Next Generation Sequencing (NGS)/Genome Wide Association Studies

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Genome Wide Association Studies[edit]

Genome wide association studies [GWAS] are frequently used to identify genetic variations between two populations of organisms--one with a specific phenotype (case) and one without (control). For example, if researchers were interested with genomic variation associated with a medical disease (e.g., cancers, Alzheimer’s, or autism), researchers can search for variants that are nonrandomly present in either a control (e.g., individuals who do not have a disease) or disease-specific population (e.g. individuals who have a disease). This allows researchers to map genes that affect specific outcomes or traits[1]. Genotypes that are disproportionately common in disease-specific populations are then candidate “genes” for the increased risk or “cause” of disease-specific phenotypes. Essentially, GWAS studies use large populations to evaluate, quantitatively, the likelihood of association between variants and phenotypic outcomes[2]. GWAS studies can also provide information on epigenetic factors in disease processes. For example, Zhang et al. (2013) observed DNA methylation differences in genes related to alcohol metabolism in individuals with alcohol dependence compared with healthy controls.[3]

Though GWAS studies can serve as a powerful tool for elucidating associations between genes and traits, there are some specific limitations. For example, genetic variants that are low in frequency and have small effects on traits are difficult to detect using GWAS, and genetic variants that are of high frequency with strong effects are very unusual for common diseases. Additionally, population stratification, linkage disequilibrium, and DNA pooling can bias the results[4].

The National Human Genome Research Institute maintains an up-to-date catalogue of GWAS studies that have published significant associations (with a p-value of less than 0.5 X 10-5 for 17 traits)[5].


  1. Carlson, C. S., Eberle, M. A., Kruglyak, L., & Nickerson, D. A. (2004). Mapping complex disease loci in whole-genome association studies. Nature, 429(6990), 446-452.
  2. Balding, D. J. (2006). A tutorial on statistical methods for population association studies. Nature Reviews Genetics, 7(10), 781-791.
  3. Zhang, R., Miao, Q., Wang, C., Zhao, R., Li, W., Haile, C. N., Hao, W., & Zhang, X. Y. (2013).Genome-wide DNA methylation analysis in alcohol dependence. Addiction Biology, 18, 392-403. doi: 10.1111/adb.12037
  4. McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P., & Hirschhorn, J. N. (2008). Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics, 9(5), 356-369.
  5. Hindorff LA, MacArthur J (European Bioinformatics Institute), Morales J (European Bioinformatics Institute), Junkins HA, Hall PN, Klemm AK, and Manolio TA. A Catalog of Published Genome-Wide Association Studies. Available at: