Academy Fellow

Early neuroimaging genetics studies were hampered by small sample sizes and the field was characterised underpowered genome-wide association studies (GWAS) in the expectation that effect sizes in the brain would be much larger than those seen in other morphological traits. This was creating a literature characterised by false positives and poor methodology with little likelihood of replication. Moreover, this issue was arising in the context of a growing awareness of the replication crisis across many fields in Neuroscience and Psychology.

To address this, with colleagues from Genetics, Neuroimaging and Psychiatry, I co-founded the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium. Our aim was to create a cross-disciplinary consortium led by methodologists to impact knowledge by increasing the power, robustness and replicability of findings in neuro-imaging studies.

ENIGMA has grown to include more than 900 actively collaborating researchers from 39 countries. We have grown from the original genetics focused working group (that I chair), developing 50 other workgroups many of which focus on examining case:control the impact of psychiatric disorders (such Schizophrenia) and non-neurological diseases (such as HIV) on brain structure and function.

We designed ENIGMA using a meta-analytic framework in which both imaging and genetic analyses were conducted at the local site level rather than to bring all the data to one site and analyse the data centrally. By adopting this approach, we were able to provide establish gold standard genetic analysis methods within the field. We make our didactic image processing and genetic analysis protocols and the GWAS results freely available online through the consortium website. Through these protocols we have helped over 50 research groups internationally to successfully clean, impute and analyze genome-wide level data for the first time.

Drawing on my previous experiences in collaborative research and training in psychology I advocated a distributed approach to the running of the consortium in which the research is supported by a group of genetic and imaging methodologists rather than a traditional steering committee approach. This has proved immensely successful resulting in a dynamic community of interdisciplinary researchers publishing a series of highly impact papers and securing significant funding. This approach has also led to invited keynote presentations about collaboration and designing consortia.

Our GWAS analysis results have been downloaded and used in LDhub analyses >500 times. The ENIGMA consortium has had a transformative effect on imaging research, increasing collaboration and the awareness of the importance of robust methodology and replication.

Coordinator Mental Health Research Program & Group Leader Psychiatric Genetics, QIMR Berghofer Medical Research Institute

OAM, FASSA, FAHMS, MAPS

1. Hibar D.P., Stein J.L., …[286 co-authors]… Thompson P.M*., Medland S.E.* (2015). Common genetic variants influence human subcortical brain structures. Nature, 520, 224-229. DOI:10.1038/nature14101.

2. Stein J.L.*, Medland S.E.*, Vasquez A.A., Hibar D.P., …[194 co-authors]… Martin N.G., Franke B., Wright M.J., Thompson P.M. (2012). Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genetics, 44, 552-561. DOI:10.1038/ng.2250.

3. Thompson P.M., Stein J.L., Medland S.E., Hibar D.P., ...[285 co-authors, contribution indicated by author order] (2014). The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8, 153-182. DOI:10.1007/s11682-013-9269-5.

4. Rietveld C.A., Medland S.E., Derringer J., …[198 co-authors]… Cesarini D., Koellinger P.D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340, 1467-1471. DOI:10.1126/science.1235488.

5. Okbay A., Beauchamp J.P., …[245 co-authors]… Medland S.E., Meyer M.N., Yang J., Johannesson M., Visscher P.M., Esko T., Koellinger P.D., Cesarini D., Benjamin D.J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533, 539-542. DOI:10.1038/nature17671.