Release 56
(Apr 24, 2025)

Reference # 21926318 Details:

Authors:Ruckert C, Stratz P, Preuss S, Bennewitz J (Contact: j.bennewitz@uni-hohenheim.de)
Affiliation:Institute of Animal Husbandry and Breeding, University of Hohenheim, D-70599 Stuttgart, Germany
Title:Mapping quantitative trait loci for metabolic and cytological fatness traits of connected F2 crosses in pigs.
Journal:Journal of Animal Science, 2012, 90(2): 399-409 DOI: 10.2527/jas.2011-4231
Abstract:

In the present study 3 connected F(2) crosses were used to map QTL for classical fat traits as well as fat-related metabolic and cytological traits in pigs. The founder breeds were Chinese Meishan, European Wild Boar, and Pietrain with to some extent the same founder animals in the different crosses. The different selection history of the breeds for fatness traits as well as the connectedness of the crosses led to a high statistical power. The total number of F(2) animals varied between 694 and 966, depending on the trait. The animals were genotyped for around 250 genetic markers, mostly microsatellites. The statistical model was a multi-allele, multi-QTL model that accounted for imprinting. The model was previously introduced from plant breeding experiments. The traits investigated were backfat depth and fat area as well as relative number of fat cells with different sizes and 2 metabolic traits (i.e., soluble protein content as an indicator for the level of metabolic turnover and NADP-malate dehydrogenase as an indicator for enzyme activity). The results revealed in total 37 significant QTL on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 14, 17, and 18, with often an overlap of confidence intervals of several traits. These confidence intervals were in some cases remarkably small, which is due to the high statistical power of the design. In total, 18 QTL showed significant imprinting effects. The small and overlapping confidence intervals for the classical fatness traits as well as for the cytological and metabolic traits enabled positional and functional candidate gene identification for several mapped QTL.

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