3 citations · 7 across the 8 of their papers we have counts for
8 papers
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…
Testing normality using the summary statistics with application to meta-analysis
Dehui Luo, Xiang Wan, Jiming Liu +1
As the most important tool to provide high-level evidence-based medicine, researchers can statistically summarize and combine data from multiple studies by conducting meta-analysis…
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…
Optimally estimating the sample mean from the sample size, median, mid-range and/or mid-quartile range
Dehui Luo, Xiang Wan, Jiming Liu +1
The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well desi…
NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data
Kai Dong, Hongyu Zhao, Xiang Wan +1
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statisti…
Exploring the genetic patterns of complex diseases via the integrative genome-wide approach
Ben Teng, Can Yang, Jiming Liu +2
Motivation: Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to id…