Biology subjects

Calus, M. P. L.

Publications and source records attributed to Calus, M. P. L..

11 recordsLinked to original sources

bioRxiv2025

Selection History Models in a Population under Ongoing Directional Selection

The aim of animal breeding is to select the genetically best animals in the current generation to improve the performance of future generations for a specific breeding goal. With the continuous shift in breeding goals towards more balanced breeding, new traits may become of interest. Knowledge of the (indirect) selection history of these traits would be insightful before a trait is included in the breeding goal. Two models, BayesS and [G], have been developed to assess the selection history of traits. BayesS estimates a parameter (s) that reflects the relationship between estimated additive effects and minor allele frequency, while [G] calculates the expected genetic change of a trait based on allele frequency changes and estimated additive marker effects. The aim of this study was to evaluate the performance of estimating s-values (based on BayesS) and [G] in an animal breeding context, focusing on their ability to detect selection for a trait with low heritability. Both [G] and s-value estimation were applied to a simulated dataset of a commercial pig breeding program under phenotypic selection, with varying heritabilities (0.05, 0.1, 0.3) and 30 generations of ongoing selection. Overall, both models were able to detect selection, where higher heritabilities and a larger sample size (for s-value estimation) or a larger selection interval (for [G]) resulted in increased detection of selection. The preferred model to identify selection varied based on the available data of the breeding population.

genetics↗

bioRxiv2025

Strategies to improve selection compared to selection based on estimated breeding values

BackgroundSelection of individuals based on their estimated breeding values aims to maximize response to selection to the next generation in the additive model. However, when the aim is not only about short-term population-wide genetic gain but also the gain over multiple generations, an optimal strategy is not as clear-cut, as the maintenance of genetic diversity may become an important factor. This study provides an extended comparison of existing selection strategies in a unifying testing pipeline using the simulation software MoBPS. ResultsApplying a weighting factor on the estimated SNP effects based on the frequency of the beneficial allele resulted in an increase of the long-term genetic gain of 1.6% after 50 generations while reducing inbreeding rates by 16.2% compared to truncation selection based on estimated breeding values. However, this also resulted in short-term losses in genetic gain of 1.2% with the break-even point reached after 25 generations. In contrast, inclusion of the average kinship of an individual to top individuals of the population as an additional trait in the selection index with a weight of 17.5% resulted in no short-term losses and increased long-term genetic gains by 4.3% while reducing inbreeding by 15.8%, achieving very similar efficiency to the use of optimum genetic contribution selection. Combining multiple diversity management strategies, with weights for each strategy optimized using an evolutionary algorithm resulted in a breeding scheme with 5.1% increased genetic gain and 37.3% reduced inbreeding rates. The proposed strategy included the use of optimum genetic contribution, weighting of SNP effects based on allele frequency, average kinship as a trait in the selection index, avoiding matings between related individuals, and lowering the proportion of selected individuals. ConclusionsThe combination of selection strategies for the management of genetic diversity was shown to be far superior to the use of any singular method tested in this study. As an efficient use of methods for the management of genetic diversity and inbreeding does not necessarily lead to short-term losses in genetic gain and comes at no extra costs, it is critical for breeding companies to implement such strategies for long-term success.

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↗

bioRxiv2022

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Recent developments allowed generating multiple high quality omics data that could increase predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here we have assessed the performance of parametric and non-parametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using best linear unbiased prediction (BLUP), while non-parametric models were implemented using the gradient boosting machine algorithm (GBM). We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While GBM models captured more phenotypic variation, their predictive performance did not exceed the BLUP models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the GBM model was able to outperform BLUP for most traits except body weight, but the same pattern was not observed when using both SNP genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

