Biology subjects

Gorjanc, G.

Publications and source records attributed to Gorjanc, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Pedigree-tracking improved population monitoring of recessive disease-causing alleles in pedigreed canine populations

Background Most monogenic inherited diseases in dogs are recessive, so their successful control depends on genetic testing to identify carriers of the disease-causing allele. However, currently used methods for disease allele monitoring in canine populations typically only consider part of the entire pedigree, since not all dogs have a known genotype through genetic test or "hereditary status" (a determinable genotype assigned to offspring based on known parental genotypes). Methods This study uses probabilistic inference with unknown parent groups (UPGs) in five Royal Kennel Club (RKC)-registered pedigrees to estimate genotype probabilities for all individuals using available genetic test results for two monogenic recessive diseases: primary lens luxation (PLL) and progressive retinal atrophy rod-cone degeneration 4 (PRA-rcd4). From these estimates, the population trends in disease-causing allele frequencies were estimated, and the inference quality was assessed through leave-one-out cross-validation and simulation. Results For dogs born after the introduction of the genetic test, the estimated disease-causing allele frequencies differed between methods. For instance, dogs born in 2020 reported up to 0.092 higher disease-causing allele frequencies with estimated genotype probabilities than those based on genetic tests and hereditary status alone. Inference performance varied across breeds, dependent on population allele frequency, with Pearson correlations between true (per genetic test result) and estimated allele dosages ranging from 0.47 to 0.67. The gene drop simulations indicated a tendency to underestimate disease-causing allele frequencies in retrospective estimates, particularly for dogs born before 1990. Impact Overall, by leveraging all available pedigree and genetic test data through probabilistic pedigree-based inference, disease-causing allele frequencies were monitored more comprehensively within the pedigreed population rather than relying on genetic test results or hereditary status alone. Such monitoring can inform responsible breeding decisions, helping to conserve genetic diversity whilst supporting improvements in future canine health and welfare.

genetics↗

bioRxiv2025

Estimating haplotype values and mutation effects in the context of a local DNA tree

BackgroundSingle nucleotide polymorphism (SNP) arrays provide genome-wide coverage of polymorphic sites across populations, but most quantitative genetics models assume that the effects of these mutations remain constant across generations and populations. This assumption overlooks population dynamics and genetic architecture that are central to trait expression, particularly when inferences are made across populations. Whole-genome sequence (WGS) data captures all variants and should, in principle, overcome these limitations, but its use has delivered only modest gains in prediction accuracy at considerable computational cost. Ancestral recombination graphs (ARGs) offer a representation of genome variations that describes how genetic variation is shaped by haplotype inheritance between generations (represented as local DNA trees) and associated mutations. This study investigates how a generative model on a local DNA tree can improve the estimation of mutation and haplotype effects, especially for rare or population-specific variants. MethodsWe developed a TBLUP approach that uses local DNA tree information to estimate haplotype and mutation effects. In each local DNA tree, branches connecting ancestral and descendant haplotypes represent DNA inheritance, and the trait associated with a branch corresponds to the mutation(s) it carries. Summing these branch-specific mutation effects from the tree root to each haplotype defines the haplotype values. This recursive structure yields a sparse and computationally efficient approach for estimating haplotype and mutation effects from the local DNA tree and phenotypes. We show how the TBLUP approach is similar and different to the SNP-BLUP and GBLUP approaches and demonstrate it with cattle mitochondrial DNA, a non-recombining genomic region, using both simulated and real data. Results and conclusionsThe TBLUP approach was computationally more efficient than SNP-BLUP/GBLUP approaches and produced more accurate estimates of haplotype values and mutation effects, which can vary between haplotypes. The accuracy increased when phenotypes were available for haplotypes across the local DNA tree rather than only for the recent haplotypes. Incorporating local DNA tree information enhances the use of genomic data in quantitative genetics. Extending the TBLUP approach to full ARGs will enable analysis across multiple local DNA trees (accounting and leveraging recombination), which will further improve quantitative genetic modelling and practical applications.

