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Archive for the ‘genetics’ Category

Sequencing Neanderthals

Just out now in Cell is a wonderful article on the full sequence of mitochondria DNA from the Neanderthal. The paper is already receiving much interest in the media, and Nature has a news story somewhat misleadingly entitled “First complete Neanderthal genome sequenced“.

Using very rigorous methods for extracting DNA material from the Vindija Cave in Croatia and keeping it clean from (human) contamination, the researchers were able to analyze 35 samples. The paper abstract basically tells the main findings:

A complete mitochondrial (mt) genome sequence was reconstructed from a 38,000 year-old Neandertal individual with 8341 mtDNA sequences identified among 4.8 Gb of DNA generated from ∼0.3 g of bone. Analysis of the assembled sequence unequivocally establishes that the Neandertal mtDNA falls outside the variation of extant human mtDNAs, and allows an estimate of the divergence date between the two mtDNA lineages of 660,000 ± 140,000 years. Of the 13 proteins encoded in the mtDNA, subunit 2 of cytochrome c oxidase of the mitochondrial electron transport chain has experienced the largest number of amino acid substitutions in human ancestors since the separation from Neandertals. There is evidence that purifying selection in the Neandertal mtDNA was reduced compared with other primate lineages, suggesting that the effective population size of Neandertals was small.

But even more interesting is that a month ago, I unexpectedly met one of the co-authors on this paper. Michael Egholm had entered my wife’s gallery for a brief visit to check out her famous microscopy paintings. While it took me some time to understand who he was, and who he worked with (such as Svante Pääbo), our conversation continued after he’d left for the US. Luckily to you English-only readers, Egholm has spent too many years to feel comfortable writing science in his native Danish. He sent me the submitted manuscript with the following comment:

The somewhat simplistic grand purpose of the Neanderthal project is to figure out what makes us human – presumably our unique brain function.  By comparing the Neanderthal sequence with Chimpanzee and Human we can ideally pin point areas on the genome that has undergone rapid evolution the last 660K years (the new split time determined in the paper). It is generally accepted that something dramatic happened within the last 100K years or so which eventually let to the exodus from Africa and population of the rest of the world along with the extinction of other hominoids.  So with the Neanderthal genome we’re on the safe side but a lot closer than our divergence from Chimpanzee (est. at 6M years).  Obviously, we will be blind in areas of the Neanderthal genome that have undergone evolution since the split from modern humans.

The mitochondrial genome is an obvious first milestone in the project because of its overrepresentation and because it does not undergo recombination the analysis is a lot simpler with respect to split time etc.  Anyways, there is one big surprise and/or coincidence in the 13 proteins that are coded for by the mtDNA in that we find one gene with 4 ns changes – this is exactly what we had hoped for and while highly statistically significant within the context of the 13 proteins of the mtDNA it is not within the context of 20K+ genes.

How’s that for an explanation?

-Thomas

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In a recent review article in Nature Reviews Neuroscience, Antonio Rangel, Colin Camerer and Read Montague suggest a framework for neuroeconomic research. Indeed, the very core of its idea is simple, but not simplistic. After reading the article, I think it will provide a useful reference for future research into neuroeconomics, aka value-based decision making. I’ve made a copy of the model here for you to see:

The caption reads:

Basic computations involved in making a choice. Value-based decision making can be broken down into five basic processes: first, the construction of a representation of the decision problem, which entails identifying internal and external states as well as potential courses of action; second, the valuation of the different actions under consideration; third, the selection of one of the actions on the basis of their valuations; fourth, after implementing the decision the brain needs to measure the desirability of the outcomes that follow; and finally, the outcome evaluation is used to update the other processes to improve the quality of future decisions.

In my own emerging work on this arena, I am trying to combine this with recent advances cognitive neuroscience. First, the advances in imaging genetics, i.e., the knowledge and study of how genetic variance leads to specific changes in neurotransmission, which in turn may affect cognition, emotion and behaviour. Second, the advances in the cognitive neuroscience of ageing, i.e, the relationship between age-related changes in brain structures and functions, and mental alterations.

Briefly put, in a just submitted manuscript, I suggest that the Rangel-Camerer-Montague framework can serve as a model for looking at genotype and age effects. This leads us to three advances: first, it provides a better way to illustrate and understand the minute details of the preference and decision making systems. Second, it serves as a demonstration that individual (and intra-individual) differences must be taken into account. The “economic agent” is not a homogenous subject, but an agent that differs from person to person and with persons over time. Finally, it may also serve as a framework for identifying potential ways to induce alterations in the systems, e.g., through medical intervention. More on this story later, given that the manuscript is accepted 😉 For now, here’s an illustration of how genotype (exemplified through COMT, MAO-A and 5-HT) and age effects may expand the model. Of course, this is only scratching the surface, but I hope you’ll see what I mean.

