ORCID Profile
0000-0001-8008-2787
Current Organisations
The University of Edinburgh
,
University of Ljubljana Biotechnical Faculty
,
MAGMA AGRO S.A.
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Publisher: Wiley
Date: 29-12-2012
DOI: 10.1111/JBG.12020
Abstract: Long-range phasing and haplotype library imputation methodologies are accurate and efficient methods to provide haplotype information that could be used in prediction of breeding value or phenotype. Modelling long haplotypes as independent effects in genomic prediction would be inefficient due to the many effects that need to be estimated and phasing errors, even if relatively low in frequency, exacerbate this problem. One approach to overcome this is to use similarity between haplotypes to model covariance of genomic effects by region or of animal breeding values. We developed a simple method to do this and tested impact on genomic prediction by simulation. Results show that the diagonal and off-diagonal elements of a genomic relationship matrix constructed using the haplotype similarity method had higher correlations with the true relationship between pairs of in iduals than genomic relationship matrices built using unphased genotypes or assumed unrelated haplotypes. However, the prediction accuracy of such haplotype-based prediction methods was not higher than those based on unphased genotype information.
Publisher: Springer Science and Business Media LLC
Date: 15-10-2021
DOI: 10.1007/S13592-021-00891-5
Abstract: Varroa mites ( Varroa destructor ) are the most significant threat to beekeeping worldwide. They are directly or indirectly responsible for millions of colony losses each year. Beekeepers are somewhat able to control varroa populations through the use of physical and chemical treatments. However, these methods range in effectiveness, can harm honey bees, can be physically demanding on the beekeeper, and do not always provide complete protection from varroa. More importantly, in some populations varroa mites have developed resistance to available acaricides. Overcoming the varroa mite problem will require novel and targeted treatment options. Here, we explore the potential of gene drive technology to control varroa. We show that spreading a neutral gene drive in varroa is possible but requires specific colony-level management practices to overcome the challenges of both inbreeding and haplodiploidy. Furthermore, continued treatment with acaricides is necessary to give a gene drive time to fix in the varroa population. Unfortunately, a gene drive that impacts female or male fertility does not spread in varroa. Therefore, we suggest that the most promising way forward is to use a gene drive which carries a toxin precursor or removes acaricide resistance alleles.
Publisher: eLife Sciences Publications, Ltd
Date: 23-05-2023
Abstract: 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 monospace stdpopsim /monospace seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of monospace stdpopsim /monospace 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 monospace stdpopsim /monospace (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 monospace stdpopsim /monospace 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.
Publisher: Springer Science and Business Media LLC
Date: 02-07-2015
Publisher: eLife Sciences Publications, Ltd
Date: 03-03-2023
Abstract: 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 monospace stdpopsim /monospace seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of monospace stdpopsim /monospace 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 monospace stdpopsim /monospace (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 monospace stdpopsim /monospace 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.
Publisher: Cold Spring Harbor Laboratory
Date: 27-08-2020
DOI: 10.1101/2020.08.27.266155
Abstract: Invasive species are among the major driving forces behind bio ersity loss. Gene drive technology may offer a humane, efficient and cost-effective method of control. For safe and effective deployment it is vital that a gene drive is both self-limiting and can overcome evolutionary resistance. We present HD-ClvR, a novel combination of CRISPR-based gene drives that eliminates resistance and localises spread. As a case study, we model HD-ClvR in the grey squirrel ( Sciurus carolinensis ), which is an invasive pest in the UK and responsible for both bio ersity and economic losses. HD-ClvR combats resistance allele formation by combining a homing gene drive with a cleave-and-rescue gene drive. The inclusion of a self-limiting daisyfield gene drive allows for controllable localisation based on animal supplementation. We use both randomly mating and spatial models to simulate this strategy. Our findings show that HD-ClvR can effectively control a targeted grey squirrel population, with little risk to other populations. HD-ClvR offers an efficient, self-limiting and controllable gene drive for managing invasive pests.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2202
DOI: 10.1038/S41598-020-61031-0
Abstract: Hybrid vigour has the potential to substantially increase the yield of self-pollinating crops such as wheat and rice, but future hybrid performance may depend on the initial strategy to form heterotic pools. We used in silico stochastic simulation of future hybrid performance in a self-pollinating crop to evaluate three strategies of forming heterotic pools in the founder population. The model included either 500, 2000 or 8000 quantitative trait nucleotides (QTN) across 10 chromosomes that contributed to a quantitative trait with population mean 100 and variance 10. The average degree of dominance at each QTN was either 0.2, 0.4 or 0.8 with variance 0.2. Three strategies for splitting the founder population into two heterotic pools were compared: (i) random split (ii) split based on genetic distance according to principal component analysis of SNP genotypes and (iii) optimized split based on F 1 hybrid performance in a diallel cross among the founders. Future hybrid performance was stochastically simulated over 30 cycles of reciprocal recurrent selection based on true genetic values for additive and dominance effects. The three strategies of forming heterotic pools produced similar future hybrid performance, and superior future hybrids to a control population selected on inbred line performance when the number of quantitative trait nucleotides was ≥2000 and/or the average degree of dominance was ≥0.4.