genomics↗

bioRxiv2025

Modelling the impacts of imports of non-native honeybees into the native Apis mellifera mellifera populationin Ireland

Human-mediated movement of organisms for agriculture and ecosystem services often results in hybridisation and introgression between populations of native and non-native species. While introgression may increase genetic diversity, it can erode unique adaptations and reduce fitness, threatening the survival of native lineages. Honeybees offer a good model with extensive records, queen trade and migratory beekeeping facilitating genetic exchange among subspecies. To explore these dynamics, we used SIMplyBee to simulate hybridisation between populations of native Apis mellifera mellifera and non-native A. m. carnica. We adopted the parameters from the Irish honeybee population that maintains relatively low levels of import, but is threatened by commercial imports. The model included colony honey yield and fitness as complex polygenic traits. We simulated varying import rates, genetic correlations between fitness in native and non-native environments, and spatial distributions of introgression over 20 years, measuring levels and rate of introgression and genetic means for both traits. Increased imports accelerated introgression and induced a trade-off between higher honey yield and lower fitness, and decreasing genetic correlations between environments amplified fitness decline. Spatial simulations showed the spread of introgression across the entire simulated area. Halting imports reversed the trend, but purging of introgressed material was slow and varied among replicates. These findings highlight the trade-off between short-term production gains and long-term losses in fitness and adaptation. Our modelling framework provides a reference for exploring introgression in other systems, emphasising that sustainable management of introgression requires restricting imports and breeding locally adapted populations rather than relying on non-native imports.

bioinformatics↗

bioRxiv2025

Modelling complex traits with ancestral recombination graphs

Ancestral recombination graphs (ARGs) are an attractive means for quantitative genetic analysis of complex traits because they encode the realized genetic relatedness between a sample of individuals in the presence of genetic drift, recombination, and mutation. Data structures for efficiently storing ARGs can also be used to rapidly process millions of genomes, and are thus promising for fitting linear mixed models (LMMs) to large phenotype and genome datasets. Here, we study the problems of variance component estimation and prediction of genetic values with ARGs, by describing a generative model of complex traits with additive effects on an ARG, and then developing algorithms that use the ARG to solve these problems efficiently on biobank-scale datasets. We observe nearly linear scaling of runtime with sample size, which is achieved by using the succinct tree sequence representation of the ARG for implicit matrix-vector products, along with modern randomized linear algebra algorithms. We estimate variance components using restricted maximum likelihood (REML), which we find performs substantially better than the Haseman- Elston method. In simulation tests, both variance component estimation and prediction of genetic values (using the best linear unbiased predictor, BLUP) perform nearly as well with inferred ARGs as with true ARGs. We also discuss interpretations of the variance component estimates as mutational variance and additive genetic variance. We provide an implementation of the algorithms as a Python package tslmm, which leverages the tree sequence library tskit.

genomics↗

bioRxiv2025

High quality genome assemblies of African cattle breeds using PacBio HiFi sequencing

Africa has a uniquely rich cattle diversity of [~]150 breeds comprising the Bos taurus indicus sub-species, Bos taurus taurus, and their crosses. These represent [~]23% of the global cattle population. However, high quality, representative assemblies are limited for African cattle and especially for indicine breeds. Here we built high quality de novo assemblies for five important African indigenous cattle breeds using PacBio HiFi sequencing: Lagune (Bos taurus taurus), Gudali, Iringa Red and Singida White (Bos taurus indicus), and Mpwapwa (Bos taurus taurus x Bos taurus indicus). These new assemblies are the most contiguous and complete African cattle assemblies produced so far, with genome sizes of 3.25 - 3.36Gb, contiguity N50s ranging from 83.59Mb to 97.87Mb and scaffold N50s from 100.30Mb to 113.37Mb. BUSCO genome completeness scores were also higher than 99.68%, indicative of highly contiguous assemblies. These improved and highly contiguous genome assemblies are consequently a valuable resource for future African and global livestock genomic studies.

