Fig. 4: Differences and similarities in pQTL regulatory effects between health and disease states. | Nature Communications

Fig. 4: Differences and similarities in pQTL regulatory effects between health and disease states.

From: Genetic profiling of the circulating proteome in common diseases suggests causal proteins and improves risk prediction

Fig. 4

A Bar and Scatter charts show the number and percentage of disease-related pQTL associations. B Comparison of absolute effect sizes of the disease-related pQTL associations (left), disease biased and non-disease associations (right). The center line of the box presents as the median, the box limits indicate upper and lower quartiles and whiskers indicate the maximum and minimum. ***P < 0.001 was calculated by a two-sided Mann-Whitney U test. C Genomic distribution of disease-specific (left), disease biased and non-disease (middle) pQTLs, alongside enrichment analysis comparing the two. Results were calculated using the two-tailed Fisher’s exact test. D Sankey plot displays the clustering results of 27 diseases based on pQTL analysis. From left to right, the figure shows the disease names, their conventional clinical classifications, and the corresponding clustering outcomes. E The results of Gene Ontology (GO) biological process (BP) enrichment proteins associated with the trans hotspot region chr8_77. F The results of Gene Ontology (GO) biological process (BP) enrichment of proteins related to cluster 7.

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