Biology subjects

Vandenplas, J.

Publications and source records attributed to Vandenplas, J..

7 recordsLinked to original sources

bioRxiv2025

Modeling missing parents in single-step test-day SNP-BLUP evaluation of dairy cattle

In many countries, single-step genomic models have replaced multiple step models for routine evaluation. These models use all available information on animals phenotypes, genotypes, and pedigrees, yet missing parental information in pedigrees remains a challenge that affects genomic breeding value (GEBV) predictions. Therefore, the choice of method for handling missing parents can affect the prediction of breeding values. Here, we compared three approaches to model missing parental information for three levels of missing pedigree data: P_Real - pedigree from routine evaluation, P_2010 - at least 20 percent of dams and 10 percent of sires born before 2019 were set to missing, and P_4020 - at least 40 percent of dams and 20 percent of sires born before 2019 were set to missing. Missing parents information was expressed through missing codes in the raw pedigree (RP) by defining genetic groups (GG) that represent missing parents grouped based on year of birth, sex, and country of origin, or by defining metafounders (MF), which represent missing parents grouped by average genetic relationships estimated from the genomic information of their descendants. The genomic breeding values for fat yield were estimated using the single-step test-day SNP-BLUP model implemented with MiXBLUP software. For the considered scenarios, the results were presented separately for sires and dams, as well as for genotyped and ungenotyped individuals. We observed differences in the prediction quality between genotyped and ungenotyped animals. While GEBV predictions for the former were generally stable across scenarios, the predictions for the ungenotyped individuals varied. In particular, the removal of parental information led to less stable results when missing parental information was expressed by MF, where insufficient pedigree completeness resulted in an overestimation of the genetic trend. In conclusion, for informative pedigrees with a small percentage of missing parents, the incorporation of GG and MF results in very similar GEBV predictions, however GG appear to be a more robust approach for ungenotyped individuals in highly incomplete pedigrees.

animal behavior and cognition↗

bioRxiv2025

Characterization of a novel heat tolerance trait and subsequent haplotype block-based analysis to identify causal regions in Dutch Holstein cattle

Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying heat tolerant animals is crucial for sustainable dairy production. This study develops a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently defines individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems, were analyzed. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature humidity index (THI) with an absolute reduction of 0.3% from THI = 30 to THI = 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically reaching 5.0% at a THI of 75. The phenotype of an animal is subsequently defined as the slope in a linear regression model of the residuals of the population-wise models against THI for milk yield and concentration of fat, protein, lactose, and specific fatty acids. Compared to reaction-norm models, individual records per cow are combined before model fitting, thereby reducing computation times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by one genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations to health and resilience. A genome-wide association study using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTLs across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the potential of breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts. Interpretive summaryThis study introduced a novel, computationally efficient method to quantify the impact of heat stress in dairy cattle and define novel heat tolerance traits based on milk production data from automated milking systems. Our results indicate quadratically increasing losses in milk yield with increasing heat load. The identified heat tolerance traits show substantial genetic variance, moderate heritabilities, and favorable correlation to key production traits. These findings highlight the potential for incorporating heat tolerance into dairy breeding goals to mitigate climate change impacts, improve animal welfare, and enhance sustainable milk production.

genetics↗

bioRxiv2025

Trajectories of genetic correlations in populations under selection: from theory to a case-study

BackgroundBreeding programs select for multiple commercial traits, aiming to achieve genetic progress for all. Often, selection is based on a selection index, i.e. a linear combination of traits with weights defined by, among other information, the genetic correlation between traits. These correlations are typically estimated as a static parameter, and assumed equal to all individuals and generations. While research on the consequences of selection to genetic variances (Bulmer effect) is widely available, only a few studies focused on the consequences of selection to genetic correlations. Our study extended the already existing inferences about how selection affects genetic variances, to how multi-trait selection affects genetic correlations. In order to further our understanding of genetic correlations, we also proposed an alternative method to calculate genetic correlations between traits at the individual level, called by us as individualized sire genetic correlation (iSGC), obtained through the estimated breeding values (EBV) from evaluated daughters. Lastly, a case-study was performed on thirty years of data from the French Holstein dairy cattle population, for five traits studied pairwise: milk and protein yield, milking speed, somatic cell score, and cow conception rate. ResultsTheory revealed that multi-trait selection leads to an attenuation (decrease) of positive genetic correlations, with potential to revert them to negative values, if initially low. Uncorrelated traits will become negatively correlated, and negative genetic correlations will be either intensified or attenuated (decrease or increase, respectively), depending on selection intensity, weights applied to the selection index, and the initial genetic correlation. ConclusionBoth theory and empirical results on real data confirm that selection does change the genetic correlation between traits in a population under selection. Moreover, empirical trajectories of the iSGC were in better agreement with the theory, than trajectories of populational genetic correlations. The iSGC searches for individual-specific patterns of correlations, and since it is measured on sires through the EBV of their daughters, it also considers the recombination of the genetic background. Along with the fact that trajectories of iSGC were in better agreement with theory, we believe it to be a potentially less biased measure of genetic correlations between traits.

genetics↗

bioRxiv2023

Accelerated computations for iterative-solver techniques in single-step BLUP models

In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an extension to the C/CUDA library miraculix, we have developed tailored solutions for the computation of genotype matrix multiplications which is a critical bottleneck in the empirical evaluation of many statistical models. We demonstrate the benefits of our solutions at the example of single-step models which make repeated use of this kind of multiplication. Targeting modern Nvidia(R) GPUs as well as a broad range of CPU architectures, our implementation significantly reduces the time required for the estimation of breeding values in large population sizes. miraculix is released under the Apache 2.0 license and is freely available at https://github.com/alexfreudenberg/miraculix.

bioinformatics↗

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↗

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↗

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↗