genomics↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2025

randPedPCA: Rapid approximation of principal components from large pedigrees

BackgroundPedigrees continue to be extremely important in agriculture and conservation genetics with the pedigrees of modern breeding programmes easily comprising millions of records. The structure of such pedigrees is challenging to visualise. Being directed acyclic graphs, pedigrees can be represented as matrices. Common choices are the numerator relationship matrix, A, and the adjacency matrix T. With these matrices, the structure of pedigrees can then, in principle, be visualised via principal component analysis (PCA). However, the naive PCA of matrices for large pedigrees is challenging due to computational and memory constraints. ResultsWe present the open-access R package randPedPCA for rapid pedigree PCA using sparse matrices and randomised linear algebra. Our rapid pedigree PCA builds on the fact that matrix-vector multiplications with the numerator relationship matrix can be carried out implicitly using the extremely sparse inverse Cholesky factor of the numerator relationship matrix. We demonstrate the utility of randPedPCA using several simulated datasets of pedigrees and SNPs. We then demonstrate the performance of randPedPCA by analysing the pedigree of the UK Labrador Retriever breeding population of almost 1.5 million individuals. ConclusionsThe structure of pedigrees can be efficiently and rapidly visualised using scatter plots of principal component scores. For large pedigrees, this is considerably faster than rendering plots of a pedigree graph.

genetics↗

bioRxiv2025

Spatial modelling improves genomic evaluation in Tanzanian smallholder admixed dairy cattle

BackgroundSmallholder dairy production systems in low-and middle-income countries are characterised by large phenotypic variance due to diverse environmental effects, farming practices, and crossbreeding. Furthermore, small herds, low genetic connectedness, and limited data recording challenge accurate separation of environmental and genetic effect in such settings, limiting genetic improvement. Here, we evaluated the impact of modelling spatial variation between herds to address these challenges and improve the accuracy of genomic evaluation for Tanzanian smallholder dairy cattle. ResultsWe analysed 19,375 test-day milk yield records of 1894 dairy cows from 1386 herds across four distinct geographical regions in Tanzania. The cows had 664,822 SNP marker genotypes after quality control and were highly admixed. We fitted a series of GBLUP models to evaluate the impact of modelling the herd effect and the spatial effect on. The herd effect was fitted as an independent random effect, while the spatial effect was fitted as a random effect with Euclidean distance-based Matern covariance function. The models were compared based on: model fit; estimates of variance components and breeding values; correlations between the estimated contribution of breeding values, herd effect, and spatial effect to phenotype values; and the accuracy of phenotype prediction in cross-validation and forward validation. The results showed large differences in milk yield between and within regions, as well as significant variation due to the spatial effect, which were not fully captured by modelling the herd effect. The results also strongly indicate that a model with just the herd effect underestimated breeding values of animals in less favourable environments and overestimated breeding values of animals in more favourable environments. ConclusionsThis study demonstrated the challenge of achieving accurate genomic evaluation in smallholder settings. By leveraging spatial modelling we maximised the use of available data and improved the separation of genetic and environmental effects. Further work is required to improve smallholder genetic evaluations by understanding environmental and genetic processes that drive the large phenotypic variance in African smallholder setting.

