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12 p, 2.1 MB |
On the holobiont 'predictome' of immunocompetence in pigs
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Calle-García, Joan (Centre de Recerca en Agrigenòmica) ;
Ramayo-Caldas, Yuliaxis (Unitat mixta d'investigació IRTA-UAB en Sanitat Animal. Centre de Recerca en Sanitat Animal) ;
Zingaretti, Laura M. (Centre de Recerca en Agrigenòmica) ;
Quintanilla, Raquel (Unitat mixta d'investigació IRTA-UAB en Sanitat Animal. Centre de Recerca en Sanitat Animal) ;
Ballester Devis, Maria (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments) ;
Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica)
Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. [...]
2023 -  10.1186/s12711-023-00803-4
Genetics, selection, evolution, Vol. 55 (may 2023)
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20 p, 3.9 MB |
Opportunities and limits of combining microbiome and genome data for complex trait prediction
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Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica) ;
Zingaretti, Laura M. (Centre de Recerca en Agrigenòmica) ;
Ramayo-Caldas, Yuliaxis (Institut de Recerca i Tecnologia Agroalimentàries) ;
de los Campos, Gustavo (Michigan State University. Department of Epidemiology & Biostatistics)
Analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: how useful can the microbiome be for complex trait prediction? Are estimates of microbiability reliable? Can the underlying biological links between the host's genome, microbiome, and phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as inputs, and (ii) using variance-component approaches (Bayesian Reproducing Kernel Hilbert Space (RKHS) and Bayesian variable selection methods (Bayes C)) to quantify the proportion of phenotypic variance explained by the genome and the microbiome. [...]
2021 - 10.1186/s12711-021-00658-7
Genetics, selection, evolution, Vol. 53 (August 2021) , art. 65
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12 p, 2.6 MB |
Transposable element polymorphisms improve prediction of complex agronomic traits in rice
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Vourlaki, Ioanna-Theoni (Centre de Recerca en Agrigenòmica) ;
Ramos-Onsins, Sebastián E. (Centre de Recerca en Agrigenòmica) ;
Casacuberta i Suñer, Josep M 1962- (Centre de Recerca en Agrigenòmica) ;
Perez-Enciso, Miguel (Universitat Autònoma de Barcelona. Departament de Patologia i de Producció Animals) ;
Castanera, Raúl (Centre de Recerca en Agrigenòmica)
Key message: Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set. [...]
2022 - 10.1007/s00122-022-04180-2
Theoretical and Applied Genetics, Vol. 135, Issue 9 (September 2022) , p. 3211-3222
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10 p, 1.2 MB |
Phenomes : the current frontier in animal breeding
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Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica) ;
Steibel, Juan P. (Michigan State University. Department of Fisheries and Wildlife)
Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. [...]
2021 - 10.1186/s12711-021-00618-1
Genetics, selection, evolution, Vol. 53 (March 2021) , art. 22
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1 p, 573.5 KB |
Correction to : Genome-wide SNP data unveils the globalization of domesticated pigs
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Yang, Bin (Jiangxi Agricultural University. National Key Laboratory for Pig Genetic Improvement and Production Technology) ;
Cui, Leilei (Jiangxi Agricultural University. National Key Laboratory for Pig Genetic Improvement and Production Technology) ;
Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica) ;
Traspov, Aleksei (Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst) ;
Crooijmans, Richard (Wageningen University. Animal Breeding and Genomics) ;
Zinovieva, Natalia (Federal Science Center for Animal Husbandry named after Academy Member L.K. Ernst) ;
Schook, Lawrence B. (University of Illinois. Institute of Genomic Biology) ;
Archibald, Alan L (University of Edinburgh. The Roslin Institute) ;
Gatphayak, Kesinee (Chiang Mai University. Animal and Aquatic Sciences) ;
Knorr, Christophe (University of Göttingen. Department of Animal Sciences) ;
Triantafyllidis, Alex (Aristotle University of Thessaloníki. Department of Genetics, Development and Molecular Biology) ;
Alexandri, Panoraia (Aristotle University of Thessaloníki. Department of Genetics, Development and Molecular Biology) ;
Semiadi, Gono (Lembaga Ilmu Pengetahuan Indonesia. Research Centre for Biology-Zoology Division) ;
Hanotte, Olivier (University of Nottingham. School of Biology) ;
Dias, Deodália (Universidade de Lisboa. Faculdade de Ciências) ;
Dovč, Peter (University of Ljubljana. Department of Animal Science) ;
Uimari, Pekka (University of Helsinki. Department of Agricultural Sciences) ;
Iacolina, Laura (University of Sassari. Department of Science for Nature and Environmental Resources) ;
Scandura, Massimo (University of Sassari. Department of Science for Nature and Environmental Resources) ;
Groenen, Martien A. M. (Wageningen University. Animal Breeding and Genomics) ;
Huang, Lusheng (Jiangxi Agricultural University. National Key Laboratory for Pig Genetic Improvement and Production Technology) ;
Megens, Hendrik-Jan (Wageningen University. Animal Breeding and Genomics)
2020 - 10.1186/s12711-020-00549-3
Genetics, selection, evolution, Vol. 52 (June 2020) . art. 30
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