Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association

Miles, Asha M.; Posbergh, Christian J.; Huson, Heather J.

doi:10.3390/ani11041147

Cited by 7 publications

(5 citation statements)

References 73 publications

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“…There are a lot of researches focused on genomics (11), transcriptomics (12), proteomics (13), and epiomics (14) in dairy cow mastitis. However, metabolomics, as the most downstream of gene expression, is gradually used to analyze the occurrence of dairy cow mastitis because it can reflect the current situation of biological systems, and it is relatively cheaper to implement (15).…”

Section: Introductionmentioning

confidence: 99%

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Fang

et al. 2021

Front. Vet. Sci.

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Cow mastitis, with high incidence rate and complex cause of disease, is one of the main diseases that affect the development of dairy industry in the world. Clinical mastitis and subclinical mastitis caused by Staphylococcus aureus, Escherichia coli, Streptococcus, and other pathogens have a huge potential safety hazard to food safety and the rapid development of animal husbandry. The economic loss caused by cow mastitis is billions of dollars every year in the world. In recent years, the omics technology has been widely used in animal husbandry with the continuous breakthrough of sequencing technology and the continuous reduction of sequencing cost. For dairy cow mastitis, the traditional diagnostic technique, such as histopathological screening, somatic cell count, milk pH test, milk conductivity test, enzyme activity test, and infrared thermography, are difficult to fully and comprehensively clarify its pathogenesis due to their own limitations. Metabolomics technology is an important part of system biology, which can simultaneously analyze all low molecular weight metabolites such as amino acids, lipids, carbohydrates under the action of complex factors including internal and external environment and in a specific physiological period accurately and efficiently, and then clarify the related metabolic pathways. Metabolomics, as the most downstream of gene expression, can amplify the small changes of gene and protein expression at the level of metabolites, which can more fully reflect the cell function. The application of metabolomics technology in cow mastitis can analyze the hetero metabolites, identify the related biomarkers, and reveal the physiological and pathological changes of cow mammary gland, so as to provide valuable reference for the prediction, diagnosis, and treatment of mastitis. The research progress of metabolomics technology in cow mastitis in recent years was reviewed, in order to provide guidance for the development of cow health and dairy industry safety in this manuscript.

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Section: Introductionmentioning

confidence: 99%

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Fang

et al. 2021

Front. Vet. Sci.

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“…Previous genome‐wide association studies used principal component analysis or factor analysis to reduce the number of linearly scored temperament or body measurements traits in Tennessee Walking horses (Staiger, Al Abri, et al, 2016; Staiger, Albright, & Brooks, 2016) or number of accelerometer gait traits in young sport horses (Ricard et al, 2020). Furthermore, genome‐wide association studies in other species such as goats and cattle could find plausible candidate region(s) or genes(s) associated with principal components or factor traits (Miles et al, 2021; Nazari‐Ghadikolaei et al, 2018). This can warrant the use our results for further studies also on genomic level such as genome‐wide association studies or potentially in future application of single‐step genomic selection in warmblood horses.…”

Section: Discussionmentioning

confidence: 99%

Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses

Nazari-Ghadikolaei

Fikse

Viklund

et al. 2023

J Animal Breeding Genetics

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Assessment protocols to describe the various aspects of conformation, gait and jumping traits on a linear scale were introduced at young horse tests for Swedish Warmblood horses in 2013. The traits scored on a linear scale are assumed to be less subjective and more easily compared across populations than the traditional evaluated traits that are scored relative to the breeding goal. However, the resulting number of traits is considerable, and several of the traits are correlated. The aim of this study was to investigate the interrelationship between the different evaluated and linearly scored traits in Swedish Warmbloods using factor analysis. In total, 20,935 horses born 1996–2017 had information on evaluated traits, and 5450 of these also had linearly scored trait records assessed since 2014 when the protocol was updated. A factor analysis with varimax rotation was performed separately for evaluated and linearly scored traits using the Psych package in R. Height at withers was included in both analyses. A total of four factors for evaluated traits and 14 factors for linearly scored traits were kept for further analysis. Missing values for individual traits in horses with linearly scored trait records were imputed based on correlated traits before factor scores were calculated using factor loadings. Genetic parameters for, and correlations between, the resulting underlying factors were estimated using multiple‐trait animal models in the BLUPF90 package. Heritability estimates were on a similar level as for the traits currently used in the genetic evaluation, ranging from 0.05 for the factor for linearly scored traits named L.behaviour (dominated by traits related to behaviour) to 0.59 for the factor for evaluated traits named E.size (dominated by height at withers and conformation). For both types of traits, separate factors were formed for jumping and gait traits, as well as for body size. High genetic correlations were estimated between such corresponding factors for evaluated traits and factors for linearly scored traits. In conclusion, factor analysis could be used to reduce the number of traits to be included in multiple‐trait genetic evaluation or in genomic analysis for warmblood horses. It can also contribute to a better understanding of the interrelationships among the assessed traits and be useful to decide on subgroups of traits to be used in several multiple‐trait evaluations on groups of original traits.

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“…CXCL3 is recognized as a proinflammatory cytokine in dairy cows with experimentally induced S. aureus clinical mastitis (Peralta et al, 2020). SFN was reported to regulate cell cycle progression in bovine mastitis via genome-wide association (Miles et al, 2021). IDH1 was identified as a candidate gene in the milk transcriptome of dairy cattle implicated in innate immunity by pathway and network analysis (Banos et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle

et al. 2021

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Background: Mastitis is the most prevalent disease in dairy cattle and one of the most significant bovine pathologies affecting milk production, animal health, and reproduction. In addition, mastitis is the most common, expensive, and contagious infection in the dairy industry.Methods: A meta-analysis of microarray and RNA-seq data was conducted to identify candidate genes and functional modules associated with mastitis disease. The results were then applied to systems biology analysis via weighted gene coexpression network analysis (WGCNA), Gene Ontology, enrichment analysis for the Kyoto Encyclopedia of Genes and Genomes (KEGG), and modeling using machine-learning algorithms.Results: Microarray and RNA-seq datasets were generated for 2,089 and 2,794 meta-genes, respectively. Between microarray and RNA-seq datasets, a total of 360 meta-genes were found that were significantly enriched as “peroxisome,” “NOD-like receptor signaling pathway,” “IL-17 signaling pathway,” and “TNF signaling pathway” KEGG pathways. The turquoise module (n = 214 genes) and the brown module (n = 57 genes) were identified as critical functional modules associated with mastitis through WGCNA. PRDX5, RAB5C, ACTN4, SLC25A16, MAPK6, CD53, NCKAP1L, ARHGEF2, COL9A1, and PTPRC genes were detected as hub genes in identified functional modules. Finally, using attribute weighting and machine-learning methods, hub genes that are sufficiently informative in Escherichia coli mastitis were used to optimize predictive models. The constructed model proposed the optimal approach for the meta-genes and validated several high-ranked genes as biomarkers for E. coli mastitis using the decision tree (DT) method.Conclusion: The candidate genes and pathways proposed in this study may shed new light on the underlying molecular mechanisms of mastitis disease and suggest new approaches for diagnosing and treating E. coli mastitis in dairy cattle.

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Direct Phenotyping and Principal Component Analysis of Type Traits Implicate Novel QTL in Bovine Mastitis through Genome-Wide Association

Cited by 7 publications

References 73 publications

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Application of Metabolomics in Diagnosis of Cow Mastitis: A Review

Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle

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