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Genetics, Selection, Evolution: GSE

Clémence Fraslin, Nicolas Dechamp, Maria Bernard, Francine Krieg, Caroline Hervet, René Guyomard, Diane Esquerré, Johanna Barbieri, Claire Kuchly, Eric Duchaud, Pierre Boudinot, Tatiana Rochat, Jean-François Bernardet, Edwige Quillet
After publication of this work [1], we noted that there was an error in Table 3 Line 4.
March 20, 2019: Genetics, Selection, Evolution: GSE
Miriam Piles, Carlos Fernandez-Lozano, María Velasco-Galilea, Olga González-Rodríguez, Juan Pablo Sánchez, David Torrallardona, Maria Ballester, Raquel Quintanilla
BACKGROUND: To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always consistent. The aim of this research was to use machine learning to identify genes associated with feed efficiency (FE) using transcriptomic (RNA-Seq) data from pigs that are phenotypically extreme for RFI. METHODS: RFI was computed by considering within-sex regression on mean metabolic body weight, average daily gain, and average backfat gain...
March 13, 2019: Genetics, Selection, Evolution: GSE
Janez Jenko, Matthew C McClure, Daragh Matthews, Jennifer McClure, Martin Johnsson, Gregor Gorjanc, John M Hickey
BACKGROUND: In livestock, deleterious recessive alleles can result in reduced economic performance of homozygous individuals in multiple ways, e.g. early embryonic death, death soon after birth, or semi-lethality with incomplete penetrance 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 conception rates, longer calving intervals, or lower survival for live born animals...
March 5, 2019: Genetics, Selection, Evolution: GSE
Stefanie Muff, Alina K Niskanen, Dilan Saatoglu, Lukas F Keller, Henrik Jensen
BACKGROUND: The animal model is a key tool in quantitative genetics and has been used extensively to estimate fundamental parameters, such as additive genetic variance or heritability. An implicit assumption of animal models is that all founder individuals derive from a single population. This assumption is commonly violated, for instance in crossbred livestock or when a meta-population is split into genetically differentiated subpopulations. Ignoring that base populations are genetically heterogeneous and thus split into different 'genetic groups' may lead to biased parameter estimates, especially for additive genetic variance...
February 28, 2019: Genetics, Selection, Evolution: GSE
Maja Winther Iversen, Øyvind Nordbø, Eli Gjerlaug-Enger, Eli Grindflek, Marcos Soares Lopes, Theo Meuwissen
BACKGROUND: In pigs, crossbreeding aims at exploiting heterosis, but heterosis is difficult to quantify. Heterozygosity at genetic markers is easier to measure and could potentially be used as an indicator of heterosis. The objective of this study was to investigate the effect of heterozygosity on various maternal and production traits in purebred and crossbred pigs. The proportion of heterozygosity at genetic markers across the genome for each individual was included in the prediction model as a fixed regression across or within breeds...
February 28, 2019: Genetics, Selection, Evolution: GSE
Pascal Duenk, Mario P L Calus, Yvonne C J Wientjes, Vivian P Breen, John M Henshall, Rachel Hawken, Piter Bijma
BACKGROUND: In pig and poultry breeding programs, the breeding goal is to improve crossbred (CB) performance, whereas selection in the purebred (PB) lines is often based on PB performance. Thus, response to selection may be suboptimal, because the genetic correlation between PB and CB performance ([Formula: see text]) is generally lower than 1. Accurate estimates of the [Formula: see text] are needed, so that breeders can decide if they should collect data from CB animals. [Formula: see text] can be estimated either from pedigree or genomic relationships, which may produce different results...
