10 citations · 36 across the 11 of their papers we have counts for
11 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…
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…
Penalized integrative analysis under the accelerated failure time model
Qingzhao Zhang, Sanguo Zhang, Jin Liu +2
For survival data with high-dimensional covariates, results generated in the analysis of a single dataset are often unsatisfactory because of the small sample size. Integrative ana…
A Penalized Multi-trait Mixed Model for Association Mapping in Pedigree-based GWAS
Jin Liu, Can Yang, Xingjie Shi +4
In genome-wide association studies (GWAS), penalization is an important approach for identifying genetic markers associated with trait while mixed model is successful in accounting…
Integrative Analysis of Prognosis Data on Multiple Cancer Subtypes using Penalization
Jin Liu, Jian Huang, Yawei Zhang +4
In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is a heterogeneous disease. Examining similarity a…