# Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing Data

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2021 · $413,026

## Abstract

Project Summary
With advancements in next-generation sequencing technologies, sequencing studies has become increasingly
used in substance dependence (SD) research. These studies generate a massive amount of sequencing data
and allow researchers to comprehensively investigate the role of a deep catalog of genetic variants in SD.
Although the ongoing sequencing studies hold great promise for unraveling novel variants that contribute to
SD, the high-dimensional data, low frequent variants, complex SD etiology, and heterogeneous SD
phenotypes create tremendous analytic and computational challenges. Developing robust and powerful
methods and computationally efficient software will address the challenges in SD sequencing data analysis
and enhance our ability to identify new SD-related variants. The goals of this application are to develop new
methods and software for designing and analyzing population-based and family-based sequencing data with
single or multiple phenotypes, and to use them in collaborative research to investigate genetic variants and
gene-gene/gene-environment (G-G/G-E) interactions associated with SD. Based on the preliminary simulation
results, our central hypothesis is that the proposed methods are more computationally efficient than existing
methods, and attain a more robust and powerful performance for various types of phenotypes. The planned
specific aims are to: 1) develop a new non-parametric method for the design and analysis of sequencing data
with one or multiple SD phenotypes; 2) develop a Joint-U method for high-dimensional G-G/G-E interaction
analysis with SD sequencing data; 3) develop a family-similarity-U method for family-based SD sequencing
data analysis, accounting for population stratification and rare variants enriched in families; and 4) facilitate the
use of the new methods through software development and collaboration. The proposed research will be
initiated by an early-stage new investigator (NIDA K01 awardee), who has assembled a team of scientists with
expertise in statistical genetics, bioinformatics/software development, SD epidemiology, behavioral genetics,
and clinical psychiatry. The successful completion of this project will address several important statistical and
computational gaps in ongoing sequencing studies, and advance the methodology and software development
for SD sequencing data analysis. The application of the new methods and software to large-scale SD
sequencing datasets also holds promise for the discovery of new SD-associated variants and G-G/G-E
interactions, which will ultimately lead to a better understanding of SD etiology, with resulting potential benefits
for SD prevention and treatment.

## Key facts

- **NIH application ID:** 10166816
- **Project number:** 5R01DA043501-05
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Qing Lu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $413,026
- **Award type:** 5
- **Project period:** 2019-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10166816

## Citation

> US National Institutes of Health, RePORTER application 10166816, Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing Data (5R01DA043501-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10166816. Licensed CC0.

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