Posts Tagged ‘Genetics’

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Nope …

“On mathematical grounds, it is difficult to understand how 10-to-the-14th synaptic connections in the human brain could be controlled by a genome with approximately 10-to-the-6th genes.”

“… the classic dichotomy between “hard-wired” nativism and the “plasticity” championed by anti-nativists was woefully off the mark. Historically, “Anti-nativists”—critics of the view that we might be born with significant mental structure prior to experience—have often attempted to downplay the significance of genes by appealing to neural plasticity, viz. the brain’s resilience to damage and its ability to modify itself in response to experience, while nativists often seem to think that their position rests on downplaying (or demonstrating limits on) plasticity.”

Well, sort of … think of genes as used for pre-wiring while experience then shapes the pre-wired system.

“… it may be more profitable to draw a distinction, between prewiring and rewiring—each of which can be had in abundance without precluding the other.”

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Probably not.  But, just in case you were wondering, it looks a lot like this.  Its (um, YOU) are a multi-level model showing overlap in genetic targets as well as signaling and neural systems between disparate latent constructs of memory and intelligence with no less than five levels of phenotype complexity (gene, signaling, neural, cognitive, and syndrome).  This super-techno-geeky-view of humanity is because:

In order to identify phenotype constructs that may ultimately be successful in genetic association studies, the field needs to move beyond the now traditional endophenotype approach and begin to build and refine multivariate multilevel phenotype models.

Check out the paper here and the super-cool PubMed exploration tools … especially PubAtlas.

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I mean, how many people are really needed to run a sufficiently powered genome-wide association study?  Are there enough people on the planet?  Heather J. Cordell’s review, Detecting gene-gene interactions that underlie human diseases, seems optimistic, but, at this point, it seems a valid question … at least if you want to detect gene-gene interactions.

“The historical lack of success in genetic studies of complex disease can largely be attributed, not to ignored biological interactions, but rather to under-powered studies that surveyed only a fraction of genetic variation …”

thanks for the pic heckyeahart

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One day, each of us may have the dubious pleasure of browsing our genomes.  What will we find?   Risk for this?  Risk for that?  Protection for this? and that?  Fast twitching muscles & wet ear wax?  Certainly.  Some of the factors will give us pause, worry and many restless nights.  Upon these genetic variants we will likely wonder, “why me? and, indeed, “why my parents (and their parents) and so on?”

Why the heck! if a genetic variant is associated with poor health, is it floating around in human populations?

A complex question, made moreso by the fact that our modern office-bound, get-married when you’re 30, live to 90+ lifestyle is so dramatically different than our ancestors. In the area of mental health, there are perhaps a few such variants – notably the deaded APOE E4 allele – that are worth losing sleep over, perhaps though, after you have lived beyond 40 or 50 years of age.

Another variant that might be worth consideration – from cradle-to-grave – is the so-called 5HTTLPR a short stretch of concatenated DNA repeats that sits in the promoter region of the 5-HTT gene and – depending on the number of repeats – can regulate the transcription of 5HTT mRNA.  Much has been written about the unfortunateness of this “short-allele” structural variant in humans – mainly that when the region is “short”, containing 14 repeats, that folks tend to be more anxious and at-risk for anxiety disorders.  Folks with the “long” (16 repeat variant) tend to be less anxious and even show a pattern of brain activity wherein the activity of the contemplative frontal cortex is uncorrelated from the emotionally active amygdala.  Thus, 5HTTLPR “long” carriers are less likely to be influenced, distracted or have their cognitive processes disrupted by activity in emotional centers of the brain.

Pity me, a 5HTTLPR “short”/”short”  who greatly envies the calm, cool-headed, even-tempered “long”/”long” folks and their uncorrelated PFC-amygdala activity.  Where did their genetic good fortune come from?

Klaus Peter Lesch and colleagues say the repeat-containing LPR DNA may be the remnants of an ancient viral insertion or transposing DNA element insertion that occurred some 40 million years ago.  In their article entitled, “The 5-HT transporter gene-linked polymorphic region (5-HTTLPR) in evolutionary perspective:  alternative biallelic variation in rhesus monkeys“, they demonstrate that the LPR sequences are not found in primates outside our simian cousins (baboons, macaques, chimps, gorillas, orangutans).  More recently, the ancestral “short” allele at the 5HTTLPR acquired some additional variation leading to the rise of the “long” allele which can be found in chimps, gorillas, orangutans and ourselves.

So I missed out on inheriting “CCCCCCTGCACCCCCCAGCATCCCCCCTGCACCCCCCAGCAT” (2 extra repeats of the ancient viral insertion) which could have altered the entire emotional landscape of my life.  Darn, to think too, that it has been floating around in the primate gene pool all these years and I missed out on it.  Drat!

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just a pointer to: Genetic Future’s pointer to the recent article, “Family become first to have DNA sequenced for non-medical reasons“.    The father suggests, “it will be ethically improper if you don’t have your children sequenced“.

Early days.

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According to the authors of  “Protective effect of CRHR1 gene variants on the development of adult depression following childhood maltreatment: replication and extension”  [PMID: 19736354], theirs is “the first instance of Genes x Environment research that stress has been ascertained by more than 1 study using the same instrument“.  The gene they speak of is the Corticotropin-releasing hormone receptor 1 (CRHR1) gene (SNPs rs7209436, rs110402, rs242924 which can form a so-called T-A-T haplotype which has been associated with protection from early life stress (as ascertained using the Childhood Trauma Questionnaire CTQ)).

The research team examined several populations of adults and, like many other studies, found that early life stress was associated with symptoms of depressive illness but, like only 1 previous study, found that the more T-A-T haplotypes a person has (0,1,or 2) the less likely they were to suffer these symptoms.

