Cue Hyunkyu Lee^1,2, Huwenbo Shi³, Bogdan Pasaniuc^4,5,6, Eleazar Eskin^4,6,7, Buhm Han^1,8,*

¹Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
²Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea
³Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA
⁴Department of Human genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
⁵Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
⁶Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
⁷Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
⁸Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Republic of Korea

^*Corresponding author

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.

Keywords : PLEIO, pleiotropy, multi-trait analysis, association mapping, meta-analysis, genetic correlation, heritability, variance component, GWAS