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. 2020 May 8;10(1):7751.
doi: 10.1038/s41598-020-64575-3.

Structural Equation Modeling for Investigating Multi-Trait Genetic Architecture of Udder Health in Dairy Cattle

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Free PMC article

Structural Equation Modeling for Investigating Multi-Trait Genetic Architecture of Udder Health in Dairy Cattle

Sara Pegolo et al. Sci Rep. .
Free PMC article

Abstract

Mastitis is one of the most prevalent and costly diseases in dairy cattle. It results in changes in milk composition and quality which are indicators of udder inflammation in absence of clinical signs. We applied structural equation modeling (SEM) - GWAS aiming to explore interrelated dependency relationships among phenotypes related to udder health, including milk yield (MY), somatic cell score (SCS), lactose (%, LACT), pH and non-casein N (NCN, % of total milk N), in a cohort of 1,158 Brown Swiss cows. The phenotypic network inferred via the Hill-Climbing algorithm was used to estimate SEM parameters. Integration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SCS, and quantified the contribution of MY and LACT acting as mediator traits to total SNP effects. Functional analyses revealed that overrepresented pathways were often shared among traits and were consistent with biological knowledge (e.g., membrane transport activity for pH and MY or Wnt signaling for SCS and NCN). In summary, SEM-GWAS offered new insights on the relationships among udder health phenotypes and on the path of SNP effects, providing useful information for genetic improvement and management strategies in dairy cattle.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Network structure inferred from the vector of the residuals using the Hill-Climbing (HC) algorithm. Network structure inferred combining the results obtained with HC algorithm and prior biological knowledge (for trait pH). Structure learning test was performed with 50,000 bootstrap samples. The percentages reported beside edges indicate the proportion of bootstrap samples supporting the edge and (in parentheses) the proportion having the direction shown.
Figure 2
Figure 2
A scheme for path analysis of SNP effects for five milk-related traits. MY: milk yield; pH: milk pH; LACT: lactose; SCS: somatic cell score; NCN: casein (expressed as % of total milk N). The blue arrows indicate the direction of relationship according to the learned causal structure. Dashed lines correspond to a negative path coefficient. λ21: MY → LACT; λ32: LACT → SCS; λ43: SCS → pH; λ52: LACT → NCN (non-casein N, expressed as % of total milk N). The grey arrows correspond to the direct effect of SNPj on the trait.
Figure 3
Figure 3
Manhattan plots for SNP effects on milk yield obtained using SEM-GWAS based on the network structure learned by Hill-Climbing algorithm. MY: milk yield.
Figure 4
Figure 4
Manhattan plots for SNP effects on milk lactose obtained using SEM-GWAS based on the network structure learned by Hill-Climbing algorithm. LACT: lactose; MY: milk yield.
Figure 5
Figure 5
Manhattan plots for SNP effects on somatic cell score obtained using SEM-GWAS based on the network structure learned by Hill-Climbing algorithm. MY: milk yield; LACT: lactose; SCS: somatic cell score.
Figure 6
Figure 6
Manhattan plots for SNP effects on milk pH obtained using SEM-GWAS based on the network structure learned by Hill-Climbing algorithm. MY: milk yield; LACT: lactose; SCS: somatic cell score.
Figure 7
Figure 7
Manhattan plots for SNP effects on non-casein N obtained using SEM-GWAS based on the network structure learned by Hill-Climbing algorithm. MY: milk yield; LACT: lactose; NCN: non- casein N (expressed as % of total milk N).
Figure 8
Figure 8
Significantly enriched GO terms and KEGG pathways for the investigated traits. (a) Milk yield (MY); (b) Lactose (LACT); (c) Somatic cell score (SCS); (d) Milk pH (pH); (e) Non-casein N (NCN, expressed as % of total milk N). SNPs obtained from MTM-GWAS (P < 0.05) were mapped to genes based on 15 kb distance from the coding region using the biomaRt R package,. The Cytoscape plugin Cluego was used to identify overrepresented pathways and GO terms based on a right-sided hypergeometric test with false discovery rate set at 0.05.

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