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Functional characterization of colorectal cancer GWAS loci
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Report from Chong In-gwa, Paek Il-gyu, Yi Tae-wi to An Chang-ho
Improving the power of GWAS Z-score imputation by leveraging functional data
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Post-GWAS methods in large scale studies of breast cancer in African Americans
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Comparisons of four commonly used methods in GWAS to detect gene-environment interactions
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Polygenic analyses of complex traits in complex populations
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Polygenes and estimated heritability of prostate cancer in an African American sample using genome-wide association study data
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Statistical methods and analyses in the Multiethnic Cohort (MEC) human gut microbiome data
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Functional characterization of colon cancer risk-associated enhancers: connecting risk loci to risk genes
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Understanding prostate cancer genetic susceptibility and chromatin regulation
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Breast epithelial cell type specific enhancers and functional annotation of breast cancer risk loci
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Identification and characterization of cancer-associated enhancers
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Two-step testing approaches for detecting quantitative trait gene-environment interactions in a genome-wide association study
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Functional characterization of a prostate cancer risk region
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Plant genome wide association studies and improvement of the linear mixed model by applying the weighted relationship matrix
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Genetic studies of cancer in populations of African ancestry and Latinos
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Hierarchical approaches for joint analysis of marginal summary statistics
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Application of genetic association methods in mice to understand phenotypes with a complex etiology
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Characterization and discovery of genetic associations: multiethnic fine-mapping and incorporation of functional information
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Bayesian hierarchical models in genetic association studies
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Population substructure and its impact on genome-wide association studies with admixed populations
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Analysis of genomic polymorphism in Arabidopsis thaliana
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Characterizing the genetic and environmental contributions to ocular and central nervous system health
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etd-KeWangjing-4686
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Efficient two-step testing approaches for detecting gene-environment interactions in genome-wide association studies, with an application to the Children’s Health Study
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