# Efficient methods for genome-wide survival analysis of early childhood caries

> **NIH NIH R03** · MICHIGAN STATE UNIVERSITY · 2022 · $159,466

## Abstract

Project Summary
Early childhood caries (ECC) is the most common chronic disease in preschool-age children in
the United States. It has been shown to have a substantial heritability, but no consensus exists
regarding ECC-associated genetic risk loci. Existing genome-wide association studies (GWAS)
of ECC are scarce and were based on single-locus association mapping using logistic regression
of binary traits (e.g., caries affection status) or count regression of quantitative traits (e.g.,
dmfs/dmft/dfs/dft). Drawbacks of those approaches include potential misspecification of the age
effect, not making full use of tooth-/tooth-surface-level caries lifetime data, potentially weak
signals from individual variants, and the burden of multiple testing correction. The first drawback
may lead to incorrect type I error rates from model-based association tests. The others hinder
statistical power. Multi-locus tests with tooth-/tooth-surface-level ages to caries or the counting
process of dmfs/dmft/dfs/dft as phenotypes can address the above drawbacks. However, caries
life course data are inevitably interval censored since continuous monitoring of caries affection or
severity is impractical in caries research. No existing multi-locus survival tests can apply to tooth-
/tooth-surface-level times to caries or the counting process of dmfs/dmft/dfs/dft subject to interval
censoring. The goal of this project is to develop two suites of methods for population-based and
family-based genetic association analyses of survival outcomes, which address the above
drawbacks and the interval censoring complexity, to dissect the genetic architecture of ECC. The
specific aims are 1) to develop two suites of set-based genetic association tests respectively for
multivariate interval-censored survival outcomes and panel count outcomes and 2) to apply the
methods to two large-scale real-world data sets on ECC, Dental Caries: Whole Genome
Association and Gene x Environment Studies and ZOE 2.0, to illustrate the utility of the methods
and discover subject matter knowledge. Additionally, the new methods will be programmed into
R packages to be disseminated through the Comprehensive R Archive Network. The successful
completion of this project will address analytic challenges that impede ECC genetic research, and
advance the statistical methodology development for population-based and family-based genetic
association analyses of survival outcomes in general. The application of the new methods to the
real data will provide new insights into the genetic etiology of ECC.

## Key facts

- **NIH application ID:** 10571345
- **Project number:** 1R03DE032357-01
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Chenxi Li
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $159,466
- **Award type:** 1
- **Project period:** 2022-09-15 → 2024-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10571345

## Citation

> US National Institutes of Health, RePORTER application 10571345, Efficient methods for genome-wide survival analysis of early childhood caries (1R03DE032357-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10571345. Licensed CC0.

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