Climbing out of the darkness - investigating the genetics of depression and other internalizing traits in Lifelines
Depression, clinically also known as major depressive disorder (MDD), is a common and severe mood disorder. Those who suffer from depression experience persistent feelings of sadness and hopelessness that causes the individual significant distress affecting their ability to function in daily life. Depression can be life threatening, not only because it significantly increases risk of dying by suicide, but also because it increases risk of developing conditions such as diabetes mellitus, heart disease and stroke. Far too long, little was known about the genetics of depression. Recent large-scale genetic studies now identified the first genetic contributions to the illness providing both novel insights and new challenges to overcome. Here, we propose to use the Lifelines cohort to investigate the clinical applicability of genetic risk predictions and further our understanding of the etiology of depression and its relation to other internalizing behaviors.
Depression has a reported heritability of ~35%, with estimates from family- and twin-based studies being higher than single nucleotide polymorphisms (SNP)-based estimates from genome-wide association studies (GWAS) so far. The most recent and largest GWAS (total of 246,363 cases and 561,190 controls), combining samples from the Psychiatric Genomics Consortium (PGC), the UK BioBank, and 23andMe, identified 102 independent variants of which 87 replicated in an independent sample. The estimate of the genome-wide SNP-based heritability (SNP-h2) on the liability scale is 8.9% (s.e. = 0.3). While this demonstrates significant heritability detected, it poses several challenges in interpretation.
First, the detected SNP-h2 is far lower than the first GWAS to detect a genome-wide significant locus for MDD. That is, the CONVERGE (China, Oxford, and Virginia Commonwealth University Experimental Research on Genetic Epidemiology) study collected data on 5278 patients with MDD and reported two significant genome-wide loci and a SNP-h2 of 20-29%. This is remarkable at first glance as the number of cases in the most recent GWAS is almost 50x greater than the number of cases included in the CONVERGE study. Closer inspection however reveals key differences in sample heterogeneity and disease definition. While the CONVERGE study included only women with severe recurrent MDD as determined by clinically established diagnostics, the recent GWAS primarily used online self-report behavioral questionnaires. This minimal phenotype, reflecting a broad definition of depression, yields low genetic specificity for MDD, including lower SNP-h2 and a greater shared genetic overlap with other neuropsychiatric traits, such as schizophrenia and bipolar disorder. The CONVERGE study on the other hand used a strictly defined MDD phenotype. As genetic risk factors are not entirely shared between the sexes, they recruited only women. Individuals were also recruited to have a history of two or more MDD episodes thereby including more severe cases who likely carry higher familial risk. CONVERGE thus included a more homogenous sample, which enabled significant identification of a genetic contribution in a relatively small cohort. More broad definitions of depression have stronger overlap with other, less heritable, internalizing traits such as neuroticism or anxiety symptoms, which often confuse the interpretation of downstream results. Indeed, when imposing more strict criteria on the definition of depression in the UK BioBank, the SNP-h2 increases even though the number of cases decreases substantially (lifetime MDD with >2 episodes; cases = 10,302, SNP-h2 ~ 32%).
These findings demonstrate that problems of heterogeneity and disease definition challenge the interpretation of genetic findings in depression so far, limiting the identification of genetic variants that are causally involved. This furthermore hinders genome-wide disease risk prediction and downstream clinical applicability. Large population cohorts with both genetic and in-depth behavioral and psychiatric phenotype data are needed to improve our understanding of the genetic architecture of depression. The Lifelines cohort represents such a sample. The richness of phenotypic data will help understand the genetic overlap of major depression with other internalizing phenotypes, such as dysthymia, depressive symptoms, neuroticism, negative affect; generalized anxiety disorder, panic disorder, social phobia, agoraphobia, anxiety symptoms. Furthermore, Lifelines has collected information on life events, such as known childhood or adulthood adversity, which are risk factors for internalizing disorders. Previous work has demonstrated that exclusion of MDD cases with adverse life events increases statistical power to detection genetic effects. In addition, information on life events will allow for modeling gene-environment interactions and may yield insight that would otherwise be missed. In summary, we believe that the Lifelines cohort presents a unique opportunity to advance our understanding of depression and its relation to other internalizing behaviors. Our proposal aims to understand the phenotypic specificity of genetic findings for depression so far and to shed more light on the core etiology of the illness.