This is an extended version of the Rangel-Camerer-Montague model. Within each processing node, two dimensions are added, here exemplified with the three primary nodes. The genotype dimension is a categorical variable that divides subjects into two or three classes, while the age dimension is continuous (inset, top left).

-Thomas

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protein.jpgEver wondered about the neurobiology of memory — how the brain stores information? And, if you know slightly more, how information is stored beyond the hippocampus, or what happens to memory during recall? If you have anything to do with memory — even having a slight interest in the topic — the journal Neurobiology of Learning and Memory now hosts a special issue on the role of protein synthesis in memory. The issue is packed with updates on the findings and controversies on this topic, and it is certain to bring you to up to date on the neurobiology of memory.

As the editor of this issue, Paul E. Gold, notes in his introduction:

The goal of collecting these papers was not to find a single clear view, laying to rest one alternative view or another—a rather delusional goal at best. Instead, the attempt was to provide a venue through which different perspectives could appear together, with the understanding that all contributors are interested in a common purpose, to identify the ways in which brains make and hold new memories.

So, this issue will probably prove important with regard to mapping out the agreements and disagreements. As Gold notes:

Across these papers, there is agreement on the basic findings. All authors agree that proteins and protein synthesis are important to memory formation, but disagree on the question of whether new protein synthesis specifically triggered by an event is important for the formation of memory for that event. Some of the alternatives suggested include protein synthesis needed to maintain cell integrity, to replenish proteins ‘consumed’ by plasticity mechanisms, and to provide particular proteins that might be modified by experience, with long-lasting modification perhaps themselves representing cellular memory.

(…)

The diversity of opinion collected in this special issue, and briefly summarized here, offers an opportunity for readers to examine how different researchers, each sharing a common goal of understanding how memories are made, can view the same data set and come away with disparate opinions. In this way, the readers may find this discourse useful in identifying the important questions, if not the answers, surrounding the roles of protein synthesis in memory.

 -Thomas

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The gene genie

1-3-1-1-2-1-3-1-0-0-0.jpgWhen it was announced in Nature in 2001 that the linguistic disorder displayed by members of an English family known as KE could be traced back to a mutation of a certain gene, FOXP2, Steven Pinker heralded this result as “the dawn of cognitive genetics”. While we are still a long way from being able to link cognitive mechanisms to the function of specific genes, it is certainly true that researchers, these days, are coming increasingly closer to understanding how the genome interfaces with the neural processes underlying cognitive behaviour. Among the many fundamental questions genomic studies are starting weigh in on is how brains have changed in evolutionary lineages, development, and individual differences in cognitive behaviour. (more…)

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gal507.jpgThe next International Imaging Genetics Conference is opening its doors now for registration. The third year in a row, building on two successful conferences, this third meeting will also house two separate workshops: one on brain imaging for geneticists; and one on genetics for brain imagers. All in the spirit of crossing the bridge between genetics, brain imaging and statistics. As this course was brilliant last year, I’m hoping to attend in January 2007, too.

Here is the announcement:

The First and Second International Imaging Genetics Conferences were held to bring together national and international experts in neuroimaging, genetics, data-mining, visualization and statistics. Targeting physicians and scientific researchers, this annual conference features presentations from investigators world-wide and held in-depth discussions within the emerging field of Imaging Genetics. Given the known importance of both genetics and environment in brain function, and the role of neuroimaging in revealing brain dysfunction, the synergism of integrating genetics with brain imaging will fundamentally change our understanding of human brain function in disease. To fully realize the promise of this synergy, we must develop novel analytic, statistical, and visualization techniques for this new field.

This international symposium was held to initially assess the state of the art in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. The Third Annual International Imaging Genetics Conference will be held on January 15th and 16th, 2007 at the Beckman Center of the National Academy of Sciences in Irvine, CA. We look forward to seeing you at this exciting upcoming event.

