Dr. Xiaoyin Li

UMD Swenson Col of Sci & Eng
UM Duluth
Duluth
Project Title: 
Developing Statistical Methods for Software for Detecting Pleiotropy Using GWAS Summary Statistics

Pleiotropy refers to the phenomenon of a single gene affecting multiple traits. It has long played a central role in theoretical, experimental, and clinical research in genetics, molecular biology, evolution, and medicine. Given this characteristic, the identification and characterization of pleiotropy are crucial for a comprehensive biological understanding of complex traits and disease states.

In recent years, genomic techniques have brought data to bear on fundamental questions about the nature and extent of pleiotropy. Many detected genetic loci harbor variants that associate with multiple, even distinct traits, which implies potential pleiotropy. However, dissecting the association pathways from a variant to multiple traits has not been well studied yet. Thus, there is a critical need to develop statistical methods in order to understand:

  • Which loci in the genome govern the co-occurrence of disorders?
  • How do we understand the mechanism that genetic variants influence pairs of traits?
  • What statistical models are best suited to identify pleiotropic variants from large-scale genetic data?

Project Investigators

Dr. Xiaoyin Li
 
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