Verification step: eQTL analysis for hub metabotypes

In a verification step, GWAS (eQTL analyses) were performed by using the most important hub metabotypes (transcripts, metabolites and proteins) of the selected modules. According to Heidt et al. [157], “a QTL analysis of expression levels of genes identifies genomic regions that are likely to contain at least one causal gene with a regulatory effect on the expression level, termed eQTL.” The positions of the resulting significant eQTL were compared with the location of candidate genes (or the associated SNPs), which was detected in the GWAS for classical phenotypes and composite module traits. This approach was motivated by the expectation, that in case of overlapping QTL regions the risk of false positive results is reduced and the biological relevance of these regions can be confirmed with higher accuracy.

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For this validation step it was necessary to identify hub metabotypes, which are the most important single values within a module. Most important key or hub metabotypes of each module were identified by three parameters: MM, MS and connectivity. The importance of each metabotype for its module (MM) is quantified by the correlation between MEs and omics expression profiles. The MS values correspond to the Pearson correlation coefficients between metabotypes and response traits (meat quality and carcass composition traits). The intramodular soft connectivity was defined as

𝐾_𝑖 = ∑ 𝑎_𝑖𝑢 𝑢≠𝑖

which is the sum of all pairwise adjacencies of a metabotype to all other metabotypes, aiu,

in the module. Within each module the metabotypes could be ranked using the absolute values of both MS, MM and connectivity to identify the key players, or so-called hub metabotypes, of a metabolic network.

According to these indicators, all module members were ranked according to their importance and stored into a sorted MM, MS and connectivity list. All metabotypes which were listed in the upper quartile of at least one of the ranking lists were used as phenotypes in the eQTL analyses described above. These metabotypes are regarded as key members of the underlying metabolic process. In the majority of cases, transcripts were the most important metabotypes of the modules.

For each important metabotype an eQTL analysis was performed which was based on the EIGENSTRAT approach with ten PCs to remove possible population stratification. Based on a genome-wide FDR value of q≤0.05 it was possible to identify significant eQTL. After eQTL analysis, corresponding genes could be identified by database query in the Ensembl genome browser 84 (www.ensembl.org).

In the detected eQTL it can be differentiated between regions, which are located close to a gene (cis-regulation) or distant (trans-regulation) [128]. Cis-regulated eQTL are more likely to represent the causative genomic region, whereas trans-regulated eQTL represent the ‘effect’, e.g. pathways that are affected by causal variations [131]. Although the most significant reported eQTL are often cis-regulated, there are some evidence that trans- regulated eQTL also might be decisive in controlling of gene expression [236].

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4.3 Results

4.3.1 Weighted network analysis

The WNA allows investigating the entire data set using the profiles of metabolites, proteins and transcripts for the construction of a weighted co-expression network. The hierarchical clustering algorithm and the following pruning process condensed the metabotypes into 30 modules. Metabotypes that were not assigned to any module (n=112), were labelled with color grey.

The relationship between meat quality, carcass composition traits and modules is given by correlation coefficients between traits and MEs (Fig. 20). For the further investigation we selected modules that showed significantly (p≤0.05) strong (r≥|0.25|) correlation coefficients to one or several meat quality or carcass composition traits (Tab. 18). Drip loss, meat color, BFT and LMCbonn were significantly correlated only to one module, whereas pH24, pH1, MFR and LMCbelly were associated with three or four different modules. The number of metabotypes per module ranged between 1,683 (module purple) and 60 (module sienna3). Most of the metabotypes within the modules belong to the class of transcripts. However, in modules sienna3 and darkolivegreen the number of metabolites and transcripts is almost balanced and module white even comprises mainly metabolites (Tab. 18).

Module purple that includes the most metabotypes is the only module, which comprises proteins (Tab. 18). While module salmon is positively correlated with MFR and negatively correlated with LMC, the correlation coefficients have reversed signs in modules darkmagenta and purple. In a similar way, adverse results can be observed for the relation between the MEs of different modules and the meat quality indicator pH24. Regarding this relationship, significant positive correlations were observed in modules purple, sienna3 and midnightblue, whereas a significant negative correlation was found in module darkgrey (Fig. 20).

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Figure 20: Correlation coefficients and corresponding p-values of module-trait relationship. Correlations of traits drip loss, pH1, pH24 and meat color to modules are characterised by color range from red (‘1’ - positive correlation) to green (‘-1’ - negative correlation). In parenthesis below correlation coefficients, the p-value is given. BFT – backfat thickness, LMC – lean meat content measured by formula of Grub in belly (LMC_belly) and by Bonner formula (LMC), MFR – meat fat ratio. ME = module eigenvalues.

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Table 18: Composition of significantly correlated modules of weighted network analysis to meat quality and composition traits.

Module No. transcripts No. metabolites (annotated/ KEGG annotated) No. proteins (entrez gene annotated) ∑ Correlated traits salmon 385 - - 385 MFR, LMCbelly, LMCbonn saddlebrown 112 2 (-) - 114 pH1 darkmagenta 62 - - 65 MFR, LMCbelly, BFT purple 1,675 1 (-) 9 (9) 1,683 MFR, LMCbelly, pH24 tan 1,442 - - 1,442 pH1 sienna3 31 29 (6/-) - 60 pH24 darkolivegreen 39 47 (14/8) - 86 pH1 midnightblue 317 - - 317 pH24 darkgrey 158 2 (1/-) - 160 pH24

white 46 83 (25/11) - 129 drip loss

lightgreen 247 1 (-) - 248 meat color

BFT – backfat thickness, LMC – lean meat content measured by formula of Grub in belly (LMCbelly) and by Bonner formula (LMCbonn), MFR – meat fat ratio.