scholarly journals Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiyuan Li ◽  
Robert Mukiibi ◽  
Yining Wang ◽  
Graham S. Plastow ◽  
Changxi Li
Keyword(s):  
Citric Acid ◽  
Beef Cattle ◽  
Candidate Genes ◽  
Feed Efficiency ◽  
Plasma Metabolites ◽  
Phenotypic Traits ◽  
Whole Genome ◽  
Biological Functions ◽  
Dna Variants ◽  
Component Traits

Abstract Background Feed efficiency is one of the key determinants of beef industry profitability and sustainability. However, the cellular and molecular background behind feed efficiency is largely unknown. This study combines imputed whole genome DNA variants and 31 plasma metabolites to dissect genes and biological functions/processes that are associated with residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) in beef cattle. Results Regression analyses between feed efficiency traits and plasma metabolites in a population of 493 crossbred beef cattle identified 5 (L-valine, lysine, L-tyrosine, L-isoleucine, and L-leucine), 4 (lysine, L-lactic acid, L-tyrosine, and choline), 1 (citric acid), and 4 (L-glutamine, glycine, citric acid, and dimethyl sulfone) plasma metabolites associated with RFI, DMI, ADG, and MWT (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using 10,488,742 imputed SNPs, 40, 66, 15, and 40 unique candidate genes were identified as associated with RFI, DMI, ADG, and MWT (P-value < 1 × 10−5), respectively. These candidate genes were found to be involved in some key metabolic processes including metabolism of lipids, molecular transportation, cellular function and maintenance, cell morphology and biochemistry of small molecules. Conclusions This study identified metabolites, candidate genes and enriched biological functions/processes associated with RFI and its component traits through the integrative analyses of metabolites with phenotypic traits and DNA variants. Our findings could enhance the understanding of biochemical mechanisms of feed efficiency traits and could lead to improvement of genomic prediction accuracy via incorporating metabolite data.

scholarly journals New insights from genome-wide association analysis using imputed whole-genome sequence: the genetic mechanisms underlying residual feed intake in chickens

2019 ◽  
Author(s):  
Shaopan Ye ◽  
Zitao Chen ◽  
Rongrong Zheng ◽  
Shuqi Diao ◽  
Jinyan Teng ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Significant Snps ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Environmental Footprint ◽  
Genome Wide ◽  
Component Traits ◽  
Genetic Mechanisms

Abstract Background: Poultry feed occupies the largest cost of poultry production, which is estimated up to 70%. Moreover, it is pressure on the agricultural industry to reduce emissions and improve its environmental footprint, simultaneously increasing output to meet the growing demand for protein worldwide. Therefore, improving feed efficiency (FE) play an important role to improve profits and their environmental footprint in broiler production. In this study, using imputed whole genome sequencing (WGS) data, genome-wide association analysis (GWAS) was performed to identify SNPs and genes associated with residual feed intake (RFI) and its component traits. Furthermore, transcriptomic analysis between high- and low-RFI groups was performed to validate candidate genes from GWAS. Results: Results showed that heritability estimates of average daily gain (ADG), average daily feed intake (ADFI) , and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes ( MTSS I-BAR domain containing 1, MTSS1; folliculin, FLCN; COP9 signalosome subunit 3, COPS3; 5',3'-nucleotidase, mitochondrial, NT5M; and gametocyte specific factor 1, GTSF1) associated with RFI, twenty significant SNPs and one candidate gene ( inositol polyphosphate multikinase, IPMK ) associated with ADG, and one significant SNP and one candidate gene ( coatomer protein complex subunit alpha, COPA ) associated with ADFI. After performing transcriptomic analysis between high- and low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras related dexamethasone induced 1 (RASD1) was the only one overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015 -4882318) is a new quantitative trait locus (QTL) associated with RFI. Conclusions: In conclusions, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection. Keywords: Whole genome sequence, GWAS, transcriptome analysis, feed efficiency, chickens.

scholarly journals Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

2018 ◽  
Vol 50 (1) ◽  
Author(s):  
Chunyan Zhang ◽  
Robert Alan Kemp ◽  
Paul Stothard ◽  
Zhiquan Wang ◽  
Nicholas Boddicker ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Feed Efficiency ◽  
Whole Genome Sequence ◽  
Sequence Variants ◽  
Whole Genome ◽  
Genomic Evaluation ◽  
Component Traits

scholarly journals Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
A. M. A. M. Zonaed Siddiki ◽  
Gous Miah ◽  
Md. Sirazul Islam ◽  
Mahadia Kumkum ◽  
Meheadi Hasan Rumi ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Molecular Mechanisms ◽  
Pleiotropic Effect ◽  
Climatic Conditions ◽  
Candidate Gene Approach ◽  
Phenotypic Traits ◽  
Whole Genome ◽  
Economic Traits ◽  
Morphological Variations ◽  
Sequence Variations

Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.

scholarly journals Liver transcriptome profiling of beef steers with divergent growth rate, feed intake, or metabolic body weight phenotypes1

