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

## TL;DR

GAMMA-RAY is a fast and user-friendly tool for analyzing multiple genetic traits together, making it easier to study how genes work together in complex biological systems.

## Contribution

GAMMA-RAY introduces a high-performance C++ implementation that streamlines multi-phenotype genetic analysis with reduced computational burden and a user-friendly interface.

## Key findings

- GAMMA-RAY significantly reduces runtime and memory usage compared to previous methods.
- The tool successfully identified putative trans-eQTLs in a yeast dataset with overlaps to previously reported eQTLs.
- Functional enrichment analysis showed biological relevance of the identified trans-eGenes.

## Abstract

Many biological traits are influenced by multiple genetic factors acting together, but traditional genetic studies often analyze one trait at a time. This approach can overlook important genetic effects that influence several traits simultaneously. To address this limitation, researchers have developed methods that analyze many traits together. However, existing approaches are often slow, require substantial computational resources, and are difficult to use in practice. In this study, we introduce GAMMA-RAY, a new software tool designed to make multi-trait genetic analysis faster, more efficient, and easier to use. GAMMA-RAY improves upon previous methods by reducing computation time and simplifying the analysis process while maintaining accurate results. Using genetic data from yeast, we demonstrate that GAMMA-RAY can effectively analyze complex patterns of gene activity. By providing both an easy-to-use web interface and a version that can be run locally, GAMMA-RAY makes advanced genetic analysis more accessible and supports future research aimed at understanding how genes jointly influence complex biological traits.

GWASs have successfully identified numerous genetic variants linked to complex traits, but traditional univariate approaches often fail to capture shared genetic architecture across multiple phenotypes. As the scale of genomic data continues to increase, the demand for more efficient multi-phenotype analysis methods has become particularly critical. In addition, the issue of population structure must also be properly addressed to ensure robust and unbiased results. Multivariate methods for multi-phenotype analysis, such as GAMMA, address this by combining linear mixed models with multivariate distance matrix regression to account for population structure; however, since these methods utilize computationally intensive models, developing efficient implementations is essential for practical analysis. Although GAMMA is a well-designed and effective tool, its original implementation relies on multiple programming environments and requires frequent data exchanges between components. These factors increase computational burden and complicate installation and execution for users unfamiliar with programming, making practical applications, particularly for high-dimensional datasets, challenging. Here, we present GAMMA-RAY, a high-performance C++ implementation that streamlines the computational pipeline, leverages parallel processing, and employs efficient matrix operations to achieve substantial reductions in runtime and memory usage. GAMMA-RAY provides both a user-friendly web-based interface for non-programmers and a standalone version for secure local execution. We further applied GAMMA-RAY to a yeast dataset and identified putative trans-eQTLs, in which several variants overlapped with previously reported cis- and trans-eQTLs. In addition, functional enrichment analysis revealed that the associated trans-eGenes are enriched, a conclusion consistently supported by biological annotation resources and underscoring the biological significance of these results.