Chromosome 9: linkage for borderline personality disorder features.

Objective A large-scale twin study implicated genetic influences on borderline personality disorder (BPO) features, with a heritability estimate of 42%. To date, no genome-wide linkage study has been conducted to identify the genomic region(s) containing the quantitative trait loci that influence the manifestation of BPD features. Methods We conducted a family-based linkage study using Merlin regress. The participating families were drawn from the community-based Netherlands Twin Register. The sample consisted of 711 sibling pairs with phenotype and genotype data, and 561 additional parents with genotype data. BPD features were assessed on a quantitative scale. Results Evidence for linkage was found on chromosomes 1, 4, 9, and 18. The highest linkage peak was found on chromosome 9p at marker D9S286 with a logarithm of odds score of 3.548 (empirical P= 0.0001). Conclusion To our knowledge, this is the first linkage study on BPD features and shows that chromosome 9 is the richest candidate for genes influencing BPD. The results of this study will move the field closer to determining the genetic etiology of BPD and may have important implications for treatment programs in the future. Association studies in this region are, however, warranted to detect the actual genes. © 2008 Wolters Kluwer Health|Lippincott Williams & Wilkins

The association of genetic predisposition to depressive symptoms with non-suicidal and suicidal self-Injuries

Non-suicidal and suicidal self-injury are very destructive, yet surprisingly common behaviours. Depressed mood is a major risk factor for non-suicidal self-injury (NSSI), suicidal ideation and suicide attempts. We conducted a genetic risk prediction study to examine the polygenic overlap of depressive symptoms with lifetime NSSI, suicidal ideation, and suicide attempts in a sample of 6237 Australian adult twins and their family members (3740 females, mean age\ua0=\ua042.4\ua0years). Polygenic risk scores for depressive symptoms significantly predicted suicidal ideation, and some predictive ability was found for suicide attempts; the polygenic risk scores explained a significant amount of variance in suicidal ideation (lowest p\ua0=\ua00.008, explained variance ranging from 0.10 to 0.16\ua0%) and, less consistently, in suicide attempts (lowest p\ua0=\ua00.04, explained variance ranging from 0.12 to 0.23\ua0%). Polygenic risk scores did not significantly predict NSSI. Results highlight that individuals genetically predisposed to depression are also more likely to experience suicidal ideation/behaviour, whereas we found no evidence that this is also the case for NSSI

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

Early Onset Alcohol Use and Self-Harm: A Discordant Twin Analysis

Background Self-harm has considerable societal and economic costs and has been extensively studied in relation to alcohol involvement. Whereas early onset alcohol use (EAU) has been causally linked to maladaptive clinical outcomes, its association with self-harm is less well characterized. The current study aimed to further examine the link between EAU and both non-suicidal self-injury (NSSI) and suicide attempt (SA), and elucidate shared familial and causal/individual-specific pathways that explain this co-occurrence. Methods Using data from 6,082 Australian same-sex twin pairs (1,732 MZ and 1,309 DZ), ages 23-40, we examined prevalence rates of NSSI and SA among twin pairs concordant and discordant for EAU. Conditional logistic regression, controlling for early clinical covariates and the influence of zygosity on EAU, was used to examine the odds ratio (OR) of self-harm within twin pairs discordant for EAU. Results Prevalence rates of both NSSI and SA were highest among twin pairs concordant for EAU and for twins who reported EAU within discordant twin pairs. Results from discordant twin analyses revealed nearly four-fold increased odds of SA for the twin who endorsed EAU, and this OR was equal across monozygotic (MZ) and dizygotic (DZ) twins. EAU also was associated with elevated odds of NSSI (OR=7.62), although this was only the case for DZ twins in discordant pairs. Conclusions The equivalent increase in odds of SA for both MZ and DZ twins suggests that causal or individual-specific influences explain the link between EAU and SA. For NSSI, elevated odds for DZ twins and nonsignificant findings for MZ twins implicate correlated genetic factors in the association between EAU and NSSI. Future studies should test mechanisms through which EAU may causally influence SA, as well as examine whether genetic risk for third variables (e.g., negative urgency, stress reactivity) may explain the genetic overlap between EAU and NSSI.Psycholog

The daily association between affect and alcohol use: a meta-analysis of individual participant data

