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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
Aniek C Bouwman, Ben J Hayes, Mario P L Calus
BACKGROUND: Genomic evaluation is used to predict direct genomic values (DGV) for selection candidates in breeding programs, but also to estimate allele substitution effects (ASE) of single nucleotide polymorphisms (SNPs). Scaling of allele counts influences the estimated ASE, because scaling of allele counts results in less shrinkage towards the mean for low minor allele frequency (MAF) variants. Scaling may become relevant for estimating ASE as more low MAF variants will be used in genomic evaluations...
October 30, 2017: Genetics, Selection, Evolution: GSE
Roger Ros-Freixedes, Serap Gonen, Gregor Gorjanc, John M Hickey
BACKGROUND: This paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population, we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes...
October 25, 2017: Genetics, Selection, Evolution: GSE
Jérôme Raoul, Andrew A Swan, Jean-Michel Elsen
BACKGROUND: Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs). METHODS: A population under selection for a maternal trait was simulated using real genotypes...
October 24, 2017: Genetics, Selection, Evolution: GSE
Rabia Letaief, Emmanuelle Rebours, Cécile Grohs, Cédric Meersseman, Sébastien Fritz, Lidwine Trouilh, Diane Esquerré, Johanna Barbieri, Christophe Klopp, Romain Philippe, Véronique Blanquet, Didier Boichard, Dominique Rocha, Mekki Boussaha
BACKGROUND: Copy number variations (CNV) are known to play a major role in genetic variability and disease pathogenesis in several species including cattle. In this study, we report the identification and characterization of CNV in eight French beef and dairy breeds using whole-genome sequence data from 200 animals. Bioinformatics analyses to search for CNV were carried out using four different but complementary tools and we validated a subset of the CNV by both in silico and experimental approaches...
October 24, 2017: Genetics, Selection, Evolution: GSE
Claudia A Sevillano, Jeremie Vandenplas, John W M Bastiaansen, Rob Bergsma, Mario P L Calus
BACKGROUND: Genomic prediction of purebred animals for crossbred performance can be based on a model that estimates effects of single nucleotide polymorphisms (SNPs) in purebreds on crossbred performance. For crossbred performance, SNP effects might be breed-specific due to differences between breeds in allele frequencies and linkage disequilibrium patterns between SNPs and quantitative trait loci. Accurately tracing the breed-of-origin of alleles (BOA) in three-way crosses is possible with a recently developed procedure called BOA...
October 23, 2017: Genetics, Selection, Evolution: GSE
Deniz Akdemir, Jean-Luc Jannink, Julio Isidro-Sánchez
BACKGROUND: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions, and there is an increased interest in using marker annotations in genome-wide prediction and association analyses...
October 17, 2017: Genetics, Selection, Evolution: GSE
Sophie Rothammer, Elisabeth Kunz, Doris Seichter, Stefan Krebs, Martina Wassertheurer, Ruedi Fries, Gottfried Brem, Ivica Medugorac
BACKGROUND: Cases of albinism have been reported in several species including cattle. So far, research has identified many genes that are involved in this eye-catching phenotype. Thus, when two paternal Braunvieh half-sibs with oculocutaneous albinism were detected on a private farm, we were interested in knowing whether their phenotype was caused by an already known gene/mutation. RESULTS: Analysis of genotyping data (50K) of the two albino individuals, their mothers and five other relatives identified a 47...
October 5, 2017: Genetics, Selection, Evolution: GSE
Julija Rusakovica, Valentin D Kremer, Thomas Plötz, Paige Rohlf, Ilias Kyriazakis
BACKGROUND: There is increasing interest in the definition, measurement and use of traits associated with water use and drinking behaviour, mainly because water is a finite resource and its intake is an important part of animal health and well-being. Analysis of such traits has received little attention, due in part to the lack of appropriate technology to measure drinking behaviour. We exploited novel equipment to collect water intake data in two lines of turkey (A: 27,415 and B: 12,956 birds)...
September 29, 2017: Genetics, Selection, Evolution: GSE
Irene van den Berg, Phil J Bowman, Iona M MacLeod, Ben J Hayes, Tingting Wang, Sunduimijid Bolormaa, Mike E Goddard
BACKGROUND: The increasing availability of whole-genome sequence data is expected to increase the accuracy of genomic prediction. However, results from simulation studies and analysis of real data do not always show an increase in accuracy from sequence data compared to high-density (HD) single nucleotide polymorphism (SNP) chip genotypes. In addition, the sheer number of variants makes analysis of all variants and accurate estimation of all effects computationally challenging. Our objective was to find a strategy to approximate the analysis of whole-sequence data with a Bayesian variable selection model...
September 21, 2017: Genetics, Selection, Evolution: GSE
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