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Buch ; Online ; E-Book: Linear Models for the Prediction of the Genetic Merit of Animals

Mrode, Raphael A. / Pocrnic, Ivan

2023

Abstract: Covering the fundamental principles of the application of linear models for the prediction of genetic merit in livestock, this new edition incorporates advances in methods of genomic prediction for pure and cross-bred animals. It provides models for the ... ...

Abstract	Covering the fundamental principles of the application of linear models for the prediction of genetic merit in livestock, this new edition incorporates advances in methods of genomic prediction for pure and cross-bred animals. It provides models for the analysis of main production traits and functional traits, and includes numerous worked examples.
Mesh-Begriff(e)	Selective Breeding/genetics ; Livestock/genetics ; Models, Genetic ; Quantitative Trait Loci/genetics ; Selection, Genetic/genetics
Thema/Rubrik (Code)	636
Sprache	Englisch
Umfang	1 online resource (518 pages)
Ausgabenhinweis	4th ed.
Verlag	CAB International
Erscheinungsort	Oxford
Dokumenttyp	Buch ; Online ; E-Book
Bemerkung	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	1-80062-050-0 ; 9781800620483 ; 978-1-80062-050-6 ; 1800620489
Datenquelle	ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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Artikel ; Online: Inheritance of the birth weights in crosses between Istrian, Awassi, East-Friesian and Travnik Pramenka sheep in Croatia

Ivan Pocrnic / Alen Dzidic

Journal of Central European Agriculture, Vol 22, Iss 2, Pp 250-

a case study

2021 Band 259

Abstract: The purpose of the study was to estimate the influence of the additive and non-additive genetic effects on the birth weight, using two different crossbreed models. Data on 1176 birth weights were collected. The animals were crosses, with different ratios ...

Abstract	The purpose of the study was to estimate the influence of the additive and non-additive genetic effects on the birth weight, using two different crossbreed models. Data on 1176 birth weights were collected. The animals were crosses, with different ratios of Awassi, East-Friesian, Istrian, and Travnik Pramenka sheep breed. The focal breed in this study was Istrian, which is an autochthonous breed in Croatia. In both models mixed procedure was used with random effect of sire and fixed effects of sex, year and month of birth, litter size, and direct inbreeding. The first model contained the additive breed effects, while the second model additionally included non-additive effects of heterosis and recombination loss. Sex, litter size, year and month of birth significantly affected birth weight in both models. Effect of direct inbreeding was negative and significant only in second model. Significant positive additive breed effect was estimated for Awassi breed in both models. The East-Friesian additive breed effect was positive in both models, but it was significant for the first one only. The positive and significant heterosis effect for the birth weight was estimated for the crosses between East-Friesian and Travnik Pramenka, as well as between Travnik Pramenka and Istrian breed. Same crosses showed significant and positive effect of recombination loss. The negative effect of recombination loss was estimated for the crosses of Awassi breed.
Schlagwörter	crossbreeding effects ; birth weight ; sheep ; inbreeding ; heterosis ; Agriculture ; S
Thema/Rubrik (Code)	630
Sprache	Bulgarisch
Erscheinungsdatum	2021-06-01T00:00:00Z
Verlag	University of Zagreb, Faculty of Agriculture
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: A method for partitioning trends in genetic mean and variance to understand breeding practices.

Oliveira, Thiago P / Obšteter, Jana / Pocrnic, Ivan / Heslot, Nicolas / Gorjanc, Gregor

Genetics, selection, evolution : GSE

2023 Band 55, Heft 1, Seite(n) 36

Abstract: Background: In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding ... ...

Abstract	Background: In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by 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 paths 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. Methods: First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the 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. Results: We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension. Conclusions: We 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 selection paths interact within a breeding programme and how they can be optimised.
Mesh-Begriff(e)	Animals ; Cattle/genetics ; Genome ; Genomics ; Monte Carlo Method ; Pedigree ; Markov Chains ; Models, Genetic ; Selection, Genetic
Sprache	Englisch
Erscheinungsdatum	2023-06-02
Erscheinungsland	France
Dokumenttyp	Journal Article
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-023-00804-3
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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

Pocrnic, Ivan / Obšteter, Jana / Gaynor, R Chris / Wolc, Anna / Gorjanc, Gregor

Frontiers in genetics

2023 Band 14, Seite(n) 1168212

Abstract: 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 ...

