Genome-wide association studies (GWAS) use high-throughput genomic technologies to scan entire genomes of large numbers of subjects quickly, in order to find genetic variants correlated with a trait or disease. Understanding the genetic architecture of complex diseases relies heavily on discovery and characterization of disease-associated variants such as single nucleotide polymorphisms (SNPs) and copy number variations (CNVs).
Complex diseases are often characterized by common variants, while the contribution of rare or low-frequency variants remains largely unknown. Large-scale GWAS using microarrays are efficient and cost-effective for identifying loci and imputing common SNP variants associated with disease. However, arrays are limited in detecting low-frequency SNP variants. The base-by-base resolution of whole-genome sequencing allows for the identification of both common and rare variants that may be associated with disease.
GWAS for many diseases and disorders have not yet been performed, and the large majority (79%) of participants in GWAS to date are of European ancestry. As the European population accounts for just ~16% of the global population, there is a recognized need for more diverse GWAS datasets.2
In addition to ethnic diversity, there is a need to perform GWAS on diverse disease indications for specific sub-groups. This will help provide clues about which genes and gene pathways could be involved in disease mechanisms and pathogenesis.
GWAS with the commonly used case-control setup approach, which compares two large groups of individuals–one case group affected by a disease and one healthy control group–have successfully identified variants for specific complex diseases, such as:
Researchers perform large GWAS studies to identify disease-associated DNA risk loci and develop polygenic risk scores.
Genome-wide association studies have identified thousands of variants with putative roles in different diseases. However, going from statistical associations to true insight into disease mechanisms remains a challenge. Recent advances in sequencing technologies have facilitated the development of strategies for assaying GWAS SNPs for potential functional relevance.