Meeting the Challenges of Neuroimaging Genetics

Proc. Natl. Acad. Sci. U.S.A.

Rajagopalan

Hou

et al. 2013

Self Cite

147

128

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.diffusion tensor imaging | neuroimaging genetics | graph theory | HARDI tractography | multiple comparisons correction H uman brain anatomy involves a complex network of structural and functional pathways that connect anatomically distinct regions. These pathways can be visualized, on a gross anatomical scale, with diffusion imaging-based tractography (1). Neural pathways change as our brains develop (2, 3) and are altered in neurodegenerative conditions, including Alzheimer's disease (4). Our individual genetic makeup exerts a strong influence on the functional synchronization of brain regions (5) and the patterning of cortical structure (6, 7), but particular genes that impact the brain's neural connectivity are still largely unknown.To empower the search for genes that affect the brain's connectivity patterns, we first studied a large twin and family cohort consisting of 366 young adults (ages 20-29 y) from 223 families. We used anatomical brain MRI combined with high-angular resolution diffusion imaging (HARDI) at high magnetic field (4 T) to subdivide cortical regions into areas of known structure and function (8), while also mapping the white matter fiber pathways between them with high-resolution tractography. We defined connectivity maps...

Section: Methodsmentioning

confidence: 99%

Genome-wide scan of healthy human connectome discoversSPON1gene variant influencing dementia severity

Proc. Natl. Acad. Sci. U.S.A.

Rajagopalan

Hou

et al. 2013

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147

128

“…All subjects were of European ancestry from 350 families. Subjects were recruited as part of a 5-y research project examining healthy young adult Australian twins using structural and functional MRI and DTI with a projected sample size of approximately 1,150 at completion (55). Subjects were screened to exclude cases of pathology known to affect brain structure.…”

Section: Methodsmentioning

confidence: 99%

Brain structure in healthy adults is related to serum transferrin and the H63D polymorphism in theHFEgene

Proc. Natl. Acad. Sci. U.S.A.

Kohannim

Hibar

et al. 2012

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Control of iron homeostasis is essential for healthy central nervous system function: iron deficiency is associated with cognitive impairment, yet iron overload is thought to promote neurodegenerative diseases. Specific genetic markers have been previously identified that influence levels of transferrin, the protein that transports iron throughout the body, in the blood and brain. Here, we discovered that transferrin levels are related to detectable differences in the macro-and microstructure of the living brain. We collected brain MRI scans from 615 healthy young adult twins and siblings, of whom 574 were also scanned with diffusion tensor imaging at 4 Tesla. Fiber integrity was assessed by using the diffusion tensor imaging-based measure of fractional anisotropy. In bivariate genetic models based on monozygotic and dizygotic twins, we discovered that partially overlapping additive genetic factors influenced transferrin levels and brain microstructure. We also examined common variants in genes associated with transferrin levels, TF and HFE, and found that a commonly carried polymorphism (H63D at rs1799945) in the hemochromatotic HFE gene was associated with white matter fiber integrity. This gene has a well documented association with iron overload. Our statistical maps reveal previously unknown influences of the same gene on brain microstructure and transferrin levels. This discovery may shed light on the neural mechanisms by which iron affects cognition, neurodevelopment, and neurodegeneration.neuroimaging genetics | twin modeling | pathway analysis | tensor-based morphometry | voxel based analysis

“…A total of 395 subjects (23.7 ± 2.2 years of age; 143 men and 252 women; 47 siblings, 141 monozygotic twins (49 pairs and 43 singletons), and 207 dizygotic twins (1 triplet, 70 pairs, and 64 singletons) from the Brisbane young adult twins and siblings study (de Zubicaray et al, 2008) were included in our study, for whom both 105-gradient DTI scans and genome-wide genotype information were available. All twins in this study are Australians of European descent.…”

Section: Participantsmentioning

confidence: 99%

Predicting White Matter Integrity from Multiple Common Genetic Variants

Kohannim

Braskie

et al. 2012

Neuropsychopharmacol

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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 singlenucleotide 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 B6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.