Abstract
Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
Keywords
Genetic architecturePersonalized medicineDiseaseBiologyProfiling (computer programming)Polygenic risk scoreGenome-wide association studyMultifactorial InheritancePersonal genomicsProbabilistic logicPrecision medicineGenetic associationComputational biologyBioinformaticsGeneticsGenomicsGenomeQuantitative trait locusComputer scienceArtificial intelligenceMedicineSingle-nucleotide polymorphismGeneGenotypePathology
MeSH Terms
Genetic Predisposition to DiseaseGenetic TestingGenome-Wide Association StudyHumansMultifactorial InheritancePrecision MedicineRisk Factors
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Publication Info
- Year
- 2018
- Type
- review
- Volume
- 19
- Issue
- 9
- Pages
- 581-590
- Citations
- 1580
- Access
- Closed
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Cite This
Ali Torkamani,
Nathan E. Wineinger,
Eric J. Topol
(2018).
The personal and clinical utility of polygenic risk scores.
Nature Reviews Genetics
, 19
(9)
, 581-590.
https://doi.org/10.1038/s41576-018-0018-x
Identifiers
- DOI
- 10.1038/s41576-018-0018-x
- PMID
- 29789686