e-QTL to p-QTL analysis: Personalized medicine research evolution to improve precision in research


e-QTL analysis (expression quantitative trait loci) involves testing the association between genetic markers or variants with the expression of a gene which is usually measured in in hundreds of unrelated individuals. Till now several studies have been conducted in this area and datasets are generated on regulatory variants in transcription factor binding sites and promoter region (local or Cis) or transcription and regulatory factors (Distal or Trans) affecting a gene expression at mRNA level. For e.g. GTEx portal. https://www.gtexportal.org/home/ is a dataset developed using e-QTL analysis in multiple tissues collected from hundreds of individuals (2). Such analyses are important in personalized medicine which includes precise selection and/ or administration of medicine. e-QTL analysis and Genome-wide association studies (GWAS) in the past helped in the drug development by providing extensive understanding on the molecular pathways involved in human disease. Such studies enabled administration of safer medicines to patients with different diseases or patients with different characteristics of same disease. With the advancement in the technology to quantify the proteins (The evolution of quantitative proteomics is well described by Schubert OT, et al (https://www.nature.com/articles/nprot.2017.040) (3). It is now possible to perform directly protein expression quantitative trait loci analysis directly measuring the end product (protein) changes in relation to the genotypes instead of relating with an intermediate product (mRNA). Such analyses can be termed as p-QTL analyses and can strengthen the information obtained before administration of the medication, and tissue specific information of the protein profile (e.g. either target related to the efficacy or a non-target related to the toxicity or an enzyme that inactivates/activates the drug or a circulating protein that can bind to the drug, etc.). Such information not only can give an idea about the drug mechanisms but also disease related information in complex traits. P-QTLs can be either close Cis or distal Trans p-QTL just like e-QTLs. This analysis can give us an idea about the level of a protein in relation to the genetic variant, but the activity regulation subjected to various other factors for e.g. phosphorylation. There are several proteomic technologies progressing to allow us to use simple instrumentation with greater sensitivity and specificity to quantify proteins Mälarstig A described comprehensive idea of p-QTL analysis in a supplement to Science available at: http://www.olink.com/science-ebook-request/ (4). In conclusion, these technologies if applied in the right direction can help to implement personalized medicine with precision. References: 1) GTEx Consortium, et al. Nature. 2017 Oct 11;550(7675):204-213. 2) Schubert OT, et al. Quantitative proteomics: challenges and opportunities in basic and applied research. Nat Protoc. 2017 Jul;12(7):1289-1294. 3) Mälarstigin A. pQTL studies and examples: Understanding biological mechanisms and creating a foundation for precision medicine. In Advancing precision medicine: Current and future proteogenomic strategies for biomarker discovery and development (Science/AAAS,Washington, DC, 2017), p. [14-17].

Posted On:13/11/2017




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