Bivariate Genome-Wide Association Study of Genetically Correlated Neuroimaging Phenotypes from DTI and MRI through a Seemingly Unrelated Regression Model

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• 作者：Neda Jahanshad (22)
  Priya Bhatt (22)
  Derrek P. Hibar (22)
  Julio E. Villalon (22)
  Talia M. Nir (22)
  Arthur W. Toga (22)
  Clifford R. Jack Jr. (23)
  Matt A. Bernstein (23)
  Michael W. Weiner (24) (25)
  Katie L. McMahon (26)
  Greig I. de Zubicaray (27)
  Nicholas G. Martin (28)
  Margaret J. Wright (28)
  Paul M. Thompson (22)
  
• 关键词：Neuroimaging genetics ; brain connectivity ; bivariate analysis ; GWAS ; genetic correlation
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8159
• 期：1
• 页码：202-210
• 全文大小：547KB
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• 作者单位：Neda Jahanshad (22)
  Priya Bhatt (22)
  Derrek P. Hibar (22)
  Julio E. Villalon (22)
  Talia M. Nir (22)
  Arthur W. Toga (22)
  Clifford R. Jack Jr. (23)
  Matt A. Bernstein (23)
  Michael W. Weiner (24) (25)
  Katie L. McMahon (26)
  Greig I. de Zubicaray (27)
  Nicholas G. Martin (28)
  Margaret J. Wright (28)
  Paul M. Thompson (22)
  
  22. Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
  23. Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
  24. Department of Radiology, Medicine, and Psychiatry, UC San Francisco, CA, USA
  25. Department of Veterans Affairs Medical Center, San Francisco, CA, USA
  26. Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
  27. School of Psychology, University of Queensland, Brisbane, Australia
  28. Queensland Institute of Medical Research, Brisbane, Australia
  

文摘

Large multisite efforts (e.g., the ENIGMA Consortium), have shown that neuroimaging traits including tract integrity (from DTI fractional anisotropy, FA) and subcortical volumes (from T1-weighted scans) are highly heritable and promising phenotypes for discovering genetic variants associated with brain structure. However, genetic correlations (r g) among measures from these different modalities for mapping the human genome to the brain remain unknown. Discovering these correlations can help map genetic and neuroanatomical pathways implicated in development and inherited risk for disease. We use structural equation models and a twin design to find r g between pairs of phenotypes extracted from DTI and MRI scans. When controlling for intracranial volume, the caudate as well as related measures from the limbic system - hippocampal volume - showed high r g with the cingulum FA. Using an unrelated sample and a Seemingly Unrelated Regression model for bivariate analysis of this connection, we show that a multivariate GWAS approach may be more promising for genetic discovery than a univariate approach applied to each trait separately.