Publisher: Cold Spring Harbor Laboratory
Date: 12-09-2018
DOI: 10.1101/414292
Abstract: Deleterious recessive alleles can result in reduced economic performance in livestock in multiple ways in homozygous in iduals: from early embryonic death, death soon after birth, to being non-lethal but causing reduced viability. While death is an easy phenotype to score, reduced viability is not as easy to identify. However, it can sometimes be observed as reduced artificial insemination (AI) conception rates, longer calving intervals, or higher hazard for live born animals. In this paper, we searched for haplotypes carrying putatively recessive lethal alleles in 132,725 genotyped Irish beef cattle from five breeds: Aberdeen Angus, Charolais, Hereford, Limousin, and Simmental. We phased the genotypes in sliding windows along the genome and used five tests to identify haplotypes with absence of or reduced homozygosity. We then corroborated the identified haplotypes with reproduction records, indicating early embryonic death, and postnatal survival records. Finally, we assessed haplotype pleiotropy by estimating substitution effects on national estimates of breeding values for 15 economically important traits in beef production. We found support for three haplotypes with carrying putatively recessive lethal alleles. The haplotypes were located on chromosome 14 in Aberdeen Angus, chromosome 19 in Charolais and chromosome 16 in Simmental. Their population frequencies is 15.2%, 14.4%, and 8.8%, respectively. All of the haplotypes showed pleiotropic effects on economically important traits for beef production. Their allele substitution effects are €3.23, €1.47, and €2.30 for the terminal index and -€3.15, -€0.75, and €1.12 for the replacement index, where one standard deviations are €18.32, €22.54, and €22.33 for terminal index and €29.52, €35.62, and €30.97 for the replacement index. We identified ZFAT as the candidate gene for lethality in Aberdeen Angus, several candidate genes for the Simmental haplotype, and no candidate genes for the Charolais haplotype. We analysed genotype, reproduction, survival, and production data to discover haplotypes carrying putatively recessive lethal alleles in Irish beef cattle. We found support for three haplotypes. All three haplotypes have pleiotropic effects on economically important traits in beef production.
Publisher: Cold Spring Harbor Laboratory
Date: 09-2021
DOI: 10.1101/2021.08.31.457499
Abstract: Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprime ’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2021
DOI: 10.1038/S41598-021-83239-4
Abstract: Invasive species are among the major driving forces behind bio ersity loss. Gene drive technology may offer a humane, efficient and cost-effective method of control. For safe and effective deployment it is vital that a gene drive is both self-limiting and can overcome evolutionary resistance. We present HD-ClvR in this modelling study, a novel combination of CRISPR-based gene drives that eliminates resistance and localises spread. As a case study, we model HD-ClvR in the grey squirrel ( Sciurus carolinensis ), which is an invasive pest in the UK and responsible for both bio ersity and economic losses. HD-ClvR combats resistance allele formation by combining a homing gene drive with a cleave-and-rescue gene drive. The inclusion of a self-limiting daisyfield gene drive allows for controllable localisation based on animal supplementation. We use both randomly mating and spatial models to simulate this strategy. Our findings show that HD-ClvR could effectively control a targeted grey squirrel population, with little risk to other populations. HD-ClvR offers an efficient, self-limiting and controllable gene drive for managing invasive pests.
Publisher: American Dairy Science Association
Date: 11-2021
Publisher: Wiley
Date: 05-10-2023
DOI: 10.1002/CSC2.21105
Publisher: Oxford University Press (OUP)
Date: 28-05-2014
DOI: 10.1093/BIOINFORMATICS/BTU206
Abstract: Summary: Multi-parent crosses of recombinant inbred lines exist in many species for fine-scale analysis of genome structure and marker–trait association. These populations encompass a wide range of crossing designs with varying potential. AlphaMPSim is a flexible simulation program that is efficiently designed for comparison of alternative designs for traits with varying genetic architectures and biallelic markers with densities up to full sequence. A large pool of founder haplotypes can be supplied by the user, or generated via integration with external coalescent simulation programs such as MaCS. From these, erse founders for multi-parent designs can be generated automatically, and users can compare designs generated from erse pedigrees. Full tracking of identity by descent status of alleles within the pedigree is undertaken, and output files are compatible with commonly available analysis packages in R. Availability and implementation: Executable versions of AlphaMPSim for Mac and Linux and a user manual are available at www.roslin.ed.ac.uk/john-hickey/software-packages/ . Contact: john.hickey@roslin.ed.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 30-04-2021
DOI: 10.1101/2021.04.30.442149
Abstract: Varroa mites ( Varroa destructor ) are the most significant threat to beekeeping worldwide. They are directly or indirectly responsible for millions of colony losses each year. Beekeepers are somewhat able to control Varroa populations through the use of physical and chemical treatments. However, these methods range in effectiveness, can harm honey bees, can be physically demanding on the beekeeper, and do not always provide complete protection from Varroa. More importantly, in some populations Varroa mites have developed resistance to available acaricides. Overcoming the Varroa mite problem will require novel and targeted treatment options. Here, we explore the potential of gene drive technology to control Varroa. We show that spreading a neutral gene drive in Varroa is possible but requires specific colony-level management practices to overcome the challenges of both inbreeding and haplodiploidy. Furthermore, continued treatment with acaricides is necessary to give a gene drive time to fix in the Varroa population. Unfortunately, a gene drive that impacts female or male fertility does not spread in Varroa. Therefore, we suggest that the most promising way forward is to use a gene drive which carries a toxin precursor or removes acaricide resistance alleles.
