Biology subjects

Obsteter, J.

Publications and source records attributed to Obsteter, J..

12 recordsLinked to original sources

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

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↗

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↗

bioRxiv2022

SIMplyBee: R package for simulating honeybee populations and breeding programs

BackgroundThe Western honeybee is an economically important species globally, but has been experiencing colony losses that lead to economical damage and decreased genetic variability. This situation is spurring additional interest in honeybee breeding and conservation programs. Stochastic simulators are essential tools for rapid and low-cost testing of breeding programs and methods, yet no existing simulator allows for a detailed simulation of honeybee populations. Here we describe SIMplyBee, a holistic simulator of honeybee populations and breeding programs. SIMplyBee is an R package and hence freely available for installation from CRAN http://cran.r-project.org/package=SIMplyBee. ImplementationSIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable a honeybee specific simulation, we extended AlphaSimR by developing classes for global simulation parameters, SimParamBee, for a honeybee colony, Colony, and multiple colonies, MultiColony. We also developed functions to address major specificities of the honeybees: honeybee genome, haplo-diploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics of honeybees. ResultsWe describe and show implementation regarding simulating a honeybee genome, creating a honeybee colony and its members, haplodiploid inheritance and complementary sex determination, colony events, creating and managing multiple colonies at once, and obtaining genomic data and honeybee quantitative genetics. Further documentation at http://SIMplyBee.info provides details on these operations and describes additional operations related to genomics, quantitative genetics, and other functionality. DiscussionSIMplyBee is a holistic simulator of honeybee populations and breeding programs that simulates individual honeybees with their genomes, colonies with colony events, and individual- and colony-level quantitative values. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and variability. Future development of SIMplyBee will focus on improving the simulation of honeybee genomes, optimizing the performance of the simulator, and including spatial awareness to crossing functions and phenotype simulation. We welcome the honeybee genetics and breeding community to join us in the future development of SIMplyBee.

genetics↗

bioRxiv2022

The benefits and perils of import in small cattle breeding programs

Small breeding programs are limited in achieving competitive genetic gain and prone to high rates of inbreeding. Thus, they often import genetic material to increase genetic gain and to limit the loss of genetic variability. However, the benefit of import depends on the strength of genotype by environment interaction. It also also diminishes the relevance of domestic selection and the use of domestic breeding animals. Introduction of genomic selection has potentially execerbated this issue, but is also opening the potential for smaller breeding program. The aim of this paper was to determine when and to what extent do small breeding programs benefit from import. We simulated two cattle breeding programs differing in selection parameters representing a large foreign and a small domestic breeding program that differ in the initial genetic mean and annual genetic gain. We evaluated a control scenario without the use of foreign sires in the domestic breeding program and 20 scenarios that varied the percentage of domestic dams mated with foreign sires, the genetic correlation between the breeding programs (0.8 or 0.9), and the time of implementing genomic selection in the domestic compared to the foreign breeding program (concurrently or with a 10-year delay). We compared the scenarios based on the genetic gain and genic standard deviation. Finally, we partitioned breeding values and genetic trends of the scenarios to quantify the contribution of domestic selection and import to the domestic genetic gain. The simulation revealed that when both breeding programs implemented genomic selection simultaneously, the use of foreign sires increased domestic genetic gain only when genetic correlation was 0.9. In contrast, when the domestic breeding program implemented genomic selection with a 10-year delay, genetic correlation of 0.8 sufficed for a positive impact of import. In that scenario, domestic genetic gain increased with the increasing use of foreign sires but with a diminishing return. The partitioning analysis revealed that the contribution of import expectedly increased with the increased use of foreign sires. However, the increase did not depend on the genetic correlation and was not proportional to the increase in domestic genetic gain. This means that a small breeding program could be overly relying on import with diminishing returns for the genetic gain and marginal benefit for the genetic variability. The benefit of import depends on an interplay of genetic correlation, extent of using foreign sires, and a breeding scheme. It is therefore crucial that small breeding programs assess the possible benefits of import beyond domestic selection. The benefit of import should be weighted against the perils of decreased use of domestic sires and decreased contribution and value of domestic selection.

genetics↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

A method for partitioning trends in genetic mean and variance to understand breeding practices

BackgroundIn breeding programmes, the observed genetic change is a sum of the contributions of different groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual groups due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. MethodsWe first extended the partitioning method to quantify the contribution of different groups to genetic variance assuming breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. ResultsWe showed how to quantify the contribution of different groups of individuals to genetic mean and variance. We showed that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed some limitations of the partitioning method under a misspecified model, suggesting the need for a genomic partitioning method. ConclusionWe presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different paths of selection interact within a breeding programme and how they can be optimised.

genetics↗

bioRxiv2020

Genomic selection for any dairy breeding program via optimized investment in phenotyping and genotyping

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario had 11 phenotype records per lactation. In genomic scenarios, we reduced phenotyping to between 10 and 1 phenotype records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic scenarios expectedly increased accuracy for young non-phenotyped male and female candidates, but also cows. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximise return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

genomics↗

bioRxiv2020

AlphaPart - R implementation of the method for partitioning genetic trends

BackgroundIn this paper we present the AlphaPart R package, an open-source software that implements a method for partitioning breeding values and genetic trends to identify sources of genetic gain. Breeding programmes improve populations for a set of traits, which can be measured with a genetic trend calculated from averaged year of birth estimated breeding values of selection candidates. While sources of the overall genetic gain are generally known, their realised contributions are hard to quantify in complex breeding programmes. The aim of this paper is to present the AlphaPart R package and demonstrate it with a simulated pig breeding example. ResultsThe package includes the main partitioning function AlphaPart, that partitions the breeding values and genetic trends by analyst defined paths, and a set of functions for handling data and results. The package is freely available from CRAN repository at http://CRAN.R-project.org/package=AlphaPart. We demonstrate the use of the package by examining the genetic gain in a pig breeding example, in which the multiplier achieved higher breeding values than the nucleus for traits measured and selected in the multiplier. The partitioning analysis revealed that these higher values depended on the accuracy and intensity of selection in the multiplier and the extent of gene flow from the nucleus. For traits measured only in the nucleus, the multiplier achieved comparable or smaller genetic gain than the nucleus depending on the amount of gene flow. ConclusionsAlphaPart implements a method for partitioning breeding values and genetic trends and provides a useful tool for quantifying the sources of genetic gain in breeding programmes. The use of AlphaPart will help breeders to better understand or improve their breeding programmes.

genomics↗

bioRxiv2019

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

This paper compares genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies showed that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ~10,500 cows. We compared different truncation selection scenarios by varying i) the method of sire selection and their use on cows or bull-dams, and ii) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared the scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, the rate of coancestry, effective population size, and the conversion efficiency. The results show that early use of genomically tested sires increased genetic gain compared to progeny testing as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. While maximizing intensity gave the lowest conversion efficiency, a faster turn-over of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny tested sires that were used over several years. Compared to truncation selection optimizing sire selection and their usage increased the conversion efficiency by either achieving comparable genetic gain for a smaller loss of genetic variation or achieving higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations to implement sustainable genomic selection.

genomics↗