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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Related Experiment Video

Updated: Jul 8, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Published on: February 21, 2015

False positives in imaging genetics.

Andreas Meyer-Lindenberg1, Kristin K Nicodemus2, Michael F Egan2

  • 1Unit for Systems Neuroscience in Psychiatry, National Institute for Mental Health, NIH, DHHS, 9000 Rockville Pike, Bethesda, MD 20892-1365, USA; Neuroimaging Core Facility, National Institute for Mental Health, NIH, DHHS, 9000 Rockville Pike, Bethesda, MD 20892-1365, USA; Clinical Brain Disorders Branch, Genes, Cognition and Psychosis Program, National Institute for Mental Health, NIH, DHHS, 9000 Rockville Pike, Bethesda, MD 20892-1365, USA; Central Institute of Mental Health, J5, 68159 Mannheim, Germany.

Neuroimage
|January 19, 2008
PubMed
Summary
This summary is machine-generated.

False positive rates in imaging genetics are low. Current statistical methods effectively control errors, ensuring reliable gene-brain association findings in neuroimaging studies.

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Area of Science:

  • Neuroimaging
  • Genetics
  • Psychiatric Research

Background:

  • Imaging genetics generates vast functional-structural brain data.
  • High potential phenotypes raise concerns about false discovery rates.
  • Empirical validation of statistical methods in imaging genetics is needed.

Purpose of the Study:

  • To provide the first empirical results on false positive rates in imaging genetics.
  • To assess the effectiveness of standard neuroimaging correction procedures.
  • To evaluate the control of Type I error rates in gene-brain association studies.

Main Methods:

  • Analyzed 720 frequent coding SNPs for brain structure (VBM) and function (n-back, emotional face task).
  • Used whole brain and regions of interest analyses with standard multiple comparison corrections.
  • Employed permutation testing to derive lower empirical estimates of false positive rates.

Main Results:

  • False positive rates were consistently below 5% (0.2-4.1%) across regions of interest and datasets at a 0.05 correction level.
  • Permutation analysis results aligned with empirically derived rates.
  • No relationship was observed between region of interest and false positive rate.

Conclusions:

  • Current neuroimaging correction procedures effectively control Type I error rates in imaging genetics.
  • Observed low false positive rates suggest statistical thresholds are conservative.
  • Empirical evidence supports the reliability of findings in imaging genetics research using standard methods.