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Related Experiment Video

Updated: Jan 7, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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ApplyPolygenicScore: An R package for applying polygenic risk score models.

Nicole Zeltser1,2, Rachel M A Dang1,2,3, Rupert Hugh-White1,2,3

  • 1Department of Human Genetics, University of California, Los Angeles, CA.

Genetics in Medicine Open
|January 5, 2026
PubMed
Summary
This summary is machine-generated.

We developed ApplyPolygenicScore, an R package to simplify polygenic score calculations. This tool aids in understanding genetic predisposition to complex traits like BMI, though non-genetic factors also play a significant role in cancer outcomes.

Keywords:
Body mass indexCancerPolygenic risk modelPolygenic risk score

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Polygenic scores (PGS) estimate genetic predisposition to complex traits by modeling contributions from multiple common variants.
  • Genome-wide association studies (GWAS) have generated numerous polygenic risk models (PGMs) for various health outcomes.
  • Applying PGMs to new genetic data is often complex and requires specialized tools.

Purpose of the Study:

  • To develop an open-source R package, ApplyPolygenicScore, for simplifying, improving, and automating the application of standardized PGMs.
  • To demonstrate the utility of ApplyPolygenicScore through a case study applying a body mass index (BMI) PGM to cancer patient data.

Main Methods:

  • The ApplyPolygenicScore R package was developed with functions for input validation, allele matching, PGS computation, and visualization.
  • A PGM for BMI was applied to genetic data from 1071 patients with bladder, liver, and endometrial cancer.
  • The package is extensively documented to ensure ease of use for the research community.

Main Results:

  • The ApplyPolygenicScore package successfully computed PGS for BMI in the cancer patient cohort.
  • The computed PGS for BMI showed a predictive relationship with BMI in cancer patients.
  • The accuracy of the BMI PGS was low, suggesting a substantial influence of non-genetic factors on BMI-related cancer outcomes.

Conclusions:

  • ApplyPolygenicScore facilitates the broader application of PGS in research beyond statistical genetics.
  • The package aims to promote wider use of PGS, enabling novel discoveries in genetic predisposition.
  • The case study highlights the importance of considering both genetic and non-genetic factors in complex diseases like cancer.