Innovative Statistical Analysis for Genome-Wide Data with General Interval-Censored Outcomes of Oral Health in Childhood Cancer Survivors

对全基因组数据的创新统计分析以及儿童癌症幸存者口腔健康的一般区间审查结果

• 批准号: 10532639
• 负责人: Yimei Li
• 金额: $ 16.23万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10532639
• 关键词: Innovative; Statistical; Analysis; Genome; Wide

Project Summary/Abstract This application will study the oral sequelae in childhood cancer survivors from the St. Jude Life cohort and Childhood Cancer Survivor Study cohort. Both disease onset and onset time were collected, but current analyses fail to analyze the disease onset time due to high rate of missing data. DNA samples were collected and sequenced but not analyzed either. We propose innovative ways to analyze the disease onset time in the presence of missing data by considering some onset time as interval-censored, and propose new methods for analyzing interval-censored outcomes with ultrahigh-dimensional genetic covariates. We will perform both single variant-based and rare variant aggregation-based analysis for the whole genome sequencing data. We aim to estimate oral disease dynamics and associated risk factors including environmental factors, genetic factors, and their interaction. Specifically, the aims are: 1). Develop nonparametric and semiparametric screening methods for ultrahigh-dimensional data with interval-censored outcomes; 2). Develop a penalized regression method for data with reduced dimensionality from Aim 1; 3). Apply the methods developed in Aim 1 and Aim 2 to the SJLIFE and CCSS data. We will develop and share multiple user-friendly R codes associated with the new methods. The main objective of the proposed research is to employ the existing methods and develop new statistical procedures to perform appropriate analysis on the whole-genome and oral health data for a deeper understanding of the genetic architecture of tooth development and disease.

项目总结/文摘

项目成果

Maximum likelihood estimation for the proportional odds model with mixed interval-censored failure time data.

• DOI: 10.1080/02664763.2020.1789077
• 发表时间: 2020
• 期刊: Journal of applied statistics
• 影响因子: 1.5
• 作者: Zhu L;Tong X;Cai D;Li Y;Sun R;Srivastava DK;Hudson MM
• 通讯作者: Hudson MM