RT Journal Article
SR Electronic
T1 A method to map and interpret pleiotropic loci using summary statistics of multiple traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.16.155879
DO 10.1101/2020.06.16.155879
A1 Cue Hyunkyu Lee
A1 Huwenbo Shi
A1 Bogdan Pasaniuc
A1 Eleazar Eskin
A1 Buhm Han
YR 2020
UL http://biorxiv.org/content/early/2020/06/20/2020.06.16.155879.abstract
AB The identification of pleiotropic loci and the interpretation of the associations at these loci are essential to understand the shared etiology of related traits. A common approach to map pleiotropic loci is to use an existing meta-analysis method to combine summary statistics of multiple traits. This strategy does not take into account 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, a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple traits. Our method maximizes power by systematically accounting for the genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with differing 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 20 novel pleiotropic loci, which showed five different patterns of associations. Our method is available at https://github.com/hanlab-SNU/PLEIO.Competing Interest StatementBuhm Han is the CTO of Genealogy Inc.