GWAS as the Detective to Find Genetic Contribution in Diseases

Introduction

In the era of unprecedented advancement in medical and technological sciences, Garrod A.E., a physician to the Hospital of Sick Children, in the year of 1902, reported a case of alkaptonuria that he described as “not the manifestation of disease but is rather of the nature of an alternative course of metabolism…”. That was the first report where the possibility of underlying “molecular evidence” behind human disease came in the lime light. Human diseases and their genetic contribution share a complex and intricate relationship, yet to be explored fully. For few cases, phenotypes (diseased condition) could directly be associated with gene, experimentally, whereas a large number of genetic associations behind disease state remained hidden.

That called for a situation where mapping of gene knowledge between afflicted and un-afflicted individuals can be mapped to find out the difference at each point (each nucleotide position or allele or variant) and the number of occurrences of the mismatched alleles in the diseased individuals (allelic frequency) with an assumption that if any allele has higher frequency to appear in the diseased individual then that is associated with the diseased trait. That can be translated as ‘scan through entire gene’ for gene to disease relation mapping or rather, as genetic language, Genome Wide Association Study (GWA or GWAS).

These GWAS data not only provide us with the information on the disease association with the gene level knowledge but also enable a deeper understanding of the entire scenario generating a landscape of gene with its minute changes that can be extrapolated to genes coding (impact on protein production) or non-coding regions (impact on protein production regulation), the transcription factor binding sites (regulating transcription), epigenetic modification probabilities (regulation in genetic coding), pathways involved (visualizing the upstream or downstream possible effects) extending to heritability of the diseases. These all impose final impact on the phenotype which is nothing but the diseased state to us. Thus, insight generated with GWAS leads us to understand the actual reason or mechanism behind disease onset that, in turn, guides scientists to find novel druggable targets for more efficient medications or some times, to look for personalized medications (Bush & Moore, 2012).

With the completion of Human Genome Project (human DNA sequence) in 2003 (International HapMap Consortium, 2003) and the International Hapmap project (haplotype map of the human genome) in 2005, scientists are well-equipped with resources to correlate genetic contribution to disease onset. Success of the GWAS reflects in identifying the genetic factor contributing to Parkinson's disease, Crohn's disease, type 2 diabetes and obesity to name a few. These GWAS data can also be accessed through various repositories. However, its smaller variant size, unavailability of replicated reports, smaller population size under study stand as limiting factors to uncover a larger portion of genetic information to understand properly. With the rapid advancement in research, these limitations will be overcome to generate a better understanding of the entire scenario of disease with GWAS concept.