# The Kinship Risk Score: An Integrative Tool to Prioritize Alcohol and Drug-Addiction Related Genes for Enhanced Risk Prediction

> **NIH NIH DP1** · EMORY UNIVERSITY · 2020 · $468,000

## Abstract

PROJECT SUMMARY/ABSTRACT
The primary goals of this project are to identify and characterize gene sets that reflect individual differences in
the propensity to develop alcohol or other forms of drug addiction and to develop a novel tool to enhance the
prediction of drug addiction risk using genome-wide data. Alcohol, tobacco, and illicit drug use and
dependence are complex biological and psychosocial problems that can be conceptualized as alternate forms
of an underlying behavioral predisposition to the development of generalized drug dependence (DD). Despite
the fact that twin and family studies suggest that genetic variation contributes to the preoccupation with alcohol
and other multiple substances of abuse, the identification of specific causal loci has been limited. In fact,
studies have confirmed that unlike monogenic disorders, alcohol and other drugs of abuse are influenced by
numerous genetic variants. While both human and animal studies of addiction have indicated that multiple
forms of drug addiction are genetically influenced, the full complement of biological mechanisms and genetic
factors are still unknown. The current application proposes a novel framework and tool that integrates
Bayesian statistics and functional genomics to enhance the identification of a set(s) of causal genetic factors
for alcohol and other drug dependence. In the first component of the framework, we will use Bayesian mixture
modeling to identify a set(s) of markers that comprise the additive genetic effect on generalized drug
dependence (DD). In the second component, we will identify cross-species-functionally-annotated gene sets
for alcohol, tobacco, and other illicit drug use/dependence, determine their relative contribution to variation in
DD, and test for enrichment of the Bayesian-derived gene set(s) in (a) gene sets ascertained using cross-
species-functional genomic studies of alcohol and other drug addiction, and (b) a range of biological gene sets
based on known molecular pathways. In the third component, we will identify combinatorial effects within and
between gene sets. In the final component, we develop prediction models, as well as test a novel kinship-
based approach to predict the risk for DD given a specified gene set. Each of these components provides
novel information that will help to formulate new research hypotheses and prediction models for addictive
behaviors and other behaviors/traits.

## Key facts

- **NIH application ID:** 9983626
- **Project number:** 5DP1DA042103-04
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Rohan Hugh Craig Palmer
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $468,000
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9983626

## Citation

> US National Institutes of Health, RePORTER application 9983626, The Kinship Risk Score: An Integrative Tool to Prioritize Alcohol and Drug-Addiction Related Genes for Enhanced Risk Prediction (5DP1DA042103-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9983626. Licensed CC0.

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