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REVIEW ARTICLE
Year : 2013  |  Volume : 1  |  Issue : 1  |  Page : 4-10

A concise history of genome-wide association studies


1 Department of Medical Genetics, University Medical Center, Utrecht, The Netherlands
2 Prince Mohammed Center for Research and Consultation Studies, University of Dammam, Kingdom of Saudi Arabia
3 Department of Experimental Cardiology, and Division Heart and Lungs, University Medical Center, Utrecht, The Netherlands
4 Department of Cardiology, Division Heart and Lungs, University Medical Center, Utrecht, The Netherlands

Correspondence Address:
Amein Al-Ali
Prince Mohammed Center for Research and Consultation Studies, University of Dammam, 31441
Kingdom of Saudi Arabia
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DOI: 10.4103/1658-631X.112902

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Genome-wide association studies (GWASs) have had a tremendous impact on the pace of genomic research of common diseases. The number of identified genetic variants associated has grown exponentially. For some diseases, such as coronary heart disease (CHD), the number of known susceptibility genes has grown from a handful to more than 45. A substantial number of genes point to unexpected mechanism involved, and functional data from the "Encyclopedia of Deoxyribonucleic Acid Elements" (ENCODE) project is helpful in uncovering the functional relevance to diseases. The rapidly evolving techniques have made the shift from family-based linkage studies to GWASs possible. Advanced single nucleotide polymorphism (SNP) arrays containing hundreds of thousands of variants efficiently assess the extent of genome-wide disease-associated genetic variation. Along with SNP arrays came breakthroughs in statistical analyses and study designs leading to the exponential growth of the GWAS catalog. Pathway analyses of GWASs results with manually curated software programs have been insightful. Next-generation sequencing (NGS) of the exome or even the whole genome will undoubtedly shift the balance in focus from common variants to more rare variations impacting common diseases. Moreover, the combined power of GWASs, sequencing, pathway analysis, and functional data to study common disease shall only be limited by our ability to comprehend.


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