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  1. Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several...

    Authors: Jonathan D’Ambrosio, Florence Phocas, Pierrick Haffray, Anastasia Bestin, Sophie Brard-Fudulea, Charles Poncet, Edwige Quillet, Nicolas Dechamp, Clémence Fraslin, Mathieu Charles and Mathilde Dupont-Nivet
    Citation: Genetics Selection Evolution 2019 51:26
  2. The cuticle is an invisible glycosylated protein layer that covers the outside of the eggshell and forms a barrier to the transmission of microorganisms. Cuticle-specific staining and in situ absorbance measur...

    Authors: Ian C. Dunn, John A. Woolliams, Peter W. Wilson, Wiebke Icken, David Cavero, Anita C. Jones, Fiona Quinlan-Pluck, Gareth O. S. Williams, Victor Olori and Maureen M. Bain
    Citation: Genetics Selection Evolution 2019 51:25
  3. In settings with social interactions, the phenotype of an individual is affected by the direct genetic effect (DGE) of the individual itself and by indirect genetic effects (IGE) of its group mates. In the pre...

    Authors: Marzieh Heidaritabar, Piter Bijma, Luc Janss, Chiara Bortoluzzi, Hanne M. Nielsen, Per Madsen, Birgitte Ask and Ole F. Christensen
    Citation: Genetics Selection Evolution 2019 51:24
  4. Fatty acids (FA) in bovine milk derive through body mobilization, de novo synthesis or from the feed via the blood stream. To be able to digest feedstuff, the cow depends on its rumen microbiome. The relative ...

    Authors: Bart Buitenhuis, Jan Lassen, Samantha Joan Noel, Damian R. Plichta, Peter Sørensen, Gareth F. Difford and Nina A. Poulsen
    Citation: Genetics Selection Evolution 2019 51:23
  5. Since the 1950s, the Norwegian–Swedish Coldblooded trotter (NSCT) has been intensively selected for harness racing performance. As a result, the racing performance of the NSCT has improved remarkably; however,...

    Authors: Brandon D. Velie, Marina Solé, Kim Jäderkvist Fegraeus, Maria K. Rosengren, Knut H. Røed, Carl-Fredrik Ihler, Eric Strand and Gabriella Lindgren
    Citation: Genetics Selection Evolution 2019 51:22
  6. Genome-wide association studies (GWAS) are widely used to identify regions of the genome that harbor genetic determinants of quantitative traits. However, the multiple-testing burden from scanning tens of mill...

    Authors: Zexi Cai, Bernt Guldbrandtsen, Mogens Sandø Lund and Goutam Sahana
    Citation: Genetics Selection Evolution 2019 51:20
  7. Growth curves have been widely used in genetic analyses to gain insights into the growth characteristics of both animals and plants. However, several questions remain unanswered, including how the initial phen...

    Authors: Akio Onogi, Atsushi Ogino, Ayako Sato, Kazuhito Kurogi, Takanori Yasumori and Kenji Togashi
    Citation: Genetics Selection Evolution 2019 51:19
  8. Societal pressures exist to reduce greenhouse gas (GHG) emissions from farm animals, especially in beef cattle. Both total GHG and GHG emissions per unit of product decrease as productivity increases. Limitati...

    Authors: Stephen A. Barwick, Anthony L. Henzell, Robert M. Herd, Bradley J. Walmsley and Paul F. Arthur
    Citation: Genetics Selection Evolution 2019 51:18
  9. Catla catla (Hamilton) fertilised spawn was collected from the Halda, Jamuna and Padma rivers in Bangladesh from which approximately 900 individuals were retained as ‘candidate founders’ of a breeding population....

    Authors: Matthew G. Hamilton, Wagdy Mekkawy and John A. H. Benzie
    Citation: Genetics Selection Evolution 2019 51:17
  10. Large-scale phenotyping for detailed milk fatty acid (FA) composition is difficult due to expensive and time-consuming analytical techniques. Reliability of genomic prediction is often low for traits that are ...

    Authors: Grum Gebreyesus, Henk Bovenhuis, Mogens S. Lund, Nina A. Poulsen, Dongxiao Sun and Bart Buitenhuis
    Citation: Genetics Selection Evolution 2019 51:16
  11. Quantitative genetic studies suggest the existence of variation at the genome level that affects the ability of cattle to resist to parasitic diseases. The objective of the current study was to identify region...

