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Genetics, Selection, Evolution: GSE

Gabriela Padilla-Jacobo, Horacio Cano-Camacho, Rigoberto López-Zavala, María E Cornejo-Pérez, María G Zavala-Páramo
BACKGROUND: The distribution of the wild turkey (Meleagris gallopavo) extends from Mexico to southeastern Canada and to the eastern and southern regions of the USA. Six subspecies have been described based on morphological characteristics and/or geographical variations in wild and domesticated populations. In this paper, based on DNA sequence data from the mitochondrial D-loop, we investigated the genetic diversity and structure, genealogical relationships, divergence time and demographic history of M...
April 17, 2018: Genetics, Selection, Evolution: GSE
John W M Bastiaansen, Henk Bovenhuis, Martien A M Groenen, Hendrik-Jan Megens, Han A Mulder
BACKGROUND: Genome editing technologies provide new tools for genetic improvement and have the potential to become the next game changer in animal and plant breeding. The aim of this study was to investigate how genome editing in combination with genomic selection can accelerate the introduction of a monogenic trait in a livestock population as compared to genomic selection alone. METHODS: A breeding population was simulated under genomic selection for a polygenic trait...
April 16, 2018: Genetics, Selection, Evolution: GSE
Martijn F L Derks, Hendrik-Jan Megens, Mirte Bosse, Jeroen Visscher, Katrijn Peeters, Marco C A M Bink, Addie Vereijken, Christian Gross, Dick de Ridder, Marcel J T Reinders, Martien A M Groenen
BACKGROUND: Deleterious genetic variation can increase in frequency as a result of mutations, genetic drift, and genetic hitchhiking. Although individual effects are often small, the cumulative effect of deleterious genetic variation can impact population fitness substantially. In this study, we examined the genome of commercial purebred chicken lines for deleterious and functional variations, combining genotype and whole-genome sequence data. RESULTS: We analysed over 22,000 animals that were genotyped on a 60 K SNP chip from four purebred lines (two white egg and two brown egg layer lines) and two crossbred lines...
April 16, 2018: Genetics, Selection, Evolution: GSE
Johannes W R Martini, Matias F Schrauf, Carolina A Garcia-Baccino, Eduardo C G Pimentel, Sebastian Munilla, Andres Rogberg-Muñoz, Rodolfo J C Cantet, Christian Reimer, Ning Gao, Valentin Wimmer, Henner Simianer
BACKGROUND: The single-step covariance matrix H combines the pedigree-based relationship matrix [Formula: see text] with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix [Formula: see text]. In particular, to improve convergence behavior of iterative approaches and to reduce inflation, two weights [Formula: see text] and [Formula: see text] have been introduced in the definition of [Formula: see text], which blend the inverse of a part of [Formula: see text] with the inverse of [Formula: see text]...
April 13, 2018: Genetics, Selection, Evolution: GSE
Harmen P Doekes, Roel F Veerkamp, Piter Bijma, Sipke J Hiemstra, Jack J Windig
BACKGROUND: In recent decades, Holstein-Friesian (HF) selection schemes have undergone profound changes, including the introduction of optimal contribution selection (OCS; around 2000), a major shift in breeding goal composition (around 2000) and the implementation of genomic selection (GS; around 2010). These changes are expected to have influenced genetic diversity trends. Our aim was to evaluate genome-wide and region-specific diversity in HF artificial insemination (AI) bulls in the Dutch-Flemish breeding program from 1986 to 2015...
April 11, 2018: Genetics, Selection, Evolution: GSE
Chunyan Zhang, Robert Alan Kemp, Paul Stothard, Zhiquan Wang, Nicholas Boddicker, Kirill Krivushin, Jack Dekkers, Graham Plastow
BACKGROUND: Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs...
April 6, 2018: Genetics, Selection, Evolution: GSE
Johan Bélteky, Beatrix Agnvall, Lejla Bektic, Andrey Höglund, Per Jensen, Carlos Guerrero-Bosagna
BACKGROUND: Domestication of animals leads to large phenotypic alterations within a short evolutionary time-period. Such alterations are caused by genomic variations, yet the prevalence of modified traits is higher than expected if they were caused only by classical genetics and mutations. Epigenetic mechanisms may also be important in driving domesticated phenotypes such as behavior traits. Gene expression can be modulated epigenetically by mechanisms such as DNA methylation, resulting in modifications that are not only variable and susceptible to environmental stimuli, but also sometimes transgenerationally stable...
