In Ayurveda system of medicine individuals are classified into seven constitution types, “Prakriti”, for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

No abstract

Copy number variations (CNVs) have provided a dynamic aspect to the apparently static human genome. We have analyzed CNVs larger than 100 kb in 477 healthy individuals from 26 diverse Indian populations of different linguistic, ethnic and geographic backgrounds. These CNVRs were identified using the Affymetrix 50K Xba 240 Array. We observed 1,425 and 1,337 CNVRs in the deletion and amplification sets, respectively, after pooling data from all the populations. More than 50% of the genes encompassed entirely in CNVs had both deletions and amplifications. There was wide variability across populations not only with respect to CNV extent (ranging from 0.04-1.14% of genome under deletion and 0.11-0.86% under amplification) but also in terms of functional enrichments of processes like keratinization, serine proteases and their inhibitors, cadherins, homeobox, olfactory receptors etc. These did not correlate with linguistic, ethnic, geographic backgrounds and size of populations. Certain processes were near exclusive to deletion (serine proteases, keratinization, olfactory receptors, GPCRs) or duplication (homeobox, serine protease inhibitors, embryonic limb morphogenesis) datasets. Populations having same enriched processes were observed to contain genes from different genomic loci. Comparison of polymorphic CNVRs (5% or more) with those cataloged in Database of Genomic Variants revealed that 78% (2473) of the genes in CNVRs in Indian populations are novel. Validation of CNVs using Sequenom MassARRAY revealed extensive heterogeneity in CNV boundaries. Exploration of CNV profiles in such diverse populations would provide a widely valuable resource for understanding diversity in phenotypes and disease.

Several studies have demonstrated the role of climatic factors in shaping skin phenotypes, particularly pigmentation. Keratinization is another well-designed feature of human skin, which is involved in modulating transepidermal water loss (TEWL). Although this physiological process is closely linked to climate, presently it is not clear whether genetic diversity is observed in keratinization and whether this process also responds to the environmental pressure. To address this, we adopted a multipronged approach, which involved analysis of 1) copy number variations in diverse Indian and HapMap populations from varied geographical regions; 2) genetic association with geoclimatic parameters in 61 populations of dbCLINE database in a set of 549 genes from four processes namely keratinization, pigmentation, epidermal differentiation, and housekeeping functions; 3) sequence divergence in 4,316 orthologous promoters and corresponding exonic regions of human and chimpanzee with macaque as outgroup, and 4) protein sequence divergence (Ka/Ks) across nine vertebrate classes, which differ in their extent of TEWL. Our analyses demonstrate that keratinization and epidermal differentiation genes are under accelerated evolution in the human lineage, relative to pigmentation and housekeeping genes. We show that this entire pathway may have been driven by environmental selection pressure through concordant functional polymorphisms across several genes involved in skin keratinization. Remarkably, this underappreciated function of skin may be a crucial determinant of adaptation to diverse environmental pressures across world populations.

Genome surveillance of the Delhi data provides a more detailed picture of diverse circulating lineages. The added value that the current study provides by clinical details of the patients is of importance.