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Neuroimage. 2014 Jul 15;95:136-50. doi: 10.1016/j.neuroimage.2014.03.033. Epub 2014 Mar 18.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling.

Author information

  • 1Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address: pkochunov@mprc.umaryland.edu.
  • 2Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA.
  • 3Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA.
  • 4Department of Statistics & Warwick Manufacturing Group, The University of Warwick, Coventry, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Oxford University, UK.
  • 5Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
  • 6Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA.
  • 7Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK.
  • 8School of Psychology, University of Queensland, Brisbane, Australia.
  • 9Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK.
  • 10Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
  • 11Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
  • 12Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
  • 13U1000 Research Unit Neuroimaging and Psychiatry, INSERM-CEA-Faculté de Médecine Paris-Sud, Orsay, France.
  • 14QIMR Berghofer Medical Research Institute, Brisbane, Australia.
  • 15Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.
  • 16Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
  • 17Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
  • 18Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.
  • 19Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, UK.
  • 20Department of Biological Psychology, VU University, Amsterdam, The Netherlands.
  • 21Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Neurology, Pediatrics, Engineering, Psychiatry, Radiology, & Ophthalmology, University of Southern California, Los Angeles, CA, USA.

Abstract

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

KEYWORDS:

Diffusion tensor imaging (DTI); Heritability; Imaging genetics; Meta-analysis; Multi-site; Reliability

PMID:
24657781
PMCID:
PMC4043878
DOI:
10.1016/j.neuroimage.2014.03.033
[PubMed - indexed for MEDLINE]
Free PMC Article
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