• Le 30 novembre 2018
    Institut de Recherche en Santé - 8 quai Moncousu - Nantes
    Amphithéâtre Denis Escande
  • 11h30

The landscape of horizontal pleiotropy in human genetic variation

The landscape of horizontal pleiotropy in human genetic variation

Marie VERBANCK, PhD, invited by Christian Dina (Eq I)
Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA

ABSTRACT

Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. However, it is unknown the extent of horizontal pleiotropy that is present in human genetic variation. Given this, we asked two key questions. First, can we quantify the amount of horizontal pleiotropy in human genetic variation? Second, what impact does horizontal pleiotropy have in causal inference using Mendelian randomization?
For the first question, we developed a method to quantify horizontal pleiotropy through a variant-level pleiotropy score using summary statistics from published genome-wide association studies (GWASs), and perform validation using simulations. When applied to 1,564 medical phenotypes measured in 337,119 humans from the UK Biobank, our pleiotropy score detected a significant excess of horizontal pleiotropy. This signal of horizontal pleiotropy was pervasive throughout the human genome and across a wide range of phenotypes, but was especially prominent in regions of high linkage disequilibrium and among highly polygenic phenotypes. We identified thousands of loci with extreme horizontal pleiotropy, a majority of which had never been reported in any published GWAS. Our results highlighted the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes, and the importance of modeling horizontal pleiotropy in genomic medicine.
For the second question, in the context of Mendelian Randomization (MR), a crucial assumption to MR is the ‘no horizontal pleiotropy’ assumption where the variant must have no other effect on disease than the effect mediated by the exposure tested for causality. Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next, we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from –131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances.
These findings suggest that horizontal pleiotropy is pervasive in human genetic variation, and has significant implications for our understanding of the genetic architecture of complex traits and disease, and causal inference testing using Mendelian randomization between complex traits and diseases.