Complicated Relationships: A Review of Biological Interaction Networks and Pathways in Animal Science

Karisa, Brian K; Moore, S. S.; Plastow, Graham

doi:10.1007/s40362-013-0005-8

Cited by 3 publications

(1 citation statement)

References 78 publications

(82 reference statements)

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“…Metabolomic studies regarding the prediction of RFI in beef cattle have already been very promising with a reported initial prediction accuracy of 95% [ 20 ; 66 ]. Metabolomic data, when coupled with genomic data, appear to increase the accuracy of trait prediction [ 67 ]. This combination potentially allows one to screen for individual animals with superior traits that could be used for breeding stock.…”

Section: Resultsmentioning

confidence: 99%

Livestock metabolomics and the livestock metabolome: A systematic review

Goldansaz¹,

Guo²,

Sajed³

et al. 2017

PLoS ONE

244

247

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Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.

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Section: Resultsmentioning

confidence: 99%

Livestock metabolomics and the livestock metabolome: A systematic review

Goldansaz¹,

Guo²,

Sajed³

et al. 2017

PLoS ONE

244

247

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Applications and Future Perspectives of Computational Approaches in Livestock Animals

Pathak,

Das,

Kumar Bora

et al. 2023

Systems Biology, Bioinformatics and Livestock Science

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Livestock is regarded as a critical point of access for enhanced food and nutrition. With the population explosion, an increase in the successful fulfillment of livestock production, including meat and dairy products, is necessary in the most ethical way. Fundamentally keeping the overall nutrition intact along with the health of both human and livestock animals is vital. Although there is an increment in production, it contributes to rising greenhouse gas (methane) emissions, thus damaging the environment. Inheriting novel technologies will not only help in the surplus upliftment of livestock products but also the emission of greenhouse gases. Omics and Systems Biology are such approaches. Omics is a combination of different aspects dealing with complete molecular levels ranging from DNA to protein, protein to metabolites, whereas Systems Biology is the analysis of both mathematical and computational along with biological system modeling. Omics gives a broad overview of both pathways and traits controlling various characters. Thus, showing detailed links between genotype-phenotype. It can yield an enormous amount of data with incredible speed. In addition, Systems Biology lines up to give an overview of the complete biological system rather than just examining a single biological molecule. It combines mathematical modelling, statistics, and bioinformatics for a better grip and understanding of the enormous data sets. In this chapter, we discuss the latest cutting-edge technologies in the field of livestock and how omics can be implemented in creating disease resistant livestock animals without hampering the quality of the products. The chapter also discusses the various applications and future scopes involving computational approaches towards animal science.

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A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle

et al. 2022

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This study aimed to perform a genome-wide association analysis (GWAS) using the Random Forest (RF) approach for scanning candidate genes for age at first calving (AFC) in Nellore cattle. Additionally, potential epistatic effects were investigated using linear mixed models with pairwise interactions between all markers with high importance scores within the tree ensemble non-linear structure. Data from Nellore cattle were used, including records of animals born between 1984 and 2015 and raised in commercial herds located in different regions of Brazil. The estimated breeding values (EBV) were computed and used as the response variable in the genomic analyses. After quality control, the remaining number of animals and SNPs considered were 3,174 and 360,130, respectively. Five independent RF analyses were carried out, considering different initialization seeds. The importance score of each SNP was averaged across the independent RF analyses to rank the markers according to their predictive relevance. A total of 117 SNPs associated with AFC were identified, which spanned 10 autosomes (2, 3, 5, 10, 11, 17, 18, 21, 24, and 25). In total, 23 non-overlapping genomic regions embedded 262 candidate genes for AFC. Enrichment analysis and previous evidence in the literature revealed that many candidate genes annotated close to the lead SNPs have key roles in fertility, including embryo pre-implantation and development, embryonic viability, male germinal cell maturation, and pheromone recognition. Furthermore, some genomic regions previously associated with fertility and growth traits in Nellore cattle were also detected in the present study, reinforcing the effectiveness of RF for pre-screening candidate regions associated with complex traits. Complementary analyses revealed that many SNPs top-ranked in the RF-based GWAS did not present a strong marginal linear effect but are potentially involved in epistatic hotspots between genomic regions in different autosomes, remarkably in the BTAs 3, 5, 11, and 21. The reported results are expected to enhance the understanding of genetic mechanisms involved in the biological regulation of AFC in this cattle breed.

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Complicated Relationships: A Review of Biological Interaction Networks and Pathways in Animal Science

Cited by 3 publications

References 78 publications

Livestock metabolomics and the livestock metabolome: A systematic review

Livestock metabolomics and the livestock metabolome: A systematic review

Applications and Future Perspectives of Computational Approaches in Livestock Animals

A Random Forest-Based Genome-Wide Scan Reveals Fertility-Related Candidate Genes and Potential Inter-Chromosomal Epistatic Regions Associated With Age at First Calving in Nellore Cattle

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