List of lab projects
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- Alzheimer’s disease (AD) gene mapping in multi-ethnic families. The project aims to identify novel AD targets using WGS and WES data from >1000 AD families.
- Duzhi Zhao.
- Alzhemier’s disease risk prediction and feature discovery. This project aims to profile Alzhemier’s disease risk factors through integrating genomic sequence features and their health records.
- Association studies of pleiotropic variants: candidate region analysis. For given regions of interest across multiple phenotypes, a number of statistical workflows will be developed for fine-mapping, pleiotropy, and mediation analysis.
- Olivia Wagner.
- Association studies of pleiotropic variants: candidate region discovery. This project aims to elucidate variants which play a role in pleiotropy for complex diseases and traits with high public health significance.
- Diana Cornejo, Haoyue Shuai and Ran Wang.
- Family-based association study design. An empirical study of several flavors of family-based vs population-based design to examine each of their advantages.
- Diana Cornejo.
- Fine-mapping methods. This project looks into fine-mapping in families, cross populations, with functional annotations and with multiple phenotypes.
- Gao Wang.
- Fine-mapping with genomic annotations. A preliminary numerical study on how genomic annotations help with fine-mapping performance.
- Gao Wang.
- Gene-gene and gene-environment interactions in age-related hearing impairment and tinnitus. Development of statistical workflows for gene by gene, gene by environment, gene by age and gene by sex interactions, with applications to complex traits association analysis.
- Molecular phenotype association mapping and GWAS integration in Alzheimer’s disease (AD). This project aims to identify genomic targets for Alzheimer’s disease through integration of a range of molecular phenotypes and AD GWAS data.
- Yuqi Miao.
- Molecular phenotype prediction and association mapping for Alzheimer’s disease (AD). This project leverages available molecular phenotype data in brains to perform TWAS for AD, in a variety of AD cohorts.
- Hao Sun.
- Multiple imputation (MI) for association analysis of imputed genotypes. Instead of analyzing genotype dosages, this project applies MI to perform association testing with imputed genotypes, providing statistical evidence of association while quantifying the reliability of the genetic data.
- Proteomics and functional genomics prediction in brains. Integration of proteomic data for brain disease associations, including building prediction models using deep learning.
- SEQSpark. Association analysis software using Apache Spark, customized for analysis tasks performed in our group.