genetics↗

bioRxiv2021

Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice

Recent literature suggests machine learning methods can capture interactions between loci and therefore could outperform linear models when predicting traits with relevant epistatic effects. However, investigating this empirically requires data with high mapping resolution and phenotypes for traits with known non-additive gene action. The objective of the present study was to compare the performance of linear (GBLUP, BayesB and elastic net [ENET]) methods to a non-parametric tree-based ensemble (gradient boosting machine - GBM) method for genomic prediction of complex traits in mice. The dataset used contained phenotypic and genotypic information for 835 animals from 6 non-overlapping generations. Traits analyzed were bone mineral density (BMD), body weight at 10, 15 and 20 weeks (BW10, BW15 and BW20), fat percentage (FAT%), circulating cholesterol (CHOL), glucose (GLUC), insulin (INS) and triglycerides (TGL), and urine creatinine (UCRT). After quality control, the genotype dataset contained 50,112 SNP markers. Animals from older generations were considered as a reference subset, while animals in the latest generation as candidates for the validation subset. We also evaluated the impact of different levels of connectedness between reference and validation sets. Model performance was measured as the Pearsons correlation coefficient and mean squared error (MSE) between adjusted phenotypes and the models prediction for animals in the validation subset. Outcomes were also compared across models by checking the overlapping top markers and animals. Linear models outperformed GBM for seven out of ten traits. For these models, accuracy was proportional to the traits heritability. For traits BMD, CHOL and GLU, the GBM model showed better prediction accuracy and lower MSE. Interestingly, for these three traits there is evidence in literature of a relevant portion of phenotypic variance being explained by epistatic effects. We noticed that for lower connectedness, i.e., imposing a gap of one to two generations between reference and validation populations, the superior performance of GBM was only maintained for GLU. Using a subset of top markers selected from a GBM model helped for some of the traits to improve accuracy of prediction when these were fitted into linear and GBM models. The GBM model showed consistently fewer markers and animals in common among the top ranked than linear models. Our results indicate that GBM is more strongly affected by data size and decreased connectedness between reference and validation sets than the linear models. Nevertheless, our results indicate that GBM is a competitive method to predict complex traits in an outbred mice population, especially for traits with assumed epistatic effects.

genetics↗

bioRxiv2021

Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP - using real data from pigs

BackgroundEmpirically assessing the impact of preselection on subsequent genetic evaluations of preselected animals requires comparison of scenarios taking into account different approaches, including scenarios without preselection. However, preselection almost always takes place in animal breeding programs, so it is difficult to have a dataset without preselection. Hence most studies on preselection used simulated datasets, concluding that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased. The aim of this study was to investigate the impact of genomic preselection (GPS) on accuracy and bias in subsequent ssGBLUP evaluations, using data from a commercial pig breeding program. MethodsWe used data on four pig production traits from one sire line and one dam line. The traits are average daily gain during performance testing, average daily gain throughout life, backfat thickness, and loin depth. As these traits had different weights in the breeding goals of the two lines, we analyzed the two lines separately. Per line, we had a reference GPS scenario which kept all available data, against which the next two scenarios were compared. We then implemented two other scenarios with additional layers of GPS by removing all animals without progeny either i) only in the validation generation, or ii) in all generations. We conducted subsequent ssGBLUP evaluations per GPS scenario, utilizing all the data remaining after implementing the GPS scenario. In computing accuracy and bias, we compared GEBV against progeny yield deviations of validation animals. ResultsResults for all traits in both lines showed marginal loss in accuracy due to the additional layers of GPS. Average accuracy across all GPS scenarios in both lines was 0.39, 0.47, 0.56, and 0.60 respectively for the four traits considered in this study. Bias was largely absent, and when present did not differ greatly among corresponding GPS scenarios. ConclusionAs preselection generally has the same effect in animal breeding programs, we concluded that impact of preselection is generally minimal on accuracy and bias in subsequent ssGBLUP evaluations of selection candidates in pigs and in other animal breeding programs.

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

Genomic prediction using individual-level data and summary statistics from multiple populations

This study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multi-population data is an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population specific information. We show that population specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e. the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enables integration of different sources of information across a wide range of settings yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics form from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions.

genomics↗

bioRxiv2018

Holstein Friesian mid-lactation Milk Polar Metabolite Composition in relation to Dietary Fat Composition and Diacylglycerol O-acyltransferase 1 Genotype

BackgroundThe metabolite composition of cow milk is dependent on a large variety of animal associated factors including diet, genotype and gut microbiome composition. The objective of this study was to investigate changes in cow milk polar metabolite composition resulting from dietary and DGAT1 (Diacylglycerol O-acyltransferase 1) genotype perturbations.\n\nMethods and ResultsCows were fed a standard diet and a diet supplemented with (poly)unsaturated fatty acids (experimental diet) for ten weeks. Metabolite profiles were determined using 1H NMR (1-Hydrogen Nuclear magnetic resonance) technology. The results showed that the diet affected the polar metabolite composition of milk via the metabolism of the cow and via the metabolism of the gut and rumen microbiota. The experimental diet reduced the metabolic rate, especially the energy metabolism and the amino-sugar and amino acid metabolism, of the cows.\n\nConclusionOur results suggests the DGAT1 genotype affects both the diet related polar metabolite metabolism of the cow as well as that of the rumen microbiota. Milk metabolite levels in animals with more DGAT1 A-alleles were higher than milk metabolite levels in animals with more K-alleles.

genomics↗

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↗