On Combining Triads and Unrelated Subjects Data in Candidate Gene Studies An Application to Data on Testicular Cancer

• Published in: Human heredity Vol. 67; no. 2; pp. 88 - 103
• Main Authors: Hsu, Li, Starr, Jacqueline R., Zheng, Yingye, Schwartz, Stephen M.
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.01.2009
• Subjects: Computer Simulation; Environment; Genetic Predisposition to Disease - genetics; Genome-Wide Association Study - methods; Heredity; Humans; Male; Models, Genetic; Original Paper; Population genetics; Research Design; Statistical methods; Testicular cancer; Testicular Neoplasms - genetics; Genetic association studies; Family studies; Testicular cancer; Population-based case control
• ISSN: 0001-5652; 1423-0062; 1423-0062
• DOI: 10.1159/000179557

Combining data collected from different sources is a cost-effective and time-efficient approach for enhancing the statistical efficiency in estimating weak-to-modest genetic effects or gene-gene or gene-environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family-based and unrelated individual-based (e.g., population-based case-control design). In this paper, we describe a general method that permits the joint estimation of effects on disease risk of genes, environmental factors, and gene-gene/gene-environment interactions under a hybrid design that includes cases, parents of cases, and unrelated individuals. We provide both asymptotic theory and statistical inference. Extensive simulation experiments demonstrate that the proposed estimation and inferential methods perform well in realistic settings. We illustrate the method by an application to a study of testicular cancer.