Biology subjects

Bijma, P.

Publications and source records attributed to Bijma, P..

9 recordsLinked to original sources

bioRxiv2024

Quantifying Key Parameters of Environmental Transmission and Age-Specific Susceptibility for Mycobacterium avium subspecies paratuberculosis (MAP)

Paratuberculosis is a chronic disease in cows and other ruminants, caused by Mycobacterium avium subspecies paratuberculosis (MAP). We developed an age-specific dose-response model and two environmental transmission models (Model A and B) to estimate key parameters based on previously published experiments for MAP in dairy cows. In the dose-response model, the age-specific susceptibility decrease rate parameter was estimated at 0.0629{square}wk-1, suggesting that a previously used parameter of 0.1{square}wk-1 may have underestimated the infection risks with increasing age. For the transmission models, Model A represents infectivity differences among transiently infectious (I tr), low shedding (Il), high shedding (Ih) individuals by varying transmission rate parameters ([Formula]) with a standardized constant shedding rate parameter of 2.10{square}wk-1, whereas Model B captures these differences by varying shedding rate parameters ([Formula]) with one transmission rate parameter of 0.0299{square}wk-1. Although both models have identical best estimates and AIC values, Model B exhibited wider 95% confidence intervals (95% CI). In both models, the MAP decay rate parameter was estimated at 0.150{square}wk-1, corresponding to a half-life of MAP of approximately 4.61 weeks, which aligns well with previously published values. To better interpret these parameters and understand how different biological assumptions about infectivity influence predicted exposure, we performed scenario analyses examining environmental contamination over time, along with infection probabilities over exposure timing, exposure duration, and the recipients age at exposure. All 95% CI are provided in the main text.

systems biology↗

bioRxiv2024

How partial phenotyping to reduce generation intervals can help to increase annual genetic gain in selected honeybee populations

Honeybee breeding is organized around annual cycles, following seasonal change. Generation intervals are thus commonly multiples of whole years. Most queens are generally raised during spring or early summer in temperate climates. A generation interval of 1 year limits phenotyping to early recordable traits, before the spring following queens births. Some traits, however, can only be recorded later, as is typically the case for total honey yield. Their recording on selection candidates thus increases the generation interval to at least 2 years, a common interval on the dam path. Using stochastic simulation, we investigated the impact of halving the dam generation interval and therefore recording only early traits on candidate dams. The generation interval on the sire path remained at 2 years with complete phenotyping. Breeding goals with varying weights on early and late traits were considered, as well as different genetic correlations between traits, from negative to positive. The acceleration of the breeding scheme generally resulted in an increased genetic gain for the two-trait breeding goals, from 0% up to +47% after 20 years of selection. Although inbreeding rates per generation were slightly lower in the accelerated breeding scheme, associate inbreeding levels were about 20-30% higher after 20 years of selection due to the 33% increased generation turnover. To avoid too high inbreeding, shortening the generation interval should thus be accompanied by strategies to limit inbreeding while still retaining most of the genetic gain, such as increasing the breeding nucleus size by relaxing selection intensity.

genetics↗

bioRxiv2024

Genomic prediction in insects: a case study on wing morphology traits in the jewel wasp Nasonia vitripennis

BackgroundBiological control is a sustainable strategy to combat agricultural pests. Yet, due to legislation, importing non-native biocontrol agents is increasingly restricted. Thus, selective breeding of biocontrol traits of native species is suggested to enhance performance of existing biocontrol agents. Genomic prediction is a new alternative to exploit genetic variation for improving biocontrol efficacy. This study aims to establish proof-of-principle for genomic prediction in insect biocontrol agents, using wing morphology traits in the parasitoid Nasonia vitripennis Walker (Pteromalidae) as a model. MethodsWe performed genomic prediction using a Genomic Best Linear Unbiased Prediction (GBLUP) model, using a total of 1,230 individuals with 8,639 SNPs generated by genotyping-by-sequencing (GBS). We used individuals from two generations from the outbred HVRx population, 717 individuals from generation G169 and 513 from generation G172. To assess genomic prediction accuracy, we used across- generation validation: forward validation for G172 from G169, backward in time validation for G169 from G172, and also 5-fold cross-validation, randomly using one fifth of the population as validiation and the others as training groups. ResultsFor size-related traits, including tibia length, wing length, width, and second moment wing area, the accuracy of genomic prediction was close to zero in both across-generation validations, but much higher in 5-fold cross-validation (ranging 0.54-0.68). For the shape-related trait wing aspect ratio, a high accuracy was found for all three validation strategies, with 0.47 for across-generation forward validation, 0.65 for across-generation backward validation, and 0.54 for 5-fold cross-validation. ConclusionPromising accuracies were observed for all traits in 5-fold cross-validation, but not in the across-generation validations. Overall, applying genomic selection in insect biocontrol agents with a relative small effective population size seems promising. However, factors such as the biology of insects, the techniques of phenotyping, and costs of large-scale genotyping still challenge the application of genomic selection to biocontrol agents.

genetics↗

bioRxiv2023

Uncertainty in the mating strategy causes bias and inaccuracy in estimates of genetic parameters in honeybees