2019 ◽  
Vol 97 (11) ◽  
pp. 4386-4404 ◽  
Author(s):  
Robert Mukiibi ◽  
Michael Vinsky ◽  
Kate Keogh ◽  
Carolyn Fitzsimmons ◽  
Paul Stothard ◽  
...  
Keyword(s):  
Body Weight ◽  
Beef Cattle ◽  
Feed Intake ◽  
Molecular Mechanisms ◽  
Molecular Genetic ◽  
Average Daily Gain ◽  
Differentially Expressed ◽  
Beef Steers ◽  
Biological Functions ◽  
Component Traits

Abstract Average daily gain (ADG) and daily dry matter intake (DMI) are key determinants of beef industry profitability. These traits together with metabolic body weight (MWT) are combined as component traits to calculate residual feed intake (RFI), a common measure of feed efficiency in beef cattle. Recently, there have been significant efforts towards molecular genetic characterization of RFI through transcriptomic studies in different breeds and tissues. However, molecular mechanisms of RFI component traits still remain predominately unexplored. Therefore, in the current study, we investigated the hepatic transcriptomic profiles and their associations with ADG, DMI, and MWT in Angus, Charolais, and Kinsella Composite (KC) populations through global RNAseq analyses. In each population and for each trait, 12 steers with extreme phenotypes (n = 6 low and n = 6 high) were analyzed for differential gene expression. These animals were from 20 beef steers of each Angus, Charolais, and KC breed population that were initially selected for a transcriptome study of RFI. At a false discovery rate <0.05 and fold change >1.5, we identified 123, 102, and 78 differentially expressed (DE) genes between high- and low-ADG animals of Angus, Charolais, and KC populations, respectively. For DMI, 108, 180, and 156 DE genes were identified between high- and low-DMI from Angus, Charolais, and KC populations, respectively, while for MWT, 80, 82, and 84 genes were differentially expressed between high- and low-MWT animals in Angus, Charolais, and KC populations, respectively. The identified DE genes were largely breed specific (81.7% for ADG, 82.7% for DMI, and 83% for MWT), but were largely involved in the same biological functions across the breeds. Among the most enriched biological functions included metabolism of major nutrients (lipids, carbohydrates, amino acids, vitamins, and minerals), small molecule biochemistry, cellular movement, cell morphology, and cell-to-cell signaling and interaction. Notably, we identified multiple DE genes that are involved in cholesterol biosynthesis, and immune response pathways for the 3 studied traits. Thus, our findings present potential molecular genetic mechanisms and candidate genes that influence feed intake, growth, and MWT of beef cattle.

scholarly journals Exploring Biological Impacts of Prenatal Nutrition and Selection for Residual Feed Intake on Beef Cattle Using Omics Technologies: A Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Aidin Foroutan ◽  
David S. Wishart ◽  
Carolyn Fitzsimmons
Keyword(s):  
Beef Cattle ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Growth And Development ◽  
Genetic Selection ◽  
Residual Feed Intake ◽  
Reproductive Capacity ◽  
Phenotypic Traits ◽  
Prenatal Nutrition ◽  
Selection For

Approximately 70% of the cost of beef production is impacted by dietary intake. Maximizing production efficiency of beef cattle requires not only genetic selection to maximize feed efficiency (i.e., residual feed intake (RFI)), but also adequate nutrition throughout all stages of growth and development to maximize efficiency of growth and reproductive capacity, even during gestation. RFI as a measure of feed efficiency in cattle has been recently accepted and used in the beef industry, but the effect of selection for RFI upon the dynamics of gestation has not been extensively studied, especially in the context of fluctuating energy supply to the dam and fetus. Nutrient restriction during gestation has been shown to negatively affect postnatal growth and development as well as fertility of beef cattle offspring. This, when combined with the genetic potential for RFI, may significantly affect energy partitioning in the offspring and subsequently important performance traits. In this review, we discuss: 1) the importance of RFI as a measure of feed efficiency and how it can affect other economic traits in beef cattle; 2) the influence of prenatal nutrition on physiological phenotypes in calves; 3) the benefits of investigating the interaction of genetic selection for RFI and prenatal nutrition; 4) how metabolomics, transcriptomics, and epigenomics have been employed to investigate the underlying biology associated with prenatal nutrition, RFI, or their interactions in beef cattle; and 5) how the integration of omics information is adding a level of deeper understanding of the genetic architecture of phenotypic traits in cattle.

scholarly journals Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
S. Lam ◽  
J. Zeidan ◽  
F. Miglior ◽  
A. Suárez-Vega ◽  
I. Gómez-Redondo ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Candidate Genes ◽  
Rna Sequencing ◽  
Feed Efficiency ◽  
Metabolic Pathways ◽  
Functional Annotation ◽  
Read Depth ◽  
Economic Benefits ◽  
Rna Seq ◽  
Positional Candidate

Abstract Background Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Results On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. Conclusion The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.

scholarly journals Development and comparison of RNA-Sequencing pipelines for more accurate SNP identification: Practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle.

