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Updated: Jul 4, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

PanEffect: a pan-genome visualization tool for variant effects in maize.

Carson M Andorf^1,2, Olivia C Haley¹, Rita K Hayford¹

¹USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.

Bioinformatics (Oxford, England)

|February 10, 2024

Summary

This summary is machine-generated.

PanEffect is a new tool for exploring genetic variant effects across multiple maize genomes. It uses AI to predict functional consequences, aiding crop improvement research.

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Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate prediction of genetic variant effects is essential for understanding traits and functions.
Existing AI tools primarily focus on single-genome analysis, lacking pan-genome scope.

Purpose of the Study:

To introduce PanEffect, a novel tool for comprehensive pan-genome analysis of coding variant effects.
To enable exploration of variant impacts across multiple maize genomes.

Main Methods:

Implementation of PanEffect within MaizeGDB for analyzing 50 maize genomes.
Utilizing the Evolutionary Scale Modeling (ESM) protein language model to calculate variant effect scores.
Visualization of over 550 million potential amino acid substitutions and 2.3 million natural variations.

Main Results:

PanEffect provides variant effect scores, indicating potential functional consequences.
The tool visualizes secondary structures and functional domains alongside variant effects.
Enables examination of benign to potentially detrimental outcomes for maize variants.

Conclusions:

PanEffect offers a platform for researchers to explore protein variants in maize.
Facilitates the identification of genetic targets for enhancing crop traits.
Supports large-scale analysis of genetic variation in crop improvement.