Deciphering mechanisms underlying the genetic variation of general production and liver quality traits in the overfed mule duck by pQTL analyses

• Main Authors: Yoannah François, Alain Vignal, Caroline Molette, Nathalie Marty-Gasset, Stéphane Davail, Laurence Liaubet, Christel Marie-Etancelin
• Format: Article
• Language: deu
• Published: BMC 2017-04-01
• Series: Genetics Selection Evolution
• Subjects: Quantitative Trait Locus; Quantitative Trait Locus Analysis; Melting Rate; Chicken Genome; Quantitative Trait Locus Detection
• Online Access: http://link.springer.com/article/10.1186/s12711-017-0313-6

Abstract Background The aim of this study was to analyse the mechanisms that underlie phenotypic quantitative trait loci (QTL) in overfed mule ducks by identifying co-localized proteomic QTL (pQTL). The QTL design consisted of three families of common ducks that were progeny-tested by using 294 male mule ducks. This population of common ducks was genotyped using a genetic map that included 334 genetic markers located across 28 APL chromosomes (APL for Anas platyrhynchos). Mule ducks were phenotyped for 49 traits related to growth, metabolism, overfeeding ability and meat and fatty liver quality, and 326 soluble fatty liver proteins were quantified. Results One hundred and seventy-six pQTL and 80 phenotypic QTL were detected at the 5% chromosome-wide significance threshold. The great majority of the identified pQTL were trans-acting and localized on a chromosome other than that carrying the coding gene. The most significant pQTL (1% genome-wide significance) were found for alpha-enolase on APL18 and fatty acid synthase on APL24. Some proteins were associated with numerous pQTL (for example, 17 and 14 pQTL were detected for alpha-enolase and apolipoprotein A1, respectively) and pQTL hotspots were observed on some chromosomes (APL18, 24, 25 and 29). We detected 66 co-localized phenotypic QTL and pQTL for which the significance of the two-trait QTL (2t-QTL) analysis was higher than that of the strongest QTL using a single-trait approach. Among these, 16 2t-QTL were pleiotropic. For example, on APL15, melting rate and abundance of two alpha-enolase spots appeared to be impacted by a single locus that is involved in the glycolytic process. On APLZ, we identified a pleiotropic QTL that modified both the blood level of glucose at the beginning of the force-feeding period and the concentration of glutamate dehydrogenase, which, in humans, is involved in increased glucose absorption by the liver when the glutamate dehydrogenase 1 gene is mutated. Conclusions We identified pleiotropic loci that affect metabolic pathways linked to glycolysis or lipogenesis, and in the end to fatty liver quality. Further investigation, via transcriptomics and metabolomics approaches, is required to confirm the biomarkers that were found to impact the genetic variability of these phenotypic traits.