Indeed, the CRHR1 gene is an important player in a complex network of hormonal signals that regulate the way the body (specifically the hypothalamic pituitary adrenal axis) transduces the effects of stress.  So it seems quite reasonable to see that individual differences in ones ability to cope with stress might correlate with genotype here.   The replication seems like a major step forward in the ongoing paradigm shift from “genes as independent risk factors” to “genetic risk factors being dependent on certain environmental forces”.  The authors suggest that a the protective T-A-T haplotype might play a role in the consolidation of emotional memories and that CRHR1 T-A-T carriers might have a somewhat less-efficient emotional memory consolidation (sort of preventing disturbing memories from making it into long-term storage in the first place?) – which is a very intriguing and testable hypothesis.

On a more speculative note … consider the way in which the stress responsivity of a developing child is tied to its mother’s own stress responsivity.  Mom’s own secretion of CRH from the placenta is known to regulate gestational duration and thus the size, heartiness and stress responsiveness of her newborn.  The genetic variations are just passed along from generation to generation and provide some protection here and there in an intertwined cycle of life.

The flowers think they gave birth to seeds,
The shoots, they gave birth to the flowers,
And the plants, they gave birth to the shoots,
So do the seeds they gave birth to plants.
You think you gave birth to the child.
None thinks they are only entrances
For the life force that passes through.
A life is not born, it passes through.

anees akbar

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Twin studies have long suggested that genetic variation is a part of healthy and disordered mental life.  The problem however – some 10 years now since the full genome sequence era began – has been finding the actual genes that account for this heritability.

It sounds simple on paper – just collect lots of folks with disorder X and look at their genomes in reference to a demographically matched healthy control population.  Voila! whatever is different is a candidate for genetic risk.  Apparently, not so.

The missing heritability problem that clouds the birth of the personal genomes era refers to the baffling inability to find enough common genetic variants that can account for the genetic risk of an illness or disorder.

There are any number of reasons for this … (i) even as any given MZ and DZ twin pair shares genetic variants that predispose them toward the similar brains and mental states, it may be the case that different MZ and DZ pairs have different types of rare genetic variation thus diluting out any similar patterns of variation when large pools of cases and controls are compared …  (ii) also, the way that the environment interacts with common risk-promoting genetic variation may be quite different from person to person – making it hard to find variation that is similarly risk-promoting in large pools of cases and controls … and many others I’m sure.

One research group recently asked whether the type of common genetic variation(SNP vs. CNV) might inform the search for the missing heritability.  The authors of the recent paper, “Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls” [doi:10.1038/nature08979] looked at an alternative to the usual SNP markers – so called common copy number variants (CNVs) – and asked if these markers might provide a stronger accounting for genetic risk.  While a number of previous papers in the mental health field have indeed shown associations with CNVs, this massive study (some 3,432 CNV probes in 2000 or so cases and 3000 controls) did not reveal an association with bipolar disorder.  Furthermore, the team reports that common CNV variants are already in fairly strong linkage disequilibrium with common SNPs and so perhaps may not have reached any farther into the abyss of rare genetic variation than previous GWAS studies.

Disappointing perhaps, but a big step forward nonetheless!  What will the personal genomes era look like if we all have different forms of rare genetic variation?

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This past friday, I attended my first meditation session at my new yoga school.  I love this school and hope – someday – to make it through the full Ashtanga series and other sequences the instructors do.  In the meantime, I found myself sitting on my folded up blanket, letting my mind wander, listening to my breath and just trying to enjoy the moment.

What a wonderful experience it was … it felt great!  … I think I my have even given my brain a rest. A simple kindness to repay it for all it has done for me!

Although I did not know what I was supposed to be “doing” during meditation, the experience itself has me hooked and fascinated with a new research article, “Genetic control over the resting brain” [doi: 10.1073/pnas.0909969107]  by David Glahn and colleages.

Reading this paper, I learned that my brain “at rest” is really very active with neural activity in a series of interconnected circuits known as the default network.  Moreover, the research team finds that many of these interconnected circuits fire together in a way that is significantly influenced by genetic factors (overall heritability of about 0.42).  By analyzing the resting state (lay in the MRI and let your mind wander) patterns of activity in 333 folks from extended pedigrees, the team shows that certain interconnections (neural activity between 2 or more regions) within the default network are more highly correlated in people who are more related to each other.  For example, the left parahippocampal region was genetically correlated with many of the other brain areas in the default network.

Of course, these genetic effects on resting state connectivity are far from determinative, and the authors noted that some interconnections within the default network were more sensitive to environmental factors – such as functional connectivity between right temporal-parietal & posterior cingulate/precuneus & medial prefronal cortex.

Wow, so my resting state activity must – at some level – as a partial product of my genome – be rather unique and special.  It certainly felt that way as my mind wandered freely during meditation class. The authors point out that their heritability study lays more groundwork for follow-up gene hunting expeditions to isolate specific genetic variants.  This will be very exciting!

Some other items from their paper that I’ll be pondering in my next meditation class are the facts that these default neural networks are already present in the infant brain!  and in our non-human primate cousins (even when they are not conscious)!  Whoa!  These genetics & resting-state brain studies will really push our sense of what it means to be human, to be unique, to be interconnected by a common (genetic) thread from generation to generation over vast spatial and temporal distances (is this karma of sorts?).

I suppose yogis & other practitioners of meditation might be bemused at this recent avenue of “cutting edge” scientific inquiry – I mean – duh?!  of course, it makes sense that by remaining calm and sitting quietly that we would discover ourselves.

