Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system...

Area of Science:

• Neuroscience
• Genetics
• Systems Biology

Background:

• Gene expression in the central nervous system (CNS) exhibits significant heritable variability, contributing to individual differences in structure, function, and behavior.
• Understanding the genetic underpinnings of these variations is crucial for dissecting complex biological systems.

Purpose of the Study:

• To develop a systematic approach for dissecting genetic networks across biological scales, from molecular to behavioral levels.
• To investigate the genetic basis of gene expression patterns and their relationship with neural phenotypes in a model system.

Main Methods:

• Utilized Affymetrix oligonucleotide arrays to profile gene expression in BXD recombinant inbred strains.
• Integrated extensive SNP and haplotype data with a legacy database of 25 years of phenotypic data.
• Developed novel analytical and graph theoretical methods to analyze shared genetic modulation of trait networks.

Main Results:

• Identified significant covariance between gene expression and pharmacological/behavioral traits, often driven by common quantitative trait loci (QTLs).
• Discovered that a few major-effect QTLs jointly modulate large sets of transcripts and neural phenotypes in tissue-specific patterns.
• Demonstrated the utility of these methods using gene sets involved in neural synapse function.

Conclusions:

• A small number of QTLs can exert broad influence over complex neural traits and gene expression networks.
• The developed analytical tools and the WebQTL resource provide a framework for hypothesis testing in systems genetics.
• This approach facilitates a deeper understanding of the genetic architecture underlying CNS variation.