# Translational Technologies Core

> **NIH NIH P50** · YALE UNIVERSITY · 2020 · $241,350

## Abstract

Translational Technologies Core
Joel Gelernter, M.D. & Alan Anticevic, Ph.D.
Abstract
The Translational Technologies Core will support and provide expertise related to genetics and
neuroimaging through the Center. It is well known that genetic factors are important in modulating risk of
alcohol dependence (AD) and related traits. Several alcohol dependence risk loci are now known, and
specific candidate genes have been identified as potentially important for the component projects in the
center. Major function of the Genetics component will be to support genotyping for each of the projects
(including pilot projects) involving human subjects, for the purpose of identifying genotype/phenotype
correlations. A microarray containing ~250k genomewide tagging SNPs, ~250k exomic putatively functional
SNPs, and 50k of added SNP content relevant to psychiatric traits (the Illumina PGC array) will be
genotyped on all CTNA subjects. The goals of the clinical components of the Center require study of
ethnically heterogeneous populations, but study of stratified samples that differ in allele frequency and
phenotype for candidate loci of interest can create artifactual association. We will therefore apply methods
to measure and statistically correct for the effects of population stratification. Genetics component
investigators will advise Center investigators on issues related to genetics studies. We will also bring to
bear a rich dataset of GWASed subjects with alcohol and other substance dependencies that can be used
to test hypothesis related to Center findings and goals; and we will continue pilot work in related topics of
interest, e.g., epigenetics of alcohol dependence. In turn, emerging evidence implicates glutamateric neural
alterations affecting structure and function across reward-related cortico-striatal- thalamic-cortical pathways
(CSTC) and higher-order prefrontal cortex (PFC) control circuits in AD. However, the neural network level
deficits that may arise as a function of AD or its genetic risk is profoundly lacking, limiting our understanding
of its neurobiology and developments of better treatments. To address this gap in knowledge, the
overarching aim of the Neuroimaging component of the Core is to leverage advances in functional and
structural neuroimaging made possible by the Human Connectome Project, to permit acquisition and
analysis at dramatically enhanced spatial and temporal resolution across the entire Center. The
Neuroimaging component will utilize analytic advances in our Center to accomplish network-driven and
hypothesis-grounded examination of multi-modal structural and functional network connectivity focused on
reward-related CSTC and PFC circuits, as well as fully data-driven methods. We aim to characterize neural
network disruptions in AD in relation to: i) drinking levels, ii) familial risk; iii) treatment response; and iv)
individual differences in clinical phenotypes – concurrently capitalizing on the synergistic recruitmen...

## Key facts

- **NIH application ID:** 9940998
- **Project number:** 5P50AA012870-20
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** JOEL GELERNTER
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $241,350
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9940998

## Citation

> US National Institutes of Health, RePORTER application 9940998, Translational Technologies Core (5P50AA012870-20). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9940998. Licensed CC0.

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