Genome‐wide Association Studies

Abstract

Abstract The genome‐wide association approach has become a reality as a result of significant advances in genomic resources such as the International HapMap Project, the high‐throughput genotyping technologies, the collection of large number of cases and controls and the development of powerful statistical analysis tools. The strength of this approach is its agnostic genome‐wide search for single nucleotide polymorphisms. The explosion of more than 100 genome‐wide association studies since 2007 has identified numerous novel genetic variants or loci that are associated with many complex diseases. These studies have also linked new molecular pathways to various diseases. This is currently the most successful approach for pinpointing common variants with modest genetic effect that are associated with common complex diseases. Beyond these discoveries, there is a need to demonstrate their biological significance and to incorporate other structural variations, gene–gene, epigenetic and environmental interactions. Key concepts In the pre‐genomic era, the genetic dissection of complex diseases is done through classical linkage studies and candidate gene‐based association studies. The classical linkage study is a powerful approach to identify rare and high penetrant disease variant or gene. The candidate gene approach in the pre‐genomic era is limited to a few genetic markers for genes that are suspected to be involved in the pathogenesis of the complex disease. The GWA approach was first proposed by Risch and Merikangas in 1996 as a statistically more powerful approach to detect common variant with modest genetic effect compared to linkage study design. International HapMap Project was initiated in 2003 to characterize the haplotype patterns in human genome and subsequently identify tagging SNPs. The human genome can be organized into haplotypes with strong LD among the SNPs. For direct association study design or the gene‐centric approach, SNPs which are likely to be functionally important are selected, e.g. nonsynonymous SNPs. Most of the GWAS was conducted in a two‐stage or multi‐stage design because this design is more cost‐effective as only a fraction of samples was genotyped with several hundred thousand SNPs. The current generation of GWAS has contributed rapidly within the last few years in uncovering novel genes associated with common complex diseases. The future GWAS will have to explore structural variations, gene–gene interactions, epigenetic and gene–environment interactions. The need to include environmental factors will require a prospective study design.