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Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters
(2019)
Given the central metabolic role of the liver, hepatic metabolites and transcripts reflect the organismal physiological state. Biochemical-clinical plasma biomarkers, hepatic metabolites, transcripts, and single nucleotide polymorphism (SNP) genotypes of some 300 pigs were integrated by weighted correlation networks and genome-wide association analyses. Network-based approaches of transcriptomic and metabolomics data revealed linked of transcripts and metabolites of the pentose phosphate pathway (PPP). This finding was evidenced by using a NADP/NADPH assay and HDAC4 and G6PD transcript quantification with the latter coding for first limiting enzyme of this pathway and by RNAi knockdown experiments of HDAC4. Other transcripts including ARG2 and SLC22A7 showed link to amino acids and biomarkers. The amino acid metabolites were linked with transcripts of immune or acute phase response signaling, whereas the carbohydrate metabolites were highly enrich in cholesterol biosynthesis transcripts. Genome-wide association analyses revealed 180 metabolic quantitative trait loci (mQTL) (p < 10-4). Trans-4-hydroxy-L-proline (p = 6 × 10-9), being strongly correlated with plasma creatinine (CREA), showed strongest association with SNPs on chromosome 6 that had pleiotropic effects on PRODH2 expression as revealed by multivariate analysis. Consideration of shared marker association with biomarkers, metabolites, and transcripts revealed 144 SNPs associated with 44 metabolites and 69 transcripts that are correlated with each other, representing 176 mQTL and expression quantitative trait loci (eQTL). This is the first work to report genetic variants associated with liver metabolite and transcript levels as well as blood biochemical-clinical parameters in a healthy porcine model. The identified associations provide links between variation at the genome, transcriptome, and metabolome level molecules with clinically relevant phenotypes. This approach has the potential to detect novel biomarkers displaying individual variation and promoting predictive biology in medicine and animal breeding.
Genomics is the field of modern biology that studies the genome as the sum of all genes of a given organism. Genomics includes the analysis of genomic variations in order to identify genetic susceptibility loci for various human diseases. Besides genomics, there are related fields summarized by the term "Omics" such as transcriptomics and proteomics, studying the sum of all transcripts and proteins in a defined biological system, respectively. Genetic variants, namely single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are used to identify genomic loci associated with human traits and diseases. Genome-wide association studies (GWASs) based on SNP data have been performed for a wide range of human traits and diseases. In the population-based Study of Health in Pomerania (SHIP) and the independent SHIP-TREND study, whole-genome genotyping data were available for 4081 and 986 individuals, respectively. In contrast to the widely used GWAS based on SNPs, association studies using CNV data are difficult to implement and thus less common. Therefore, one aim of this work was to detect CNVs using the whole-genome genotyping data available for 4081 individuals from SHIP. Another aim was to develop an efficient workflow for the analysis of these CNVs. As most common genetic variants exhibit only relatively small effects on phenotypic variability, large sample sizes are needed to maximize the statistical power to detect such effects. Therefore, the integration of data from multiple collaborating studies is indispensable. In this context, several CNV studies with the SHIP data have been performed and published, for example on body mass index (BMI) phenotypes where the SHIP cohort was used as a population-based control. Trait-associated genetic markers identified through GWASs are often intergenic or synonymous coding, and those loci identified through whole-genome CNV analyses often contain multiple genes, making it difficult to identify the causal variants. In this context, the functional analysis of identified loci aids in determining causal variant(s). One possibility to conduct functional analysis is the expression quantitative trait loci (eQTL) analysis, defined as the association of genome-wide genotyping data with genome-wide gene expression data based on measured transcriptomes. This allows the identification of genetic variants influencing the expression levels of defined genes. A further example are transcriptome-wide association analysis (TWAS), defined as the association of phenotype data with whole-genome expression data. Thus, another aim of this work was to establish an analysis pipeline for processing such expression data, which were available for about 1000 individuals from the SHIP-TREND study. Here, array-based gene expression data were generated using RNA prepared from whole-blood. Interpretation of TWAS results is often difficult, because of possible reverse causation on gene expression data. Furthermore, technical errors of measurement may bias the results. In a comprehensive work, biological and technical factors influencing measured gene expression data have been identified and were subsequently taken into account to improve the association analyses. To further elucidate the molecular mechanisms underlying the relationship of gene expression levels with human traits or diseases, pathway analyses using the Ingenuity Pathway Analysis (IPA) tool have been performed in connection with the TWAS. As for GWASs, the associations identified in TWAS usually exhibit only small effect sizes, highlighting the need for larger studies or meta-analysis to identify all susceptibility variants. In this context several eQTL- and TWAS meta-analyses using the SHIP-TREND data have been performed, for example on the phenotypes age, sex, BMI, smoking status and serum lipid traits. The results of these analyses are in preparation for publication and the most advanced example, the correlation of expression data with BMI, is presented here. The integration of whole-genome genotyping and expression data provides new functional information of the underlying biological mechanisms of complex human traits and diseases. Within the frame of this work, this could be demonstrated for the example of susceptibility to Helicobacter pylori infection.