Journal
JOURNAL OF NEUROSCIENCE
Volume 39, Issue 44, Pages 8778-8787Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.0888-19.2019
Keywords
fMRI; FOXP2; individual differences; KIAA0319/TTRAP/THEM2; language; neuroimaging genetics
Categories
Funding
- Max Planck Society
- Donders Centre for Cognitive Neuroimaging
- National Institutes of Health [R00-HD057522, R01-DC016607, R01-DC016950]
- Simons Foundation
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A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/ cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.
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