01.Association06.Genetic association studies - sporedata/researchdesigneR GitHub Wiki
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Used when evaluating the association between genetic markers as risk factors for conditions or clinical outcomes.
- Polygenic scores are used to predict the risk of a determined condition or outcome based on genetic conditions. For example, create a decision support system [1].
- Dataset with genetic markers either conditions or clinical outcomes as well as other predictors.
Genetic Association studies make use of genetic markers as risk factors for conditions or clinical outcomes.
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Polygenic scores:
- Are important to understand the genetic basis of disease and the multiple methods to evaluate it [2].
- Algorithms for constructing polygenic risk scores (PRSs) have two steps. First, a procedure for ‘variable selection’ to determine which SNPs need to be included in the model. Second, a procedure for the estimation of weights needs to be constructed to attached to the selected variables [3].
- PRSs are calculated as a weighted sum of the risk alleles of single-nucleotide polymorphisms (SNPs). It is then validated regarding their potential their predictive ability regarding disease outcomes. PRSs can then be included as part of decision support systems [4].
- Bayesian frameworks to accomplish these goals are available [5].
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- Articles
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From genetic loci identification, it is possible to calculate the polygenic risk prediction and its impacts on the overall burden of disease.
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Genetic approaches can measure how different genetic variants might impact disease.
[1] Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics. 2018 Sep;19(9):581-90.
[2] Witte JS, Visscher PM, Wray NR. The contribution of genetic variants to disease depends on the ruler. Nature Reviews Genetics. 2014 Nov;15(11):765-76.
[3] Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics. 2016 Jul;17(7):392.
[4] Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics. 2018 Sep;19(9):581-90.
[5] Stahl EA, Wegmann D, Trynka G, Gutierrez-Achury J, Do R, Voight BF, Kraft P, Chen R, Kallberg HJ, Kurreeman FA, Kathiresan S. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nature genetics. 2012 May;44(5):483-9.