【Abstract】The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the  MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.

【个人简介】Nianjun Liu教授，曾获北京大学数学本科、硕士学位，耶鲁大学生物统计硕士、博士学位，是多个期刊例如《Frontiers in Statistical Genetics and Methodology》， 《Frontiers in Nutrition Methodology》，《Biometrics & Biostatistics International Journal》，《Austin Statistics》, 《Enliven: Biostatistics and Metrics》等的编委；曾获2015年度统计遗传学研究最佳论文奖（by The Science Unbound Foundation）。