Abstract
BackgroundFor decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F2 intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome.ResultsSixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter.ConclusionsThe medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken.
Highlights
Genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens
Phenotypic and genetic correlations between residuals A description of traits, along with Pearson correlation coefficients and estimated genetic correlations between adjusted traits are in Tables 1 and 2, respectively
Glucose was negatively correlated with non-esterified fatty acids (NEFA) (−0.20) but positively correlated with body temperature (0.28)
Summary
Genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. For body weight (BW), which has a moderate heritability (~0.35), standard selection has been very efficient because measurement of this phenotype is easy and inexpensive, even for large populations For body composition, such as breast muscle (BMW) or abdominal fat (AFW) weight, despite a greater heritability (~0.50), genetic progress has been slower for two reasons: (1) intensity of selection has been lower because of the difficulty and cost to measure these phenotypes, and (2) genetic evaluations have been less accurate because evaluation of candidates is based on information from relatives only. Whole-genome sequencing of multiple chicken breeds has led to the identification of thousands of single nucleotide polymorphisms (SNPs), providing very high-density genetic maps compared to the previously low-density microsatellite marker maps [7]
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