February 19, 2019: Genetics, Selection, Evolution: GSE
Claire Oget, Charlotte Allain, David Portes, Gilles Foucras, Alessandra Stella, Jean-Michel Astruc, Julien Sarry, Gwenola Tosser-Klopp, Rachel Rupp
BACKGROUND: The identification of loci associated with resistance to mastitis or of the causative mutations may be helpful in breeding programs for dairy sheep as it is for cattle worldwide. Seven genomic regions that control milk somatic cell counts, an indirect indicator of udder infection, have already been identified in sheep (Spanish Churra, French Lacaune and Italian Sardinian-Lacaune backcross populations). In this study, we used a 960 custom-designed ovine single nucleotide polymorphism (SNP) chip in Lacaune and Manech Tête Rousse dairy sheep to validate these seven genomic regions associated with mastitis...
February 13, 2019: Genetics, Selection, Evolution: GSE
Tong Yin, Sven König
BACKGROUND: Body weight (BW) at different ages are of increasing importance in dairy cattle breeding schemes, because of their strong correlation with energy efficiency traits, and their impact on cow health, longevity and farm economy. In total, 15,921 dairy cattle from 56 large-scale test-herds with BW records were genotyped for 45,613 single nucleotide polymorphisms (SNPs). This dataset was used for genome-wide association studies (GWAS), in order to localize potential candidate genes for direct and maternal genetic effects on BW recorded at birth (BW0), at 2 to 3 months of age (BW23), and at 13 to 14 months of age (BW1314)...
February 6, 2019: Genetics, Selection, Evolution: GSE
Sanne van den Berg, Jérémie Vandenplas, Fred A van Eeuwijk, Aniek C Bouwman, Marcos S Lopes, Roel F Veerkamp
BACKGROUND: Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS. METHODS: Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line)...
January 24, 2019: Genetics, Selection, Evolution: GSE
Thomas J Lopdell, Kathryn Tiplady, Christine Couldrey, Thomas J J Johnson, Michael Keehan, Stephen R Davis, Bevin L Harris, Richard J Spelman, Russell G Snell, Mathew D Littlejohn
BACKGROUND: Over many years, artificial selection has substantially improved milk production by cows. However, the genes that underlie milk production quantitative trait loci (QTL) remain relatively poorly characterised. Here, we investigate a previously reported QTL located at the CSF2RB locus on chromosome 5, for several milk production phenotypes, to better understand its underlying genetic and molecular causes. RESULTS: Using a population of 29,350 taurine dairy cows, we conducted association analyses for milk yield and composition traits, and identified highly significant QTL for milk yield, milk fat concentration, and milk protein concentration...
January 24, 2019: Genetics, Selection, Evolution: GSE
Sunduimijid Bolormaa, Amanda J Chamberlain, Majid Khansefid, Paul Stothard, Andrew A Swan, Brett Mason, Claire P Prowse-Wilkins, Naomi Duijvesteijn, Nasir Moghaddar, Julius H van der Werf, Hans D Daetwyler, Iona M MacLeod
BACKGROUND: The use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep. RESULTS: The accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino...
January 17, 2019: Genetics, Selection, Evolution: GSE
Congying Chen, Chenlong Liu, Xinwei Xiong, Shaoming Fang, Hui Yang, Zhiyan Zhang, Jun Ren, Yuanmei Guo, Lusheng Huang
BACKGROUND: The size and type of ears are important conformation characteristics that distinguish pig breeds. A significant quantitative trait locus (QTL) for ear size has been identified on SSC5 (SSC for Sus scrofa chromosome) but the underlying causative gene and mutation remain unknown. Thus, our aim was to identify the gene responsible for enlarged ears in pig. RESULTS: First, we narrowed down the QTL region on SSC5 to a 137.85-kb interval that harbors only the methionine sulfoxide reductase B3 (MSRB3) gene...
December 27, 2018: Genetics, Selection, Evolution: GSE
Patrik Waldmann
BACKGROUND: Genome-wide marker data are used both in phenotypic genome-wide association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies include high-dimensional data with thousands to millions of single nucleotide polymorphisms (SNPs) recorded in hundreds to a few thousands individuals. Different machine-learning approaches have been used in GWAS and GWP effectively, but the use of neural networks (NN) and deep-learning is still scarce. This study presents a NN model for genomic SNP data...