Abstract	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° and 65°. 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.
Sprache	Englisch
Erscheinungsdatum	2023-05-10
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2023.1168212
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Search for new antimicrobials: spectroscopic, spectrometric, and in vitro antimicrobial activity investigation of Ga(III) and Fe(III) complexes with aroylhydrazones.

Klarić, David / Pocrnić, Marijana / Lež, Dražen / Polović, Saša / Kontrec, Darko / Kosalec, Ivan / Budimir, Ana / Galić, Nives

Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry

2022 Band 27, Heft 8, Seite(n) 715–729

Abstract: The in vitro antimicrobial activity of Fe(III) and Ga(III) complexes with N'-(2,3-dihydroxy-phenylmethylidene)-3-pyridinecarbohydrazide ( ... ...

Abstract	The in vitro antimicrobial activity of Fe(III) and Ga(III) complexes with N'-(2,3-dihydroxy-phenylmethylidene)-3-pyridinecarbohydrazide (H
Mesh-Begriff(e)	Ferric Compounds/chemistry ; Anti-Infective Agents/pharmacology ; Anti-Infective Agents/chemistry ; Ligands ; Escherichia coli ; Spectrum Analysis ; Pyridines ; Coordination Complexes/pharmacology ; Coordination Complexes/chemistry
Chemische Substanzen	carbohydrazide (W8V7FYY4WH) ; Ferric Compounds ; Anti-Infective Agents ; Ligands ; Pyridines ; Coordination Complexes
Sprache	Englisch
Erscheinungsdatum	2022-10-11
Erscheinungsland	Germany
Dokumenttyp	Journal Article
ZDB-ID	1464026-0
ISSN	1432-1327 ; 0949-8257
ISSN (online)	1432-1327
ISSN	0949-8257
DOI	10.1007/s00775-022-01967-y
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Optimisation of the core subset for the APY approximation of genomic relationships

Pocrnic, Ivan / Lindgren, Finn / Tolhurst, Daniel / Herring, William O. / Gorjanc, Gregor

Genet Sel Evol. 2022 Dec., v. 54, no. 1 p.76-76

2022

Abstract: BACKGROUND: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed ... ...

Abstract	BACKGROUND: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. METHODS: We derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection. RESULTS: All core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. CONCLUSIONS: Our results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals.
Schlagwörter	algorithms ; genomics ; genotype ; genotyping ; models ; population structure ; principal component analysis ; single nucleotide polymorphism ; swine
Sprache	Englisch
Erscheinungsverlauf	2022-12
Umfang	p. 76.
Erscheinungsort	BioMed Central
Dokumenttyp	Artikel ; Online
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-022-00767-x
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction.

Misztal, Ignacy / Tsuruta, Shogo / Pocrnic, Ivan / Lourenco, Daniela

Journal of animal science

2020 Band 98, Heft 12

Abstract: Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to ... ...

Abstract	Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.
Mesh-Begriff(e)	Algorithms ; Animals ; Cattle/genetics ; Genome ; Genomics ; Genotype ; Models, Genetic ; Pedigree ; Phenotype ; Swine/genetics
Sprache	Englisch
Erscheinungsdatum	2020-11-19
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	390959-1
ISSN	1525-3163 ; 0021-8812
ISSN (online)	1525-3163
ISSN	0021-8812
DOI	10.1093/jas/skaa374
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Optimisation of the core subset for the APY approximation of genomic relationships.

Pocrnic, Ivan / Lindgren, Finn / Tolhurst, Daniel / Herring, William O / Gorjanc, Gregor

Genetics, selection, evolution : GSE

2022 Band 54, Heft 1, Seite(n) 76

Abstract: Background: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed ... ...