Influential psychological theories hypothesize that people consume alcohol in response to the experience of both negative and positive emotions. Despite two decades of daily diary and ecological momentary assessment research, it remains unclear whether people consume more alcohol on days they experience higher negative and positive affect in everyday life. In this preregistered meta-analysis, we synthesized the evidence for these daily associations between affect and alcohol use. We included individual participant data from 69 studies (N = 12,394), which used daily and momentary surveys to assess affect and the number of alcoholic drinks consumed. Results indicate that people are not more likely to drink on days they experience high negative affect, but are more likely to drink and drink heavily on days high in positive affect. People self-reporting a motivational tendency to drink-to-cope and drink-to-enhance consumed more alcohol, but not on days they experienced higher negative and positive affect. Results were robust across different operationalizations of affect, study designs, study populations, and individual characteristics. These findings challenge the long-held belief that people drink more alcohol following increases in negative affect. Integrating these findings under different theoretical models and limitations of this field of research, we collectively propose an agenda for future research to explore open questions surrounding affect and alcohol use.The present study was funded by the Canadian Institutes of Health Research Grant MOP-115104 (Roisin M. O’Connor), Canadian Institutes of Health Research Grant MSH-122803 (Roisin M. O’Connor), John A. Hartford Foundation Grant (Paul Sacco), Loyola University Chicago Research Support Grant (Tracy De Hart), National Institute for Occupational Safety and Health Grant T03OH008435 (Cynthia Mohr), National Institutes of Health (NIH) Grant F31AA023447 (Ryan W. Carpenter), NIH Grant R01AA025936 (Kasey G. Creswell), NIH Grant R01AA025969 (Catharine E. Fairbairn), NIH Grant R21AA024156 (Anne M. Fairlie), NIH Grant F31AA024372 (Fallon Goodman), NIH Grant R01DA047247 (Kevin M. King), NIH Grant K01AA026854 (Ashley N. Linden-Carmichael), NIH Grant K01AA022938 (Jennifer E. Merrill), NIH Grant K23AA024808 (Hayley Treloar Padovano), NIH Grant P60AA11998 (Timothy Trull), NIH Grant MH69472 (Timothy Trull), NIH Grant K01DA035153 (Nisha Gottfredson), NIH Grant P50DA039838 (Ashley N. Linden-Carmichael), NIH Grant K01DA047417 (David M. Lydon-Staley), NIH Grant T32DA037183 (M. Kushner), NIH Grant R21DA038163 (A. Moore), NIH Grant K12DA000167 (M. Potenza, Stephanie S. O’Malley), NIH Grant R01AA025451 (Bruce Bartholow, Thomas M. Piasecki), NIH Grant P50AA03510 (V. Hesselbrock), NIH Grant K01AA13938 (Kristina M. Jackson), NIH Grant K02AA028832 (Kevin M. King), NIH Grant T32AA007455 (M. Larimer), NIH Grant R01AA025037 (Christine M. Lee, M. Patrick), NIH Grant R01AA025611 (Melissa Lewis), NIH Grant R01AA007850 (Robert Miranda), NIH Grant R21AA017273 (Robert Miranda), NIH Grant R03AA014598 (Cynthia Mohr), NIH Grant R29AA09917 (Cynthia Mohr), NIH Grant T32AA07290 (Cynthia Mohr), NIH Grant P01AA019072 (P. Monti), NIH Grant R01AA015553 (J. Morgenstern), NIH Grant R01AA020077 (J. Morgenstern), NIH Grant R21AA017135 (J. Morgenstern), NIH Grant R01AA016621 (Stephanie S. O’Malley), NIH Grant K99AA029459 (Marilyn Piccirillo), NIH Grant F31AA022227 (Nichole Scaglione), NIH Grant R21AA018336 (Katie Witkiewitz), Portuguese State Budget Foundation for Science and Technology Grant UIDB/PSI/01662/2020 (Teresa Freire), University of Washington Population Health COVID-19 Rapid Response Grant (J. Kanter, Adam M. Kuczynski), U.S. Department of Defense Grant W81XWH-13-2-0020 (Cynthia Mohr), SANPSY Laboratory Core Support Grant CNRS USR 3413 (Marc Auriacombe), Social Sciences and Humanities Research Council of Canada Grant (N. Galambos), and Social Sciences and Humanities Research Council of Canada Grant (Andrea L. Howard)