Related posts here, here, here

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Just a pointer to a great book – The Loss of Sadness: How Psychiatry Transformed Normal Sorrow into Depressive Disorder by Allan V. Horwitz and Jerome C. Wakefield.  Its an in-depth treatment on the many reasons and contexts in which we – quite naturally – feel sad and depressed and the way in which diagnostic criteria can distort the gray area between normal sadness and a psychiatric disorder.  I really enjoyed the developmental perspective on the natural advantages of negative emotions in childhood (a signal to attract caregivers) as well as the detailed evolution of the DSM diagnostic criteria.  The main gist of the book is that much of what psychiatrists treat as emotional disorders are more likely just the natural responses to the normal ups and downs of life – not disorders at all.  A case for American consumers as pill-popping suckers to medical-pharma-marketing overreach (here’s a related post on this overreach notion pointing to the work of David Healy).

Reading the book makes me feel liberated from the medical labels that are all too readily slapped on healthy people.  There is much that is healthy about sadness and many reasons and contexts in which its quite natural.  From now on, instead of trying to escape from, or rid myself of sadness, I will embrace it and let myself feel it and work through it.  Who knows, maybe this is a good first step in a healthy coping process.

If depressed emotional states are more a part of the normal range of emotions (rather than separate disordered states) then does this allow us to make predictions about the underlying genetic bases for these states?    Perhaps not.   However, on page 172, the authors apply their critical view to the highly cited Caspi et al., article (showing that 5HTT genotype interacts with life stress in the presentation of depressive illness – critiqued here).  They note that the incidence of depression at 17% in the sample is much too high – most certainly capturing a lot of normal sadness.  Hence, the prevalent short allele in the 5HTT promoter might be better thought of as a factor that underlies how healthy people respond to social stress – rather than as a drug target or risk factor for psychiatric illness.

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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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** PODCAST accompanies this post**

I have a little boy who loves to run and jump and scream and shout – a lot.  And by this, I mean running – at full speed and smashing his head into my gut,  jumping – off the couch onto my head,  screaming – spontaneous curses and R-rated body parts and bodily functions.  I hope you get the idea.  Is this normal? or (as I oft imagine) will I soon be sitting across the desk from a school psychologist pitching me the merits of an ADHD diagnosis and medication?

Of course, when it comes to behavior, there is not a distinct line one can cross from normal to abnormal.  Human behavior is complex, multi-dimensional and greatly interpreted through the lens of culture.  Our present culture is highly saturated by mass-marketing, making it easy to distort a person’s sense of “what’s normal” and create demand for consumer products that folks don’t really need (eg. psychiatric diagnoses? medications?).   Anyhow, its tough to know what’s normal.  This is an important issue to consider for those (mass-marketing hucksters?) who might be inclined to promote genetic data as “hard evidence” for illness, disorder or abnormality of some sort.

With this in mind, I really enjoyed a recent paper by Stollstorff et al., “Neural response to working memory load varies by dopamine transporter genotype in children” [doi:10.1016/j.neuroimage.2009.12.104] who asked how the brains of healthy children functioned, even though they carry a genotype that has been widely associated with the risk of ADHD.  Healthy children who carry genetic risk for ADHD. Hmm, might this be my boy?

The researchers looked at a 9- vs. 10-repeat VNTR polymorphism in the 3′-UTR of the dopamine transporter gene (DAT1).  This gene – which encodes the very protein that is targeted by so many ADHD medications – influences the re-uptake of dopamine from the synaptic cleft.  In the case of 10/10 genotypes, it seems that DAT1 is more highly expressed, thus leading to more re-uptake and hence less dopamine in the synaptic cleft.  Generally, dopamine is needed to enhance the signal/noise of neurotransmission, so – at the end of the day – the 10/10 genotype is considered less optimal than the 9/9-repeat genotype.  As noted by the researchers, the ADHD literature shows that the 10-repeat allele, not the 9-repeat, is most often associated with ADHD.

The research team asked these healthy children (typically developing children between 7 and 12 years of age) to perform a so-called N-back task which requires that children remember words that are presented to them one-at-a-time.  Each time a new word is presented, the children had to decide whether that word was the same as the previous word (1-back) or the previous, previous word (2-back).  Its a maddening task and places an extreme demand on neural circuits involved in active maintenance of information (frontal cortex) as well as inhibition of irrelevant information that occurs during updating (basal ganglia circuits).

As the DAT1 protein is widely expressed in the basal ganglia, the research team asked where in the brain was variation in the DAT1 (9- vs. 10-repeat) associated with neural activity?  and where was there a further difference between 1-back and 2-back?  Indeed, the team finds that brain activity in many regions of the basal ganglia (caudate, putamen, substantia nigra & subthalamic nucleus) were associated with genetic variation in DAT1.  Neat!  the gene may be exerting an influence on brain function (and behavior) in healthy children, even though they do not carry a diagnosis.  Certainly, genes are not destiny, even though they do influence brain and behavior.

What was cooler to me though, is the way the investigators examined the role of genetic variation in the 1-back (easy or low load condition) vs. 2-back (harder, high-load condition) tasks.  Their data shows that there was less of an effect of genotype on brain activation in the easy tasks.  Rather, only when the task was hard, did it become clear that the basal ganglia in the 10/10 carriers was lacking the necessary brain activation needed to perform the more difficult task.  Thus, the investigators reveal that the genetic risk may not be immediately apparent under conditions where heavy “loads” or demands are not placed on the brain.  Cognitive load matters when interpreting genetic data!

