Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Xiaofeng Zhu, Tao Feng, Bamidele O. Tayo, Jingjing Liang, J. Hunter Young, Nora Franceschini, Jennifer A. Smith, Lisa R. Yanek, Yan V. Sun, Todd L. Edwards, Wei Chen, Mike Nalls, Ervin Fox, Michele Sale, Erwin Bottinger, Charles Rotimi, Yongmei Liu, Barbara McKnight, Kiang Liu, Donna K. ArnettAravinda Chakravati, Richard S. Cooper, Susan Redline, Daniel Levy

Research output: Contribution to journalArticlepeer-review

234 Scopus citations


Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple - even distinct - traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10-8) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10-7) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

Original languageEnglish
Pages (from-to)21-36
Number of pages16
JournalAmerican Journal of Human Genetics
Issue number1
StatePublished - Jan 8 2015

Bibliographical note

Publisher Copyright:
© 2015 The American Society of Human Genetics.

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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