In spite of many successes, genome-wide association studies (GWAS) face two major challenges. The ?rst is its limited statistical power even with thousands of individuals in a typical GWAS, thus missing many associated genetic variants, mostly single nucleotide polymorphisms (SNPs), due to their small effect sizes. The second is that even for those few identi?ed associated SNPs, since they often do not reside in protein-coding regions, it is dif?cult to interpret their function and thus missing biological insights about the disease (or other complex traits). This is evident with the limited success in genetic studies on Alzheimer's disease (AD) in spite of multiple genetic loci that have been identi?ed in the last few years. A new gene-based association test called PrediXcan was recently proposed to integrate GWAS with a reference eQTL dataset, alleviating the above two problems in boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of PrediXcan, we propose more powerful gene-based association tests, integrating GWAS with one or more sources of genomic and imaging endophenotypes. We also extend the proposed methods to the case with only GWAS summary statistics. The proposed methods are to be applied to several AD-related GWAS, eQTL and neuroimaging datasets for new discovery of AD-associated genetic variants. We will develop and distribute software implementing the proposed methods.

Public Health Relevance

This proposed research is expected to not only advance integrative GWAS data analysis and interpretation for more powerful identi?cation of novel genetic variants associated with Alzheimer's disease, but also contribute valuable computational tools to the elucidation of genetic components for other common diseases and complex traits, including psychiatric and mental disorders.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Behavioral Genetics and Epidemiology Study Section (BGES)
Program Officer
Miller, Marilyn
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Minnesota Twin Cities
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
Deng, Yangqing; Pan, Wei (2018) Significance Testing for Allelic Heterogeneity. Genetics 210:25-32
Wu, Chong; Pan, Wei (2018) Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia. Genet Epidemiol 42:303-316
Park, Jun Young; Wu, Chong; Pan, Wei (2018) An adaptive gene-level association test for pedigree data. BMC Genet 19:68
Wu, Chong; Pan, Wei (2018) Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways. Genetics 209:699-709
Deng, Yangqing; Pan, Wei (2018) Improved Use of Small Reference Panels for Conditional and Joint Analysis with GWAS Summary Statistics. Genetics 209:401-408
Xu, Zhiyuan; Wu, Chong; Pan, Wei et al. (2017) Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage 159:159-169
Xu, Zhiyuan; Wu, Chong; Wei, Peng et al. (2017) A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics 207:893-902
Deng, Yangqing; Pan, Wei (2017) Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses. Genetics 207:1285-1299