This result made me think that genes in the brain might be a lot like genes in muscles.  Individual differences in muscle strength are not associated with genotype when kids are lifting feathers.  Only when kids are actually training and using their muscles, might one start to see that some genetically advantaged kids have muscles that strengthen faster than others.  Does this mean there is a “weak muscle gene” – yes, perhaps.  But with the proper training regimen, children carrying such a “weak muscle gene” would be able to gain plenty of strength.

I guess its off to the mental and physical gyms for me and my son.

** PODCAST accompanies this post** also, here’s a link to the Vaidya lab!

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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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If you’re a coffee drinker, you may have noticed the new super-sized portions available at Starbucks.  On this note, it may be worth noting that caffeine is a potent psychoactive substance of which – too much – can turn your buzz into a full-blown panic disorder.  The Diagnostic and Statistical Manual for psychiatry outlines a number of caffeine-related conditions mostly involving anxieties that can arise when the natural alertness-promoting effects are pushed to extremes.  Some researchers have begun to explore the way the genome interacts with caffeine and it is likely that many genetic markers will surface to explain some of the individual differences in caffeine tolerance.

Here’s a great paper, “Association between ADORA2A and DRD2 Polymorphisms and Caffeine-Induced Anxiety” [doi: 10.1038/npp.2008.17] wherein polymorphisms in the adenosine A2A receptor (ADORA2A encodes the protein that caffeine binds to and antagonizes) – as well as the dopamine D2 receptor (DRD2 encodes a protein whose downstream signals are normally counteracted by A2A receptors) — show associations with anxiety after the consumption of 150mg of caffeine (about an average cup of coffee – much less than the super-size, super-rich cups that Starbucks sells).  The variants, rs5751876 (T-allele), rs2298383 (T-allele) and rs4822492 (G-allele) from the ADORA2A gene as well as rs1110976 (-/G genotype) from the DRD2 gene showed significant increases in anxiety in a test population of 102 otherwise-healthy light-moderate regular coffee drinkers.

My own 23andMe data only provides a drop of information suggesting I’m protected from the anxiety-promoting effects.  Nevertheless, I’ll avoid the super-sizes.
rs5751876 (T-allele)  C/C – less anxiety
rs2298383 (T-allele) – not covered
rs4822492 (G-allele) – not covered
rs1110976 (-/G genotype) – not covered

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Just a pointer to onetime University of Edinburgh Professor C.H. Waddington’s 1972 Gifford Lecture on framing the genes vs. environment debate of human behavior.  Although Waddington is famous for his work on population genetics and evolutionary change over time, several of his concepts are experiencing some resurgence in the neuroimaging and psychological development literatures these days.

One term, CHREOD, combines the Greek word for “determined” or “necessary” and the word for “pathway.” It describes a system that returns to a steady trajectory in contrast to homeostasis which describes a system which returns to a steady state.  Recent reviews on the development of brain structure have suggested that the “trajectory” (the actual term “chreod” hasn’t survived) as opposed to any specific time point is the essential phenotype to use for understanding how genes relate to psychological development.  Another term, CANALIZATION, refers to the ability of a population to produce the same phenotype regardless of variability in its environment or genotype.  A recent neonatal twin study found that the heritability of grey matter in neonatal humans was rather low.  However it seems to then rise until young adulthood – as genetic programs presumably kick-in – and then decline again.  Articles by neurobiologist Jay N. Giedd and colleagues have suggested that this may reflect Waddington’s idea of canalization.  The relative influence of genes vs. environment may change over time in ways that perhaps buffer against mutations and/or environmental insults to ensure the stability and robustness of functions and processes that are both appropriate for survival and necessary for future development.  Another Waddington term, EPIGENETIC LANDSCAPE, refers to the limitations on how much influence genes and environment can have on the development of a given cell or structure.  Certainly the environment can alter the differentiation, migration, connectivity, etc. of neurons by only so much.  Likewise, most genetic mutations have effects that are constrained or compensated for by the larger system as well.

Its amazing to me how well these evolutionary genetic concepts capture the issues at the nexus of of genetics and cognitive development.  From his lecture, it is clear that Waddington was not unaware of this.  Amazing to see a conceptual roadmap laid out so long ago.  Digging the book cover art as well!

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We hope, that you choke, that you choke.
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Coping with fear and anxiety is difficult.  At times when one’s life, livelihood or loved one’s are threatened, we naturally hightenen our senses and allocate our emotional and physical resources for conflict.  At times, when all is well, and resources, relationships and relaxation time are plentiful, we should unwind and and enjoy the moment.  But most of us don’t.  Our prized cognitive abilities to remember, relive and ruminate on the bad stuff out there are just too well developed – and we suffer – some more than others  (see Robert Saplosky’s book “Why Zebras Don’t Get Ulcers” and related video lecture (hint – they don’t get ulcers because they don’t have the cognitive ability to ruminate on past events).  Such may be the flip side to our (homo sapiens) super-duper cognitive abilities.

Nevertheless, we try to understand our fears and axieties and understand their bio-social-psychological bases. A recent paper entitled, “A Genetically Informed Study of the Association Between Childhood Separation Anxiety, Sensitivity to CO2, Panic Disorder, and the Effect of Childhood Parental Loss” by Battaglia et al. [Arch Gen Psychiatry. 2009;66(1):64-71] brought to mind many of the complexities in beginning to understand the way in which some individuals come to suffer more emotional anguish than others.  The research team addressed a set of emotional difficulties that have been categorized by psychiatrists as “panic disorder” and involving sudden attacks of fear, sweating, racing heart, shortness of breath, etc. which can begin to occur in early adulthood.

