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PRSice-2: Polygenic Risk Score software for biobank-scale data.

Shing Wan Choi1,2, Paul F O'Reilly1,2

  • 1MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, UK, SE5 8AF.

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|July 16, 2019
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Summary
This summary is machine-generated.

PRSice-2 is a new software tool that automates polygenic risk score (PRS) analyses for large genetic datasets. It is faster and more memory-efficient than previous tools, enabling advanced genetic discovery.

Keywords:
GWASimputationpolygenic risk score

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Polygenic risk score (PRS) analyses are crucial in biomedical research for understanding trait etiology, controlling for genomic profiles, and strengthening causal inference.
  • Large-scale biobank projects are generating vast genetic and phenotypic data, necessitating efficient and scalable computational methods.
  • Existing software for PRS analysis requires optimization to handle the increasing size and complexity of biobank data.

Purpose of the Study:

  • Introduce PRSice-2, a software program designed for automated and simplified PRS analyses.
  • Address the need for efficient and scalable tools to process large-scale genetic and phenotypic data from biobanks.
  • Provide a robust solution for researchers conducting PRS analyses on massive datasets.

Main Methods:

  • Developed PRSice-2, a software tool written in C++ with an R script for plotting.
  • Implemented features for handling both genotyped and imputed data.
  • Incorporated support for different inheritance models and simultaneous evaluation of multiple traits.

Main Results:

  • PRSice-2 demonstrates significant improvements in speed and memory efficiency compared to PRSice-1, LDpred, and lassosum.
  • The software provides empirical association P-values that are free from overfitting inflation.
  • PRSice-2 achieves comparable predictive power to existing methods while offering superior performance.

Conclusions:

  • PRSice-2 offers a highly efficient and scalable solution for PRS analyses on large-scale data.
  • Its performance enhancements are critical for future sophisticated PRS applications, including high-dimensional and gene set-based analyses.
  • The software is freely available, promoting wider adoption and advancement in genetic research.