Monday January 15th:

  • Nicholas Schork, UCSD “Multivariate Analysis of Combined Imaging and Genomic Data”
  • Eleazer Eskin, UCSD “Analysis of Complex Traits Through Intermediate Phenotypes.”
  • Tom Nichols, University of Michigan “Statistical Challenges & Opportunities in Imaging Genetics”
  • Fabio Macciardi, University of Toronto “Integrating Imaging Genetics Methods in Schizophrenia.”
  • David Goldman, NIAAA “Genes and Neurobiologies in the Addictions”
  • David Goldstein, Duke Institute for Genome Sciences and Policy “Neuropsychiatric pharmacogenetics”
  • Daniel Weinberger, NIMH/NIH: TBA

Tuesday January 16th:

  • Joseph Callicott, NIMH “Does risk for schizophrenia arise from multiple genes in vulnerable pathways? Evidence from DISC1 and FEZ1”
  • Lisa Eyler, UCSD “Genetics of Brain and Cognition: A Twin Study of Aging”
  • Fei Wang, Peking University “Neuregulin 1 Genetic Variation and anterior cingulum integrity in schizophrenia and in health.”
  • Andreas Meyer-Lindenberg, NIMH/NIH “Genetic characterization of prefrontal-subcortical interactions in humans.”

**New for 2007** Sunday January 14th:

*** Half-day Workshop tutorials will be offered the day before the conference at the Beckman Center- see website for details***

Workshop 1: What Geneticists need to know about Brain Imaging
Workshop 2: What Brain Imagers need to know about Genetics

Registration and conference information can be found at the conference website

-Thomas

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har1expression.jpgThe race is on to pinpoint how the human genome has changed since the last common ancestor of chimps and hominids. With more and more genomes being sequenzed it has become possible to compare species and locate the regions where DNA has remained static over the last ~5 million years, and where it has evolved rapidly. An extremely exiting paper reporting such a genome-wide scan in a range of animals, including humans, has been put on-line today at the Nature website. The paper identifies 49 so-called Human Accelerated Regions (HAR) where sequences are evolutionary conserved among many mammals but has diverged rapidly in humans since the last chimp-human ancestor. The fastest among these, dubbed HAR1, has accrued 18 changes in this time. As it turns out, HAR1 is not a protein-coding sequence but belongs to a non-coding RNA sequence. This is rather interesting since geneticists (and evolutionary psychologists) generally assume that adaptations work on protein-coding genes. This new result may indicate that many of the adaptations setting the human genome apart from the chimp genome can have taken place outside of the genome’s protein-coding sequences.

Equally interesting is the fact that one of the two RNA genes containing HAR1, HAR1F, is expressed in human Cajal-Retzius cells that are thought to play a crucial role in redirecting migrating neurons during development. Thus, HAR1F may possibly be linked to the expansion of the human cortex starting 2 million years ago. In a short news article published in today’s Nature Gerton Lunter speculates on what HAR1F‘s functional role might be:

What the gene does is a mystery, but there are some guesses. “Given that it’s changed so dramatically only for humans, it might be involved in humanspecific brain wiring,” says Gerton Lunter at the University of Oxford, UK. One thing is becoming clear: proteincoding genes may not be the movers and shakers of human evolution scientists once thought. “We should stop looking at proteins and start looking at noncoding DNA,” says Lunter. “Everything points in that direction.”

Now, if we could just manage to extract DNA samples from the various hominids fossils linked with major evolutionary changes (homo erectus and so on), we might some day end up with a clear picture of how the hominid brain has evolved. What an amazing triumph that would be!

By the way, in last week’s Science Paul Mellars had a nice review showing how genetical analysis also illuminates our understanding of the dispersal of modern human populations out of Africa some 60,000 years ago. You might want to read that as well.

References

Pollard, K. (2006): An RNA gene expressed during cortical development evolved rapidly in humans. Nature, in press.

Mellars, P. (2006): Going east: New genetic and archaeological perspectives on the modern human colonization of Eurasia. Science 313: 796-800.

-Martin

UPDATE: You can find nice comments on this truly pathbreaking paper at Carl Zimmer’s, John Hawks’ and the Gene Expression weblogs.

