Emerging face of genetics, genomics and diabetes

Abstract

Type 2 diabetes mellitus (T2DM) has become a global public health issue encompassing even children and youth in recent decades. It is a complex disease influenced by genetic and environmental factors. Although much attention is paid to environmental factors since they are identifiable and potentially modifiable, beginning the turn of the 21st century, there has been an explosion of activities that are aimed to screen the genome for identifying T2DM susceptibility genes. This gained momentum with a confluence of different scientific disciplines that was showcased by the millennial Human Genome Project and its aftermath. Currently, the next generation sequencing accelerated the identification of potential causal genes/variants (especially low frequency and rare variants) influencing complex diseases such as T2DM. Also, the other technologies such as transcriptomics, proteomics, metabolomics and epigenomics have emerged as additional tools to identify the molecular factors underling the phenotypic expression of T2DM. The voluminous biological data generated by these studies has necessitated or contributed to bioinformatics, a confluence of biology and its various flavours, information technology, computational biology, algorithms, matching, statistics, mathematics, nanotechnology, ethics among others.The areas of analysis using bioinformatics in diabetes can be approached as employing datasets from genome or amino acid sequences, structures of biological molecules and functional genomics experiments. Nevertheless analyses extend to other types of data including evolutionary trees, metabolic pathways and the semantics of published literature.Mathematical models and analysis and statistical analyses are required to obtain critical information from the large amounts of data that is generated by genomic and other approaches. Newer algorithms to cluster data from a wide scatter will be relevant: for example, K-means can select best density in a self-adoptive manner and initialize r empirically; an improved method over K means, the Automatic Generation of Distance for Density based Clustering (AGDDC) was developed by Karteeka Pavan et al. to optimize number of clusters [1]. This was developed to find initial optimum centroids based on density clustering. It can be applied to relevant genes related to the pathogenesis of T2DM.The challenge in modern biology and of modern diabetes, is a profusion of data. Bioinformatic methods aid in curating and synthesizing the deluge [2]. The Human Genomics Project (HGP) which heralded the situation where data overran the capacity to cull out the information, simultaneously saw sequencing of genes of other species including vertebrates, invertebrates, fungi, bacteria and plants. The underlying theme was to know how genetic architecture and genes evolved over time. Considering that the basic building blocks were the four nucleotides across all life forms, it was logical to compare the genomes of different species to identify where and how divergence of genes occurred and how newer metabo...