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Posts Tagged ‘23andMe’

Check out the Interpretome! developed by students and staff at Stanford University.

– I have 17 European alleles and 3 East Asian alleles … the genetic proof is in … white boys can’t jump.
– I have 17 out of 32 Type 2 Diabetes risk alleles … put down those carbs now … and 19 out of 30 Coronary Artery Disease risk alleles … and go for a jog.
– I have a combined Risk of Narcolepsy: 2.92 … but the score jumps to 85 with an issue of GENETICS in my hand.
– I’m not exactly on the leading edge of human evolution … a 72/110 of positive selection score.
– I’d better start saving for a long-ass retirement … probability of extreme longevity: 78.2

More on the interpretome here, here and here!

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Wobble base pair guanine uracil (GU)

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Hands shake and wobble as the decades pass … moreso in some.

A recently evolved “T” allele (rs12720208) in the  3′ untranslated region (3′ UTR) of the FGF20 gene has been implicated in the risk of Parkinson’s Disease … namely by creating a wobbly G:U base-pair between microRNA-433 (miR-433) and the FGF20 transcript.  Since the normal function of microRNA-433 is to repress translation of proteins (such as FGF20), it is suspected that the PD risk “T” allele carriers make relatively more FGF20 … which, in turn … leads to the production of higher levels of alpha-synuclein (the main component of Lewy body fibrils, a pathological marker of diseases such as PD).  This newly evolved T-allele has also been associated with brain structural differences in healthy individuals.

My hands will shake and wobble as the decades pass … but not because I carry the G:U wobble pairing between miR-433:FGF20.  My 23andMe profile shows that I carry 2 C alleles and will produce the thermodynamically favorable G:C pairing.  Something to keep in mind as I lose my mind in the decades to come.

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“Listen Eric, you should think about how useful your newfangled Personal Genome is going to be.  There are a lot of reasons why all this information doesn’t tell you much”

“For example, have you thought about epigenetic effects that might be environmentally induced and can be transmitted across multiple subsequent generations?  Genotypes of individuals in previous generations might even be a better predictor of phenotype than an individual’s own genotype.”

“I know that Copy-Number Polymorphic (CNP) duplications are highly variable among individual and are considered inaccessible by most existing genotyping and sequencing technologies, but I’m still getting my genome sequenced anyway.”

“Can you please help Eric understand that rare variants and large variants (deletions, duplications and inversions) are individually rare, but collectively common in the human population might account for much more of heritability than common variation.  Nothing is known about these rare variants!”

“Yeah, Eric doesn’t realize that a very large number of closely linked genes can exhibit context-dependent and non-additive effects.”

“Gene by environment innnterraaaaactiiooon … coooool.”

–real science here.

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Crocus (cropped)
Image by noahg. via Flickr

If you’ve started to notice the arrival of spring blossoms, you may have wondered, “how do the blossoms know when its spring?”  Well, it turns out that its not the temperature, but rather, that plants sense the length of the day-light cycle in order to synchronize their  own life cycles with the seasons.  According to the photoperiodism entry for wikipedia, “Many flowering plants use a photoreceptor protein, such as phytochrome or cryptochrome, to sense seasonal changes in night length, or photoperiod, which they take as signals to flower.”

It turns out that humans are much the same. Say wha?!

Yep, as the long ago descendants of single cells who had to eek out a living during day (when the sun emits mutagenic UV radiation) and night cycles, our very own basic molecular machinery that regulates the transcription, translation, replication and a host of other cellular functions is remarkably sensitive – entrained – in a clock-like fashion to the rising and setting sun.  This is because, in our retinas, there are light-sensing cells that send signals to the suprachiasmatic nucleus (SCN) which then – via the pineal gland – secretes systemic hormones such as melatonin that help synchronize cells and organs in your brain and body.  When this process is disrupted, folks can feel downright lousy, as seen in seasonal affective disorder (SAD), delayed sleep phase syndrome (DSPS) and other circadian rhythm disorders.

If you’re skeptical, consider the effects of genetic variation in genes that regulate our circadian rhythms, often called “clock” genes – very ancient genes that keep our cellular clocks synchronized with each other and the outside environment.  Soria et al., have a great paper entitled, “Differential Association of Circadian Genes with Mood Disorders: CRY1 and NPAS2 are Associated with Unipolar Major Depression and CLOCK and VIP with Bipolar Disorder” [doi: 10.1038/npp.2009.230] wherein they reveal that normal variation in these clock genes is associated with mood regulation.

A few of the highlights reported are rs2287161 in the CRY1 gene,  rs11123857 in the NPAS2 gene, and rs885861 in the VIPR2 gene – where the C-allele, G-allele and C-allele, respectively, were associated with mood disorders.

I’m not sure how one would best interpret genetic variation of such circadian rhythm genes.  Perhaps they index how much a person’s mood could be influenced by changes or disruptions to the normal rhythm??  Not sure.  My 23andMe data shows the non-risk AA genotype for rs11123857 (the others are not covered by 23andMe).

