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. 2015 Nov 10:5:16387.
doi: 10.1038/srep16387.

The genetics of feed conversion efficiency traits in a commercial broiler line

Henry Reyer  1 , Rachel Hawken  2 , Eduard Murani  1 , Siriluck Ponsuksili  3 , Klaus Wimmers  1
Affiliations

The genetics of feed conversion efficiency traits in a commercial broiler line

Henry Reyer et al. Sci Rep. .

Abstract

Individual feed conversion efficiency (FCE) is a major trait that influences the usage of energy resources and the ecological footprint of livestock production. The underlying biological processes of FCE are complex and are influenced by factors as diverse as climate, feed properties, gut microbiota, and individual genetic predisposition. To gain an insight to the genetic relationships with FCE traits and to contribute to the improvement of FCE in commercial chicken lines, a genome-wide association study was conducted using a commercial broiler population (n = 859) tested for FCE and weight traits during the finisher period from 39 to 46 days of age. Both single-marker (generalized linear model) and multi-marker (Bayesian approach) analyses were applied to the dataset to detect genes associated with the variability in FCE. The separate analyses revealed 22 quantitative trait loci (QTL) regions on 13 different chromosomes; the integration of both approaches resulted in 7 overlapping QTL regions. The analyses pointed to acylglycerol kinase (AGK) and general transcription factor 2-I (GTF2I) as positional and functional candidate genes. Non-synonymous polymorphisms of both candidate genes revealed evidence for a functional importance of these genes by influencing different biological aspects of FCE.

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Conflict of interest statement

All authors declare no competing financial interests, except RH who is employed by Cobb-Vantress, Inc. Cobb-Vantress is involved in broiler breeding and research in genetic improvement of broilers lines.

Figures

Figure 1
Figure 1. Values of LD versus physical distances on representations of macro- (chromosome 1), intermediate (chromosome 6), and microchromosomes (chromosome 19).
Values of r2 between markers were averaged in non-overlapping windows of 1 kb and plotted against the distances between SNP markers. Nonlinear curves were fitted to estimate the average distance between markers showing extent of ‘useful’ LD (r2 ≥ 0.25).
Figure 2
Figure 2. Manhattan plots of genome-wide association analysis results for body weight and feed efficiency traits in a commercial broiler line (n = 859) using single-marker analysis implemented in a generalized linear model.
Chromosomes 29 and 30 represent linkage groups LGE22C19W28_E50C23 and LGE64, respectively. The threshold for suggestive significance was set at -log10(p-value) = 4.3. Detailed information about SNP exceeding the threshold levels are listed in Supplementary Table S2.
Figure 3
Figure 3. Manhattan plots for genome-wide association using a Bayesian multi-marker approach (Bayes B).
Observed traits were body weight at 36 and 46 days of age and feed conversion ratio, body weight gain, and feed intake recorded during a feeding trial between days 39 and 46. The horizontal line represents the threshold of suggestive linkage with the trait at Bayes Factor = 3. Chromosome 29 represents linkage group LGE22C19W28_E50C23, and chromosome 30 is linkage group LGE64. Detailed information about SNP exceeding the threshold levels are listed in Supplementary Table S3.
Figure 4
Figure 4. Linkage map of markers spanning the genomic region including the AGK locus on chicken chromosome 1.
Depicted is the LD (D’ value in diamond) between markers including genotype information of 240 broilers also genotyped for AGK SNP c.1166. Linkage blocks were defined using the ‘solid spine of LD’ algorithm implemented in the Haploview 4.2 software.
Figure 5
Figure 5. Linkage map of the genomic region including the GTF2I locus on chicken chromosome 19.
Depicted is the LD (D’ value in diamond) between markers including genotype information of 240 broilers also genotyped for GTF2I SNP c.2011. Linkage blocks were defined using the ‘solid spine of LD’ algorithm implemented in the Haploview 4.2 software.
See this image and copyright information in PMC

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