding value (GEBV) for CB performance. GEBVs are the result of combining estimated effects of single nucleotide polymorphisms (SNPs) with the animal’s genotype. Using CB genomic information allows to estimate SNP allele effects accounting for the CB genetic background. The genome of CB animals is a mosaic of genomic regions inherited from the different parental breeds, therefore, this thesis aimed to investigate whether SNP alleles have different effects depending from which parental breed the allele was inherited and study the impact on GEBV of PB animals for CB performance when breed-specific allele effects were taken into consideration. Firstly, I showed that around 94 % of alleles of three-way CB pigs can be assigned a breed of origin. Knowing this, allowed me to implement a model that accounts for breed-specific effects of all SNP alleles. Using results of this model, I showed that estimated effects and explained variance of SNPs strongly associated with CB performance are different depending upon from which parental breed they were inherited, however, the majority of the genomic regions are not or only weakly associated with CB performance. Therefore, I implemented a new model that allows to estimate breed-specific effects only for alleles of SNPs strongly associated with CB performance, and for the rest of the SNPs assumes that allele effects are the same across breeds. Differences of prediction accuracies between models were generally small. When the estimated genetic correlation between the performance of PB and CB animals per breed of origin differed largely across models, it was better to use models that make a distinction of alleles according to their breed of origin and whether or not they were associated to the trait.</p
Genetics of crossbreeding
Full text linkIn pig and poultry breeding programs, animals from genetically distinct purebred breeding lines are mated to produce crossbred animals, which provide food products to consumers. Although the aim of such breeding programs is to improve the performance of the crossbreds, selection takes place in the purebred lines, and is usually based on purebred performance. This strategy may be suboptimal because the genetic correlation between purebred and crossbred performance () is usually lower than one. When is lower than one, it may be beneficial to make selection decisions based on information on crossbred performance instead of purebred performance. This is, however, a challenging task, because purebred animals cannot be tested directly for performance at the crossbred level. Now, with the recent developments in genomic prediction, it has become possible to estimate breeding values for crossbred performance of purebred animals. In this thesis, I studied the genetics of crossbreeding, with a focus on genomic prediction for crossbred performance in purebred lines. First, I illustrate how interactions between genes can lead to differences in genetic trait expression between lines, and how such interactions can lead to values that are lower than one. The results show that decreases as the genetic distance between parental lines increases. I derive expressions for based on genetic parameters in the parental lines, which allows breeders to estimate bounds of without having to collect crossbred data. Second, I show that genotype-based models lead to larger estimated with smaller standard errors than pedigree-based models. In contrast to my expectation, considering breed-of-origin of alleles in genotype-based models does not yield different estimates of . Third, I investigate the benefit of training the genomic prediction model with crossbred instead of purebred data. The results show that crossbred data improves the accuracy of breeding values for a trait with an of 0.8, but not for a trait with an of 0.96. Furthermore, taking the breed-of-origin of alleles into account is beneficial for a trait with an of 0.8, but not for a trait with an of 0.96. Finally, I discuss the relationship between and heterosis in the presence of gene interactions, and strategies to estimate breeding values for crossbred performance of purebreds. The results in this thesis improve our understanding of the genetics of crossbreeding, and facilitate the optimization of breeding programs that aim to improve crossbred performance with selection in purebred breeding lines
Impact of preselection in genomic evaluations
No full textThe development of genomic evaluation models over the last two decades has resulted in faster genetic improvement of animals, compared to when only pedigree-based genetic evaluation models were used. In large animal breeding programs, selection of parents of the next generation usually takes place in multiple stages, and the initial stages of this selection are collectively called preselection. Preselection takes place when selection candidates are young, sometimes even before they have records for any breeding goal trait. As the preselected animals grow older, they generally get records for more breeding goal traits, and they are re-evaluated in subsequent evaluations to select the final set of parents of the next generation. Impact of preselection on accuracy and bias of subsequent genomic evaluation of preselected animals is poorly understood. The same applies for the role of genotypes of preculled animals (i.e. animals removed from the breeding program at preselection stage) in subsequent genomic evaluation of their preselected sibs. In this thesis, I used single-step genomic best linear unbiased prediction (ssGBLUP) as the representative genomic evaluation model, and used simulated and real datasets to investigate the impact of i) types and intensities of preselection and ii) genotypes and phenotypes of different groups of animals, on accuracy and bias in ssGBLUP evaluation of preselected animals. I showed that preselection, regardless of its type and intensity, results in some accuracy loss in subsequent ssGBLUP evaluation of preselected animals, compared to a scenario without preselection. I explained that the accuracy loss is mainly due to loss of relatives with records. I also showed that ssGBLUP evaluates preselected animals without preselection bias, regardless of type and intensity of preselection. I further showed that genotypes of preculled animals are only needed in subsequent ssGBLUP evaluation of their genomically preselected sibs if some of their parents are not genotyped. The results of this thesis also showed that if ssGBLUP is used in subsequent evaluation of genomically preselected animals, realized genetic gain is only slightly lower compared to a scenario without preselection. To minimize this loss of genetic gain as a result of genomic preselection, I recommended that commercial animal breeding programs genotype as many young selection candidates as economically possible
