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Genome-wide association studies for 30 haematological and blood clinical-biochemical traits in Large White pigs reveal genomic regions affecting intermediate phenotypes

Haematological and clinical-biochemical parameters are considered indicators of the physiological/health status of animals and might serve as intermediate phenotypes to link physiological aspects to production and disease resistance traits. The dissection of the genetic variability affecting these p...

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Detalles Bibliográficos
Autores principales: Bovo, Samuele, Mazzoni, Gianluca, Bertolini, Francesca, Schiavo, Giuseppina, Galimberti, Giuliano, Gallo, Maurizio, Dall’Olio, Stefania, Fontanesi, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504931/
https://www.ncbi.nlm.nih.gov/pubmed/31065004
http://dx.doi.org/10.1038/s41598-019-43297-1
Descripción
Sumario:Haematological and clinical-biochemical parameters are considered indicators of the physiological/health status of animals and might serve as intermediate phenotypes to link physiological aspects to production and disease resistance traits. The dissection of the genetic variability affecting these phenotypes might be useful to describe the resilience of the animals and to support the usefulness of the pig as animal model. Here, we analysed 15 haematological and 15 clinical-biochemical traits in 843 Italian Large White pigs, via three genome-wide association scan approaches (single-trait, multi-trait and Bayesian). We identified 52 quantitative trait loci (QTLs) associated with 29 out of 30 analysed blood parameters, with the most significant QTL identified on porcine chromosome 14 for basophil count. Some QTL regions harbour genes that may be the obvious candidates: QTLs for cholesterol parameters identified genes (ADCY8, APOB, ATG5, CDKAL1, PCSK5, PRL and SOX6) that are directly involved in cholesterol metabolism; other QTLs highlighted genes encoding the enzymes being measured [ALT (known also as GPT) and AST (known also as GOT)]. Moreover, the multivariate approach strengthened the association results for several candidate genes. The obtained results can contribute to define new measurable phenotypes that could be applied in breeding programs as proxies for more complex traits.