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Statistical models and computational tools for predicting complex traits and diseases.

Wonil Chung1,2

  • 1Department of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.

Genomics & Informatics
|January 11, 2022
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Summary
This summary is machine-generated.

Predicting complex traits and diseases using genetic variants is advancing personalized medicine. This review covers statistical methods and tools for accurate polygenic risk score (PRS) computation from large biobanks.

Keywords:
PRS modelscomputational toolspolygenic risk score

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Predicting individual traits and diseases from genetic variants is crucial for personalized medicine.
  • Genome-wide association studies (GWAS) and large biobanks enable accurate polygenic predictions.
  • Existing polygenic risk score (PRS) tools are widely used but often without a deep understanding of their models.

Purpose of the Study:

  • To review recent statistical methodologies for polygenic risk score (PRS) computation.
  • To survey computational tools used for PRS estimation.
  • To elucidate the underlying statistical models and parameter specifications for optimal PRS performance.

Main Methods:

  • Review of statistical methodologies for polygenic risk score (PRS) calculation.
  • Analysis of computational tools for PRS estimation.
  • Examination of parameter specification in PRS models.

Main Results:

  • Numerous statistical methods and computational tools for PRS computation have been developed.
  • Large-sample biobanks facilitate highly accurate polygenic predictions.
  • Understanding PRS models and parameters is essential for researchers.

Conclusions:

  • Accurate polygenic risk score (PRS) computation relies on understanding diverse statistical models and tools.
  • The advancement of PRS methodologies is key to realizing personalized medicine.
  • Further research into PRS parameter optimization is beneficial.