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Recently Thomas wrote about a paper by Yulia Kovas and Robert Plomin in the May issue of TICS discussing the implications of the fact that a great number of genes – dubbed “generalist” genes – affect not one, but most cognitive abilities. One obvious implication is that, if most genes being expressed in the brain affect several areas of the brain, the massive modularity hypothesis (MMH) might not hold true. As Kovas and Plomin wrote in the conclusion to their paper:

Our opinion outlined in this article is that the generalist genes hypothesis is correct and that genetic input into brain structure and function is general (distributed) not specific (modular). The key genetic concepts of pleiotropy and polygenicity increase the plausibility of this opinion. Generalist genes have far-reaching implications for cognitive neuroscience because their pleiotropic and polygenic effects perfuse the transcriptome, the proteome and the brain. This is more than a ‘life-is-complicated’ message. DNA and RNA microarrays provide powerful tools that will ultimately make it possible for cognitive neuroscience to incorporate the trait-specific genome and transcriptome even if hundreds of genes affect individual differences in a particular brain or cognitive trait. The more immediate impact of generalist genes will be to change the way in which we think about the relationship among the genome, the transcriptome and the ‘phenome’ of the brain and cognition.

As Thomas was quick to remark, this idea is of course sure to infuriate proponents of the MMH. Therefore, it comes as no surprise that Gary Marcus and Hugh Rabagliati has a letter in next month’s TICS criticizing Kovas and Plomin’s article. Here is their argument for upholding the MMH:

Genes are in essence instructions for fabricating biological structure. In the construction of a house, one finds both some repeated motifs and some specializations for particular rooms. Every room has doors, electrical wiring, insulation and walls built upon a frame of wooden studs. However, the washroom and kitchen vary in the particulars of how they use plumbing array fixtures, and only a garage is likely to be equipped with electric doors (using a novel combination of electrical wiring and ‘doorness’). Constructing a home requires both domain-general and domain-specific techniques. The specialization of a given room principally derives from the ways in which high-level directives guide the precise implementation of low-level domain-general techniques. When it comes to neural function, the real question is how ‘generalist genes’ fit into the larger picture. Continuing the analogy, one might ask whether different ‘rooms’ of the brain are all built according to exactly the same plan, or whether they differ in important ways, while depending on common infrastructure. Kovas and Plomin presume that the sheer preponderance of domain-general genes implies a single common blueprint for the mind, but it is possible that the generalist genes are responsible only for infrastructure (e.g. the construction of receptors, neurotransmitters, dendritic spines, synaptic vesicles and axonal filaments), with a smaller number of specialist genes supervising in a way that still yields a substantial amount of modular structure.

The interesting thing about this discussion between Plomin and Marcus is the fact that the question that they raise can be investigated empirically, as Kovas and Plomin note in a reply to Marcus and Rabagliati:

Finding high genetic correlations means that genes must be generalists at the psychometric level at which
these traits have been assessed. Therefore, a genetic polymorphism that is associated with individual differences in a particular cognitive ability will also be associated with other abilities. The question is how these generalist genes work in the brain. Does a genetic polymorphism affect just one brain structure or function, which then affects many cognitive processes, as suggested by a modular view of brain structure and function (mechanism 1 in [Kovas and Plomin’s original article])? This model assumes that brain structures and functions are not genetically correlated – genetic correlations arise only at the level of cognition. Another possibility, which we think is more probable, is that the origin of the general effect of a genetic polymorphism is in the brain because the polymorphism affects many brain structures and functions (mechanisms 2 and 3 in [Kovas and Plomin’s original article]). Of course, some polymorphisms might have general effects via mechanism 1 and other polymorphisms might have general effects via mechanisms 2 and 3, as Marcus and Rabagliati suggest. Fortunately, this is an empirical issue about DNA polymorphisms that does not require resorting to metaphors such as house-building. We did not say that the case for mechanism 3 was proven, which is what Marcus and Rabagliati imply with their partial quote. The full quote from our article is: ‘In our opinion, these two key genetic concepts of pleiotropy and polygenicity suggest that the genetic input into brain structure and function is general not modular’. Pleiotropy (in which a gene affects many traits) is a general rule of genetics. Polygenicity (in which many genes affect a trait) is becoming another rule of genetics for complex traits and common disorders. As we point out, polygenicity greatly multiplies and magnifies the pleiotropic effects of generalist genes. A more empirical reason for suggesting that the origin of generalist genes is in the brain is that gene-expression maps of the brain generally indicate widespread expression of cognition related genes throughout the brain.

I second that sentiment. It would be a big step forward if the massively modularity discussion would move beyond mere speculation and become grounded in empirical data.

References

Kovas, Y. & Plomin, R. (2006): Generalist genes: implications for the cognitive sciences. Trends in Cognitive Science 10: 198-203.

Marcus, G. & Rabagliata, H (2006): Genes and domain specificity. Trends in Cognitive Science, in press.

Kovas, Y. & Plomin, R. (2006): Response to Marcus and Rabagliata. Trends in Cognitive Science, in press.

-Martin

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