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Updated: Mar 29, 2026

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GAMMA-RAY: A Fully Automated and Rapid System for High-Dimensional Multi-Phenotype Analysis Considering Population

Taegun Kim1, Jaeseung Song2, Jong Wha Joanne Joo1,3

  • 1Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea.

Biology
|March 27, 2026
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Summary
This summary is machine-generated.

GAMMA-RAY offers a faster, user-friendly C++ implementation for multi-phenotype analysis, improving the identification of genetic variants linked to complex traits and addressing population structure in genomic data.

Keywords:
GWASmulti-phenotype analysisparallel computingpopulation structure correctionweb-based interface

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-Wide Association Studies (GWASs) identify genetic variants for complex traits.
  • Univariate methods struggle with shared genetic architecture across phenotypes.
  • Increasing genomic data necessitates efficient multi-phenotype analysis and population structure control.

Purpose of the Study:

  • To develop an efficient, high-performance implementation for multivariate multi-phenotype analysis.
  • To streamline computational pipelines and reduce runtime and memory usage.
  • To provide accessible tools for both programmers and non-programmers.

Main Methods:

  • Developed GAMMA-RAY, a C++ implementation of the GAMMA method.
  • Utilized parallel processing and efficient matrix operations.
  • Integrated a web-based interface and a standalone version for local execution.

Main Results:

  • GAMMA-RAY significantly reduced runtime and memory usage compared to previous implementations.
  • Applied to a yeast dataset, it identified putative trans-eQTLs, with overlapping variants and enriched trans-eGenes.
  • Results were supported by biological annotation resources, highlighting biological significance.

Conclusions:

  • GAMMA-RAY provides an efficient and user-friendly solution for high-dimensional multi-phenotype analysis.
  • The tool facilitates the discovery of genetic architecture underlying complex traits.
  • The identified trans-eQTLs and trans-eGenes in yeast demonstrate the biological relevance of the method.