A Review on the Challenges in Indian Genomics Research for Variant Identification and Interpretation.

Today, genomic data holds great potential to improve healthcare strategies across various dimensions - be it disease prevention, enhanced diagnosis, or optimized treatment. The biggest hurdle faced by the medical and research community in India is the lack of genotype-phenotype correlations for Indians at a population-wide and an individual level. This leads to inefficient translation of genomic information during clinical decision making. Population-wide sequencing projects for Indian genomes help overcome hurdles and enable us to unearth and validate the genetic markers for different health conditions. Machine learning algorithms are essential to analyze huge amounts of genotype data in synergy with gene expression, demographic, clinical, and pathological data. Predictive models developed through these algorithms help in classifying the individuals into different risk groups, so that preventive measures and personalized therapies can be designed. They also help in identifying the impact of each genetic marker with the associated condition, from a clinical perspective. In India, genome sequencing technologies have now become more accessible to the general population. However, information on variants associated with several major diseases is not available in publicly-accessible databases. Creating a centralized database of variants facilitates early detection and mitigation of health risks in individuals. In this article, we discuss the challenges faced by genetic researchers and genomic testing facilities in India, in terms of dearth of public databases, people with knowledge on machine learning algorithms, computational resources and awareness in the medical community in interpreting genetic variants. Potential solutions to enhance genomic research in India, are also discussed.

A non-invasive approach to explore the discriminatory potential of the urinary volatilome of invasive ductal carcinoma of the breast.

Worldwide, breast invasive ductal carcinoma (IDC) accounts for the majority of the reported cases of this form of cancer. IDC effective management, as for any form of cancer, would greatly benefit from early diagnosis. This, however, due to various socio-economic reasons, is very far for the reality in developing countries like India, where cancer diagnosis is often carried out at late stages when disease management is troublesome. With the present work, we aim to evaluate a simple analytical methodology to identify a set of volatile organic compounds (VOCs) in urine samples, as a biosignature for IDC. Using solid-phase microextraction followed by gas chromatography/mass spectrometry, a panel of 14 urinary VOCs was found to discriminate IDC (n = 65) from a healthy control (HC) group (n = 70) through multivariate statistical treatments. Furthermore, metabolic pathway analysis revealed various dysregulated pathways involved in IDC patients hinting that their detailed investigations could lead to novel mechanistic insights into the disease pathophysiology. In addition, we validated the expression pattern of five of these VOCs namely 2-ethyl-1-hexanol, isolongifolenone, furan, dodecanoic acid, 2-methoxy-phenol in another external cohort of 59 urinary samples (IDC = 32 and HC = 27) and found their expression pattern to be consistent with the primary sample set. To our knowledge, this is the first study exploring breast IDC volatome alterations in Indian patients.