题目：A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics

主讲人：哈佛大学公共卫生学院生物统计系 张豪宇博士

主持人：西南财经大学统计学院 刘耀午老师

时间：2020年1月13日（星期一）15:00-16:00

地点：西南财经大学光华校区光华楼1007会议室

报告摘要：

Cancers are routinely classified into subtypes according to various features, including histo-pathological characteristics and molecular markers. Previous genome-wide association studies (GWAS) 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 all possible subtypes defined by the cross-classification of the tumor characteristics of interest. 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. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. We also identified 32 novel breast cancer susceptibility loci using both standard methods and MTOP from a GWAS analysis including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry.

主讲人简介：

张豪宇博士现为哈佛大学公共卫生学院生物统计系博士后，导师是林希虹院士。他在浙江大学数学系完成本科学习后，在约翰霍普金斯大学生物统计系取得博士学位，导师是Nilanjan Chatterjee教授。他的主要研究兴趣为统计遗传学。