Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies

Abstract

With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely ‘depressions’ rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.

Genome-wide association meta-analysis of childhood and adolescent internalising symptoms

“…The difficulty in identifying causal loci for early-life internalising symptoms is not novel, and resembles the trajectory of GWAS investigations of adult internalising disorders. GWAS studies of adult depression also made slow progress due to limited sample sizes and heterogeneity (54-56). As depression is a polygenic disorder influenced by many genetic variants of small effect and has several potential sources of heterogeneity, including a diverse presentation of symptoms, large sample sizes were required to achieve success in identifying specific genomic loci (23, 25).…”

Resting-state brain metabolic fingerprinting clusters (biomarkers) and predictive models for major depression in multiple myeloma patients

“…As a quantitative molecular imaging modality, the exploratory studies in late 19 th and early 21 st century revealed metabolic changes in multiple areas of major depression patients as compared to healthy control subjects. However, there were discrepancy of anatomic locations and hyper/hypo-metabolic changes among published FDG PET/CT studies due to heterogeneity of clinical syndrome and genetics of depression [ 33 , 34 ], and relative small sample sizes and ROI definitions published [ 11 , 13 ].…”

Exploring the Role of Contactins across Psychological, Psychiatric and Cardiometabolic Traits within UK Biobank

“…Subtypes of MDD may have different genetic risk factors, for example, childhood-onset MDD is genetically more similar to schizophrenia and bipolar disorder than to adult-onset MDD [ 46 ]. However, large-scale studies assessing whether different clinical presentations of MDD have different genetic risk factors are in the early stages [ 47 ], despite increasing evidence for different biological pathways being implicated (HPA-axis dysregulation for melancholic depression and inflammation in atypical depression) [ 8 , 46 , 48 ]. It remains to be seen whether the CNTN family influences subtypes of depression specifically.…”