BackgroundWith the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not. We also investigated two alternative strategies to accommodate phenotypes of open-mated DPQs in the genetic evaluation, adding either a dummy pseudo sire in the pedigree, or a non-genetic effect to the statistical evaluation model to account for the origin of the DPQs mates. ResultsWhen breeding queens were inseminated with semen from drones of a single DPQ and this was known, estimates of genetic parameters and genetic trends were more precise. If they were inseminated using drones from a single or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in the estimates. For colony phenotypes of open-mated DPQs, adding a dummy pseudo sire in the pedigree for each mating location led to considerable overestimation of genetic variances, while correcting for the mating area by adding a non-genetic effect in the evaluation model gave unbiased estimates. ConclusionsKnowing only the dam of the DPQ(s) in the mating may lead to erroneous assumptions on how DPQs were used and cause severe biases in estimates of genetic parameters and genetic trends. Therefore, keeping track in the pedigree of which DPQ(s), and not only which dam of DPQ(s) are used, is recommended. 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.

genetics↗

bioRxiv2023

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations

Genomic selection has become the dominant tool for genetic improvement in livestock and plants. Therefore, its sustainability is essential for global food production. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allele architecture are different for genomic selection and whether they depend on the genetic architectures of traits. Here we investigate the allele frequency changes of loci and new causal mutations under fifty 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 by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and five 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 large effect with 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, it is very important to be able to minimize the impact of hitchhiking and to limit the loss of favorable alleles more that current genomic selection methods do.

genetics↗

bioRxiv2021

The quantitative genetics of the endemic prevalence of infectious diseases: Indirect Genetic Effects dominate heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. Despite the availability of well-established epidemiological theory, 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 the transmission dynamics of infectious diseases for heritable variation and response to selection of the host population, our current theoretical framework of quantitative genetics excludes these dynamics. As a consequence, we do not know which genetic effects of the host population determine the prevalence of an infection, and have no concepts of breeding value and heritable variation for endemic prevalence. Here we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for R0 and for the endemic prevalence of an infectious disease. We first identify the genetic factors that determine the prevalence of an infection, using an approach founded in epidemiological theory. Subsequently we investigate the population level consequences of individual genetic variation, both for R0 and for the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for both prevalence and individual binary disease status, and show that these parameters depend strongly on the level of the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases when prevalence approaches zero, while heritability of individual disease status goes to zero. As a consequence, response of prevalence to selection accelerates considerably when prevalence goes down, in contrast to predictions from classical theory. Finally, we show that most of the heritable variation for the endemic prevalence of an infection is hidden due to indirect genetic effects, suggesting a key role for kin-group selection both in the evolutionary history of current populations and for genetic improvement strategies in animals and plants.

genetics↗

bioRxiv2021

The long-term effects of genomic selection: Response to selection, additive genetic variance and genetic architecture

Genomic selection has revolutionized genetic improvement in animals and plants, but little is known of its long term effects. Here we investigate the long-term effects of genomic selection on the change in the genetic architecture of traits over generations. We defined the genetic architecture as the subset, allele frequencies and statistical additive effects of causal loci. 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. The observed change in genetic architecture over generations was similar for genomic and pedigree selection, and slightly smaller for phenotypic selection. Short-term response was highest with genomic selection, while long-term response was highest with phenotypic selection, especially when non-additive effects were present. This was mainly because the loss in genetic variance and in segregating loci was much greater with genomic selection. Compared to pedigree selection, genomic selection lost a similar amount of the genetic variance but maintained more segregating loci, which on average had lower minor allele frequencies. For all selection methods, the presence of epistasis limited the changes in allele frequency and the fixation of causal loci, and substantially changed the statistical additive effects over generations. Our results show that non-additive effects can have a substantial impact on the change in genetic architecture. Therefore, non-additive effects can substantially impact the accuracy and future genetic gain of genomic selection.

genetics↗

bioRxiv2018

Required marker properties for unbiased estimates of the genetic correlation between populations

Populations generally differ in environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in both populations is known as the additive genetic correlation between populations and can differ from one. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect bias of the estimated genetic correlation. We simulated two populations that were separated for 50 generations. Markers and causal loci were selected to either have similar or different allele frequencies in the two populations. Differences in consistency of LD between populations were obtained by using different marker density panels. Results showed that when the difference in allele frequencies of causal loci between populations was reflected by the markers, genetic correlations were only slightly underestimated using markers. This was even the case when LD patterns, measured by LD statistic r, were different between populations. When the difference in allele frequencies of causal loci between populations was not reflected by the markers, genetic correlations were severely underestimated. We conclude that for an unbiased estimate of the genetic correlation between populations, marker allele frequencies should reflect allele frequencies of causal loci so that marker-based relationships can accurately predict the relationships at causal loci, i.e. E(Gcausal loci|Gmarkers) = Gmarkers. Differences in LD between populations have little effect on the estimated genetic correlation.

genetics↗

bioRxiv2017

Properties Of Genomic Relationships For Estimating Current Genetic Variances Within And Genetic Correlations Between Populations

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate the genomic relationship matrix affect the estimate of genetic variances, covariances and correlations. The aim of this paper is to define a multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that current genetic variances are estimated when the current population is used as base population of the relationship matrix. In this paper, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations and validated it using simulations. When the scaling factors of the genomic relationship matrix fulfill the property [Formula], the genetic correlation is estimated even though estimated variance components are not necessarily related to the current population. When this property is not met, the correlation based on estimated variance components should be multiplied by [Formula] to rescale the genetic correlation. In this study we present a genomic relationship matrix which directly results in current genetic variances as well as genetic correlations between populations.

genetics↗