3 papers
stat.ME2018
Bayesian Weighted Mendelian Randomization for Causal Inference based on Summary Statistics
Jia Zhao, Jingsi Ming, Xianghong Hu +3
The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on an…
stat.AP2018
BIVAS: A scalable Bayesian method for bi-level variable selection with applications
Mingxuan Cai, Mingwei Dai, Jingsi Ming +3
In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to…
stat.ME2017
LSMM: A statistical approach to integrating functional annotations with genome-wide association studies
Jingsi Ming, Mingwei Dai, Mingxuan Cai +3
Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still…