30 citations · 76 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
Prediction analysis for microbiome sequencing data
Tao Wang, Can Yang, Hongyu Zhao
One primary goal of human microbiome studies is to predict host traits based on human microbiota. However, microbial community sequencing data present significant challenges to the…
GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data
Dongjun Chung, Can Yang, Cong Li +2
Genome-wide association studies (GWAS) suggests that a complex disease is typically affected by many genetic variants with small or moderate effects. Identification of these risk v…
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…