List of lab projects

This page is automatically generated from individual Markdown files and sorted alphabetically. You can use the Search Bar on the side of the wiki to look for contents of interest. Please do not edit this page manually. Notice that access to project details are only available to members of the lab.

A

• ADSP functional genomics consortium brain xQTL project. A consortium project in collaboration with ADSP researchers across the country, to curate a comprehensive multi-omics QTL resource for brain tissues, and to integrate the resource for Alzheimer’s disease studies.
  • Hao Sun and Xuanhe Chen.
• 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 of Hispanics, European and African Americans.
  • Yiu Liu and Chong Li.
• Alzhemier’s disease analysis in Biobank studies. This project aims to profile Alzhemier’s disease risk factors through integrating genomics and other phenotype records in Biobank data, including prediction of AD phenotype and investigation of study designs (sample matching and case-only design).
  • Rui Dong.
• 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, including new and efficient colocalization method we develop for analyzing multiple phenotypes.
  • Balewgizie Tegegne.

C

• Computational protocol for molecular QTL analysis. We develop this protocol as a companion to the brain xQTL project, aiming to create a transparent and reproducible computational protocol for multi-omics QTL studies.
  • Hao Sun.

D

• Dynamic statistical comparisons. This is continued development of the DSC software for benchmarking computational experiments in statistics and computational biology (collaboration with Professor Matthew Stephens’ group and Professor Bo Peng’s group).
  • Gao Wang, Yuxin Zou, Bo Peng and Matthew Stephens.

F

• Family-based association study design. An empirical study of several flavors of family-based vs population-based design to examine each of their advantages (initialized by Professor Suzanne Leal and supervised by Gao Wang).
  • Bingsong Zhang, Tianyi Liu.
• Fine-mapping in Alzheimer’s disease families. We aim to extend the SuSiE model for fine-mapping into a generalized linear mixed model framework with applications to fine-mapping in families with Alzheimer’s disease.
  • Hyeonju Kim.
• Fine-mapping of functional data in genomic applications. This project aims to develop a new method for variable selection in functional data with applications to methylation and histone acetylation QTL fine-mapping (collaboration with Professor Matthew Stephens’ group).
  • Yuqi Miao and William Denault.
• Fine-mapping using summary statistics. We have previously developed SuSiE for fine-mapping.
  • Yuxin Zou.
• Fine-mapping with multiple traits. We extend our previous work SuSiE to analyze multiple phenotypes.
  • Yuxin Zou and William Denault.
• Functional genomics annotation and prediction in brains with application to fine-mapping. Integration of functional annotation and molecular phenotype for brain disease fine-mapping from a large resource of genomic annotations associations, including experimental data as well as deep learning based feature predictions.
  • Anmol Singh and Tabassum Fabiha.

L

• Linkage analysis with continued development of tools for WGS data.. We develope SEQLinkage 2.0 with various improvements over the previous release in 2015, and apply it to linkage analysis of the Alzheimer’s disease family at Columbia Neurology.
  • Yin Huang.

M

• 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 with new statistical methods we developed for TWAS (collaboration with Professor Fabio Morgante’s group).
  • Hao Sun, Chunming Liu and Fabio Morgante.

V

• Variable selection in association studies with copy number variants. We experiment application of fine-mapping to copy number variation data in case-control studies, with new methods developed using variational Bayes as well as traditional MCMC algorithms (collaboration with Professor Xin He’s group).
  • Min Qiao and Bohan Jiang.