Nature didn’t want our commentary ;) … so we publish it here instead.
A recent study published in Nature by the CONVERGE consortium1 identified two Single Nucleotide Polymorphisms (SNPs) for Major Depressive Disorder (MDD) that replicated across two samples of Han-Chinese women with recurrent depression. The report was accompanied by an editorial2 that hailed the findings as biologically and diagnostically relevant, suggesting that large-scale exploratory genome-wide studies offer enticing prospects towards aiding diagnosis and the development of new drugs.
We disagree with the editorial’s interpretation (and most of the media coverage) of these CONVERGE results, which contrast with the careful phrasing of the authors themselves.
Although the two SNPs discovered in the comparatively homogenous CONVERGE sample did replicate in a similarly ascertained group, the editorial fails to mention that they did not in the more heterogeneous Psychiatric Genomics Consortium (PGC) data also examined by the authors. Moreover, in polygenic risk score analysis, the genetic signal in the PGC sample explained less than 0.1% of disease risk in the CONVERGE data, implying a fundamental lack of overlap in genetic risk signal across samples.
The laudable effort of the CONVERGE consortium to ensure genetically and phenotypically homogenous samples confirms the elusiveness of the genetics of MDD. Hailing the results as robust insights into the biology of depression detracts from the true scientific relevance of the study: genetic effects for MDD are, even in large homogenous samples, small and do not generalize. Given the hitherto negative results of genetic MDD studies4,5, slogging along on this current road of ever-larger samples and discovering at best small effects is not an alluring prospect, especially so considering that these effects are likely not specific to MDD6. Instead, we suggest revising complex psychiatric phenotypes such as MDD that were transferred unquestioningly from psychiatry to genetics. Incorporating recently proposed network models7, symptom- rather than syndrome-level analyses8, and the development of new instruments that tap variation along the entire continuum9,10 (i.e., in both “cases” and “controls”) offer promising ways forward.
Dr. Eiko I. Fried, University of Leuven, Belgium
Dr. Sophie van der Sluis, VU Medical Center, Amsterdam, The Netherlands
Dr. Angelique O. J. Cramer, University of Amsterdam, The Netherlands
- Cai, N. et al. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–91 (2015).
- Ledford, H. First robust genetic links to depression emerge. Nature 523, 268–269 (2015).
- Keener, A. B. Genetic Variants Linked to Depression. Sci. (2015).
- Hek, K., Demirkan, A., Lahti, J. & Terracciano, A. A Genome-Wide Association Study of Depressive Symptoms. Biol. Psychiatry 73(7), 667–78 (2013).
- Daly, J. et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013).
- Kendler, K. S. ‘A gene for…’: the nature of gene action in psychiatric disorders. Am. J. Psychiatry 162, 1243–52 (2005).
- Cramer, A. O. J., Kendler, K. S. & Borsboom, D. Where are the Genes? The Implications of a Network Perspective on Gene Hunting in Psychopathology. Eur. J. Pers. 286, 270–271 (2011).
- Fried, E. I. & Nesse, R. M. Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential. BMC Med. 13, 1–11 (2015).
- Lee, S. H. & Wray, N. R. Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy. PLoS One 8, e71494 (2013).
- Van der Sluis, S., Posthuma, D., Nivard, M. G., Verhage, M. & Dolan, C. V. Power in GWAS: lifting the curse of the clinical cut-off. Mol. Psychiatry 18, 2–3 (2012).