genomics↗

bioRxiv2025

On ARGs, pedigrees, and genetic relatedness matrices

Genetic relatedness is a central concept in genetics, underpinning studies of population and quantitative genetics in human, animal, and plant settings. It is typically stored as a genetic relatedness matrix (GRM), whose elements are pairwise relatedness values between individuals. This relatedness has been defined in various contexts based on pedigree, genotype, phylogeny, coalescent times, and, recently, ancestral recombination graph (ARG). ARG-based GRMs have been found to better capture the structure of a population and improve association studies relative to the genotype GRM. However, calculating GRMs and further operations with them is fundamentally challenging due to inherent quadratic time and space complexity. Here, we first discuss the different definitions of relatedness in a unifying context, making use of the additive model of a quantitative trait to provide a definition of "branch relatedness" and the corresponding "branch GRM". We explore the relationship between branch relatedness and pedigree relatedness through a case study of French-Canadian individuals that have a known pedigree. Through the tree sequence encoding of an ARG, we then derive an efficient algorithm for computing products between the branch GRM and a general vector, without explicitly forming the branch GRM. This algorithm leverages the sparse encoding of genomes with the tree sequence and hence enables large-scale computations with the branch GRM. We demonstrate the power of this algorithm by developing a randomized principal components algorithm for tree sequences that easily scales to millions of genomes. All algorithms are implemented in the open source tskit Python package. Taken together, this work consolidates the different notions of relatedness as branch relatedness and by leveraging the tree sequence encoding of an ARG it provides efficient algorithms that enable computations with the branch GRM that scale to mega-scale genomic datasets.

genomics↗

bioRxiv2025

Evaluating genotyping strategies for a small managed population with simulation

BackgroundCollecting genomic information is crucial to advance breeding for complex traits such as health, welfare, and behaviour in domesticated populations. For that purpose, different data collection scenarios can be envisioned based on the number of individuals, the number of markers, and the genotyping technology. This study developed a simulation framework, based on a service dog population, aiming to identify an optimal and cost-effective genotyping strategy that would support the implementation of genomic selection, investigation of the genetic architecture of traits of interest, and track loci of interest. MethodsWe simulated a population based on the existing pedigree, using the gene drop method in AlphaSimR. The existing pedigree was extended with additional progeny generations to evaluate the outcomes of different genotyping strategies in the future. We generated genotype data based on existing high-coverage whole-genome sequences (WGS) for the current breeding dogs and evaluated different scenarios for genotyping the progeny. The genotyping options considered SNP arrays of various densities and WGS callsets produced from different sequencing depths. We then phased and imputed the genotype data to high-coverage WGS using AlphaPeel. ResultsAll scenarios were compared based on individual imputation accuracy against the simulated true whole-genome genotype. Averaged over five generations of simulated progeny, low-pass sequencing (0.5 to 2X depth) achieved accuracies of 0.998 to 0.999. The accuracy of SNP array genotyping (25K to 710K markers) was lower, with means of 0.911 to 0.938. ConclusionsOur simulation was tailored to identify the most cost-effective and efficient strategy for downstream use in genomic selection and genetic research into traits and loci of interest. Low-pass sequencing outperformed SNP array genotyping in imputation accuracy of whole-genome genotypes as expected. Additionally, low-pass sequencing technology was the most affordable genotyping approach currently available for dogs. Thus, it appears to be the optimal choice for balancing the goals of regimented breeding programmes such as those that produce service dogs. This simulation framework could also be adapted to address other objectives for breeding organisations working with small populations.

genomics↗

bioRxiv2025

Leveraging ancestral recombination graphs for quantitative genetic analysis of rice yield in indica and japonica subspecies

Rice (Oryza sativa L.) has two main subspecies, indica and japonica, which coexist in many regions but are often treated separately during breeding. Combining both subspecies in quantitative genetic analyses could enhance genetic improvement, however, this requires appropriately modelling their genetic history. The ancestral recombination graph (ARG) is an effective population genetics tool that comprehensively and succinctly represents a species genetic history. This study evaluated the use of an ARG, encoded as a tree sequence, to improve quantitative genetic analyses of indica and japonica rice. Using data from Uruguays National Rice Breeding Program, we inferred ancestral alleles, constructed and dated an ARG, and examined its application in genomic prediction and genome-wide association studies. We compared the predictive ability of a branch-based relationship matrix (BRM) built from an ARG against conventional relationship matrices from pedigree and single nucleotide polymorphism (SNP) site data. We then estimated BRMs SNP site effects to identify potential sites of interest and better understand how these map onto the tree sequence branches. The results showed that the ARG captured key biological signals, encoded genomic data more efficiently than conventional formats, and resulted in the highest predictive ability when combining both subspecies. Although the ARG-based approach did not substantially outperform conventional approaches for between-species prediction, this approach holds promise for plant breeding with larger datasets and could enhance genome-wide association studies by elucidating haplotype ancestry and the evolution of their value. Overall, our results demonstrated the potential of ARGs for the quantitative genetic analysis of diverse populations.

