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Am J Hum Genet. 2015 Jan 8;96(1):21-36. doi: 10.1016/j.ajhg.2014.11.011. Epub 2014 Dec 11.

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

Author information

1
Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: xiaofeng.zhu@case.edu.
2
Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; College of Mathematical Science, Heilongjiang University, Harbin 150080, P.R. China.
3
Department of Public Health Science, Loyola University Chicago Stritch School of Medicine, Maywood, IL 60153, USA.
4
Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
5
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
6
Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA.
7
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
8
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
9
Center for Human Genetics Research, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, TN 37212, USA.
10
Tulane Center for Cardiovascular Health, Tulane University, New Orleans, LA 70112, USA.
11
Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA.
12
Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39126, USA.
13
University of Virginia Center for Public Health Genomics, Charlottesville, VA 22908, USA.
14
The Charles Bronfman Institute for Personalized Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA.
15
Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA.
16
Department of Epidemiology & Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA.
17
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
18
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
19
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
20
Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
21
Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA.

Abstract

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.

PMID:
25500260
PMCID:
PMC4289691
DOI:
10.1016/j.ajhg.2014.11.011
[Indexed for MEDLINE]
Free PMC Article
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