    Authors: Alan J. Twomey, Donagh P. Berry, Ross D. Evans, Michael L. Doherty, David A. Graham and Deirdre C. Purfield
    Citation: Genetics Selection Evolution 2019 51:15
  12. In this paper, we simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it. Deleterious variants can be identified by bioinformatics...

    Authors: Martin Johnsson, R. Chris Gaynor, Janez Jenko, Gregor Gorjanc, Dirk-Jan de Koning and John M. Hickey
    Citation: Genetics Selection Evolution 2019 51:14
  13. We used stable isotope profiling (15N and 13C) to obtain indicator phenotypes for feed efficiency in aquaculture. Our objectives were to (1) examine whether atom percent of stable isotopes of nitrogen and carbon ...

    Authors: Hanne Dvergedal, Jørgen Ødegård, Margareth Øverland, Liv Torunn Mydland and Gunnar Klemetsdal
    Citation: Genetics Selection Evolution 2019 51:13
  14. In quail, two feather colour phenotypes i.e. fawn-2/beige and yellow are associated with the ASIP locus. The aim of our study was to characterize the structural modifications within this locus that explain the ye...

    Authors: Annie Robic, Mireille Morisson, Sophie Leroux, David Gourichon, Alain Vignal, Noémie Thebault, Valérie Fillon, Francis Minvielle, Bertrand Bed’Hom, Tatiana Zerjal and Frédérique Pitel
    Citation: Genetics Selection Evolution 2019 51:12
  15. After publication of this work [1], we noted that there was an error in Table 3 Line 4.

    Authors: 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 and Edwige Quillet
    Citation: Genetics Selection Evolution 2019 51:11

    The original article was published in Genetics Selection Evolution 2018 50:60

  16. 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. Th...

    Authors: Miriam Piles, Carlos Fernandez-Lozano, María Velasco-Galilea, Olga González-Rodríguez, Juan Pablo Sánchez, David Torrallardona, Maria Ballester and Raquel Quintanilla
    Citation: Genetics Selection Evolution 2019 51:10
  17. 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 inc...

    Authors: Janez Jenko, Matthew C. McClure, Daragh Matthews, Jennifer McClure, Martin Johnsson, Gregor Gorjanc and John M. Hickey
    Citation: Genetics Selection Evolution 2019 51:9
  18. 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. T...

    Authors: Maja Winther Iversen, Øyvind Nordbø, Eli Gjerlaug-Enger, Eli Grindflek, Marcos Soares Lopes and Theo Meuwissen
    Citation: Genetics Selection Evolution 2019 51:8
  19. 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...

    Authors: Stefanie Muff, Alina K. Niskanen, Dilan Saatoglu, Lukas F. Keller and Henrik Jensen
    Citation: Genetics Selection Evolution 2019 51:7
  20. 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 ...

    Authors: Pascal Duenk, Mario P. L. Calus, Yvonne C. J. Wientjes, Vivian P. Breen, John M. Henshall, Rachel Hawken and Piter Bijma
    Citation: Genetics Selection Evolution 2019 51:6
  21. 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 con...

    Authors: Claire Oget, Charlotte Allain, David Portes, Gilles Foucras, Alessandra Stella, Jean-Michel Astruc, Julien Sarry, Gwenola Tosser-Klopp and Rachel Rupp
    Citation: Genetics Selection Evolution 2019 51:5
  22. 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 an...

    Authors: Tong Yin and Sven König
    Citation: Genetics Selection Evolution 2019 51:4
  23. 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. ...

    Authors: 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 and Mathew D. Littlejohn
    Citation: Genetics Selection Evolution 2019 51:3
  24. 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 th...

    Authors: Sanne van den Berg, Jérémie Vandenplas, Fred A. van Eeuwijk, Aniek C. Bouwman, Marcos S. Lopes and Roel F. Veerkamp
    Citation: Genetics Selection Evolution 2019 51:2
  25. 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 ran...

    Authors: 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 and Iona M. MacLeod
    Citation: Genetics Selection Evolution 2019 51:1
  26. 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 chromoso...