Publisher: Cold Spring Harbor Laboratory
Date: 25-05-2020
DOI: 10.1101/2020.05.24.113258
Abstract: Hybrid crop breeding programs using a two-part strategy produced the most genetic gain, but a maximum avoidance of inbreeding crossing scheme was required to increase long-term genetic gain. The two-part strategy uses outbred parents to complete multiple generations per year to reduce the generation interval of hybrid crop breeding programs. The maximum avoidance of inbreeding crossing scheme manages genetic variance by maintaining uniform contributions and inbreeding coefficients across all crosses. This study performed stochastic simulations to quantify the potential of a two-part strategy in combination with two crossing schemes to increase the rate of genetic gain in hybrid crop breeding programs. The two crossing schemes were: (i) a circular crossing scheme, and (ii) a maximum avoidance of inbreeding crossing scheme. The results from this study show that the implementation of genomic selection increased the rate of genetic gain, and that the two-part hybrid crop breeding program generated the highest genetic gain. This study also shows that the maximum avoidance of inbreeding crossing scheme increased long-term genetic gain in two-part hybrid crop breeding programs completing multiple selection cycles per year, as a result of maintaining higher levels of genetic variance over time. The flexibility of the two-part strategy offers further opportunities to integrate new technologies to further increase genetic gain in hybrid crop breeding programs, such as the use of outbred training populations. However, the practical implementation of the two-part strategy will require the development of bespoke transition strategies to fundamentally change the data, logistics, and infrastructure that underpin hybrid crop breeding programs. Hybrid crop breeding programs using a two-part strategy produced the most genetic gain by using outbred parents to complete multiple generations per year. However, a maximum avoidance of inbreeding crossing scheme was required to manage genetic variance and increase long-term genetic gain.
Publisher: The Company of Biologists
Date: 07-2017
DOI: 10.1242/JEB.156646
Abstract: Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva – because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour–taste associative learning, as well as light/dark–electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages.
Publisher: Cold Spring Harbor Laboratory
Date: 31-10-2022
DOI: 10.1101/2022.10.29.514266
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 07-04-2023
DOI: 10.1007/S00122-023-04305-1
Abstract: The inclusion of multiple traits and multiple environments within a partially separable factor analytic approach for genomic selection provides breeders with an informative framework to utilise genotype by environment by trait interaction for efficient selection. This paper develops a single-stage genomic selection (GS) approach which incorporates information on multiple traits and multiple environments within a partially separable factor analytic framework. The factor analytic linear mixed model is an effective method for analysing multi-environment trial (MET) datasets, but has not been extended to GS for multiple traits and multiple environments. The advantage of using all information is that breeders can utilise genotype by environment by trait interaction (GETI) to obtain more accurate predictions across correlated traits and environments. The partially separable factor analytic linear mixed model (SFA-LMM) developed in this paper is based on a three-way separable structure, which includes a factor analytic matrix between traits, a factor analytic matrix between environments and a genomic relationship matrix between genotypes. A diagonal matrix is then added to enable a different genotype by environment interaction (GEI) pattern for each trait and a different genotype by trait interaction (GTI) pattern for each environment. The results show that the SFA-LMM provides a better fit than separable approaches and a comparable fit to non-separable and partially separable approaches. The distinguishing feature of the SFA-LMM is that it will include fewer parameters than all other approaches as the number of genotypes, traits and environments increases. Lastly, a selection index is used to demonstrate simultaneous selection for overall performance and stability. This research represents an important continuation in the advancement of plant breeding analyses, particularly with the advent of high-throughput datasets involving a very large number of genotypes, traits and environments.
Publisher: Cold Spring Harbor Laboratory
Date: 02-11-2019
DOI: 10.1101/827956
Abstract: Genetic evaluation is a central component of a breeding program. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination create strong genetic connectedness that enables the genetic and environmental effects of an in idual animal’s phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions. The results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For ex le, the accuracy of genomic prediction of young bulls from an average herd size of 1 (μ=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows. This study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be in idually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.
Publisher: Oxford University Press (OUP)
Date: 13-12-2021
Abstract: Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Gregor Gorjanc.