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

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ajhg.2014.11.011 Byline: 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... Full description

Journal Title: American journal of human genetics January 8, 2015, Vol.96(1), pp.21-36
Main Author: Zhu, Xiaofeng
Other Authors: Feng, Tao , Tayo, Bamidele O , Liang, Jingjing , Young, J Hunter , Franceschini, Nora , Smith, Jennifer A , Yanek, Lisa R , Sun, Yan V , Edwards, Todd L , Chen, Wei , Nalls, Mike , Fox, Ervin , Sale, Michele , Bottinger, Erwin , Rotimi, Charles , Liu, Yongmei , Mcknight, Barbara , Liu, Kiang , Arnett, Donna K , Chakravati, Aravinda , Cooper, Richard S , Redline, Susan , Zhu, Xiaofeng
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1537-6605 ; DOI: 10.1016/j.ajhg.2014.11.011
Link: http://search.proquest.com/docview/1645779226/?pq-origsite=primo
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title: Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.
format: Article
creator:
  • Zhu, Xiaofeng
  • Feng, Tao
  • Tayo, Bamidele O
  • Liang, Jingjing
  • Young, J Hunter
  • Franceschini, Nora
  • Smith, Jennifer A
  • Yanek, Lisa R
  • Sun, Yan V
  • Edwards, Todd L
  • Chen, Wei
  • Nalls, Mike
  • Fox, Ervin
  • Sale, Michele
  • Bottinger, Erwin
  • Rotimi, Charles
  • Liu, Yongmei
  • Mcknight, Barbara
  • Liu, Kiang
  • Arnett, Donna K
  • Chakravati, Aravinda
  • Cooper, Richard S
  • Redline, Susan
  • Zhu, Xiaofeng
subjects:
  • Blood Pressure–Genetics
  • Genetic Loci–Genetics
  • Genome-Wide Association Study–Genetics
  • Humans–Genetics
  • Hypertension–Genetics
  • Models, Biological–Genetics
  • Phenotype–Genetics
  • Polymorphism, Single Nucleotide–Genetics
ispartof: American journal of human genetics, January 8, 2015, Vol.96(1), pp.21-36
description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ajhg.2014.11.011 Byline: 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. Arnett, Aravinda Chakravati, Richard S. Cooper, Susan Redline 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 x 10.sup.-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 x 10.sup.-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. Author Affiliation: (1) Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA (2) College of Mathematical Science, Heilongjiang University, Harbin 150080, P.R. China (3) Department of Public Health Science,
language: eng
source:
identifier: E-ISSN: 1537-6605 ; DOI: 10.1016/j.ajhg.2014.11.011
fulltext: fulltext
issn:
  • 15376605
  • 1537-6605
url: Link


