A Mixed-Model Approach for Powerful Testing of Genetic Associations with Cancer Risk Incorporating Tumor Characteristics

• Activity Number: 40 - Statistical Methods for Microbiome and Tumor Data
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Haoyu Zhang* and Ni Zhao and Thomas U. Ahearn and William Wheeler and Montserrat Garcia-Closas and Nilanjan Chatterjee
• Companies: Johns Hopkins University and Johns Hopkins University and National Cancer Institute and Information Management Services, Inc. and National Cancer Institute and Johns Hopkins University
• Keywords: Two-stage polytomous model; Susceptibility variants; Cancer subtypes; EM algorithm; Score tests; Etiologic heterogeneity

Abstract

Cancers are routinely classified into subtypes according to various features, including histo-pathological characteristics and molecular markers. Genomic investigations have reported heterogeneous association between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose score tests for genetic associations using a mixed-effect two-stage polytomous model (MTOP). In the first stage, a standard polytomous model is used to specify for all subtypes defined by the cross-classification of different markers. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization (EM) algorithm to account for missing data on tumor markers. We derived the two independent score statistics