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

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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.