1. Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line
- Author
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E. Le Bihan-Duval, Olivier Demeure, Christelle Hennequet-Antier, Cécile Berri, L. Salles, Sophie Allais, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité de Recherches Avicoles (URA), Institut National de la Recherche Agronomique (INRA), Société SASSO, Recherches Avicoles (SRA), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)
- Subjects
Genetic Markers ,Male ,Linkage disequilibrium ,Animal breeding ,Meat ,chicken ,Quantitative Trait Loci ,poulet ,qualité de la viande ,Quantitative trait locus ,Biology ,lignée génétique ,Crossbreed ,Linkage Disequilibrium ,03 medical and health sciences ,Bayes' theorem ,technological quality ,Animals ,Selection (genetic algorithm) ,030304 developmental biology ,2. Zero hunger ,Genetics ,0303 health sciences ,qtl ,[SDV.BA]Life Sciences [q-bio]/Animal biology ,Body Weight ,0402 animal and dairy science ,food and beverages ,Bayes factor ,Bayes Theorem ,04 agricultural and veterinary sciences ,General Medicine ,040201 dairy & animal science ,Genetic architecture ,qualité technologique ,Body Composition ,Animal Science and Zoology ,Female ,carcasse qualité ,Chickens - Abstract
Slow-growing chicken lines are valuable genetic resources for the development of well-perceived alternative free-range production. While there is no constraint on increasing growth rate, breeding programs have to evolve in order to include new traits improving the positioning of such lines in the growing market for parts and processed products. In this study, we used dense genotyping to fine map QTL for chicken growth, body composition, and meat quality traits in view of developing new tools for selection of a slow-growing line. The dataset included a total of 836 birds (10 sires, 87 dams, 739 descendants) and 40,203 SNP. QTL for the 15 traits analyzed were detected by 3 different methods, i.e., linkage and linkage disequilibrium haplotype-based analysis (LDLA), family-based single marker association (FASTA), and Bayesian multi-marker regression (Bayes Cπ). After filtering for QTL redundancy, we found 16, 16, and 9 QTL when using the FASTA, LDLA, and Bayes Cπ methods, respectively, with a threshold of 2.49 × 10-5 for FASTA and LDLA, and a Bayes factor of 150 for the Bayes Cπ analysis. They comprised 17 QTL for body weight, 9 QTL for body composition, and 15 QTL for breast meat quality or behavior at slaughter. The 3 methods agreed in the detection of highly significant QTL such as that detected on GGA24 for body weight at 3, 6, and 9 wk, and the 2 QTL detected on GGA17 and GGA18 for breast meat yield. Several significant QTL were also detected for the different components of breast meat quality. This study provided new locations for investigation in order to improve our understanding of the genetic architecture of growth, carcass composition, and meat quality in the chicken and to develop molecular tools for the selection of these traits in a slow-growing line.
- Published
- 2018