The performance of birds appears to vary among the flock of growing broilers which may in part be due to variation in their gut microbiota. In the view of poultry industry, it is desirable to minimise such variation. We investigated metagenomic profile of fecal bacteria in birds with high and low feed conversion ratio (FCR) to identify microbial community linked to low and high FCR by employing high throughput pyrosequencing of 16S rRNA genomic targets. Therefore feeding trial was investigated in order to identify fecal bacteria consistently linked with better feed conversion ratio in bird performance as measured by body weight gain. High-throughput 16S rRNA gene based pyrosequencing was used to provide a comparative analysis of fecal microbial diversity. The fecal microbial community of birds was predominated by Proteobacteria (48.04 % in high FCR and 49.98 % in low FCR), Firmicutes (26.17 % in high FCR and 36.23 % in low FCR), Bacteroidetes (18.62 % in high FCR and 11.66 % in low FCR), as well as unclassified bacteria (15.77 % in high FCR and 14.29 % in low FCR), suggesting that a large portion of fecal microbiota is novel and could be involved in currently unknown functions. The most prevalent bacterial classes in high FCR and low FCR were Gammaproteobacteria, Clostridia and Bacteroidia. However in low FCR birds Phascolarctobacterium, Faecalibacterium and Clostridium predominated among the Clostridia. In FCR comparison of fecal bacteria, about 36 genera were differentially abundant between high and low FCR birds. This information could be used to formulate effective strategies to improve feed efficiency and feed formulation for optimal gut health.

Individual weight gain in broiler growers appears to vary, which may in part be due to variation in their gut microbiota. In this paper we analyse the fecal microbiota of low and high feed conversion ratio (FCR) broilers. After shotgun sequencing of the fecal microbiome, we used the SEED database to identify the microbial diversity and metabolic potential in low and high FCR birds. The domain-level breakdown of our samples was bacteria (>95 %), eukaryotes (>2 %), archaea (>0.2 %), and viruses (>0.2 %). At the phylum level, Proteobacteria (78.83 % in low and 52.04 % in high FCR), Firmicutes (11.97 % in low and 27.53 % in high FCR) and Bacteroidetes (7.10 % in low FCR and 17.53 % in high FCR) predominated in the fecal microbial community. Poultry fecal metagenomes revealed the sequences related to 33 genera in both low and high FCR with significantly different proportion. Functional analysis revealed that genes for the metabolism of carbohydrates, amino acids and derivatives and protein metabolism were most abundant in SEED subsystem in both samples. Genes associated with stress, virulence, cell wall and cell capsule were also abundant. Indeed, genes associated with sulphur assimilation, flagellum and flagellar motility were over represented in low FCR birds. This information could help in developing strategies to improve feed efficiency and feed formulation for broiler chickens.

Feed conversion ratio (FCR) is an economically important trait in broilers and feed accounts for a significant proportion of the costs involved in broiler production. To explore the contribution of functional variants to FCR trait, we analyzed coding and non-coding single-nucleotide variants (SNVs) across the genome by exome sequencing in seven pairs of full-sibs broilers with divergent FCR and with a sequence coverage at an average depth of fourfold. We identified 192,119 high-quality SNVs, including 30,380 coding SNVs (cSNVs) in the experimental population. We discovered missense SNVs in PGM2, NOX4, TGFBR3, and TMX4, and synonymous SNVs in TSNAX, ITA, HSP90B1, and COL18A1 associated with FCR. Haplotype analyses of genome-wide significant SNVs in PGM2, PHKG1, DGKZ, and SOD2 were also observed with suggestive evidence of haplotype association with FCR. Single-variant and FCR QTL-related genes-based association analyses of SNVs identified newly associated genes for FCR in the regions subjected to targeted exome sequencing. The top seven SNVs were next evaluated in independent replication data sets where SNV chr. 3: 13,990,160 (c. 961G>C) at TMX4 was replicated (p < 0.05). Collectively, we have detected SNVs associated with FCR in broiler as well as identification of SNVs in known FCR QTL region. These findings should facilitate the discovery of causative variants for FCR and contribute to marker-assisted selection.