报告摘要：

Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently Border et al. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM) leading to many false positive findings. Here we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in GWAS applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short range LD but also long-range LD induced by xAM.

 

个人简介：

马诗洋，上海交通大学医学院/数学科学学院副研究员，博士生导师，2023年入选国家海外青年高层次人才计划，上海市海外高层次人才计划，参与科技部重点研发项目。2019年获美国罗切斯特大学统计学博士学位，之后在哥伦比亚大学生物统计系著名统计遗传学家Iuliana Ionita-Laza教授的指导下从事博士后研究工作，2022年底全职回国加入上海交通大学。主要研究方向为统计遗传学和生物医学统计。近年来在国际知名期刊发表论文15篇，发表的杂志包括PNAS， Genome Biology， American Journal of Human Genetics，Nature Communications， Statistics in Medicine等，单篇文章引用量上百次。主持上海市启明星项目，上海市卫健委卫生行业临床研究专项和上海交通大学“交大之星”计划医工交叉研究基金。