Abstract
Association studies have enabled the exploration of alternative, more efficient methods for early detection, prevention and treatment of diseases by providing valuable insight into their genetic foundation. Genome wide association studies (GWASs) have been particularly informative with respect to complex diseases whose manifestation depends on a multitude of genetic and environmental factors. In these studies, common Single Nucleotide Polymorphisms (SNPs) are used to locate and identify regions of the genome that may be causative of common complex diseases. These studies have uncovered a number of loci of interest for several diseases and have also allowed for the development of genetic counseling with improved individual disease risk assessment. With the more accurate prediction of the probability of disease development, progression and treatment success, GWASs have also brought about the age of personalized medicine. Despite these promising outcomes, skepticism concerning the power of these studies and their impact on patient care exists. This uncertainty stems from the many inherent limitations of this relatively young technique. This chapter explores the underlying concepts of GWASs, their contributions to research, clinical and commercial development, and their limitations with the hopes of providing a better understanding of the impact of these SNP-based association studies can have on public health.
| Original language | English |
|---|---|
| Title of host publication | Single Nucleotide Polymorphisms: Human Variation and a Coming Revolution in Biology and Medicine |
| Place of Publication | usa |
| Publisher | Springer International Publishing |
| Pages | 51-76 |
| Number of pages | 26 |
| ISBN (Electronic) | 9783031056161 |
| ISBN (Print) | 9783031056147 |
| DOIs | |
| State | Published - Aug 8 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Genotype
- GWAS
- Linkage disequilibrium
- Personalized medicine
- Phenotype
- SNP
- Understanding disease origin
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