01.Association06.Genetic association studies - sporedata/researchdesigneR GitHub Wiki

1. Use cases: in which situations should I use this method?

  1. 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].

2. Input: what kind of data does the method require?

  • Dataset with genetic markers either conditions or clinical outcomes as well as other predictors.

3. Algorithm: how does the method work?

Model mechanics

Genetic Association studies make use of genetic markers as risk factors for conditions or clinical outcomes.

  • 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].

Model mechanics

Describing in words

Describing in images

Describing with code

Breaking down equations

Suggested companion methods

Learning materials

  1. Books *
  2. Articles

4. Output: how do I interpret this method's results?

Typical tables and plots and corresponding text description

  • Figure below [5]. Imgur

  • See figure 2A [6].

Metaphors

  1. From genetic loci identification, it is possible to calculate the polygenic risk prediction and its impacts on the overall burden of disease.

  2. Genetic approaches can measure how different genetic variants might impact disease.

Reporting guidelines

5. SporeData-specific

Templates

Data science functions

General description

Clinical areas of interest

Variable categories

Linkage to other datasets

Limitations

Related publications

SporeData data dictionaries

References

[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.

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