The challenges of Complex Trait Genetics

Professor Danielle Posthuma speaks to Innovation News Network about her work at the Complex Traits Genetics lab.

Professor Danielle Posthuma is statistical geneticist at the Vrije Universiteit (VU) Amsterdam and VU University Medical Center Amsterdam (VU), Neuroscience Campus Amsterdam. The Complex Traits Genetics lab (CTGlab) was setup in 2008 by Danielle Posthuma as a small research group within the Center for Neurogenomics and Cognitive Research (CNCR) at the VU and the Department of Clinical Genetics of the VUMC. Research in her group focuses on the genetic and environmental causes of individual differences in behaviour, cognition and mental and physical health.

Here, Posthuma speaks to Innovation News Network about her research into areas as diverse as neurodegeneration, psychiatric genomics, and stem cell biology, and the advances that her lab has made.

How would you define the Complex Traits Genetics lab and the work that takes place there? What makes you unique?

Our main aim is to identify the genetic causes and understand underlying biological mechanisms of a variety of human traits, most of which are linked to the brain. As such, we investigate psychiatric, cognitive and neurodegenerative traits but sometimes also height and BMI, typically as a control trait. We are, for the most part, a ‘dry’ lab, conducting in silico analyses, and developing and applying novel statistical methods, all aiming to enable gene discovery. However, about 25% of our work involves wet lab techniques, as part of our stem cell lab, where we conduct functional work aiming to gain mechanistic insight. Having both a wet and a dry component in the group ensures there is a strong connection between the two. Our embedment in the CNCR/Amsterdam neuroscience ensures we have a close link to a wide range of functional experimental settings that can be applied to testing hypotheses generated by our gene-discovery efforts.

As part of our gene discovery efforts we use large genetically informative data sets, mostly in collaboration with large international consortia, such as the Psychiatric Genomics Consortium (PGC). My group is actively involved in such consortia and has a leading role in several of them (focusing for example on Alzheimer’s Disease, intelligence, antisocial behaviour and schizophrenia). We are always renewing and developing statistical tools to optimally analyse genetically informative data sets. One such tool is for gene set analysis (MAGMA), which we created to allow for a closer look at biological pathways, and a more recent tool is FUMA (, which can be used to interpret results from genome-wide association studies using a large collection of external biological resources.

When I compare our lab with other similar ones, I get the sense that what makes us truly unique is the fact that we combine so many different areas of expertise: my group consists of 20-25 people and they are mathematicians, statistics specialists, bioinformatics experts, stem cell biologists, neurologists, medical experts, and psychologists. It is a good mix of people all working towards the same goal but coming in from different perspectives. We try to remain open to new ways of looking at the data. Internally we have a very collaborative atmosphere and externally we are very much in favour of sharing our tools and results as soon as possible without conditions to the scientific community.

Is the lack of a common language a challenge when working across disciplines?

Yes, that is indeed one of the main challenges, and while this was a significant issue when we first started with this level of multidisciplinarity, it has now been some eight years, and it has got easier with time. We have weekly meetings with the whole group and what helps is that no-one is afraid to ask a lot of questions.

What work have you done in the area of neurodegenerative disorders?

At the VUMC there is a strong research component in neurodegeration, led by the VUMC Alzheimer Center. We have several ongoing collaborations with the Alzheimer Center, focusing on Alzheimer’s Disorder and Fronto Temporal Dementia. Apart from that, I co-lead the PGC Alzheimer’s workgroup where we aim to conduct large scale genome-wide association (GWA) analyses. Here we use genetic data from a combination of clinically defined cases and controls as well as data from the wonderful UK biobank resource of 500,000 individuals.

Although the UK biobank does not contain many individuals diagnosed with Alzheimer’s, it does include information on whether the individual’s parents had Alzheimer’s. This enables us to define a proxy phenotype for individuals who have one or both parents with this Alzheimer’s disease. If Alzheimer’s disease is heritable then children of parents with the disease are expected to be enriched for genetic variants that are important for Alzheimer’s. Of course, such a proxy-case status is less ideal than having the case status of the individual him/herself, and effects will be diluted, but the large sample size of the proxy case-controls in combination with a clinical case- control part enables us to detect novel genomic risk loci and genes for Alzheimer’s disease.

We are using extensive functional interpretation and post-GWAS analyses to interpret these findings and generate hypotheses for functional follow-up.The next step is to carry out functional experiment that can prove causality.

With regard to psychiatric genomics, where do you feel the biggest challenges and opportunities lie? Where will your own work focus here?

Psychiatric genomics is a fast moving field – this is mainly thanks to large-scale collaborations, the open sharing policy of many researchers and the availability of large-scale biological resources that aid in understanding GWAS results. Our challenges lie in making sure we use the available data and resources in statistically and biologically sound ways, generating mechanistic hypotheses and formulating the appropriate functional experiments that can prove how genetic variation leads to cellular or system differences and ultimately to phenotypic differences.

How difficult is it to obtain research funding?

In the Netherlands it is extremely difficult – the total budget for research is not very high, there is a lot of competition and, especially for young researchers, this can be very demotivating. In the current system in the Netherlands, the university does not provide basic funding, all researchers need to provide their own funding either via teaching or via grants.

At the EU level, there are more funding opportunities, yet this is also very competitive, and usually there is a lot of administrative work involved.

How is your work on stem cell biology progressing?

I decided to attract a team leader in stem cell biology to my group approximately five years ago, because I was looking for a functional, experimental set-up that could be used to investigate the effects of multiple genetic variants each of small effect at the same time.

Traditional experimental models for investigating the effects of genes involve for example creating a knock out in a mouse for a particular gene that has been implicated in disease. But if there are hundreds or thousands of implicated genes, knock-out studies are not feasible. Most of the traits that I am interested in, like schizophrenia, autism, depression or intelligence, are polygenic. GWAS studies are now pointing towards the genes and we are in need of well-tailored functional set-ups to investigate causal relations of the combined effects of genes on disease risk.

The advent of induced pluripotent stem cell (iPSC) models allow us to create neuronal or glial cells from individuals that are for example selected on a high (or low) polygenic risk score for a certain disease. This opens up a wealth of opportunities, and enables us to investigate genetic defects in living, human, neuronal cells from genetically informative individuals. IPSC models is thus one of the most promising functional models for the follow up of GWAS studies for polygenic traits.

What are your thoughts on the developing work around the CRISPR system in this regard?

CRISPR/CAS9 is a wonderful tool to manipulate genetic variants. It would be great to be able to upscale this so that we can work on multiple genes at the same time.


Prof. dr Danielle Posthuma

Team Leader, Psychiatric & Statistical Genomics

Complex Trait Genetics

Neuroscience Campus Amsterdam

+31 20 5982823

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