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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This p-QtL analysis can be performed on a large scale using cells representing a population and specific ethnicity. Such work has been reported early in 2013 by Wu et al in their publication titled "Variation and Genetic Control of Protein Abundance in Humans" in Nature journal. one can access full text here : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789121/ . They have used lymphoblastoid cell lines which were sequenced under HapMap and 1000 genomes project.

PM Team
30/11/2017


One question often asked by the young researchers is that how and when to decide which mechanisms to investigate that facilitates our understanding on the function of any protein modulated by the genetic variant? So a static genetic marker could be identified that can explain a phenotype, which can be helpful for e.g. in stratification of patients A clear and definitive questioning stratergy is what PM Team thinks should be developed by the young researchers instead of using all the advanced technology and resources we have. One must clearly look for supporting and the contradicting literature before initiating a study after asking a precise, clear research question. Which is the best was than collecting the data possible using technology and resources and then deciding what we can get out of all this data. What if there is a genetic variant correlation with mRNA sequence – Then functional problem might exists or at least this variant is in LD with some other variant which is changing the protein sequence and thus function. Possibility of changes in amount of the protein exists (if this variant responsible for change in amino acid sequence is linked to another variant that is changing the transcription process) via transcription mechanisms. What if a genetic variant is correlated with the amount of mRNA—Functional relevance in relation to the amount of protein available, but one must remember that even more amount is produced, if the person has a defective variant in the exonic region (homozygous status), then it is meaningless. What is there is no correlation of mRNA and the protein levels probably other epigenetic mechanisms such as miRNA at the level of translation plays a role. What if there is no correlation between amount of the protein and its function, possibly post-translational mechanisms plays a major role. Thus, one must think of the steps and approaches, before the study plan instead of using fancy technologies available (to make a meaningful outcome that can have a real translation in clinic). 1) First, observe the function change (Functional studies, In silico simulations) due to a genetic variation resulting an amino acid change, or frame shift or splicing defect or stop codon then investigate them in a large clinical studies of homogeneous population (if possible). Vice versa possible to first find an association by candidate gene selection based either on the physiology or expert opinion or by open approach and then investigate the functionality of the identified variants in functional studies. 2) If the associated variant with phenotype is in promoter region or intronic or intergenic region, look for correlation of this variant with mRNA expression and then the protein levels. Till now the focus is mainly on the genetic mechanisms especially on the variants 3) What if there is no correlation of the promoter region variant and the mRNA levels? Then focus on the epigenetic mechanisms such as methylation to explain why there is no direct relation of variants with mRNA expression. 4) What if you have the correlation of variants with mRNA expression but not with protein expression, then focus on the miRNA mediated mechanisms. 5) What if there is correlation of the variants with protein levels but the levels of proteins not related to the function observed. Then focus on the post-translational mechanisms. Thus, one must identify the level of regulation that is distributed at various levels of either transcription, or translation or post-translational level. Please note that complex scenarios might exists than this simple explanation.

PM Team
24/11/2017