Associations between polygenic liability to psychopathology and non-suicidal versus suicidal self-injury.

Little is known about how non-suicidal and suicidal self-injury are differentially genetically related to psychopathology and related measures. This research was conducted using the UK Biobank Resource, in participants of European ancestry (N = 2320 non-suicidal self-injury [NSSI] only; N = 2648 suicide attempt; 69.18% female). We compared polygenic scores (PGS) for psychopathology and other relevant measures within self-injuring individuals. Logistic regressions and likelihood ratio tests (LRT) were used to identify PGS that were differentially associated with these outcomes. In a multivariable model, PGS for anorexia nervosa (odds ratio [OR] = 1.07; 95% confidence intervals [CI] 1.01; 1.15) and suicidal behavior (OR = 1.06; 95% CI 1.00; 1.12) both differentiated between NSSI and suicide attempt, while the PGS for other phenotypes did not. The LRT between the multivariable and base models was significant (Chi square = 11.38, df = 2, p = 0.003), and the multivariable model explained a larger proportion of variance (Nagelkerke's pseudo-R2 = 0.028 vs. 0.025). While NSSI and suicidal behavior are similarly genetically related to a range of mental health and related outcomes, genetic liability to anorexia nervosa and suicidal behavior is higher among those reporting a suicide attempt than those reporting NSSI-only. Further elucidation of these distinctions is necessary, which will require a nuanced assessment of suicidal versus non-suicidal self-injury in large samples.

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

Power, measurement error, and pleiotropy robustness in twin-design extensions to Mendelian Randomization.

Mendelian Randomization (MR) has become an important tool for causal inference in the health sciences. It takes advantage of the random segregation of alleles to control for background confounding factors. In brief, the method works by using genetic variants as instrumental variables, but it depends on the assumption of exclusion restriction, i.e., that the variants affect the outcome exclusively via the exposure variable. Equivalently, the assumption states that there is no horizontal pleiotropy from the variant to the outcome. This assumption is unlikely to hold in nature, so several extensions to MR have been developed to increase its robustness against horizontal pleiotropy, though not eliminating the problem entirely (Sanderson et al. 2022). The Direction of Causation (DoC) model, which affords information from the cross-twin cross-trait correlations to estimate causal paths, was extended with polygenic scores to explicitly model horizontal pleiotropy and a causal path (MR-DoC, Minica et al 2018). MR-DoC was further extended to accommodate bidirectional causation (MR-DoC2 ; Castro-de-Araujo et al. 2023). In the present paper, we compared the power of the DoC model, MR-DoC, and MR-DoC2. We investigated the effect of phenotypic measurement error and the effect of misspecification of unshared (individual-specific) environmental factors on the parameter estimates.

MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives.

Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minica et al. in Behav Genet 48:337-349,  https://doi.org/10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50,  https://doi.org/10.1007/BF01067552 , 1993).