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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Genetical genomic analysis of complex phenotypes using the PhenoGen website.

Beth Bennett¹, Laura M Saba, Cheryl K Hornbaker

¹Department of Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO 80045-0511, USA. beth.bennett@ucdenver.edu

Behavior Genetics

|December 25, 2010

Summary

PhenoGen offers an interactive resource for mouse and rat gene expression data across multiple strains. This tool aids in identifying candidate genes by mapping expression quantitative trait loci (eQTLs) and analyzing genetic variance.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Genomics
Bioinformatics
Neuroscience

Background:

Mouse and rat models are crucial for understanding complex biological systems.
Gene expression data provides insights into cellular function and disease mechanisms.
Quantitative genetics approaches are essential for dissecting genetic contributions to traits.

Purpose of the Study:

To introduce PhenoGen, an interactive online resource for gene expression data.
To provide a platform for analyzing gene expression across diverse mouse and rat strains.
To facilitate candidate gene identification using expression quantitative trait loci (eQTLs).

Main Methods:

Archiving DNA microarray data from 20 inbred mouse strains and three recombinant inbred (RI) panels.
Enabling user data uploads and application of analytical tools.
Estimating genetic variance (heritability) of transcript levels using single-animal arrays and multiple strain samples.
Performing genetic mapping of expression quantitative trait loci (eQTLs).

Main Results:

PhenoGen provides a comprehensive archive of gene expression data.
The resource allows for the estimation of heritability for individual transcript levels.
Overlapping eQTLs with phenotypic QTLs enables powerful candidate gene identification.

Conclusions:

PhenoGen serves as a valuable resource for scientific discovery in quantitative genetics.
The platform can be utilized for teaching quantitative genetics principles.
The integration of expression and phenotypic data aids in understanding gene function and trait heritability.