Bioinformatics, Genomics and Diabetes
Bioinformatic analysis has been a key in unraveling the genetic basis of diabetes mellitus, which figured predominantly among target diseases for research after the human genome project. Despite extensive research the genetic contribution using current me
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Bioinformatics, Genomics and Diabetes Gumpeny Ramachandra Sridhar and Gumpeny Lakshmi
Abstract Bioinformatic analysis has been a key in unraveling the genetic basis of diabetes mellitus, which figured predominantly among target diseases for research after the human genome project. Despite extensive research the genetic contribution using current methods explains less than 10 % of predisposition. Data from next generation sequencing is bound to alter diagnosis, pathogenesis and treatment targets. Insight into the fine genetic architecture allows a fine grained classification of the diabetes spectrum, allowing primary preventive methods in at-risk individuals. In this quest the role of computational, statistical and pattern recognition would play increasingly major roles.
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Introduction
Diabetes mellitus is a metabolic disorder with increasing prevalence the world over. It accounts for substantial disability death, economic and socioeconomic loss. It results from an imbalance between the need and availability in the body for insulin, a protein hormone secreted by β cells of pancreas. There is a complex interaction of genetic factors, environment and lifestyle in the expression of the disease. Extensive work on the genetic basis has generated enormous data, which in the current state explains for only a minor part of its cause. Next generation technologies applied to diabetes is providing insights to the elaborate checks and balances in normal physiology, whose disturbances result in susceptibility to and expression of diabetes. A number of hitherto unexplored regulatory factors such as regulatory RNA, epigenetic influences and microbiota found in the gut have come to the forefront in the cause and course of this disease.
G. Ramachandra Sridhar (&) G. Lakshmi Endocrine and Diabetes Centre, 15-12-15 Krishnanagar, Visakhapatnam 530 002, India e-mail: [email protected] © The Author(s) 2016 P.V. Lakshmi et al. (eds.), Computational Intelligence Techniques in Health Care, Springer Briefs in Forensic and Medical Bioinformatics, DOI 10.1007/978-981-10-0308-0_1
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G. Ramachandra Sridhar and G. Lakshmi
Background
Diabetes, with a worldwide prevalence of 382 million people in 2013 is projected to nearly double that figure by 2035 [1]. Arising from a genetic underpinning which interacts with environmental factors, current genetic technologies have identified many common variants which contribute to it. However these explain only a small fraction of diabetes heritability. Newer technologies can improve our genetic understanding of diabetes [2]. Diabetes mellitus is broadly classified into Type 1 diabetes, which often presents in the young as a result of pancreatic β cell loss. It is characterized by insulin deficiency and circulating autoimmune markers such as antibodies to glutamic acid dehydrogenase. The proportion of people with type 1 diabetes is less, ranging from 2 to 20 %. Heritability was explained up to 80 % by genetic factors, principally HLA class II alleles, other loci encompassing insulin gene, CTLA
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