Right off the bat, it seems that one of the difficulties in understanding such an emotional state(s) are the conventions (important for $$ billing purposes) used to describe the relationship between “healthy” and “illness” or “disorder”.  I mean, honestly, who hasn’t experienced what could be described as a mild panic disorder once or twice?  I have, but perhaps that doesn’t amount to a disorder.  A good read on the conflation of normal stress responses and disordered mental states is “Transforming Normality into Pathology: The DSM and the Outcomes of Stressful Social Arrangements” by Allan V. Horwitz.

Another difficulty in understanding how and why someone might experience such a condition has to do with the complexities of their childhood experience (not to mention genes). Child development and mental health are inextrictably related, yet, the relationship is hard to understand.  Certainly, the function of the adult brain is the product of countless developmental unfoldings that build upon one another, and certainly there is ample evidence that when healthy development is disrupted in a social or physical way, the consequences can be very unfortunate and long-lasting. Yet, our ability to make sense of how and why an individual is having mental and/or emotional difficulty is limited.  Its a complex, interactive and emergent set of processes.

What I liked about the Battaglia et al., article was the way in which they acknowledged all of these complexities and – using a multivariate twin study design – tried to objectively measure the effects of genes and environment (early and late) as well as candidate biological pathways (sensitivity to carbon dioxide).  The team gathered 346 twin pairs (equal mix of MZ and DZ) and assessed aspects of early and late emotional life as well as the sensitivity to the inhalation of 35% CO2 (kind of feels like suffocating and is known to activate fear circuitry perhaps via the ASC1a gene).   The basic notion was to parcel out the correlations between early emotional distress and adult emotional distress as well as with a very specific physiological response (fear illicited by breathing CO2).  If there were no correlation or covariation between early and late distress (or the physiological response) then perhaps these processes are not underlain by any common mechanism.

However, the team found that there was covariation between early life emotion (criteria for separation anxiety disorder) and adult emotion (panic disorder) as well as the physiological/fear response illicited by CO2.  Indeed there seems to be a common, or continuous, set of processes whose disruption early in development can manifest as emotional difficulty later in development.  Furthermore, the team suggests that the underlying unifying or core process is heavily regulated by a set of additive genetic factors.  Lastly, the team finds that the experience of parental loss in childhood increased (but not via an interaction with genetic variation) the strength of the covariation between early emotion, late emotion and CO2 reactivity.  The authors note several limitations and cautions to over-interpreting these data – which are from the largest such study of its kind to date.

For individuals who are tangled in persistent ruminations and emotional difficulties, I don’t know if these findings help.  They seem to bear out some of the cold, cruel logic of life and evolution – that our fear systems are great at keeping us alive when we’ve had adverse experience in childhood, but not necessarily happy.  On the other hand, the covariation is weak, so there is no such destiny in life, even when dealt unfortunate early experience AND genetic risk.  I hope that learning about the science might help folks cope with such cases of emotional distress.

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DON’T tell the grant funding agencies, but, in at least one way, the effort to relate genetic variation to individual differences in cognitive function is a totally intractable waste of money.

Let’s say we ask a population of folks to perform a task – perhaps a word memory task – and then we use neuroimaging to identify the areas of the brain that (i) were associated with performance of the task, and (ii) were not only associated with performance, but were also associated with genetic variation in the population.  Indeed, there are already examples of just this type of “imaging-genetic” study in the literature.  Such studies form a crucial translational link in understanding how genes (whose biochemical functions are most often studied in animal models) relate to human brain function (usually studied with cognitive psychology). However, do these genes relate to just this task? What if subjects were recalling objects? or feelings?  What if subjects were recalling objects / experiences / feelings / etc. from their childhoods?  Of course, there are thousands of common cognitive operations one’s brain routinely performs, and, hence, thousands of experimental paradigms that could be used in such “imaging-genetic” gene association studies.  At more than $500/hour (some paradigms last up to 2 hours) in imaging costs, the translational genes-to-cognition endeavor could get expensive!

DO tell the grant funding agencies that this may not be a problem any longer.

The recent paper by Liu and colleagues “Prefrontal-Related Functional Connectivities within the Default Network Are Modulated by COMT val158met in Healthy Young Adults” [doi: 10.1523/jneurosci.3941-09.2010] suggests an approach that may simplify matters.  Their approach still involves genotyping (in this case for rs4680) and neuroimaging.  However, instead of performing a specific cognitive task, the team asks subjects to lay in the scanner – and do nothing.  That’s right – nothing – just lay still with eyes closed and just let the mind wander and not to think about anything in particular – for a mere 10 minutes.  Hunh?  What the heck can you learn from that?

It turns out that one can learn a lot.  This is because the neural pathways that the brain uses when you are actively doing something (a word recall task) are largely intact even when you are doing nothing.  Your brain does not “turn off” when you are laying still with your eyes closed and drifting in thought.  Rather, your brain slips into a kind of default pattern, described in studies of  “default networks” or “resting-state networks” where wide-ranging brain circuits remain dynamically coupled and actively exchange neural information.  One really great paper that describes these networks is a free-and-open article by Hagmann et al., “Mapping the Structural Core of Human Cerebral Cortex” [doi: 10.1371/journal.pbio.0060159] from which I’ve lifted their Figure 1 above.  The work by Hagmann et al., and others show that the brain has a sort of “connectome” where there are thousands of “connector hubs” or nodes that remain actively coupled (meaning that if one node fires, the other node will fire in a synchronized way) when the brain is at rest and when the brain is actively performing cognitive operations.  In a few studies, it seems that the strength of functional coupling in certain brain areas at rest is correlated (positively and negatively) with the activation of these areas when subjects are performing a specific task.