April 2, 2018: Genetics, Selection, Evolution: GSE
Chao Ning, Dan Wang, Xianrui Zheng, Qin Zhang, Shengli Zhang, Raphael Mrode, Jian-Feng Liu
BACKGROUND: Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets. RESULTS: We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis...
March 26, 2018: Genetics, Selection, Evolution: GSE
Amanda J Cross, Brittney N Keel, Tami M Brown-Brandl, Joseph P Cassady, Gary A Rohrer
BACKGROUND: Heat stress has a negative impact on pork production, particularly during the grow-finish phase. As temperature increases, feeding behaviour changes in order for pigs to decrease heat production. The objective of this study was to identify genetic markers associated with changes in feeding behaviour due to heat stress. Feeding data were collected on 1154 grow-finish pigs using an electronic feeding system from July 2011 to March 2016. In this study, days were classified based on the maximum temperature humidity index (THI) during the day as "Normal" (< 23...
March 25, 2018: Genetics, Selection, Evolution: GSE
Kathryn E Kemper, Philip J Bowman, Benjamin J Hayes, Peter M Visscher, Michael E Goddard
BACKGROUND: Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into 'unassociated' (no association with any trait) and 'associated' (associated with one or more traits)...
March 24, 2018: Genetics, Selection, Evolution: GSE
Hadi Esfandyari, Peer Berg, Anders Christian Sørensen
BACKGROUND: Genomic selection can be applied to select purebreds for crossbred performance (CP). The average performance of crossbreds can be considered as the summation of two components, i.e. the breed average (BA) of the parental breeds and heterosis (H) present in crossbreds. Selection of pure breeds for CP based on genomic estimated breeding values for crossbred performance (GEBV-C) or for purebred performance (GEBV-P) may differ in their ability to exploit BA and H and can affect the merit of crossbreds in both the short and long term...
March 22, 2018: Genetics, Selection, Evolution: GSE
Martijn F L Derks, Juan M Herrero-Medrano, Richard P M A Crooijmans, Addie Vereijken, Julie A Long, Hendrik-Jan Megens, Martien A M Groenen
BACKGROUND: Sex-linked slow (SF) and fast (FF) feathering rates at hatch have been widely used in poultry breeding for autosexing at hatch. In chicken, the sex-linked K (SF) and k+ (FF) alleles are responsible for the feathering rate phenotype. Allele K is dominant and a partial duplication of the prolactin receptor gene has been identified as the causal mutation. Interestingly, some domesticated turkey lines exhibit similar slow- and fast-feathering phenotypes, but the underlying genetic components and causal mutation have never been investigated...
March 22, 2018: Genetics, Selection, Evolution: GSE
Otsanda Ruiz-Larrañaga, Jorge Langa, Fernando Rendo, Carmen Manzano, Mikel Iriondo, Andone Estonba
BACKGROUND: The current large spectrum of sheep phenotypic diversity results from the combined product of sheep selection for different production traits such as wool, milk and meat, and its natural adaptation to new environments. In this study, we scanned the genome of 25 Sasi Ardi and 75 Latxa sheep from the Western Pyrenees for three types of regions under selection: (1) regions underlying local adaptation of Sasi Ardi semi-feral sheep, (2) regions related to a long traditional dairy selection pressure in Latxa sheep, and (3) regions experiencing the specific effect of the modern genetic improvement program established for the Latxa breed during the last three decades...
March 22, 2018: Genetics, Selection, Evolution: GSE
Jørgen Ødegård, Ulf Indahl, Ismo Strandén, Theo H E Meuwissen
BACKGROUND: For marker effect models and genomic animal models, computational requirements increase with the number of loci and the number of genotyped individuals, respectively. In the latter case, the inverse genomic relationship matrix (GRM) is typically needed, which is computationally demanding to compute for large datasets. Thus, there is a great need for dimensionality-reduction methods that can analyze massive genomic data. For this purpose, we developed reduced-dimension singular value decomposition (SVD) based models for genomic prediction...
February 28, 2018: Genetics, Selection, Evolution: GSE
Małgorzata Zbawicka, María I Trucco, Roman Wenne
BACKGROUND: Throughout the world, harvesting of mussels Mytilus spp. is based on the exploitation of natural populations and aquaculture. Aquaculture activities include transfers of spat and live adult mussels between various geographic locations, which may result in large-scale changes in the world distribution of Mytilus taxa. Mytilus taxa are morphologically similar and difficult to distinguish. In spite of much research on taxonomy, evolution and geographic distribution, the native Mytilus taxa of the Southern Hemisphere are poorly understood...
February 22, 2018: 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
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