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Genetics, Selection, Evolution: GSE

Hanne M Nielsen, Birgitte Ask, Per Madsen
BACKGROUND: Average daily gain (ADG) in pigs is affected by the so-called social (or indirect) genetic effects (SGE). However, SGE may differ between sexes because boars grow faster than gilts and their social behaviours differ. We hypothesized that direct genetic effects (DGE) and SGE for ADG in pigs differ between boars and gilts and that accounting for these differences will improve the predictive ability of a social genetic effects model (SGM). Our data consisted of ADG from 30 to 94 kg for 32,212 uncastrated males (boars) and 48,252 gilts that were raised in sex-specific pens...
February 1, 2018: Genetics, Selection, Evolution: GSE
Emily H Waide, Christopher K Tuggle, Nick V L Serão, Martine Schroyen, Andrew Hess, Raymond R R Rowland, Joan K Lunney, Graham Plastow, Jack C M Dekkers
BACKGROUND: Genomic prediction of the pig's response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. RESULTS: Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0...
February 1, 2018: Genetics, Selection, Evolution: GSE
Kasper Janssen, Helmut Saatkamp, Hans Komen
BACKGROUND: Profitability of breeding programs is a key determinant in the adoption of selective breeding, and can be evaluated using cost-benefit analysis. There are many options to design breeding programs, with or without a multiplier tier. Our objectives were to evaluate different breeding program designs for aquaculture and to optimize the number of selection candidates for these programs. METHODS: The baseline was based on an existing breeding program for gilthead seabream, where improvement of the nucleus had priority over improvement of the multiplier tier, which was partly replaced once every 3 years...
January 29, 2018: Genetics, Selection, Evolution: GSE
Luis Varona, Andrés Legarra, William Herring, Zulma G Vitezica
BACKGROUND: The quantitative genetics theory argues that inbreeding depression and heterosis are founded on the existence of directional dominance. However, most procedures for genomic selection that have included dominance effects assumed prior symmetrical distributions. To address this, two alternatives can be considered: (1) assume the mean of dominance effects different from zero, and (2) use skewed distributions for the regularization of dominance effects. The aim of this study was to compare these approaches using two pig datasets and to confirm the presence of directional dominance...
January 26, 2018: Genetics, Selection, Evolution: GSE
Theo H E Meuwissen, Ulf G Indahl, Jørgen Ødegård
BACKGROUND: Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model...
December 27, 2017: Genetics, Selection, Evolution: GSE
Claudia A Sevillano, Jeremie Vandenplas, John W M Bastiaansen, Rob Bergsma, Mario P L Calus
After publication of our article [1], we found a typo in the formula to build the genomic relationship matrix using allele frequencies across all genotyped pigs (matrix) and the genomic relationship matrix using breed-specific allele frequencies (matrix), and we noted that the description to this formula is not very clear.
December 27, 2017: Genetics, Selection, Evolution: GSE
Jean-Michel Elsen
BACKGROUND: Formulae to predict the precision or accuracy of genomic estimated breeding values (GEBV) are important when modelling selection schemes. Simple versions of such formulae have been proposed in the past, based on a number of simplifying hypotheses, including absence of linkage disequilibrium and linkage between loci, a population made up of unrelated individuals, and that all genetic variability of the trait is explained by the genotyped loci. These formulae were based on approximations that were not always clear...
December 27, 2017: Genetics, Selection, Evolution: GSE
Marina Solé, Ann-Stephan Gori, Pierre Faux, Amandine Bertrand, Frédéric Farnir, Mathieu Gautier, Tom Druet
BACKGROUND: Inbreeding coefficients can be estimated either from pedigree data or from genomic data, and with genomic data, they are either global or local (when the linkage map is used). Recently, we developed a new hidden Markov model (HMM) that estimates probabilities of homozygosity-by-descent (HBD) at each marker position and automatically partitions autozygosity in multiple age-related classes (based on the length of HBD segments). Our objectives were to: (1) characterize inbreeding with our model in an intensively selected population such as the Belgian Blue Beef (BBB) cattle breed; (2) compare the properties of the model at different marker densities; and (3) compare our model with other methods...
December 22, 2017: Genetics, Selection, Evolution: GSE
Gota Morota
BACKGROUND: Deterministic formulas for the accuracy of genomic predictions highlight the relationships among prediction accuracy and potential factors influencing prediction accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control prediction accuracy. RESULTS: The software to simulate deterministic formulas for genomic prediction accuracy was implemented in R and encapsulated as a web-based Shiny application...
December 20, 2017: Genetics, Selection, Evolution: GSE
Joanna J Ilska, Theo H E Meuwissen, Andreas Kranis, John A Woolliams
BACKGROUND: Molecular data is now commonly used to predict breeding values (BV). Various methods to calculate genomic relationship matrices (GRM) have been developed, with some studies proposing regression of coefficients back to the reference matrix of pedigree-based relationship coefficients (A). The objective was to compare the utility of two GRM: a matrix based on linkage analysis (LA) and anchored to the pedigree, i.e. [Formula: see text] and a matrix based on linkage disequilibrium (LD), i...
December 11, 2017: Genetics, Selection, Evolution: GSE
Grum Gebreyesus, Mogens S Lund, Bart Buitenhuis, Henk Bovenhuis, Nina A Poulsen, Luc G Janss
BACKGROUND: Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability...