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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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According to wikipedia, “Jean Philippe Arthur Dubuffet (July 31, 1901 – May 12, 1985) was one of the most famous French painters and sculptors of the second half of the 20th century.”  “He coined the term Art Brut (meaning “raw art,” often times referred to as ‘outsider art’) for art produced by non-professionals working outside aesthetic norms, such as art by psychiatric patients, prisoners, and children.”  From this interest, he amassed the Collection de l’Art Brut, a sizable collection of artwork, of which more than half, was painted by artists with schizophrenia.  One such painting that typifies this style is shown here, entitled, General view of the island Neveranger (1911) by Adolf Wolfe, a psychiatric patient.

Obviously, Wolfe was a gifted artist, despite whatever psychiatric diagnosis was suggested at the time.  Nevertheless, clinical psychiatrists might be quick to point out that such work reflects the presence of an underlying thought disorder (loss of abstraction ability, tangentiality, loose associations, derailment, thought blocking, overinclusive thinking, etc., etc.) – despite the undeniable aesthetic beauty in the work.  As an ardent fan of such art,  it made me wonder just how “well ordered” my own thoughts might be.  Given to being rather forgetful and distractable, I suspect my thinking process is just sufficiently well ordered to perform the routine tasks of day-to-day living, but perhaps not a whole lot more so.  Is this bad or good?  Who knows.

However, Krug et al., in their recent paper, “The effect of Neuregulin 1 on neural correlates of episodic memory encoding and retrieval” [doi:10.1016/j.neuroimage.2009.12.062] do note that the brains of unaffected relatives of persons with mental illness show subtle differences in various patterns of activation.  It seems that when individuals are using their brains to encode information for memory storage, unaffected relatives show greater activation in areas of the frontal cortex compared to unrelated subjects.  This so-called encoding process during episodic memory is very important for a healthy memory system and its dysfunction is correlated with thought disorders and other aspects of cognitive dysfunction.  Krug et al., proceed to explore this encoding process further and ask if a well-known schizophrenia risk variant (rs35753505 C vs. T) in the neuregulin-1 gene might underlie this phenomenon.  To do this, they asked 34 TT, 32 TC and 28 CC individuals to perform a memory (of faces) game whilst laying in an MRI scanner.

The team reports that there were indeed differences in brain activity during both the encoding (storage) and retrieval (recall) portions of the task – that were both correlated with genotype – and also in which the CC risk genotype was correlated with more (hyper-) activation.  Some of the brain areas that were hyperactivated during encoding and associated with CC genotype were the left middle frontal gyrus (BA 9), the bilateral fusiform gyrus and the left middle occipital gyrus (BA 19).  The left middle occipital gyrus showed gene associated-hyperactivation during recall.  So it seems, that healthy individuals can carry risk for mental illness and that their brains may actually function slightly differently.

As an ardent fan of Art Brut, I confess I hoped I would carry the CC genotype, but alas, my 23andme profile shows a boring TT genotype.  No wonder my artwork sucks.  More on NRG1 here.

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Recreated :File:Neuron-no labels2.png in Inksc...
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The A-to-T SNP rs7794745 in the CNTNAP2 gene was found to be associated with increased risk of autism (see Arking et al., 2008).  Specifically, the TT genotype, found in about 15% of individuals, increases these folks’ risk by about 1.2-1.7-fold.  Sure enough, when I checked my 23andMe profile, I found that I’m one of these TT risk-bearing individuals.  Interesting, although not alarming since me and my kids are beyond the age where one typically worries about autism.  Still, one can wonder if such a risk factor might have exerted some influence on the development of my brain?

The recent paper by Tan et al., “Normal variation in fronto-occipital circuitry and cerebellar structure with an autism-associated polymorphism of CNTNAP2” [doi:10.1016/j.neuroimage.2010.02.018 ] suggests there may be subtle, but still profound influences of the TT genotype on brain development in healthy individuals.  According to the authors, “homozygotes for the risk allele showed significant reductions in grey and white matter volume and fractional anisotropy in several regions that have already been implicated in ASD, including the cerebellum, fusiform gyrus, occipital and frontal cortices. Male homozygotes for the risk alleles showed greater reductions in grey matter in the right frontal pole and in FA in the right rostral fronto-occipital fasciculus compared to their female counterparts who showed greater reductions in FA of the anterior thalamic radiation.”

The FA (fractional anisotropy – a measurement of white-matter or myelination) results are consistent with a role of CNTNAP2 in the establishment of synaptic contacts and other cell-cell contacts especially at Nodes of Ranvier – which are critical for proper function of white-matter tracts that support rapid, long-range neural transmission.  Indeed, more severe mutations in CNTNAP2  have been associated with cortical dysplasia and focal epilepsy (Strauss et al., 2006).

Subtle changes perhaps influencing long-range information flow in my brain – wow!

More on CNTNAP2 … its evolutionary history and role in language development.

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