2 citations · 4 across the 4 of their papers we have counts for
7 papers · 1 filter
Accounting for correlated horizontal pleiotropy in two-sample Mendelian randomization using correlated instrumental variants
Qing Cheng, Baoluo Sun, Yingcun Xia +1
Mendelian randomization (MR) is a powerful approach to examine the causal relationships between health risk factors and outcomes from observational studies. Due to the proliferatio…
BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data
Min Zhou, Mingwei Dai, Yuan Yao +3
Detecting interaction effects among predictors on the response variable is a crucial step in various applications. In this paper, we first propose a simple method for sure screenin…
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…
VIMCO: Variational Inference for Multiple Correlated Outcomes in Genome-wide Association Studies
Xingjie Shi, Yuling Jiao, Yi Yang +4
In Genome-Wide Association Studies (GWAS) where multiple correlated traits have been measured on participants, a joint analysis strategy, whereby the traits are analyzed jointly, c…
LPG: a four-groups probabilistic approach to leveraging pleiotropy in genome-wide association studies
Yi Yang, Mingwei Dai, Jian Huang +4
To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding a light on the genet…
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…