    Authors: Congying Chen, Chenlong Liu, Xinwei Xiong, Shaoming Fang, Hui Yang, Zhiyan Zhang, Jun Ren, Yuanmei Guo and Lusheng Huang
    Citation: Genetics Selection Evolution 2018 50:72
  27. 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...

    Authors: Zulma G. Vitezica, Antonio Reverter, William Herring and Andres Legarra
    Citation: Genetics Selection Evolution 2018 50:71
  28. 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 ...

    Authors: Patrik Waldmann
    Citation: Genetics Selection Evolution 2018 50:70
  29. 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 syn...

    Authors: Martin Johnsson, Roger Ros-Freixedes, Gregor Gorjanc, Matt A. Campbell, Sudhir Naswa, Kimberly Kelly, Jonathan Lightner, Steve Rounsley and John M. Hickey
    Citation: Genetics Selection Evolution 2018 50:69
  30. 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 bre...

    Authors: 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 and Bingwang Du
    Citation: Genetics Selection Evolution 2018 50:68
  31. 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, ca...

    Authors: Andrew Whalen, Roger Ros-Freixedes, David L. Wilson, Gregor Gorjanc and John M. Hickey
    Citation: Genetics Selection Evolution 2018 50:67
  32. 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 ...

    Authors: Andre L. S. Garcia, Brian Bosworth, Geoffrey Waldbieser, Ignacy Misztal, Shogo Tsuruta and Daniela A. L. Lourenco
    Citation: Genetics Selection Evolution 2018 50:66
  33. 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 add...

    Authors: Yvonne C. J. Wientjes, Mario P. L. Calus, Pascal Duenk and Piter Bijma
    Citation: Genetics Selection Evolution 2018 50:65
  34. 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 th...

    Authors: Roger Ros-Freixedes, Mara Battagin, Martin Johnsson, Gregor Gorjanc, Alan J. Mileham, Steve D. Rounsley and John M. Hickey
    Citation: Genetics Selection Evolution 2018 50:64
  35. Coccidiosis is a major contributor to losses in poultry production. With emerging constraints on the use of in-feed prophylactic anticoccidial drugs and the relatively high costs of effective vaccines, there a...

    Authors: Kay Boulton, Matthew J. Nolan, Zhiguang Wu, Androniki Psifidi, Valentina Riggio, Kimberley Harman, Stephen C. Bishop, Pete Kaiser, Mitchell S. Abrahamsen, Rachel Hawken, Kellie A. Watson, Fiona M. Tomley, Damer P. Blake and David A. Hume
    Citation: Genetics Selection Evolution 2018 50:63
  36. Availability of whole-genome sequence data for a large number of cattle and efficient imputation methodologies open a new opportunity to include rare and low-frequency variants (RLFV) in genomic prediction in ...

    Authors: Qianqian Zhang, Goutam Sahana, Guosheng Su, Bernt Guldbrandtsen, Mogens Sandø Lund and Mario P. L. Calus
    Citation: Genetics Selection Evolution 2018 50:62
  37. Authors: Alessandra Stella, Ezequiel Luis Nicolazzi, Curtis P. Van Tassell, Max F. Rothschild, Licia Colli, Benjamin D. Rosen, Tad S. Sonstegard, Paola Crepaldi, Gwenola Tosser-Klopp and Stephane Joost
    Citation: Genetics Selection Evolution 2018 50:61
  38. Patterns of homozygosity can be influenced by several factors, such as demography, recombination, and selection. Using the goat SNP50 BeadChip, we genotyped 3171 goats belonging to 117 populations with a world...

    Authors: Francesca Bertolini, Tainã Figueiredo Cardoso, Gabriele Marras, Ezequiel L. Nicolazzi, Max F. Rothschild and Marcel Amills
    Citation: Genetics Selection Evolution 2018 50:59
  39. Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We ...

    Authors: Licia Colli, Marco Milanesi, Andrea Talenti, Francesca Bertolini, Minhui Chen, Alessandra Crisà, Kevin Gerard Daly, Marcello Del Corvo, Bernt Guldbrandtsen, Johannes A. Lenstra, Benjamin D. Rosen, Elia Vajana, Gennaro Catillo, Stéphane Joost, Ezequiel Luis Nicolazzi, Estelle Rochat…
    Citation: Genetics Selection Evolution 2018 50:58
  40. Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems an...

