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Graphical Models in Genetics, Genomics, and Metagenomics
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
There has been a recent interest in integrating summary data from GWAS and eQTL studies in order to identify genes whose expression levels are associated with complex trait because of pleiotropy. One advantage of such approaches is that the summary-level data from GWAS and eQTL studies can come from two completely different sets of individuals, thereby effectively increasing the sample size for association analysis. The rationale for such an integrative analysis is articulated in [50] and is illustrated in Figure 21.4. If the phenotypic difference is caused by a genetic variant mediated by gene expression or transcription, then we should expect simultaneous association between phenotype, gene expression and the genetic variant (see Figure 21.4 (a) and also [50]). Such a simultaneous association can be due to causality with gene expression as mediator, pleiotropy where the same causal variant is associated with both phenotype and gene expression, or due to linkage where the shared association is because of linkage disequilibrium (LD) with two distinct causal variants, one affecting gene expression and another affecting phenotype (see Figure 21.4 (b)).
Metabolomics approach to biomarkers of dry eye disease using 1H-NMR in rats
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Jung Dae Lee, Hyang Yeon Kim, Jin Ju Park, Soo Bean Oh, Hyeyoon Goo, Kyong Jin Cho, Suhkmann Kim, Kyu-Bong Kim
Recently, the emergence of omics has dramatically increased the understanding of more complex diseases, and we can better understand the process or degree of diseases using biomarkers. Metabolomics is a recent addition to these techniques and has emerged as a powerful tool in biological research (Kim and Lee 2009; Bonvallot et al. 2014; Rinschen et al. 2019). For the past 15 years, metabolomics has been used for clinical and animal studies of several diseases including ocular pathology (Chen et al. 2019, 2011; Galbis-Estrada et al. 2014, 2015; Kim et al. 2013, 2010; Lee et al. 2020; Rosique et al. 2019; Tran et al. 2016). Metabolomics potentially identifies biomarkers and functional pathways of diseases (Kim and Lee 2009; Bonvallot et al. 2014; Jang et al. 2018; Rinschen et al. 2019). Metabolomics enables simultaneous analysis of hundreds of metabolites from biological samples, thus providing a glimpse of the metabolomics state of a tissue. Ryu et al. (2018, 2019) identified the biomarker and expected metabolic pathway for cisplatin- and gentamicin-initiated nephrotoxicity by applying metabolomics to rat serum and urine. Lee et al. (2020) found endogenous metabolites associated with pulmonary damage by polyhexamethylene guanidine phosphate (PHMG-p) using metabolomics in rat serum and urine. The power of metabolomics as an intermediate phenotype in the analysis of complex traits was demonstrated in several experiments, including ophthalmologic studies (Chen et al. 2011; Galbis-Estrada et al. 2014; Pieragostino et al. 2017). In addition, Young and Wallace (2009) when evaluating eye diseases indicated that metabolite-specific multiplexing analysis might provide uniquely useful data. Studies to date focused primarily on analysis of tears samples from DED patients with the determination of DED metabolite biomarkers (Chen et al. 2019, 2011; Galbis-Estrada et al. 2014, 2015; Hagan, Martin, and Enríquez-de-Salamanca 2016; Jiang et al. 2020; Lam et al. 2014; Lauwen et al. 2017; Nazifova-Tasinova et al. 2020; Yazdani et al. 2019). However, the benefits derived to obtaining a metabolic profile from urine analysis for various diseases have not been fully explored and thus metabolomics studies for DED are still limited. The aim of this study was thus to examine and characterize metabolic changes as biomarkers in DED development in a rat model using 1H-NMR spectroscopy.