Whole Exome Sequencing in Healthy Individuals of Extreme Constitution Types Reveals Differential Disease Risk: A Novel Approach towards Predictive Medicine

Tahseen Abbas, Gaura Chaturvedi, P. Prakrithi, Ankit Kumar Pathak, Rintu Kutum, Pushkar Dakle, Ankita Narang, Vijeta Manchanda, Rutuja Patil, Dhiraj Aggarwal, Bhushan Girase, Ankita Srivastava, Manav Kapoor, Ishaan Gupta, Rajesh Pandey, Sanjay Juvekar, Debasis Dash, Mitali Mukerji, Bhavana Prasher

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Precision medicine aims to move from traditional reactive medicine to a system where risk groups can be identified before the disease occurs. However, phenotypic heterogeneity amongst the diseased and healthy poses a major challenge for identification markers for risk stratification and early actionable interventions. In Ayurveda, individuals are phenotypically stratified into seven constitution types based on multisystem phenotypes termed “Prakriti”. It enables the prediction of health and disease trajectories and the selection of health interventions. We hypothesize that exome sequencing in healthy individuals of phenotypically homogeneous Prakriti types might enable the identification of functional variations associated with the constitution types. Exomes of 144 healthy Prakriti stratified individuals and controls from two genetically homogeneous cohorts (north and western India) revealed differential risk for diseases/traits like metabolic disorders, liver diseases, and body and hematological measurements amongst healthy individuals. These SNPs differ significantly from the Indo-European background control as well. Amongst these we highlight novel SNPs rs304447 (IFIT5) and rs941590 (SERPINA10) that could explain differential trajectories for immune response, bleeding or thrombosis. Our method demonstrates the requirement of a relatively smaller sample size for a well powered study. This study highlights the potential of integrating a unique phenotyping approach for the identification of predictive markers and the at-risk population amongst the healthy.

Original languageEnglish
Article number489
JournalJournal of Personalized Medicine
Volume12
Issue number3
DOIs
StatePublished - Mar 2022
Externally publishedYes

Keywords

  • ayurgenomics
  • deep phenotypes
  • exome sequencing
  • exomes
  • extreme phenotypes
  • precision medicine
  • risk stratification

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