Do Candidate Genes Affect the Brain's White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans (original) (raw)
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Effects of the BDNF Val66Met Polymorphism on White Matter Microstructure in Healthy Adults
Neuropsychopharmacology, 2012
The BDNF Val 66 Met polymorphism, a possible risk variant for mental disorders, is a potent modulator of neural plasticity in humans and has been linked to deficits in gray matter structure, function, and cognition. The impact of the variant on brain white matter structure, however, is controversial and remains poorly understood. Here, we used diffusion tensor imaging to examine the effects of BDNF Val 66 Met genotype on white matter microstructure in a sample of 85 healthy Caucasian adults. We demonstrate decreases of fractional anisotropy and widespread increases in radial diffusivity in Val/Val homozygotes compared with Met-allele carriers, particularly in prefrontal and occipital pathways. These data provide an independent confirmation of prior imaging genetics work, are consistent with complex effects of the BDNF Val 66 Met polymorphism on human brain structure, and may serve to generate hypotheses about variation in white matter microstructure in mental disorders associated with this variant.
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014
Several genetic variants are thought to influence white matter (WM) integrity, measured with diffusion tensor imaging (DTI). Voxel based methods can test genetic associations, but heavy multiple comparisons corrections are required to adjust for searching the whole brain and for all genetic variants analyzed. Thus, genetic associations are hard to detect even in large studies. Using a recently developed multi-SNP analysis, we examined the joint predictive power of a group of 18 cholesterol-related single nucleotide polymorphisms (SNPs) on WM integrity, measured by fractional anisotropy. To boost power, we limited the analysis to brain voxels that showed significant associations with total serum cholesterol levels. From this space, we identified two genes with effects that replicated in individual voxel-wise analyses of the whole brain. Multivariate analyses of genetic variants on a reduced anatomical search space may help to identify SNPs with strongest effects on the brain from a broad panel of genes.
2018
Microstructural changes of white matter (WM) tracts are known to be associated with various neuropsychiatric disorders/diseases. Heritability of structural changes of WM tracts has been examined using diffusion tensor imaging (DTI) in family-based studies for different age groups. The availability of genetic and DTI data from recent large population-based studies offers opportunity to further improve our understanding of genetic contributions. Here, we analyzed the genetic architecture of WM tracts using DTI and single-nucleotide polymorphism (SNP) data of unrelated individuals in the UK Biobank (n ∼ 8000). The DTI parameters were generated using the ENIGMA-DTI pipeline. We found that DTI parameters are substantially heritable on most WM tracts. We observed a highly polygenic or omnigenic architecture of genetic influence across the genome as well as the enrichment of SNPs in active chromatin regions. Our bivariate analyses showed strong genetic correlations for several pairs of WM ...
NeuroImage
Identifying genes that contribute to white matter microstructure should provide insights into the neurobiological processes that regulate white matter development, plasticity and pathology. We detected five significant SNPs using genome-wide association analysis on a global measure of fractional anisotropy in 776 individuals from large extended pedigrees. Genetic correlations and genome-wide association results indicated that the genetic signal was largely homogeneous across white matter regions. Using RNA transcripts derived from lymphocytes in the same individuals, we identified two genes (GNA13 and CCDC91) that are likely to be cis-regulated by top SNPs, and whose expression levels were also genetically correlated with fractional anisotropy. A transcript of HTR7 was phenotypically associated with FA, and was associated with an intronic genome-wide significant SNP. These results encourage further research in the mechanisms by which GNA13, HTR7 and CCDC91 influence brain structure, and emphasize a role for g-protein signaling in the development and maintenance of white matter microstructure in health and disease.
Genes, Brain and Behavior, 2010
The brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family and involved in nerve growth and survival. It has also become a major research focus in the investigation of both cognitive and affective processes in the human brain in the last years. Especially, a single nucleotide polymorphism on the BDNF gene called BDNF Val66Met gained a lot of attention, because of its effect on activity-dependent BDNF secretion and its link to negative emotionality and impaired memory processes. A well-replicated finding from genetic structural imaging showed that carriers of the less frequent 66Met allele show diminished gray matter volume in several areas of the temporal lobe. New imaging techniques like diffusion tensor imaging now allow investigating the influence of BDNF Val66Met on white matter integrity. We applied tractbased spatial statistics in a brain image dataset including n = 99 healthy participants. No significant differences between the 66Met and homozygous 66Val carriers were observed when correcting for multiple comparisons. In summary, the BDNF Val66Met polymorphism seems not to play a substantial role with respect to the modulation of the white matter integrity in healthy subjects. Although not in the focus of this study, we also investigated the influence of Eysenck's Personality Questionnaire on the white matter tracts. No significant results could be observed.
Predominantly global genetic influences on individual white matter tract microstructure
NeuroImage
Individual differences in white matter tract microstructure, measured with diffusion tensor imaging (DTI), demonstrate substantial heritability. However, it is unclear to what extent this heritability reflects global genetic influences or tract-specific genetic influences. The goal of the current study was to quantify the proportion of genetic and environmental variance in white matter tracts attributable to global versus tract-specific influences. We assessed fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) across 11 tracts and 22 subdivisions of these tracts in 392 middle-aged male twins from the Vietnam Era Twin Study of Aging (VETSA). In principal component analyses of the 11 white matter tracts, the first component, which represents the global signal, explained 50.1% and 62.5% of the variance in FA and MD, respectively. Similarly, the first principal component of the 22 tract subdivisions explained 38.4% and 47.0% of the variance in FA and MD, respectively. Twin modeling revealed that DTI measures of all tracts and subdivisions were heritable, and that genetic influences on global FA and MD accounted for approximately half of the heritability in the tracts or tract subdivisions. Similar results were observed for the AD and RD diffusion metrics. These findings underscore the importance of controlling for DTI global signals when measuring associations between specific tracts and outcomes such as cognitive ability, neurological and psychiatric disorders, and brain aging.
Predicting White Matter Integrity from Multiple Common Genetic Variants
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2012
Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effec...
NeuroImage, 2013
The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at