Anu Acharya

Genome-wide polygenic risk scores (PRS) for lifestyle disorders, like Type 2 Diabetes (T2D), are useful in identifying at-risk individuals early on in life, and to guide them towards healthier lifestyles. The current study was aimed at developing PRS for the Indian population using imputed genotype data from UK Biobank and testing the developed PRS on data from GenomegaDB™ of Indians living in India. 984 T2D cases and 2,818 controls were selected from Indian participants of UK Biobank to… Show more

Genome-wide polygenic risk scores (PRS) for lifestyle disorders, like Type 2 Diabetes (T2D), are useful in identifying at-risk individuals early on in life, and to guide them towards healthier lifestyles. The current study was aimed at developing PRS for the Indian population using imputed genotype data from UK Biobank and testing the developed PRS on data from GenomegaDB™ of Indians living in India. 984 T2D cases and 2,818 controls were selected from Indian participants of UK Biobank to develop the PRS. Summary statistics available for South Asians, from the DIAMANTE consortium, were used to weigh genetic variants. LDpred2 algorithm was used to adjust the effect of linkage disequilibrium among the variants. The association of PRS with T2D, tested with logistic regression, was found to be very significant (AUC = 0.75, OR = 3.06 [95%CI = 2.78 - 3.36]). When participants were divided into four PRS quartile groups, the odds of developing T2D increased sequentially with higher PRS groups. The highest PRS group (top 25%) showed 6.6 fold increased risk compared to the rest of the participants (75%). The derived PRS was found to be significantly associated with T2D in the test dataset of 446 Indians (AUC = 0.6, OR = 1.48 [95%CI = 1.21 - 1.8]). Our results indicate the importance of developing population-specific PRS for T2D to stratify the individuals, and to recommend personalized preventive measures. Show less

Low- and middle-income countries contain the majority of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). India has the second-highest number of reported cases, but most seroprevalence estimates come from cities. Cities, with denser population, are more vulnerable to SARS-CoV-2. However, millions of city workers fled to rural India where lockdown was less stringent.

We assessed SARS-CoV-2 prevalence among volunteers from population-representative… Show more

Low- and middle-income countries contain the majority of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). India has the second-highest number of reported cases, but most seroprevalence estimates come from cities. Cities, with denser population, are more vulnerable to SARS-CoV-2. However, millions of city workers fled to rural India where lockdown was less stringent.

We assessed SARS-CoV-2 prevalence among volunteers from population-representative households in urban and rural areas of the state of Karnataka (population, 67.5 million). Show less

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… Show more

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. Show less

The adiponectin gene, ADIPOQ, encodes an adipocytokine, known as adiponectin hormone. This hormone is known to be associated with insulin sensitization, fat metabolism, immunity, and inflammatory response. Polymorphisms in ADIPOQ gene lower the adiponectin levels, increasing the risk for diabetes and cardiovascular diseases.

Pharmaceutical Outsourcing: Discovery and Preclinical Services is the first in a series on pharmaceutical outsourcing. This first book is written for all practitioners in the pharmaceutical and biotech world and is about managing projects in drug discovery and preclinical development. The purpose envisioned by the authors and editors is to provide an understanding of how outsourcing works from the perspective of sponsor, internal customer, service provider, outsourcing service marketplace… Show more

Pharmaceutical Outsourcing: Discovery and Preclinical Services is the first in a series on pharmaceutical outsourcing. This first book is written for all practitioners in the pharmaceutical and biotech world and is about managing projects in drug discovery and preclinical development. The purpose envisioned by the authors and editors is to provide an understanding of how outsourcing works from the perspective of sponsor, internal customer, service provider, outsourcing service marketplace, principal investigator, project leader, and consultant. The authors of this book and the companies they represent hail from the Americas, Europe, Asia, and Australia, underscoring the fact that drug discovery is an international effort. The scope of the businesses covered include the one-person consulting company through to a sponsor among the largest in the industry. Written in the styles unique to each author, the reader will enjoy getting into the minds of the writer. Our intention is to provide a story for each aspect of the process as you move from target validation, genomic profiling, screening, medicinal chemistry, modeling and informatics, safety evaluation, therapeutic target confirmation, through to protecting the results of the research, the intellectual property. Emerging trends in drug discovery support a rapidly growing business model in outsourcing, the virtual pharma company. In recognition of this important element are several success stories and learnings on arbitraging risks in discovery. Outsourcing provides the ultimate flexibility in managing projects that may quickly grow, progress in directions unanticipated, or fail early. Last but not least, this book provides insights in working with companies outside your region, with business cultures unfamiliar or unknown. Relationship building is about understanding your sponsor or customer in their cultural space and being able to establish the rapport for clear and open communications. Priceless! Show less