PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics

Cue Hyunkyu Lee, Huwenbo Shi, Bogdan Pasaniuc, Eleazar Eskin, Buhm Han

Research output: Contribution to journalArticlepeer-review


Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

Original languageEnglish
Pages (from-to)36-48
Number of pages13
JournalAmerican Journal of Human Genetics
Issue number1
StatePublished - 7 Jan 2021


  • GWAS
  • association mapping
  • genetic correlation
  • heritability
  • meta-analysis
  • multi-trait analysis
  • pleiotropy
  • variance component

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