December 22, 2018: Genetics, Selection, Evolution: GSE
Zulma G Vitezica, Antonio Reverter, William Herring, Andres Legarra
BACKGROUND: Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter size in pigs were estimated by Bayesian methods for five nested models with additive, dominance, and pairwise epistatic effects in a pig dataset, and including genomic inbreeding as a covariate. RESULTS: Estimates of additive and non-additive (dominance and epistatic) variance components were obtained for litter size...
December 22, 2018: Genetics, Selection, Evolution: GSE
Jing Dong, Chuan He, Zhibing Wang, Yanqing Li, Shanshan Li, Lin Tao, Jiebo Chen, Donghua Li, Fenxia Yang, Naibin Li, Quan Zhang, Li Zhang, Guangqin Wang, Fisayo Akinyemi, He Meng, Bingwang Du
BACKGROUND: Highly diversified in morphology and structure, feathers have evolved into various forms. Frizzle feathers, which result from a developmental defect of the feather, are observed in several domestic chicken breeds. The frizzle phenotype is consistent with incomplete dominance of a major gene, but the molecular mechanisms that underlie this phenotype remain obscure. Kirin, a Chinese indigenous chicken breed that originated in the Guangdong province, is famous for its frizzle feathers...
December 20, 2018: Genetics, Selection, Evolution: GSE
Martin Johnsson, Roger Ros-Freixedes, Gregor Gorjanc, Matt A Campbell, Sudhir Naswa, Kimberly Kelly, Jonathan Lightner, Steve Rounsley, John M Hickey
BACKGROUND: In this work, we investigated sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs. A functional CD163 protein is required for infection by porcine reproductive and respiratory syndrome virus, which is a serious pathogen with major impacts on pig production. RESULTS: We used targeted pooled sequencing of the exons of CD163 to detect sequence variants in 35,000 pigs of diverse genetic backgrounds and to search for potential stop-gain and frameshift indel variants...
December 20, 2018: Genetics, Selection, Evolution: GSE
Andrew Whalen, Roger Ros-Freixedes, David L Wilson, Gregor Gorjanc, John M Hickey
BACKGROUND: In this paper, we extend multi-locus iterative peeling to provide a computationally efficient method for calling, phasing, and imputing sequence data of any coverage in small or large pedigrees. Our method, called hybrid peeling, uses multi-locus iterative peeling to estimate shared chromosome segments between parents and their offspring at a subset of loci, and then uses single-locus iterative peeling to aggregate genomic information across multiple generations at the remaining loci...
December 18, 2018: Genetics, Selection, Evolution: GSE
Yvonne C J Wientjes, Mario P L Calus, Pascal Duenk, Piter Bijma
BACKGROUND: Generally, populations differ in terms of environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in two populations is known as the additive genetic correlation between populations and thus, can differ from 1. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect the bias of the estimated genetic correlation...
December 14, 2018: Genetics, Selection, Evolution: GSE
Andre L S Garcia, Brian Bosworth, Geoffrey Waldbieser, Ignacy Misztal, Shogo Tsuruta, Daniela A L Lourenco
BACKGROUND: Catfish farming is the largest segment of US aquaculture and research is ongoing to improve production efficiency, including genetic selection programs to improve economically important traits. The objectives of this study were to investigate the use of genomic selection to improve breeding value accuracy and to identify major single nucleotide polymorphisms (SNPs) associated with harvest weight and residual carcass weight in a channel catfish population. Phenotypes were available for harvest weight (n = 27,160) and residual carcass weight (n = 6020), and 36,365 pedigree records were available...
December 14, 2018: Genetics, Selection, Evolution: GSE
Roger Ros-Freixedes, Mara Battagin, Martin Johnsson, Gregor Gorjanc, Alan J Mileham, Steve D Rounsley, John M Hickey
BACKGROUND: Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing...
December 13, 2018: Genetics, Selection, Evolution: GSE
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