genetics↗

bioRxiv2024

The principles of expected and realised genetic relatedness among individual honeybees

Monitoring honeybee genetic variability is essential to manage global and local ge-netic diversity. Coefficients of relatedness are regularly used to measure genetic similar-ity within and between populations and their individuals. Although the haplo-diploid inheritance of honeybees is well understood, interpreting the various types of related-ness coefficients based on pedigree and genotype data is a challenge for researchers and practitioners in honeybee breeding. To demonstrate the principles of genetic relatedness in honeybees and its different individual-based estimators, we simulated three honeybee populations each containing 400 colonies over 10 years using the stochastic simulator SIMplyBee. We kept two populations closed and hybridised the third one by importing drones from one of the closed populations. We evaluated the relatedness between indi-viduals within a colony, between queens of the same population, and between queens of different populations. We calculated three types of relatedness: expected identity by descent using pedigree information, realised identity by descent using pedigree and genotype information, and identity by state using genotype information. Our results demonstrated an alignment of mean relatedness across different types when calculated using the same founder population, regardless of their data source. Identity by state relatedness varied significantly when calculated with different founder populations. Al-though this is an expected result, it shows that caution is needed when comparing values between studies using different populations with different allele frequencies. We expectedly showed increased relatedness over time in closed populations and decreased in the hybrid population. Our results underscore the significance of understanding the methodology for computing relatedness coefficients.

genetics↗

bioRxiv2024

Accurate determination of breed origin of alleles in a simulated smallholder crossbred dairy cattle population

BackgroundAccurate assignment of breed origin of alleles at a heterozygote locus may help to introduce a resilient or adaptive haplotype in crossbreeding. In this study, we developed and tested a method to assign breed of origin for individual alleles in crossbred dairy cattle. After generations of mating within and between local breeds as well as the importation of exotic bulls, five rounds of selected crossbred cows were simulated to mimic a dairy breeding programme in the low- and middle-income countries (LMICs). In each round of selection, the alleles of those crossbred animals were phased and assigned to their breed of origin (being either local or exotic). ResultsAcross all core lengths and modes of phasing (with offset or no), the average percentage of alleles correctly assigned a breed origin was 95.76%, with only 1.39% incorrectly assigned and 2.85% missing or unassigned. On consensus, the average percentage of alleles correctly assigned a breed origin was 93.21%, with only 0.46% incorrectly assigned and 6.33% missing or unassigned. This high proportion of alleles correctly assigned a breed origin resulted in a high core-based mean accuracy of 0.99 and a very high consensus-based mean accuracy of 1.00. The algorithms assignment yield and accuracy were affected by the choice of threshold levels for the best match of assignments. The threshold level had the opposite effect on assignment yield and assignment accuracy. A less stringent threshold generated higher assignment yields and lower assignment accuracy. ConclusionsWe developed an algorithm that accurately assigns a breed origin to alleles of crossbred animals designed to represent breeding programmes in the LMICs. The developed algorithm is straightforward in its application and does not require prior knowledge of pedigree, which makes it more relevant and applicable in LMICs breeding programmes.

genetics↗

bioRxiv2024

Unlocking genome-based prediction and selection in conifers: the key role of within-family prediction accuracy