    Authors: Francesca Bertolini, Bertrand Servin, Andrea Talenti, Estelle Rochat, Eui Soo Kim, Claire Oget, Isabelle Palhière, Alessandra Crisà, Gennaro Catillo, Roberto Steri, Marcel Amills, Licia Colli, Gabriele Marras, Marco Milanesi, Ezequiel Nicolazzi, Benjamin D. Rosen…
    Citation: Genetics Selection Evolution 2018 50:57
  41. Genetic isolation of breeds may result in a significant loss of diversity and have consequences on health and performance. In this study, we examined the effect of geographic isolation on caprine genetic diver...

    Authors: Taina F. Cardoso, Marcel Amills, Francesca Bertolini, Max Rothschild, Gabriele Marras, Geert Boink, Jordi Jordana, Juan Capote, Sean Carolan, Jón H. Hallsson, Juha Kantanen, Agueda Pons and Johannes A. Lenstra
    Citation: Genetics Selection Evolution 2018 50:56
  42. International standard panels of single nucleotide polymorphisms (SNPs) have replaced microsatellites in several species for parentage assessment and assignment (PA) purposes. However, such a resource is still...

    Authors: Andrea Talenti, Isabelle Palhière, Flavie Tortereau, Giulio Pagnacco, Alessandra Stella, Ezequiel L. Nicolazzi, Paola Crepaldi and Gwenola Tosser-Klopp
    Citation: Genetics Selection Evolution 2018 50:55
  43. Bacterial cold-water disease, which is caused by Flavobacterium psychrophilum, is one of the major diseases that affect rainbow trout (Oncorhynchus mykiss) and a primary concern for trout farming. Better knowledg...

    Authors: 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 and Edwige Quillet
    Citation: Genetics Selection Evolution 2018 50:60

    The Correction to this article has been published in Genetics Selection Evolution 2019 51:11

  44. Copy number variations (CNV) are an important source of genetic variation that has gained increasing attention over the last couple of years. In this study, we performed CNV detection and functional analysis f...

    Authors: Wioleta Drobik-Czwarno, Anna Wolc, Janet E. Fulton and Jack C. M. Dekkers
    Citation: Genetics Selection Evolution 2018 50:54
  45. Cross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validat...

    Authors: Andres Legarra and Antonio Reverter
    Citation: Genetics Selection Evolution 2018 50:53

    The Correction to this article has been published in Genetics Selection Evolution 2019 51:69

  46. A breeding program for commercial broiler chicken that is carried out under strict biosecure conditions can show reduced genetic gain due to genotype by environment interactions (G × E) between bio-secure (B) ...

    Authors: Thinh T. Chu, Setegn W. Alemu, Elise Norberg, Anders C. Sørensen, John Henshall, Rachel Hawken and Just Jensen
    Citation: Genetics Selection Evolution 2018 50:52
  47. The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) method, such as single-step genomic BLUP (ssGBLUP), simultaneously analyses phenotypic, pedigree, and genomic informat...

    Authors: Jérémie Vandenplas, Herwin Eding, Mario P. L. Calus and Cornelis Vuik
    Citation: Genetics Selection Evolution 2018 50:51
  48. High resistance (the ability of the host to reduce pathogen load) and tolerance (the ability to maintain high performance at a given pathogen load) are two desirable host traits for producing animals that are ...

    Authors: Graham Lough, Andrew Hess, Melanie Hess, Hamed Rashidi, Oswald Matika, Joan K. Lunney, Raymond R. R. Rowland, Ilias Kyriazakis, Han A. Mulder, Jack C. M. Dekkers and Andrea Doeschl-Wilson
    Citation: Genetics Selection Evolution 2018 50:50
  49. Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP mo...

    Authors: Biaty Raymond, Aniek C. Bouwman, Yvonne C. J. Wientjes, Chris Schrooten, Jeanine Houwing-Duistermaat and Roel F. Veerkamp
    Citation: Genetics Selection Evolution 2018 50:49

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