Using genomic information to conserve genetic diversity in livestock
Full text linkConcern about the status of livestock breeds and their conservation has increased as selection and small population sizes caused loss of genetic diversity. Meanwhile, dense SNP chips and whole genome sequences (WGS) became available, providing opportunities to accurately quantify the impact of selection on genetic diversity and develop tools to better preserve such genetic diversity for long-term perspectives. This thesis aimed to infer the impact of selection and mitigate its effects on genetic diversity using genomic information. One of the advantages of WGS information, compared to pedigree and SNP chip information, is that it provides information on all variants, including rare ones, and ‘true’ relationships between individuals may be estimated thus being useful for evaluating genetic diversity. Taking into account rare variants had significant effects on estimated relationships. Moreover, optimal contribution (OC) strategy was used to perform selection either in a breeding program, maximising genetic merit while minimising loss of genetic diversity, or to build a gene bank, only maximising the conserved genetic diversity, with the aim to quantify loss of genetic diversity due to selection decisions. More genetic diversity was conserved when genomic information was used for selection decisions instead of pedigree and WGS information revealed a high loss of genetic diversity due to losing rare variants. Ways to reduce the loss of genetic diversity during a genomic selection program were investigated. The choice of individuals to update the reference population was proposed as a promising way to better conserve genetic diversity in a breeding population. In fact, changes in the reference population will lead to changes in prediction equations and thus ultimately to a shift in long-term selection decisions. Differences between reference population design using either random, truncation or OC selection of individuals, on the breeding population were modest but OC achieved conservation of more genetic diversity in the breeding population with only a small reduction in long-term genetic gain. Finally the potential of gene bank material as additional source of genetic diversity in the breeding population was examined, using the Dutch MRY cattle breed as a case study. Including old bulls, containing more genetic diversity than recent bulls, in the population of fathers for the next generation, selected with OC, resulted in both a slightly higher genetic merit and more genetic diversity conserved. The impact of selection on genetic diversity can be monitored by estimating the loss of rare variants over time. For the long-term perspectives of populations it is important to use specialised methods and genomic information to balance between selection response and conservation of genetic diversity. </p
Prospects of whole-genome sequence data in animal and plant breeding
No full textThe rapid decrease in costs of DNA sequencing implies that whole-genome sequence data will be widely available in the coming few years. Whole-genome sequence data includes all base-pairs on the genome that show variation in the sequenced population. Consequently, it is assumed that the causal mutations (e.g. quantitative trait loci; QTL) are included, which allows testing a given trait directly for association with a QTL, and might lead to discovery of new QTL or higher accuracies in genomic predictions compared to currently available marker panels. The main aim of this thesis was to investigate the benefits of using whole-genome sequence data in breeding of animals and plants compared to currently available marker panels. First the potential and benefits of using whole-genome sequence data were studied in (dairy) cattle. Accuracy of genotype imputation to whole-genome sequence data was generally high, depending on the used marker panel. In contrast to the expectations, genomic prediction showed no advantage of using whole-genome sequence data compared to a high density marker panel. Thereafter, the use of whole-genome sequence data for QTL detection in tomato (S. Lycopersicum) was studied. In a recombinant inbred line (RIL) population, more QTL were found when using sequence data compared to a marker panel, while increasing marker density was not expected to provide additional power to detect QTL. Next to the RIL population, also in an association panel it was shown that, even with limited imputation accuracy, the power of a genome-wide association study can be improved by using whole-genome sequence data. For successful application of whole-genome sequence data in animals or plants, genotype imputation will remain important to obtain accurate sequence data for all individuals in a cost effective way. Sequence data will increase the power of QTL detection in RIL populations, association panels or outbred populations. Added value of whole-genome sequence data in genomic prediction will be limited, unless more information is known about the biological background of traits and functional annotations of DNA. Also statistical models that incorporate this information and that can efficiently handle large datasets have to be developed.</p
Genomic breeding value prediction:methods and procedures
No full textAnimal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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