Posts Tagged ‘brain structure’


The above images are eigenfaces … which are statistically distilled basic components of human faces … from which ANY human face can be reconstructed as a combination of the above basic components.  It’s a great mathematical trick – particularly if you’re into the whole mass surveillance and electronic police state thing.

If you are more into the whole, helping people and medical care thing, check out the global consortia at ENIGMA who have been carrying out massive genetic and brain scanning studies – like this one involving 437,607 SNPs in 31,622 voxels in 731 subjects using their new method, vGeneWAS, to study Alzheimer’s Disease:

“We hypothesized that vGeneWAS would, in some situations, have greater power to detect associations than existing SNP-based methods. One such situation might be when a gene contains many loci with weak individual effects. In addition, we expected that vGeneWAS would have greater overall power than mass SNP-based methods, like vGWAS, because of the drastic reduction in the effective number of statistical tests performed.”

The vGeneWAS method relies on the calculation of “eigenSNPs” which are eigenvectors that describe a matrix of n subjects by m SNPs in an individual gene (an n-x-m matrix of 1’s,0’s,-1’s for aa, aA, AA genotypes).  EigenSNPs are sort of like eigenfaces insofar as eigenSNPs (which are not actual SNPs) capture the majority of variance, or the basic essence of an individual gene … but seriously, you should read the original article ’cause every stats test I ever took totally punched me in the face.

In any case, the eigenSNP-by-voxel method pulled out some legit results such as rs2373115 (where the G-allele confers risk) in the GAB2 gene  which has repeatedly been implicated in the risk of age-related late-onset Alzheimer’s Disease (in folks who carry ApoE4  rs429358(C) alleles).  The authors found that the genetic risk of AD conferred by GAB2 may arise by way of GAB2’s effect on brain structure in the periventricular areas, which have been known to be among the first brain regions to show AD-related changes (time-lapse movie of AD tissue loss in the brain).

Picture 2

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An historic find has occurred in the quest (gold-rush, if you will) to link genome variation with brain structure-function variation.  This is the publication of the very first genome-wide (GWAS) analysis of individual voxels (voxels are akin to pixels in a photograph, but are rather 3D cubes of brain-image-space about 1mm on each side) of brain structure – Voxelwise genome-wide association study (vGWAS) [doi: 10.1016/j.neuroimage.2010.02.032] by Jason Stein and colleagues under the leadership of Paul M. Thompson, a  leader in the area of neuroimaging and genetics – well-known for his work on brain structure in twin and psychiatric patient populations.

In an effort to discover genes that contribute to individual differences in brain structure, the authors took on the task of statistically analyzing the some 31,622 voxels (per brain) obtained from high-resolution structural brain scans; with 448,293 Illumina SNP genotypes (per person) with minor allele frequencies greater than 0.1 (common variants); in 740 unrelated healthy caucasian adults.  When performed on a voxel-by-voxel basis, this amounts to some 14 billion statistical tests.

Yikes!  A statistical nightmare with plenty of room for false positive results, not to mention the recent disillusionment with the common-variant GWAS approach?  Certainly.  The authors describe these pitfalls and other scenarios wherein false data is likely to arise and most of the paper addresses the pros and cons of different statistical analysis strategies – some which are prohibitive in their computational demands.  Undaunted, the authors describe several approaches for establishing appropriate thresholds and then utilize a ‘winner take all’ analysis strategy wherein a single ‘most-associated winning snp’ is identified for each voxel, which when clustered together in hot spots (at P = 2 x 10e-10), can point to specific brain areas of interest.

Using this analytical approach, the authors report that 8,212 snps were identified as ‘winning, most-associated’ snps across the 31,622 voxels.  They note that there was not as much symmetry with respect to winning snps in the left hemispere and corresponding areas in the right hemisphere, as one might have expected.  The 2 most significant snps across the entire brain and genome were rs2132683 and rs713155 which were associated with white matter near the left posterior lateral ventricle.  Other notable findings were rs2429582 in the synaptic (and possible autism risk factor) CADPS2 gene which was associated with temporal lobe structure and rs9990343 which sits in an intergenic region but is associated with frontal lobe structure.  These and several other notable snps are reported and brain maps are provided that show where in the brain each snp is associated.

As in most genome-wide studies, one can imagine that the authors were initially bewildered by their unexpected findings.  None of the ‘usual suspects’ such as neurotransmitter receptors, transcription factors, etc. etc. that dominate the psychiatric genetics literature.  Bewildered, perhaps, but maybe thats part of the fun and excitement of discovery!  Very exciting stuff to come I’ll bet as this new era unfolds!

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DCDC2 (gene)
Image via Wikipedia

A recent analysis of brain structure in healthy individuals who carry a common 2,445-bp deletion in intron 2 of the doublecortin domain containing 2 (DCDC2) gene found that heterozygotes for the deletion showed higher grey matter volumes for several brain areas known to be involved in the processing of written and spoken language (superior, medial and inferior temporal cortex, fusiform, hippocampal / parahippocampal, inferior occipito-parietal, inferior and middle frontal gyri, especially in the left hemisphere) [doi:10.1007/s11682-007-9012-1].  The DCDC2 gene sits within a well known locus frequently found to be associated with developmental dyslexia, and associations of reading disability with DCDC2 have been confirmed in population-based studies.  dcdc2rnai Further work on DCDC2 (open access) shows that the DNA that is deleted in the 2,445-bp deletion in intron 2 carries a number of repeating sequences to which developmental transcription factors bind and that inhibition of DCDC2 results in altered neuronal migration (the right-hand panel shows altered radial migration when DCDC2 is inhibited).  Perhaps the greater grey matter volumes are related to this type of neuronal migration finding?  Will be interesting to follow this story further!

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