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titleMeta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.
creatorZhu, Xiaofeng ; Feng, Tao ; Tayo, Bamidele O ; Liang, Jingjing ; Young, J Hunter ; Franceschini, Nora ; Smith, Jennifer A ; Yanek, Lisa R ; Sun, Yan V ; Edwards, Todd L ; Chen, Wei ; Nalls, Mike ; Fox, Ervin ; Sale, Michele ; Bottinger, Erwin ; Rotimi, Charles ; Liu, Yongmei ; Mcknight, Barbara ; Liu, Kiang ; Arnett, Donna K ; Chakravati, Aravinda ; Cooper, Richard S ; Redline, Susan ; Zhu, Xiaofeng
contributorFranceschini, Nora (correspondence author) ; Fox, Ervin (record owner) ; Zhang, Zhaogong ; Edwards, Todd L ; Nalls, Michael A ; Sung, Yun Ju ; Tayo, Bamidele O ; Sun, Yan V ; Gottesman, Omri ; Adeyemo, Adebawole ; Johnson, Andrew D ; Young, J Hunter ; Rice, Ken ; Duan, Qing ; Chen, Fang ; Li, Yun ; Tang, Hua ; Fornage, Myriam ; Keene, Keith L ; Andrews, Jeanette S ; Smith, Jennifer A ; Faul, Jessica D ; Guangfa, Zhang ; Guo, Wei ; Liu, Yu ; Murray, Sarah S ; Musani, Solomon K ; Srinivasan, Sathanur ; Velez Edwards, Digna R ; Wang, Heming ; Becker, Lewis C ; Bovet, Pascal ; Bochud, Murielle ; Broeckel, Ulrich ; Burnier, Michel ; Carty, Cara ; Chen, Wei-Min ; Chen, Guanjie ; Chen, Wei ; Ding, Jingzhong ; Dreisbach, Albert W ; Evans, Michele K ; Guo, Xiuqing ; Garcia, Melissa E ; Jensen, Rich ; Keller, Margaux F ; Lettre, Guillaume ; Lotay, Vaneet ; Martin, Lisa W ; Morrison, Alanna C ; Mosley, Thomas H ; Ogunniyi, Adesola ; Palmas, Walter ; Papanicolaou, George ; Penman, Alan ; Polak, Joseph F ; Ridker, Paul M ; Salako, Babatunde ; Singleton, Andrew B ; Shriner, Daniel ; Taylor, Kent D ; Vasan, Ramachandran ; Wiggins, Kerri ; Williams, Scott M ; Yanek, Lisa R ; Zhao, Wei ; Zonderman, Alan B ; Becker, Diane M ; Berenson, Gerald ; Boerwinkle, Eric ; Bottinger, Erwin ; Cushman, Mary ; Eaton, Charles ; Heiss, Gerardo ; Hirschhron, Joel N ; Howard, Virginia J ; Lanktree, Matthew B ; Liu, Kiang ; Liu, Yongmei ; Loos, Ruth ; Margolis, Karen ; Psaty, Bruce M ; Schork, Nicholas J ; Weir, David R ; Rotimi, Charles N ; Sale, Michele M ; Harris, Tamara ; Kardia, Sharon L R ; Hunt, Steven C ; Arnett, Donna ; Redline, Susan ; Cooper, Richard S ; Risch, Neil ; Rao, D C ; Rotter, Jerome I ; Chakravarti, Aravinda ; Reiner, Alex P ; Levy, Daniel ; Keating, Brendan J ; Zhu, Xiaofeng ; Zhu, Xiaofeng ; Zhu, Xiaofeng
ispartofAmerican journal of human genetics, January 8, 2015, Vol.96(1), pp.21-36
identifierE-ISSN: 1537-6605 ; DOI: 10.1016/j.ajhg.2014.11.011
subjectBlood Pressure–Genetics ; Genetic Loci–Genetics ; Genome-Wide Association Study–Genetics ; Humans–Genetics ; Hypertension–Genetics ; Models, Biological–Genetics ; Phenotype–Genetics ; Polymorphism, Single Nucleotide–Genetics
languageeng
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descriptionTo link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ajhg.2014.11.011 Byline: 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. Arnett, Aravinda Chakravati, Richard S. Cooper, Susan Redline 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 x 10.sup.-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 x 10.sup.-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. Author Affiliation: (1) Department of Epidemiology & Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA (2) 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 Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA (5) Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA (6) Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA (7) Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA (8) Center for Human Genetics Research, Division of Epidemiology, Department of Medicine, Vanderbilt University, Nashville, TN 37212, USA (9) Tulane Center for Cardiovascular Health, Tulane University, New Orleans, LA 70112, USA (10) Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA (11) Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39126, USA (12) University of Virginia Center for Public Health Genomics, Charlottesville, VA 22908, USA (13) The Charles Bronfman Institute for Personalized Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA (14) Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892, USA (15) Department of Epidemiology & Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA (16) Department of Biostatistics, University of Washington, Seattle, WA 98195, USA (17) Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA (18) Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA (19) Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA (20) Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA Article History: Received 21 August 2014; Accepted 17 November 2014 Article Note: (miscellaneous) Published: December 11, 2014
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16Liu, Yongmei
17Mcknight, Barbara
18Liu, Kiang
19Arnett, Donna K
20Chakravati, Aravinda
21Cooper, Richard S
22Redline, Susan
titleMeta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.
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1Genetic Loci–Genetics
2Genome-Wide Association Study–Genetics
3Humans–Genetics
4Hypertension–Genetics
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13Duan, Qing
14Chen, Fang
15Li, Yun
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19Andrews, Jeanette S
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22Guangfa, Zhang
23Guo, Wei
24Liu, Yu
25Murray, Sarah S
26Musani, Solomon K
27Srinivasan, Sathanur
28Velez Edwards, Digna R
29Wang, Heming
30Becker, Lewis C
31Bovet, Pascal
32Bochud, Murielle
33Broeckel, Ulrich
34Burnier, Michel
35Carty, Cara
36Chen, Wei-Min
37Chen, Guanjie
38Chen, Wei
39Ding, Jingzhong
40Dreisbach, Albert W
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42Guo, Xiuqing
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titleMeta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.
authorZhu, Xiaofeng ; Feng, Tao ; Tayo, Bamidele O ; Liang, Jingjing ; Young, J Hunter ; Franceschini, Nora ; Smith, Jennifer A ; Yanek, Lisa R ; Sun, Yan V ; Edwards, Todd L ; Chen, Wei ; Nalls, Mike ; Fox, Ervin ; Sale, Michele ; Bottinger, Erwin ; Rotimi,...
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4Hypertension–Genetics
5Models, Biological–Genetics
6Phenotype–Genetics
7Polymorphism, Single Nucleotide–Genetics
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