Abstract Number:
3889
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
jiwon park (1), Rongjie Liu (2), Chao Huang (3)
Institutions:
(1) Johns hopkins bloomberg school of public health, N/A, (2) N/A, N/A, (3) FSU, N/A
Co-Author(s):
First Author:
jiwon park
Johns hopkins bloomberg school of public health
Presenting Author:
jiwon park
Johns hopkins bloomberg school of public health
Abstract Text:
With the burgeoning interest in pleiotropy, where a single genetic variant affects multiple traits, the PLACO method was proposed to identify pleiotropic variants between two case-control traits, inclusive of sample overlap scenarios. We introduce the modified PLACO method, a novel scalable statistical approach based on GWAS summary statistics data for enhanced detection of pleiotropic variants across correlated quantitative or qualitative traits. By testing the composite null hypothesis that a variant is linked to at most one trait, the modified PLACO effectively controls type 1 errors and increases detection power for pleiotropy, especially in highly correlated traits. Applied to lipid traits- triglyceride and HDL levels-it unveils shared genetic regions overlooked by conventional methods, later validated by larger datasets. This demonstrates its ability to discover novel associations in traits often missed due to small sample sizes, later validated by larger datasets. This study highlights modified PLACO's potential for discovering novel genetic associations and offers a robust framework for pleiotropy analysis of two traits, regardless of their correlation or sample overlap.
Keywords:
GWAS|composite null hypothesis|pleiotropy| | |
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