Dataset Information

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach.

ABSTRACT: BACKGROUND:Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600?K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations. RESULTS:Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35?days-of-age (BW35), and 41?days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1-4, 6-7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. CONCLUSIONS:The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.

SUBMITTER: Moreira GCM

PROVIDER: S-EPMC6836328 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach.

Moreira Gabriel Costa Monteiro GCM Poleti Mirele Daiana MD Pértille Fábio F Boschiero Clarissa C Cesar Aline Silva Mello ASM Godoy Thaís Fernanda TF Ledur Mônica Corrêa MC Reecy James M JM Garrick Dorian J DJ Coutinho Luiz Lehmann LL

BMC genetics 20191106 1

<h4>Background</h4>Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F<sub>2</sub> Chicken Resource population. Regions exhibiting s ...[more]

PMID: 31694549

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Project description:Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

Project description:BackgroundImproving the feed efficiency would increase profitability for producers while also reducing the environmental footprint of livestock production. This study was conducted to investigate the relationships among feed efficiency traits and metabolizable efficiency traits in 180 male broilers. Significant loci and genes affecting the metabolizable efficiency traits were explored with an imputation-based genome-wide association study. The traits measured or calculated comprised three growth traits, five feed efficiency related traits, and nine metabolizable efficiency traits.ResultsThe residual feed intake (RFI) showed moderate to high and positive phenotypic correlations with eight other traits measured, including average daily feed intake (ADFI), dry excreta weight (DEW), gross energy excretion (GEE), crude protein excretion (CPE), metabolizable dry matter (MDM), nitrogen corrected apparent metabolizable energy (AMEn), abdominal fat weight (AbF), and percentage of abdominal fat (AbP). Greater correlations were observed between growth traits and the feed conversion ratio (FCR) than RFI. In addition, the RFI, FCR, ADFI, DEW, GEE, CPE, MDM, AMEn, AbF, and AbP were lower in low-RFI birds than high-RFI birds (P?< 0.01 or P?< 0.05), whereas the coefficients of MDM and MCP of low-RFI birds were greater than those of high-RFI birds (P?< 0.01). Five narrow QTLs for metabolizable efficiency traits were detected, including one 82.46-kb region for DEW and GEE on Gallus gallus chromosome (GGA) 26, one 120.13-kb region for MDM and AMEn on GGA1, one 691.25-kb region for the coefficients of MDM and AMEn on GGA5, one region for the coefficients of MDM and MCP on GGA2 (103.45-103.53?Mb), and one 690.50-kb region for the coefficient of MCP on GGA14. Linkage disequilibrium (LD) analysis indicated that the five regions contained high LD blocks, as well as the genes chromosome 26 C6orf106 homolog (C26H6orf106), LOC396098, SH3 and multiple ankyrin repeat domains 2 (SHANK2), ETS homologous factor (EHF), and histamine receptor H3-like (HRH3L), which are known to be involved in the regulation of neurodevelopment, cell proliferation and differentiation, and food intake.ConclusionsSelection for low RFI significantly decreased chicken feed intake, excreta output, and abdominal fat deposition, and increased nutrient digestibility without changing the weight gain. Five novel QTL regions involved in the control of metabolizable efficiency in chickens were identified. These results, combined through nutritional and genetic approaches, should facilitate novel insights into improving feed efficiency in poultry and other species.

Project description:BackgroundFeed efficiency is a major driver of the sustainability of pig production systems. Understanding the biological mechanisms that underlie these agronomic traits is an important issue for environment questions and farms' economy. This study aimed at identifying genomic regions that affect residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during nine generations (LRFI, low RFI; HRFI, high RFI).ResultsWe built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). Forty-five chromosomal regions were detected in the global-GWAS, whereas 28 and 42 regions were detected in the HRFI-GWAS and LRFI-GWAS, respectively. Among these 45 regions, only 13 were shared between at least two analyses, and only one was common between the three GWAS but it affects different traits. Among the five quantitative trait loci (QTL) detected for RFI, two were close to QTL for meat quality traits and two pinpointed novel genomic regions that harbor candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or in lipid metabolism-related signaling pathways. In most cases, different QTL regions were detected between the three designs, which suggests a strong impact of the dataset structure on the detection power and could be due to the changes in allelic frequencies during the establishment of lines.ConclusionsIn addition to efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted chromosomal regions that affect production traits and presented significant changes in allelic frequencies across generations. Further analyses are needed to estimate whether these regions correspond to traces of selection or result from genetic drift.

Project description:The objective of this study was to identify genomic regions and metabolic pathways associated with dry matter intake, average daily gain, feed efficiency and residual feed intake in an experimental Nellore cattle population. The high-density SNP chip (Illumina High-Density Bovine BeadChip, 777k) was used to genotype the animals. The SNP markers effects and their variances were estimated using the single-step genome wide association method. The (co)variance components were estimated by Bayesian inference. The chromosome segments that are responsible for more than 1.0% of additive genetic variance were selected to explore and determine possible quantitative trait loci. The bovine genome Map Viewer was used to identify genes. In total, 51 genomic regions were identified for all analyzed traits. The heritability estimated for feed efficiency was low magnitude (0.13±0.06). For average daily gain, dry matter intake and residual feed intake, heritability was moderate to high (0.43±0.05; 0.47±0.05, 0.18±0.05, respectively). A total of 8, 17, 14 and 12 windows that are responsible for more than 1% of the additive genetic variance for dry matter intake, average daily gain, feed efficiency and residual feed intake, respectively, were identified. Candidate genes GOLIM4, RFX6, CACNG7, CACNG6, CAPN8, CAPN2, AKT2, GPRC6A, and GPR45 were associated with feed efficiency traits. It was expected that the response to selection would be higher for residual feed intake than for feed efficiency. Genomic regions harboring possible QTL for feed efficiency indicator traits were identified. Candidate genes identified are involved in energy use, metabolism protein, ion transport, transmembrane transport, the olfactory system, the immune system, secretion and cellular activity. The identification of these regions and their respective candidate genes should contribute to the formation of a genetic basis in Nellore cattle for feed efficiency indicator traits, and these results would support the selection for these traits.

Dataset Information

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach.

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Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach.

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