2020 ◽  
Author(s):  
S. Lam ◽  
J. Zeidan ◽  
F. Miglior ◽  
A. Suárez-Vega ◽  
I. Gómez-Redondo ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Candidate Genes ◽  
Rna Sequencing ◽  
Feed Efficiency ◽  
Read Depth ◽  
Economic Benefits ◽  
Rna Seq ◽  
Snp Detection ◽  
Positional Candidate ◽  
Variant Detection

Abstract Optimization of an RNA-Sequencing (RNA-Seq) pipeline can maximize power and accuracy for identifying genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study determined an optimized pipeline for variant detection using a dataset with multiple samples and tissues. The RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n=6 low-RFI, n=6 high-RFI) were used. Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by group for low-RFI and for high-RFI for each tissue, iii) merged samples by group and tissue for low- and high-RFI for both tissues. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Approaches were compared by comparing read depth, overlap of SNP detection results, and following SNP annotation for positional candidate genes. On average, total reads detected for Approach i) individual liver and muscle samples were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), total reads detected for each merged group of samples was 162,030,705, and for Approach iii) was 324,061,410, revealing the highest read depth. Additionally, Approach iii) encompassed the majority of localized positional genes detected by each approach, suggesting Approach iii) be applied to maximize detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive rate. Approach iii) was used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Annotation of moderate to high functional impact SNPs revealed co-localized positional candidate genes for each RFI group (2,886 for low-RFI, 3,075 for high-RFI), which were significantly (P<0.05) associated with immune and metabolism pathways. The most optimized RNA-Seq pipeline allowed for more accurate identification of SNP, associated positional candidate genes, and associated metabolic pathways in muscle and liver tissues, providing insight on the genetic architecture of feed efficiency in beef cattle.

Endocannabinoid concentrations in plasma during the finishing period are associated with feed efficiency and carcass composition of beef cattle

2017 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
V. M. Artegoitia ◽  
A. P. Foote ◽  
R. G. Tait ◽  
L. A. Kuehn ◽  
R. M. Lewis ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Feed Efficiency ◽  
Carcass Composition

scholarly journals RAPID COMMUNICATION: Residual feed intake in beef cattle is associated with differences in protein turnover and nutrient transporters in ruminal epithelium

2019 ◽  
Vol 97 (5) ◽  
pp. 2181-2187
Author(s):  
Ahmed A Elolimy ◽  
Emad Abdel-Hamied ◽  
Liangyu Hu ◽  
Joshua C McCann ◽  
Daniel W Shike ◽  
...  
Keyword(s):  
Amino Acid ◽  
Beef Cattle ◽  
Protein Degradation ◽  
Insulin Signaling ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Protein Turnover ◽  
Residual Feed Intake ◽  
Ubiquitin Proteasome ◽  
Ruminal Epithelium

Abstract Residual feed intake (RFI) is a widely used measure of feed efficiency in cattle. Although the precise biologic mechanisms associated with improved feed efficiency are not well-known, most-efficient steers (i.e., with low RFI coefficient) downregulate abundance of proteins controlling protein degradation in skeletal muscle. Whether cellular mechanisms controlling protein turnover in ruminal tissue differ by RFI classification is unknown. The aim was to investigate associations between RFI and signaling through the mechanistic target of rapamycin (MTOR) and ubiquitin-proteasome pathways in ruminal epithelium. One hundred and forty-nine Red Angus cattle were allocated to 3 contemporary groups according to sex and herd origin. Animals were offered a finishing diet for 70 d to calculate the RFI coefficient for each. Within each group, the 2 most-efficient (n = 6) and least-efficient animals (n = 6) were selected. Compared with least-efficient animals, the most-efficient animals consumed less feed (P &lt; 0.05; 18.36 vs. 23.39 kg/d DMI). At day 70, plasma samples were collected for insulin concentration analysis. Ruminal epithelium was collected immediately after slaughter to determine abundance and phosphorylation status of 29 proteins associated with MTOR, ubiquitin-proteasome, insulin signaling, and glucose and amino acid transport. Among the proteins involved in cellular protein synthesis, most-efficient animals had lower (P ≤ 0.05) abundance of MTOR, p-MTOR, RPS6KB1, EIF2A, EEF2K, AKT1, and RPS6KB1, whereas MAPK3 tended (P = 0.07) to be lower. In contrast, abundance of p-EEF2K, p-EEF2K:EEF2K, and p-EIF2A:EIF2A in most-efficient animals was greater (P ≤ 0.05). Among proteins catalyzing steps required for protein degradation, the abundance of UBA1, NEDD4, and STUB1 was lower (P ≤ 0.05) and MDM2 tended (P = 0.06) to be lower in most-efficient cattle. Plasma insulin and ruminal epithelium insulin signaling proteins did not differ (P &gt; 0.05) between RFI groups. However, abundance of the insulin-responsive glucose transporter SLC2A4 and the amino acid transporters SLC1A3 and SLC1A5 also was lower (P ≤ 0.05) in most-efficient cattle. Overall, the data indicate that differences in signaling mechanisms controlling protein turnover and nutrient transport in ruminal epithelium are components of feed efficiency in beef cattle.

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