In the genetic study reported by Liu and colleagues, they found that genotype (N=57) at the dopaminergic COMT gene correlated with differences in the functional connectivity (synchronization of firing) of nodes in the prefrontal cortex.  This result is eerily similar to results found for a number of specific tasks (N-back, Wisconsin Card Sorting, Gambling, etc.) where COMT genotype was correlated with the differential activation of the frontal cortex during the task.  So it seems that one imaging paradigm (lay still and rest for 10 minutes) provided comparable insights to several lengthy (and diverse) activation tasks.  Perhaps this is the case. If so, might it provide a more direct route to linking genetic variation with cognitive function?

Liu and colleagues do not comment on this proposition directly nor do they seem to be over-interpreting their results in they way I have editorialized things here.  They very thoughtfully point out the ways in which the networks they’ve identified and similar and different to the published findings of others.  Certainly, this study and the other one like it are the first in what might be a promising new direction!

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Darwin's finches or Galapagos finches. Darwin,...
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In his book, The Beak of the Finch, Jonathan Weiner describes the great diversity of finches on the Galapagos Islands – so much diversity – that Darwin himself initially thought the finch variants to be completely different birds (wrens, mockingbirds, blackbirds and “gross-bills”).  It turns out that one of the pivotal events in Charles Darwin‘s life was his work in 1837 with the great ornithologist John Gould who advised that the birds were actually closely related finches and also specific to separate islands!

Fast-forward to 2009, and we are well on our way to understanding how closely related species can, via natural selection of genetic variation, diverge across space and time. The BMP4 and CaM genes, for example, have been associated with beak morphology in what are now known as Darwin’s Finches.  Wonderful indeed, but now consider, for a moment, the variability – not of finch beaks – but of human cognition.

If you’ve ever been a part of a team or group project at work or school, you know that very few people THINK just like you.  Indeed, variability in human cognition can be the source of a lot of frustration.  Let’s face it, people have different experiences stored away (in a highly distributed fashion) in their memory banks, and each persons brain is extensively wired with trillions of synapses.  Of course! nobody thinks like you.  How could such a complex organ function exactly the same way in 2 separate individuals.

Perhaps then, if you were an alien visitor (as Darwin was to the Galapagos Islands) and you watched 5 separate individuals devise a plan to – oh lets just say, to improve healthcare accessibility and affordability – and you measured individuals based solely on their “thinking patterns” you might conclude (as Darwin did) that you were dealing with 5 separate “species”.  Just flip the TV between FOX, CNN, CNBC, CSPAN and MSNBC if you’re not convinced!

However, if you were to take a more in-depth approach and crack open a current issue of a neuroimaging journal – you might come to the exact opposite conclusion.  That’s right.  If you looked at patterns of brain activity and other indirect measures of neural network dynamics (what I casually meant by “thinking patterns” ) you would mostly see conclusions drawn from studies where many individuals are pooled into large groups and then probed for forms of brain activity that are common rather than different.  Most studies today show that humans use a common set of neural systems to perform mental operations (e.g., recalling events and information).  Brain structures including the hippocampus, frontal cortex, thalamus, parietal cortex are all known to be involved in deciding whether or not you have seen something before.  Thus, if you perform an fMRI brain scanning study on individuals and ask them to complete an episodic memory recall task (show them a list of words before scanning and then – when they are in the scanner – ask them to respond to words they remember seeing), you will likely observe that all or most individuals show some BOLD response activity in these structures.

OK great! But can you imagine where we would be if Charles Darwin returned home from his voyage and said, “Oh, just a bunch of birds out there … you know, the usual common stuff … beaks, wings, etc.”  I’d rather not imagine.

Enter Professor Michael Miller and colleagues and their recent paper, “Unique and persistent individual patterns of brain activity across different memory retrieval tasks” [doi:10.1016/j.neuroimage.2009.06.033].  This paper looks – not just at the common stuff – but the individual differences in BOLD responses among individuals who perform a number of different memory tasks.  The team reports that there are dramatic differences in the patterns of brain activity between individuals.  This can be seen very clearly in Figure 1 which shows left hemisphere activity associated with memory recall.  The group data (N=14) show nice clean frontal parietal activations – but when the data is broken down on an individual-by-individual basis, you might – without knowing that the all subjects were performing the same recall tasks – suspect that each person was doing or “thinking” something quite different.  The research team then re-scanned each subject several months later and asked whether the individual differences were consistent from person to person. Indeed, the team shows that the 2nd brain scan is much more similar to the first (correlations were about 0.5) and that the scan-rescan data for an individual was more similar than the correlation between any single person and the rest of the group (about 0.25).  Hence, as the authors state, “unique patterns of brain activity persist across different tasks”.

Vive la difference!  Yes, the variability is – if you’re interested in using genetics to understand human history and cognitive development – the really exciting part!  Of course, genetics is not the main reason for the stable individual-to-individual differences in brain activity.  There are likely to be many factors that could alter the neural dynamics of broadly distributed neural networks used for memory recall.  Environment, experience, gender are just a few factors that are known to influence the function of these networks.  The authors reveal that individuals may also differ in the strategies and criteria they use to make decisions about whether they can recall or detect a previously viewed item.  Some people will respond only when they are very certain (high criteria) and others will respond even if they feel only slightly sure they’ve seen an item before (low criteria).  The authors show in Figure 5 that the folks who showed similar decision criteria are more likely to have similar patterns of brain activity.

Perhaps then, the genetic differences that (partially) underlie individual differences in brain activity might relate to personality or other aspects of decision making?  I don’t have a clue, but I do know that this approach – of looking carefully at individual differences – is a step forward to doing what Darwin (and don’t forget John Gould!) is so well known for.  Understand where the variation comes from, and you will understand where you come from!

