Human Variome Analysis and Implications in Medicine

Yellapragada SubbaRow Memorial lecture (mentioned in my earlier post) was also marked by five alumna of the School of Biotechnology returning back to showcase their work. One of them, Dr. Yasha Hasija, assistant professor at the Delhi Technological University, talked on how analysis of the human variome would enable genome based prediction of complex disorders. Hers was the talk that I enjoyed the most due to my inclination towards systems medicine and here is a brief review of it. Predictive medicine aims to either individually predict disease occurrence or to individually predict further disease course in already diseased patients. The technical challenges to predictive medicine have been outlined in one of my earlier posts here.


After Koch’s germ theory of disease, most diseases were seen as solely due to microbes (or say environment). As exceptions were identified, they were attributed exclusively to genetic factors. Most complex disorders, in fact, lie somewhere in between with the nature-versus-nurture debate yet unsolved. Even diseases which are not genetic at all, it seems, affect different individuals differently – the difference lies in the individuals’ genetic predisposition for the particular disease. How can we, then, expect a single drug to be effective for all patients? Another challenge besides complexity is epigenetics – DNA and chromatin modifications that persist from one cell to another, without change in the DNA sequence. The epigenome is concerned with changes in gene expression with respect to the environment. It has also been hypothesized that drugs may alter epigenetic homeostasis by direct or indirect mechanisms. Systems medicine employing microarray analysis of gene expressions and methylation patterns may lead to better understanding of long-term side effects of drugs. The ENCODE project, discussed in one of my posts here, showed last September that much of our DNA which was earlier considered “junk” is not that idle.

Single nucleotide polymorphisms

Variances in DNA sequences occurring at a single base pair in more than 1% of the population are known as single nucleotide polymorphisms (SNPs). As of June 26th 2012, dbSNP listed 18, 78, 52, 828 SNPs in humans which comprise approximately 5.8% of the total genome. Due to high cost of whole genome sequencing, analysis of SNPs provides a shortcut. International HapMap Project aims at analysing all the SNPs in humans and their relation with diseases. SNPedia is a wiki devoted to the medical consequences of SNPs and includes software to analyze personal genomes. Some diseases, like sickle cell anaemia, are caused invariably due to SNPs. More complex diseases are controlled by many genes which may comprise a particular SNP. Also, SNPs have been found to be associated with effectiveness of particular drugs and hence can be used as drug response markers. The field concerned with analysing genomic information to assess pharmaceutical response is termed pharmacogenomics (pharmacoepigenomics for the epigenome)

Individual parameter analysis

In diseased patients, real-time monitoring of disease may lead to better therapy.

Dr Yasha Hasija during her talk. The slide shows a complex model of connections between diseases.
Dr Yasha Hasija during her talk. The slide shows a complex model of connections between diseases.

Diseases are primarily caused due to molecular changes in heterogeneous cellular systems or organs (cytomes). It can be inferred that information on disease course prediction and diagnosis should be collectable at the cellular level. This evidence based medicine at the cellular level may also diminish the number or length of clinical therapy trials by utilizing the predictive data pattern changes in patient cytomes as indicators of therapeutic effects instead of the entire patient. It seems particularly vital in case of diseases which progress rapidly or are life threatening or whose treatment has significant side effects. Cytometric analysis of disease associated molecular alterations in cytomes will revolutionize how individual patients are taken care of. Hood et al., Institute for Systems Biology hold a patent for multiparametric analysis for predictive medicine. The invention allows determination of comparative expression profile in an individual by comparing expression levels of a sample of molecules in a population of molecules in a specimen from the individual. The data can be used to sense perturbations in the comparative expression and, hence, detect disease at the earliest. 

Aging and related disorders

Advancements in medical science have led to increase in life expectancy, but not yet to improving the quality of life of the aged. Chronic degenerative diseases (CDD) like cancer, cardiovascular, autoimmune or neurodegenerative diseases are a major cause of death amongst the elderly. Cytomic markers or/and existence of certain SNPs in the genome may indicate high possibility of occurrence of CDD in individual patients, long before the disease actually manifests. dbAARD is a database of aging and age related disorders, managed by the Delhi Technological University.

She also discussed the Genetic susceptibility to TB and the possibility of finding novel medicines through Open source drug discovery (OSDD). OSSD provides the talented minds a platform to collaborate and contribute to develop affordable healthcare for neglected tropical diseases such as malaria, tuberculosis and leishmaniasis. It is hoped that open source biology would prove to be as beneficial to the growth of biotechnology as open source computing proved to information technology.


Read more here:

  1. Csoka, A. B., Szyf, M., Epigenetic side-effects of common pharmaceuticals: A potential new field in medicine and pharmacology, Medical Hypotheses, November 2009, pages 770-780
  2. Valet, G., Predictive medicine by cytomics: potential and challenges, Journal of Biological Regulators and Homeostatic Agents, June 2002
  3. Licastro, F., Caruso, C., Predictive diagnostics and personalized medicine for the prevention of chronic degenerative diseases, Immunity and Ageing, 2010

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