December 5, 2017: Genetics, Selection, Evolution: GSE
Uche Godfrey Okeke, Deniz Akdemir, Ismail Rabbi, Peter Kulakow, Jean-Luc Jannink
BACKGROUND: Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2)...
December 4, 2017: Genetics, Selection, Evolution: GSE
Jean-Jacques Colleau, Isabelle Palhière, Silvia T Rodríguez-Ramilo, Andres Legarra
BACKGROUND: Pedigree-based management of genetic diversity in populations, e.g., using optimal contributions, involves computation of the [Formula: see text] type yielding elements (relationships) or functions (usually averages) of relationship matrices. For pedigree-based relationships [Formula: see text], a very efficient method exists. When all the individuals of interest are genotyped, genomic management can be addressed using the genomic relationship matrix [Formula: see text]; however, to date, the computational problem of efficiently computing [Formula: see text] has not been well studied...
December 1, 2017: Genetics, Selection, Evolution: GSE
Juan P Sánchez, Mohamed Ragab, Raquel Quintanilla, Max F Rothschild, Miriam Piles
BACKGROUND: Improving feed efficiency ([Formula: see text]) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs ([Formula: see text]) should be of value for further research on biological aspects of [Formula: see text]. Here, we present a random regression model that extends the classical definition of [Formula: see text] by including animal-specific needs in the model...
December 1, 2017: Genetics, Selection, Evolution: GSE
Caroline Morgenthaler, Mathieu Diribarne, Aurélien Capitan, Rachel Legendre, Romain Saintilan, Maïlys Gilles, Diane Esquerré, Rytis Juras, Anas Khanshour, Laurent Schibler, Gus Cothran
BACKGROUND: Curly horses present a variety of curl phenotypes that are associated with various degrees of curliness of coat, mane, tail and ear hairs. Their origin is still a matter of debate and several genetic hypotheses have been formulated to explain the diversity in phenotype, including the combination of autosomal dominant and recessive alleles. Our purpose was to map the autosomal dominant curly hair locus and identify the causal variant using genome-wide association study (GWAS) and whole-genome sequencing approaches...
November 15, 2017: Genetics, Selection, Evolution: GSE
Salvatore Mastrangelo, Marco Tolone, Maria T Sardina, Gianluca Sottile, Anna M Sutera, Rosalia Di Gerlando, Baldassare Portolano
BACKGROUND: Because very large numbers of single nucleotide polymorphisms (SNPs) are now available throughout the genome, they are particularly suitable for the detection of genomic regions where a reduction in heterozygosity has occurred and they offer new opportunities to improve the accuracy of inbreeding ([Formula: see text]) estimates. Runs of homozygosity (ROH) are contiguous lengths of homozygous segments of the genome where the two haplotypes inherited from the parents are identical...
November 14, 2017: Genetics, Selection, Evolution: GSE
Mohammed K Abo-Ismail, Luiz F Brito, Stephen P Miller, Mehdi Sargolzaei, Daniela A Grossi, Steve S Moore, Graham Plastow, Paul Stothard, Shadi Nayeri, Flavio S Schenkel
BACKGROUND: Our aim was to identify genomic regions via genome-wide association studies (GWAS) to improve the predictability of genetic merit in Holsteins for 10 calving and 28 body conformation traits. Animals were genotyped using the Illumina Bovine 50 K BeadChip and imputed to the Illumina BovineHD BeadChip (HD). GWAS were performed on 601,717 real and imputed single nucleotide polymorphism (SNP) genotypes using a single-SNP mixed linear model on 4841 Holstein bulls with breeding value predictions and followed by gene identification and in silico functional analyses...
November 7, 2017: Genetics, Selection, Evolution: GSE
Heidi Signer-Hasler, Alexander Burren, Markus Neuditschko, Mirjam Frischknecht, Dorian Garrick, Christian Stricker, Birgit Gredler, Beat Bapst, Christine Flury
BACKGROUND: Domestication, breed formation and intensive selection have resulted in divergent cattle breeds that likely exhibit their own genomic signatures. In this study, we used genotypes from 27,612 autosomal single nucleotide polymorphisms to characterize population structure based on 9214 sires representing nine Swiss dairy cattle populations: Brown Swiss (BS), Braunvieh (BV), Original Braunvieh (OB), Holstein (HO), Red Holstein (RH), Swiss Fleckvieh (SF), Simmental (SI), Eringer (ER) and Evolèner (EV)...
November 7, 2017: Genetics, Selection, Evolution: GSE
Beatriz Gutiérrez-Gil, Cristina Esteban-Blanco, Pamela Wiener, Praveen Krishna Chitneedi, Aroa Suarez-Vega, Juan-Jose Arranz
BACKGROUND: With the aim of identifying selection signals in three Merino sheep lines that are highly specialized for fine wool production (Australian Industry Merino, Australian Merino and Australian Poll Merino) and considering that these lines have been subjected to selection not only for wool traits but also for growth and carcass traits and parasite resistance, we contrasted the OvineSNP50 BeadChip (50 K-chip) pooled genotypes of these Merino lines with the genotypes of a coarse-wool breed, phylogenetically related breed, Spanish Churra dairy sheep...
November 7, 2017: Genetics, Selection, Evolution: GSE
Jie He, Yunfeng Zhao, Jingli Zhao, Jin Gao, Dandan Han, Pao Xu, Runqing Yang
BACKGROUND: Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. METHODS: We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia...
November 2, 2017: Genetics, Selection, Evolution: GSE
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