Abstract	Background: By entering the era of mega-scale genomics, we are facing many computational issues with standard genomic evaluation models due to their dense data structure and cubic computational complexity. Several scalable approaches have been proposed to address this challenge, such as the Algorithm for Proven and Young (APY). In APY, genotyped animals are partitioned into core and non-core subsets, which induces a sparser inverse of the genomic relationship matrix. This partitioning is often done at random. While APY is a good approximation of the full model, random partitioning can make results unstable, possibly affecting accuracy or even reranking animals. Here we present a stable optimisation of the core subset by choosing animals with the most informative genotype data. Methods: We derived a novel algorithm for optimising the core subset based on a conditional genomic relationship matrix or a conditional single nucleotide polymorphism (SNP) genotype matrix. We compared the accuracy of genomic predictions with different core subsets for simulated and real pig data sets. The core subsets were constructed (1) at random, (2) based on the diagonal of the genomic relationship matrix, (3) at random with weights from (2), or (4) based on the novel conditional algorithm. To understand the different core subset constructions, we visualise the population structure of the genotyped animals with linear Principal Component Analysis and non-linear Uniform Manifold Approximation and Projection. Results: All core subset constructions performed equally well when the number of core animals captured most of the variation in the genomic relationships, both in simulated and real data sets. When the number of core animals was not sufficiently large, there was substantial variability in the results with the random construction but no variability with the conditional construction. Visualisation of the population structure and chosen core animals showed that the conditional construction spreads core animals across the whole domain of genotyped animals in a repeatable manner. Conclusions: Our results confirm that the size of the core subset in APY is critical. Furthermore, the results show that the core subset can be optimised with the conditional algorithm that achieves an optimal and repeatable spread of core animals across the domain of genotyped animals.
Mesh-Begriff(e)	Swine ; Animals ; Models, Genetic ; Genome ; Genomics/methods ; Genotype ; Algorithms
Sprache	Englisch
Erscheinungsdatum	2022-11-22
Erscheinungsland	France
Dokumenttyp	Journal Article
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-022-00767-x
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Temporal and genomic analysis of additive genetic variance in breeding programmes.

Lara, Letícia A de C / Pocrnic, Ivan / Oliveira, Thiago de P / Gaynor, R Chris / Gorjanc, Gregor

Heredity

2021 Band 128, Heft 1, Seite(n) 21–32

Abstract: Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of ... ...

Abstract	Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.
Mesh-Begriff(e)	Breeding ; Genome ; Genomics/methods ; Linkage Disequilibrium ; Models, Genetic ; Pedigree ; Phenotype ; Selection, Genetic
Sprache	Englisch
Erscheinungsdatum	2021-12-15
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2423-5
ISSN	1365-2540 ; 0018-067X
ISSN (online)	1365-2540
ISSN	0018-067X
DOI	10.1038/s41437-021-00485-y
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study.

Pocrnic, Ivan / Lourenco, Daniela A L / Masuda, Yutaka / Misztal, Ignacy

Genetics, selection, evolution : GSE

2019 Band 51, Heft 1, Seite(n) 75

Abstract: Background: The dimensionality of genomic information is limited by the number of independent chromosome segments (M: Results: The simulation included datasets with different population sizes and amounts of phenotypic information. Computations were ... ...

Abstract	Background: The dimensionality of genomic information is limited by the number of independent chromosome segments (M Results: The simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added. Conclusions: A small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.
Mesh-Begriff(e)	Algorithms ; Animals ; Computer Simulation ; Genomics/methods ; Phenotype ; Population Density
Sprache	Englisch
Erscheinungsdatum	2019-12-12
Erscheinungsland	France
Dokumenttyp	Journal Article
ZDB-ID	1005838-2
ISSN	1297-9686 ; 0754-0264 ; 0999-193X
ISSN (online)	1297-9686
ISSN	0754-0264 ; 0999-193X
DOI	10.1186/s12711-019-0516-0
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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