eQTL mapping

Several groups will collect tissue from specific brain regions. The Sequencing Core (Core C) will perform RNA-Seq on these brain regions. We will use RNA-Seq data to quantify gene expression in each brain region and use these data to map eQTLs. We will also analyze allele-specific expression (ASE) as another indicator of regulatory variation.
We are creating the RatGTEx portal to provide gene expression and eQTL data for different rat tissues. The pilot version of this portal is available at ratgtex.org. In addition to data downloads, it has interactive visualizations that can display results for queried genes, variants, and tissues. It will specify a standard processing pipeline and data formats to facilitate comparisons across datasets.

The abundance of a gene transcript is a quantitative trait and can be mapped to genetic loci. We anticipate many of our behavioral QTL will overlap with multiple genes, while we assume only one of them is the causal gene for the trait. In these cases, the presence of an eQTL for one particular gene in the QTL region (i.e., a cis-eQTL) suggests that genetic variants regulate both the trait and the expression of that gene. It is very likely that this genetic variation regulates the behavioral phenotype by changing the expression of this particular gene (as opposed to other genes within the QTL).
We use naïve animals to generate these eQTL data because we assume this difference in gene expression causes the variation in behavioral phenotype among the HS population. By using treatment naïve brains, these data will also be useful for for other projects on genetic analysis in HS rats. Because the brain is a highly heterogeneous tissue with tightly regulated gene expression patterns, it is important to obtain brain samples with high anatomical precision, while maintaining RNA quality. We developed a dissection method using 50 µm cryosections, which produces RNA with high quality.
P50 Project 2 We already collected data (n=88 rats, 440 RNA-seq samples) from the nucleus accumbens core, lateral habenula, infralimbic cortex, prelimbic cortex, and orbitofrontal cortex; all of which play key roles in many of the addiction-related behaviors. In the renewal we choose to work on three new brain regions: posterior ventral tegmental area, nucleus accumbens core, and prelimbic cortex. The main cell types in these regions are : glutamatergic in prelimbic cortex, median spiny neurons in nucleus accumbens core and dopaminergic in posterior ventral tegmental area. Measuring more brain regions will allow us to detect more eQTL because gene expression is highly heterogeneous in the brain and many genes are only expressed in a few limited locations.

Full RatGTex pipeline and protocol can be found here 

Munro et al., The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats. Nucleic Acids Res. 2022

Samples collected from HS rats that are available for eQTL mapping

ProjectInvestigatorTissueNPrior experienceStatus
Pilot:
Creating the dataset for TWAS in HS rats
Francesca Telese, UCSDBrain: whole hemisphere340NaiveComplete,
the data is in RatGTEx
U01DA044468:
Genomic analysis of avoidance learning in addiction
Tom Jhou,
Medical University of South Carolina
Brain: rostromedial tegmental nucleus80Extreme high and low phenotypes
in cocaine-avoidance and
punishment resistance
In progress
P50 DA037844 Y5-10, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: prelimbic cortex200NaiveIn progress
U01DA046077:
Identification of Genetic Features of Delay Discounting
Using a Heterogeneous Stock Rat Model
Suzanne Mitchell,
Robert Hitzemann,
OSHU
Brain: prelimbic cortex194Behavior testing: Delay discountingComplete,
the data is in RatGTEx
P50 DA037844 Y1-5, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: prelimbic cortex82NaiveComplete,
the data is in RatGTEx
U01DA044468;
Genomic analysis of avoidance learning in addiction
Tom Jhou,
Medical University of South Carolina
Brain: prelimbic cortex80Extreme high and low phenotypes
in cocaine-avoidance and
punishment resistance
In progress
P50 DA037844 Y5-10, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: posterior ventral tegmental area200NaiveIn progress
P50 DA037844 Y5-10, Project 1:
Neurogenetic Substrates of Cocaine Addiction
Paul Meyer,
U at Buffalo
Brain: posterior ventral tegmental area180Exposure to cocaineIn progress
P50 DA037844 Y1-5, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: orbitofrontal cortex82NaiveComplete,
the data is in RatGTEx
P50 DA037844 Y5-10, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: nucleus accumbens core200NaiveIn progress
U01DA046077:
Identification of Genetic Features of Delay Discounting
Using a Heterogeneous Stock Rat Model
Suzanne Mitchell,
Robert Hitzemann,
OSHU
Brain: nucleus accumbens core193Behavior testing: Delay discountingComplete,
the data is in RatGTEx
P50 DA037844 Y5-10, Project 1:
Neurogenetic Substrates of Cocaine Addiction
Paul Meyer,
U at Buffalo
Brain: nucleus accumbens core180Exposure to cocaineIn progress
P50 DA037844 Y1-5, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: nucleus accumbens core77NaiveComplete,
the data is in RatGTEx
U01 DA050239:
Single-cell Resolution analysis of chromatin
accessibility and gene expression changes
in a model of drug addiction
Francesca Telese, UCSDBrain: nucleus accumbens100Exposure to oxycodoneCollected
P50 DA037844 Y1-5, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: lateral habenula82NaiveComplete,
the data is in RatGTEx
U01DA044468;
Genomic analysis of avoidance learning in addiction
Tom Jhou,
Medical University of South Carolina
Brain: lateral habenula80Extreme high and low phenotypes
in cocaine-avoidance and
punishment resistance
In progress
P50 DA037844 Y5-10, Project 1:
Neurogenetic Substrates of Cocaine Addiction
Paul Meyer,
U at Buffalo
Brain: insular cortex180Exposure to cocaineIn progress
P50 DA037844 Y1-5, Project 2:
Socially-acquired nicotine self-administration
Hao Chen, UTHSCBrain: infralimbic cortex83NaiveComplete,
the data is in RatGTEx
R01DK120667:
Systems genetics to identify neuronal genes for diet-induced obesity
Leah Solberg Woods,
WFU
Brain: hypothalamus500High fat and Low fat diets, males and femalesIn progress
R01DK120667:
Systems genetics to identify neuronal genes for diet-induced obesity
Leah Solberg Woods,
WFU
Brain: hippocampus500High fat and Low fat diets, males and femalesIn progress
U01DA046077:
Identification of Genetic Features of Delay Discounting
Using a Heterogeneous Stock Rat Model
Suzanne Mitchell,
Robert Hitzemann,
OSHU
Brain: basolateral amygdala191Behavior testing: Delay discountingComplete,
the data is in RatGTEx
R01 EY021200:
Genetic Modulators of Glaucoma
Monica Jablonski, UTHSCEye53Healthy HS ratsComplete,
the data is in RatGTEx
R01DK106386: ystems genetics of adiposity traits in outbred ratsLeah Solberg Woods,
WFU
Liver 401All males, normal chowComplete,
the data is in RatGTEx
R01DK106386: ystems genetics of adiposity traits in outbred ratsLeah Solberg Woods,
WFU
Adipose tissue411All males, normal chowComplete,
the data is in RatGTEx