Beyond the grave: Do the dead have rights?

Positive and negative affect reflect the extent to which a person feels positive or negative emotions (e.g., excited, interested; nervous, distressed), respectively. Positive and negative affect can be experienced in a state (temporary) or trait (stable) manner and are distinct transdiagnostic features of many health conditions, particularly depression and anxiety. Another critical yet often overlooked transdiagnostic feature of many health conditions is cognitive dysfunction. Despite altered affect and cognitive dysfunction being burdensome and pervasive features of many conditions, they remain poorly understood and have few efficacious treatments. Understanding the genetic architecture of mental health and cognitive phenotypes, and their relation to each other, can provide aetiological and biological insights which may facilitate identification of novel treatment targets. Although observational studies have found associations between mental health and cognition (although see), causality remains unclear. Reported associations may reflect: (i) poorer mental health causing poorer cognition, (ii) poorer cognition causing poorer mental health, (iii) confounding via shared risk factors (e.g., stress, sedentary behaviour). Crucially, studies investigating causal relationships between more narrowly defined mental health phenotypes (e.g., affect) and cognitive domains (e.g., memory, attention) are needed. Evidence of bidirectional causality would strengthen the case that interventions indirectly targeting poor mental health (either diagnostic conditions or transdiagnostic phenotypes) may prevent and treat cognitive dysfunction, and vice versa. Two approaches that can shed light on the aetiology, comorbidity, and/or causal relationships between phenotypes are genetic correlations (using all genome-wide variants) and Mendelian randomization (MR) (typically using variants that reach genome-wide significance threshold). Recent genetic correlation studies highlight the substantial pleiotropic genetic architecture of various cognitive phenotypes with mental health traits. Hagenaars et al. reported large genetic overlap between cognitive measures in UK Biobank (verbal-numerical reasoning, reaction time, memory) and mental health conditions including, but not limited to, major depressive disorder. However, it is unclear why the genetic overlap arises. Some possible explanations include genetic variants impacting traits via shared heritable risk factors (e.g., same neural circuitry), potential causal relationships between traits (i.e., genetic variants impacting cognition which then impacts mental health), or other factors (e.g., assortative mating, linkage disequilibrium). MR can test for causality given certain assumptions are met (see Supplementary Methods). It does this by using genetic variants robustly associated with the exposure as a proxy for it. The properties of genetic variants (random assignment from parents, fixed at conception) mean that they are less likely to be associated with confounders and overcome issues of reverse causality. These methods provide powerful tools given the availability of large genome-wide association studies (GWAS) on well-defined phenotypes. To date, GWAS of mental health phenotypes have largely been conducted on diagnostic categories such as depression and anxiety. However, these conditions are heterogeneous, with diagnosed individuals showing diverse symptom profiles. For example, a diagnosis of depression requires ≥ 5/9 symptoms to be present within a two-week period, one of which must be low mood or anhedonia. In addition, symptoms are often not specific to a condition, meaning that a GWAS of one condition may in reality capture a broader phenotype. One potential approach to reducing phenotypic heterogeneity, while addressing the overlap with other conditions, is to focus on traits that more closely map onto biological systems (positive and negative affect, specific cognitive domains). In line with the Research Domain Criteria framework, these are often transdiagnostic.