ContextGenomic selection is a promising approach for forest tree breeding. However, its advantage in terms of prediction accuracy over conventional pedigree-based methods is unclear and within-family accuracy is rarely assessed. AimsWe used an pedigree-based model (ABLUP) with corrected pedigree data as a baseline reference for assessing the prediction accuracy of genome-based model (GBLUP) at the global and within-family levels in maritime pine (Pinus pinaster Ait). MethodsWe sampled 39 full-sib families, each comprising 10 to 40 individuals, to constitute an experimental population of 833 individuals. A stochastic simulation model was also developed to explore other scenarios of heritability, training set size and tagging density. ResultsPrediction accuracies with GBLUP and ABLUP were similar and accuracy with GBLUP within-family was on average zero with large variation between families. Simulations revealed that the number of individuals in the training set was the principal factor limiting GBLUP accuracy in our study and likely in many forest tree breeding programmes. Accurate within-family prediction is possible if 40-65 individuals per full-sib family are included in the genomic training set, from a total of 1600-2000 individuals in the training set. ConclusionSuch conditions lead to a significant advantage of GBLUP over ABLUP in terms of prediction accuracy and more clearly justify the switch to genome-based prediction and selection in forest trees.

genomics↗

bioRxiv2023

Plant breeding simulations with AlphaSimR

Plant breeding plays a crucial role in the development of high-performing crop varieties that meet the demands of society. Emerging breeding techniques offer the potential to improve the precision and efficiency of plant breeding programs; however, their optimal implementation requires refinement of existing breeding programs or the design of new ones. Stochastic simulations are a cost-effective solution for testing and optimizing new breeding strategies. The aim of this paper is to provide an introduction to stochastic simulation with software AlphaSimR for plant breeding students, researchers, and experienced breeders. We present an overview of how to use the software and provide an introductory AlphaSimR vignette as well as complete AlphaSimR scripts of breeding programs for self-pollinated, clonal, and cross-pollinated plants, including relevant breeding techniques, such as backcrossing, speed breeding, genomic selection, index selection, and others. Our objective is to provide a foundation for understanding and utilizing simulation software, enabling readers to adapt the provided scripts for their own use or even develop completely new plant breeding programs. By incorporating simulation software into plant breeding education and practice, the next generation of plant breeders will have a valuable tool in their quest to provide sustainable and nutritious food sources for a growing population.

genetics↗

bioRxiv2023

Accounting for nuclear and mito genome in dairy cattle breeding - a simulation study

Mitochondria play a significant role in numerous cellular processes through proteins encoded by both nuclear genome (nDNA) and mito genome (mDNA). While the variation in nDNA is influenced by mutations and recombination of parental genomes, the variation in mDNA is solely driven by mutations. In addition, mDNA is inherited in a haploid form, from the dam. Cattle populations show significant variation in mDNA between and within breeds. Past research suggests that variation in mDNA accounts for 1-5% of the phenotypic variation in dairy traits. Here we simulated a dairy cattle breeding program to assess the impact of accounting for mDNA variation in pedigree-based and genome-based genetic evaluations on the accuracy of estimated breeding values for mDNA and nDNA components. We also examined the impact of alternative definitions of breeding values on genetic gain, including nDNA and mDNA components that both impact phenotype expression, but mDNA is inherited only maternally. We found that accounting for mDNA variation increased accuracy between +0.01 and +0.05 for different categories of animals, especially for young bulls (+0.05) and females without genotype data (between +0.01 and +0.03). Different scenarios of modelling and breeding value definition impacted genetic gain. The standard approach of ignoring mDNA variation achieved competitive genetic gain. Modelling, but not selecting on mDNA expectedly reduced genetic gain, while optimal use of mDNA variation recovered the genetic gain.

genetics↗

bioRxiv2023

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

genetics↗

bioRxiv2023

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45{degrees} and 65{degrees}. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

genetics↗