I will follow this literature more closely in the months to come.

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The human brain is renown for its complexity.  Indeed, while we often marvel at the mature brain in its splendid form and capability, its even more staggering to consider how to build such a powerful computing machine.  Admittedly, mother nature has been working on this for a long time – perhaps since the first neuronal cells and cell networks appeared on the scene hundreds of millions of years ago.  In that case, shouldn’t things be pretty well figured out by now?  Consider the example of Down syndrome, a developmental disability that affects about 1 in 800 children.  In this disability, a mere 50% increase in a relative handful of genes is enough to alter the development of the human brain.  To me, its somehow surprising that the development of such a complex organ can be so sensitive to minor disruptions – but perhaps that’s the main attribute of the design – to factor-in aspects of the early environment whilst building.  Perhaps?

So what are these genes that, in the case of Down syndrome, can alter the course of brain development?  Well, it is widely known that individuals with Down syndrome have an extra copy of chromosome 21.  However, the disorder does not necessarily depend on having an extra copy of each and every gene on chromosome 21.   Rare partial trisomies of only 5.4 million base-pairs on 21q22 can produce the same developmental outcomes as the full chromosome trisomy.  Also, it turns out that mice have a large chunk of mouse chromosome 16 that has the very same linear array of genes (synteny) found on human chromosome 21 (see the figure here).  In mice that have an extra copy of about 104 genes, (the Ts65Dn segment above) many of the developmental traits related to brain structure and physiology are observed.  In mice that have an extra copy of about 81 genes, this is also the case (the Ts1Cje segment).

To focus this line of research even further, the recent paper by Belichenko et al., “The “Down Syndrome Critical Region” Is Sufficient in the Mouse Model to Confer Behavioral, Neurophysiological, and Synaptic Phenotypes Characteristic of Down Syndrome” [DOI:10.1523/JNEUROSCI.1547-09.2009]  examine brain structure, physiology and behavior in a line of mice that carry an extra copy of just 33 genes (this is the Ts1Rhr segment seen in the figure above).  Interestingly, these mice display many of the various traits (admittedly mouse versions) that have been associated with Down syndrome – thus greatly narrowing the search from a whole chromosome to a small number of genes.  20 out of 48 Down syndrome-related traits such as enlargement of dendritic spines, reductions of dendritic spines, brain morphology and various behaviors were  observed.  The authors suggest that 2 genes in this Ts1Rhr segment, in particular, look like intriguing candidates.  DYRK1A a gene, that when over-expressed can lead to hippocampal-dependent learning deficits, and KCNJ6, a potassium channel which could readily drive neurons to hyperpolarize if over-expressed.

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*** PODCAST accompanies this post ***

Nowadays, it seems that genomics is spreading beyond the rarefied realm of science and academia into the general, consumer-based popular culture.  Quelle surprise!?  Yes, the era of the personal genome is close at hand, even as present technology  provides – directly to the general consumer public – a  genome-wide sampling of many hundreds of thousands of single nucleotide variants.   As curious early adopters begin to surf their personal genomic information, one might wonder how they, and  homo sapiens in general, will ultimately utilize their genome information.  Interestingly, some have already adapted the personal genome to facilitate what homo sapiens loves to do most – that is, to interact with one another.  They are at the vanguard of a new and hip form of social interaction known as “personal genome sharing”.  People connecting in cyberspace – via  haplotype or sequence alignment – initiating new social contacts with distant cousins (of which there may be many tens of thousands at 5th cousins and beyond).  Sharing genes that regulate the social interaction of sharing genes, as it were.

A broader view of social genes, within the context of our neo-Darwinian synthesis, however, shows that the relationship between the genome and social behavior can be rather complex.  When genes contribute directly to the fitness of an organism (eg. sharper tooth and claw), it is relatively straightforward to explain how novel fitness-conferring genetic variants increase in frequency from generation to generation.  Even when genetic variants are selfish, that is, when they subvert the recombination or gamete production machinery, in some cases to the detriment of their individual host, they can still readily spread through populations.  However, when a new genetic variant confers a fitness benefit to unrelated individuals by enhancing a cooperative or reciprocally-altruistic form of social interaction, it becomes more difficult to explain how such a novel genetic variant can take hold and spread in a large, randomly mating population.  Debates on the feasibility natural selection acting “above the level of the individual” seem settled against this proposition.  However, even in the face of such difficult population genetic conundrums, research on the psychology, biology and evolutionary genetics of social interactions continues unabated.  Like our primate and other mammalian cousins, with whom homo sapiens shares some 90-99% genetic identity, we are an intensely social species as our literature, poetry, music, cinema, not to mention the more recent twittering, myspacing, facebooking and genome-sharing demonstrate.

Indeed, many of the most compelling examples of genetic research on social interactions are those that reveal the devastating impacts on psychological development and function when social interaction is restricted.  In cases of maternal and/or peer-group social separation stress, the effects on gene expression in the brain are dramatic and lead to long-lasting consequences on human emotional function.  Studies on loneliness by John Cacioppo and colleagues reveal that even the perception of loneliness is aversive enough to raise arousal levels which, may, have adaptive value.  A number of specific genes have been shown to interact with a history of neglect or maltreatment in childhood and, subsequently, increase the risk of depression or emotional lability in adulthood.  Clearly then, despite the difficulties in explaining how new “social genes” arise and take hold in populations, the human genome been shaped over evolutionary time to function optimally within the context of a social group.

