Polygenic. Schizophrenia is a complex neurodevelopmental disorder that is assumed to be caused by a mixture of genetic and non-genetic factors. The genetic component in schizophrenia is thought to be polygenic, i.e. due to the interaction of multiple genetic factors. Rare variants may play a particular role in this presumable polygenic genetic architecture, but so far this component of the genetic morbidity has been hard to pin down. Now, a recent study in Nature explores the role of rare, disruptive mutations in schizophrenia using large-scale population-based exome sequencing. Let’s find out about a new level of exome-wide honesty and why even a gene with 10 disruptive mutations in cases and none in controls is only mentioned in passing. Continue reading
Heterogeneity. Family-based exome sequencing or trio exome sequencing for de novo mutations is currently the method of choice to identify genetic risk factors in neurodevelopmental disorders. However, given the increasingly recognized variability in the human genome, the hunt for causative de novo mutations is sometimes an uphill battle – it is impossible to distinguish causal mutations from random events unless genes are affected repeatedly. In a recent publication in Nature, Fromer and colleagues present the most comprehensive search for de novo mutations in schizophrenia to date. They observe an incredible genetic heterogeneity that reflects the genetic architecture of neurodevelopmental disorders. Continue reading
Time flies – already thursday night again. Here are updates on study designs to identify rare pathogenic mutations in neurodevelopment diseases, an epilepsy animal model study as well as novel statistical frameworks for large genetic screens.
The placebo effect. In a recent paper in Science Translational Medicine the group of Kam-Hansen investigated the effect of altered placebo and drug labeling changes and its outcome in patients with episodic migraine. Their results suggest that the placebo accounted for more than 50% of the drug effect.
Why are some brain disorders so common? Schizophrenia, autism and epilepsy each affect about 1% of the world’s population, over their lifetimes. Why are the specific phenotypes associated with those conditions so frequent? More generally, why do particular phenotypes exist at all? What constrains or determines the types of phenotypes we observe, out of all the variations we could conceive of? Why does a system like the brain fail in particular ways when the genetic program is messed with? Here, I consider how the difference between “concrete” and “emergent” properties of the brain may provide an explanation, or at least a useful conceptual framework. Continue reading
GABA, postsynaptic. The molecular structure of the postsynapse has long been a mystery. Why do receptors cluster at a particular site and don’t simply float around all over the plasma membrane? The identification of postsynaptic scaffolding proteins answered some of these questions. However, it also became clear that inhibitory synapses are completely different from excitatory synapses. Now, a recent paper in Human Molecular Genetics finds that exonic deletions in gephyrin, the main structural protein of the inhibitory synapse, predispose to various neurodevelopmental disorders. Continue reading
Genotype to phenotype. Recurrent microdeletions at various sites in the human genome are known risk factors for a broad range of neurodevelopmental disorders including epilepsy, autism, intellectual disability and schizophrenia. Despite the fact that the pathogenic role is well established, the mechanisms linking the microdeletion to the neurodevelopmental phenotype remain obscure. In contrast to monogenic disorders, various genes are included and functional studies are difficult. Now, a recent paper in Cell examines the role of a specific microRNA that is dysregulated in the 22q11.2 microdeletion. The results are surprising. Continue reading
And the hairball. What is the value of network analysis of genetic data except for being an undefined label for any work including the use of external data sources for the evaluation of hmm, some genetic data? Let’s be specific: what is the value of this recent high-profile paper in Nature Neuroscience describing the distribution of variants in a schizophrenia network? Continue reading
The power, over and over again. I must admit that I am thoroughly confused by power calculations for rare genetic variants, particularly for de novo variants that are identified through trio exome sequencing. Carolien has recently written a post about the results we can expect from exome sequencing studies. For a current grant proposal, I have now tried to estimate the rate of de novos using a small simulation experiment. And I have realized that we need to re-think the concept of power. Continue reading
The story continues. This week, I am trying to catch up with a number of recent papers in the field of neurogenetics. A recent publication in Nature Genetics highlights the role of de novo mutations identified through exome sequencing in schizophrenia. The authors also look at control data and compare their findings with the growing body of data available for autism research. And while many aspects regarding de novo mutations become more clear with every study published, the real difference is sometimes difficult to grasp. Continue reading
Heritability 2.0. Genome-wide association studies (GWAS) have acquired a slightly negative connotation in the last two years as the results of the enormous efforts were moderate at best. Even though several hundreds of variants have been identified as susceptibility genes for various diseases, the identified genetic risk factors only explain a tiny fraction of the risk for these diseases. Much of what causes common and rare diseases is still unknown – there is a vast discrepancy between population estimates of the genetic contribution and the contribution explained through identified genetic risk factors. This phenomenon has been labeled the “missing heritability”. Now, a recent study using novel statistical tools for GWAS data finds that there is not that much missing after all… Continue reading