Genetic diversity is closely linked to phenotypic differences. They are
also associated to the
varying degrees of disease susceptibility or altered drug response across
individuals within a
given population. Several such examples were observed from the Indian
Genome Variation
project implemented during the years 2003-2008. In addition to heterozygous
polymorphisms,
the consanguineous marriage structure that is prevalent in our society, has
resulted in
numerous rare diseases due to the expression of recessive alleles. While
whole genome sequencing enables the cataloging of genetic variations,
assessing its effect on gene
expression by quantifying transcript levels i.e., transcriptomics, is vital
to identify the causal
variants. Apart from genetic effects, the environment (smoking, diet,
exposure to pollutants etc.)
plays an important role in gene regulation. Analysis of DNA methylation
profiles and chromatin
accessibility changes, i.e., epigenomics, in response to such factors is
thus crucial in the context of molecular medicine. Similarly, the
microbiome is yet another tier in elucidating the phenotypic manifestation
of health and disease. The integrative interpretation of such multi-omic
datasets for medicine or “multi-omic medicine”, will enable a tailored
approach to the diagnosis, treatment and management of diseases within
individuals or ethnically homogenous groups.

In this talk I will outline my plan of developing and executing a
computational framework which would integrate these “-omic” technologies to
discern the molecular mechanisms of disease. This in turn would enable
precise, predictive and personalized medicine. I will also highlight
strategies for translating this approach to a clinical setting by using
cell-free DNA and single-cell methods that can make these assays faster,
less invasive, less expensive, more informative, and widely applicable.

My recent work has included investigating the effects of genetic and
somatic mutations in the
mitochondrial DNA (mtDNA). Each cell has hundreds to thousands of
mitochondria, which in
turn contain 5-10 molecules of circular, ~16.5kb mtDNA. The mtDNA are
nearly 10 times more
likely to attain somatic mutations compared to nuclear DNA. These mutations
tend to
accumulate, and consequently, may outcompete the wild-type mtDNA molecules.
The balance
of mutant mtDNA versus the normal mtDNA (heteroplasmy) is a key determinant
of
mitochondrial function. It is estimated that one in 200 humans harbors a
pathogenic mtDNA
mutation. Such disorders are usually manifest during aging as in the case
of Alzheimer’s and
Parkinson’s disease. In this talk I will give an overview of a novel
technology that I’m developing
- Single Cell Analysis of Mitochondrial Polymorphisms through sequencing
(‘SCAMP-seq’) that
is aimed to accurately assess this mutational load, at single cell
resolution. I will touch upon
various application areas and technology development plans.