2 citations · 4 across the 4 of their papers we have counts for
9 papers
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…
REMI: Regression with marginal information and its application in genome-wide association studies
Jian Huang, Yuling Jiao, Jin Liu +1
In this study, we consider the problem of variable selection and estimation in high-dimensional linear regression models when the complete data are not accessible, but only certain…
Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy
Mingwei Dai, Xiang Wan, Hao Peng +5
A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of va…