Data management is boring and quite likely, you are not happy about how you do it today. Research data collected in practice typically evokes complaints when it finally reaches the statisticians or bioinformaticians as they claim that it is not being properly organized. That’s not because you made a mistake: it’s simply hard to do when you set up your small scale study. Tools like Excel will give you practically infite freedom with little guidance and every study seems to be so different from the last. Basic data management isn’t really taught in most research environments unless you’re talking about clinical trials that require elaborate and specialized software systems. Luckily, there is a Coursera course starting on June 2nd, 2014 that teaches the basics of data management. While it is focussed on the tool the organizers provide, the contents of the course should allow you to build better data structures.
The Epilepsy Genetics Initiative. If you had told me last week that the next era of epilepsy genetics would be announced by an animated cartoon, I wouldn’t have believed you. Earlier this week, the Epilepsy Genetics Initiative (EGI) was launched, an emerging large exome repository that will help us connect dots in epilepsy genetics research by centralizing genetic data for research. These are the three things that I have learned from the EGI launch. Continue reading
Silence. You might wonder why you hear very little about ARX in exome studies these days. The X-chromosomal aristaless related homeobox gene was one of the first genes for epilepsies and brain malformations to be discovered. Mutations in ARX can be identified in male patients with a variety of neurodevelopmental disorders including idiopathic West Syndrome – accordingly, it’s on the differential list for patients with Infantile Spasms without a known cause. Let me tell you about the problems that the ARX gene poses for exome sequencing. Continue reading
Genomics meets linkage. This blog post is about family studies in epilepsy genetics. One of my tasks for the next two months is to write the “Trilateral Grant” – we were invited to submit a full proposal for a German-Israeli-Palestinian grant by the German Research Foundation (DFG) on the genetics of familial epilepsies. As keeping up our blogging schedule will be my other big task for the coming months, I thought that I could combine both and explore some topics regarding family studies on this blog. Let’s start with a sobering fact – small dominant families remain difficult to solve, not because of too little but rather too much genetic data. Continue reading
Do you still draw your pedigrees by hand? Or generate them using some website, take a screenshot of it (with Photoshop) and paste it into a Powerpoint file that you convert to PDF, send it by mail to a colleague, who then tries to extract the information into a text file representing the pedigree structure in a computer readable format? Continue reading
Architecture. Even though we often write about novel gene findings in the epilepsies, we assume that most epilepsies are complex genetic or polygenic. Polygenic inheritance suggests the genetic architecture is composed of multiple interacting genetic risk factors, each contributing a small proportion to the disease risk. However, when using the phrase genetic architecture, sometimes I am not quite sure what I actually mean by this. For example, how many genes are needed? This is why I wanted to build a model genetic architecture and explore what happens if we build a genetic disease solely from rare risk variants. Follow me to a brief back-of-the-envelope calculation of how this might work.
Beyond SCN1A. Dravet Syndrome is a severe fever-associated epileptic encephalopathy. While the large majority of patients with Dravet Syndrome carry mutations in the SCN1A gene, the genetic basis is unknown in up to 20% of patients. Some female patients with Dravet-like epilepsies have mutations in PCDH19, but other than this, no additional major gene for typical Dravet Syndrome is known. In a recent paper in Neurology, de novo mutations in GABRA1 and STXBP1 are identified as novel causes for Dravet Syndrome. In addition, several SCN1A-negative patients were shown to have mutations in SCN1A that were initially missed. Continue reading
Lessons. Today was the first day of our bioinformatics workshop in Leuven, Belgium. We started out with some basic command line programming and eventually moved on to working with R Studio. What is this all about? It’s about getting some basic understanding of what your computer does and how your computer handles files. It’s about good data and bad data and losing the fear of the command line. We collected responses from the participants today about today’s take home messages. Continue reading
Living in Cologne is a little tough at the moment. Currently, we are in the middle of the Cologne Carnival, the world’s oldest carnival, which started in 1829. Until the upcoming Wednesday the entire city is one big festival. In addition to the 1 million Cologne citizens probably another million tourists will join. Due to this (positive) distraction I will write less than usual. However, I still consider this week’s publications noteworthy. 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