An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics

Xiaofeng Zhu, Xiaoyin Li, Rong Xu, Tao Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Motivation: The overall association evidence of a genetic variant with multiple traits can be evaluated by cross-phenotype association analysis using summary statistics from genome-wide association studies. Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits. Results: Here, we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits.

Original languageEnglish (US)
Pages (from-to)1390-1400
Number of pages11
JournalBioinformatics
Volume37
Issue number10
DOIs
StatePublished - May 15 2021

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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