From this perspective, a new paper, “Oxytocin receptor genetic variation relates to empathy and stress reactivity in humans” by Sarina Rodrigues and colleagues [doi.org/10.1073/pnas.0909579106] may be of broad interest as a recent addition to a long-standing, but now very rapidly growing, flow of genetic research on genes and social interactions.  The research team explored just a single genetic variant in the gene encoding the receptor for a small neuropeptide known as oxytocin, a protein with well-studied effects on human social interactions.  Intra-nasal administration of oxytocin, for example, has been reported to enhance eye-gaze, trust, generosity and the ability to infer the emotional state of others.  In the Rodrigues et al., study, a silent G to A change (rs53576) within exon 3 of the oxytocin receptor (OXTR) gene is used to subgroup an ethnically diverse population of 192 healthy college students who participated in assessments for pro-social traits such as the “Reading the Mind in the Eyes” (RMET) test of empathetic accuracy as well as measures of dispositional empathy.  Although an appraisal of emotionality in others is not a cooperative behavior per se, it has been demonstrated to be essential for healthy social function.  The Rodrigues et al., team find that the subgroup of students who carried the GG genotype were more accurate and able to discern the emotional state of others than students who carried the A-allele.  Such molecular genetic results are an important branching point to further examine neural and cognitive mechanisms of empathy as well as long-standing population genetic concerns of how new genetic variants like the A-allele of rs53576 arose and managed to take-hold in human populations.

Regarding the latter, there are many avenues for inquiry, but oxytocin’s role in the regulation of the reproductive cycle and social behavior stands out as an ideal target for natural selection.  Reproductive and behavioral-genetic factors that influence the ritualized interactions between males and females have been demonstrated to be targets of natural selection during the process of speciation.  New variants can reduce the cross-mating of closely related species who might otherwise mate and produce sterile or inviable hybrid offspring.  So-called pre-mating speciation mechanisms are an efficient means, therefore, to ensure that reproduction leads to fit and fertile offspring.  In connection with this idea, reports of an eye-gaze assessment similar to the RMET test used by Rodrigues et al., revealed that women’s pupils dilate more widely to photos of men they were sexually attracted to during their period of the menstrual cycle of greatest fertility, thus demonstrating a viable link between social preference and reproductive biology.  However, in the Rodrigues et al., study, it was the G-allele that was associated with superior social appraisal and this allele is not the novel allele, but rather the ancestral allele that is carried by chimpanzees, macaques and orangutans.  Therefore, it does not seem that the novel A-allele would have been targeted by natural selection in this type of pre-mating social-interaction scenrio.  Might other aspects of OXTR function provide more insight then?  Rodrigues et al.,  explore the role of the gene beyond the social interaction dimension and note that OXTR is widely expressed in limbic circuitry and also plays a broader modulatory role in many emotional reactivity.  For this reason, they sought to assess the stress responsivity of the participants via changes in heart-rate that are elicited by the unpredictable onset of an acoustic startle.  The results show that the A-allele carriers showed greater stress reactivity and also greater scores on a 12-point scale of affective reactivity.  Might greater emotional vigilance in the face of adversity confer a fitness advantage for A-allele carriers? Perhaps this could be further explored.

Regarding the neural and cognitive mechanisms of empathy and other pro-social traits, the Rodrigues et al., strategy demonstrates that when human psychological research includes genetic information it can more readily be informed by a wealth of non-human animal models.  Comparisons of genotype-phenotype correlations at the behavioral, physiological, anatomical and cellular levels across different model systems is one general strategy for generating hypotheses about how a gene like OXTR mediates and moderates cognitive function and also why it (and human behavior) evolved.  For example, mice that lack the OXTR gene show higher levels of aggression and deficits in social recognition memory.  In humans, genetic associations of the A-allele with autism, and social loneliness form possible translational bridges.  In other areas of human psychology such as in the areas of attention and inhibition, several genetic variants correlate with specific  mental operations and areas of brain activation.  The psychological construct of inhibition, once debated purely from a behavioral psychological perspective, is now better understood to be carried out by a collection of neural networks that function in the lateral frontal cortex as well as basal ganglia and frontal midline.  Individual differences in the activation of these brain regions have been shown to relate to genetic differences in a number of dopaminergic genes, whose function in animal models is readily linked to the physiologic function of specific neural circuits and types of synapses.  In the area of social psychology, where such types of neuroimaging-genetic studies are just getting underway, the use of “hyper-scanning”, a method that involves the simultaneous neuroimaging of two or more individuals playing a social game (prisoners dilemma) reveals a co-activation of dopamine-rich brain areas when players are able to make sound predictions of other participant’s choices.  These types of social games can model specific aspects of reciprocal social interactions such as trust, punishment, policing, sanctions etc. that have been postulated to support the evolution of social behavior via reciprocal altruism.  Similar imaging work showed that intra-nasal administration of oxytocin potently reduced amygdala activation and decreased amygdala coupling to brainstem regions implicated in autonomic and behavioural manifestations of fear.  Such recent examples affirm the presence of a core neural circuitry involved in social interaction whose anatomical and physiological properties can be probed using genetic methods in human and non-human populations.

Although there will remain complexities in explaining how new “social genes” can arise and move through evolutionary space and time (let alone cyberspace!) the inter-flows of genetic data and social psychological function in homo sapiens will likely increase.  The rising tide should inevitably force both psychologists and evolutionary biologists to break out of long-standing academic silos and work together to construct coherent models that are consistent with cognitive-genetic findings as well as population- genetic and phylogenetic data.  Such efforts will heavily depend on a foundation of psychological research into “social genes” in a manner illustrated by Rodrigues et al.

*** PODCAST accompanies this post *** Thanks agian Dr. Rodrigues!!!

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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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