Posts Tagged ‘GWAS’


Don’t worry about your general cognitive ability genes. Otherwise, check out this study led by Drs. Joe Trampush and Anil Malhotra from the Feinstein Institute showing that the less frequent and non-ancestral (A) alelle of rs1906252 was associated with higher Spearman’s General Intelligence (g-factor) scores.  This SNP sits 700 kilobases upstream of a putative ubiquitin ligase subunit (FBXL4) connected to severe psychomotor retardation. Loss-of-function in other ubiquitin ligase subunits have also been implicated in mental retardation.

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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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THE ultimate guide to your genome … ‘nuf said.

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.


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A LOT of genetic data is out there … and more coming all the time … easy to get excited about, but hard to make sense of.  Here’s an epic story of just one SNP.

One of the best research teams in the business performed a genomewide association study (GWAS) of neuroticism in 1,227 US Caucasion participants and found associations (P values of 10−5 to 10−6) with several markers – including rs7151262 in the MAMDC1 gene.  Later they replicated the finding in a German sample of 1,880 (P values in the same directions 0.006–0.025).

Very exciting to ponder the ways in which this SNP might relate to the development of brain systems that process emotional information!

More recently, they attempted another replication of the MAMDC1 gene for association with neuroticism in 2,722 US Caucasion participants.  This time they report, “the current analysis failed to detect a significant association signal“.

Some 5,829 people were involved in the research and the data suggest that rs7151262 may or may not contribute to one’s neurotic tendencies.  If you knew your rs7151262 genotype would it change the way you think about yourself?

I don’t know … the confusion over the (+) vs. (-) association data would make me … well, neurotic.

thanks for the photo jinxmesomethingcrazy.

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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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Where da rodents kick it
Image by Scrunchleface via Flickr

A recent GWAS study identified the 3′ region of the liver- (not brain) expressed PECR gene (rs7590720(G) and rs1344694(T)) on chromosome 2 as a risk factor for alcohol dependency.  These results, as reported by Treutlein et al., in “Genome-wide Association Study of Alcohol Dependence” were based on a population of 487 male inpatients and a follow-up re-test in a population of 1024 male inpatients and 996 control participants.

The authors also asked whether lab rats who – given the choice between water-based and ethanol-spiked beverages over the course of 1 year – showed differential gene expression in those rats that were alcohol preferrers vs. alcohol non-preferring rats.  Among a total of 542 genes that were found to be differentially expressed in the amygdala and caudate nucleus of alcohol vs. non-alcohol-preferring rat strains,  a mere 3 genes – that is the human orthologs of these 3 genes – did also show significant association with alcohol dependency in the human populations.  Here are the “rat genes” (ie. human homologs that show differential expression in rats and association with alcohol dependency in humans): rs1614972(C) in the alcohol dehydrogenase 1C (ADH1C) gene, rs13273672(C) in the GATA binding protein 4 (GATA4) gene, and rs11640875(A) in the cadherin 13 (CDH13) gene.

My 23andMe profile gives a mixed AG at rs7590720, and a mixed GT at rs1344694 while I show a mixed CT at rs1614972, CT at rs13273672 and AG at rs11640875.  Boooring! a middling heterozygote at all 5 alcohol prefer/dependency loci.   Were these the loci for chocolate prefer/dependency I would be a full risk-bearing homozygote.


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