There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD).

In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10–20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers—C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2)—measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response.

Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.

Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Suicidal behavior is moderately heritable and a consequence of a combination of the diathesis traits for suicidal behavior and suicide-related major psychiatric disorders. Here, we sought to examine shared polygenic effects between various psychiatric disorders/traits and suicidal behavior and to compare the shared polygenic effects of various psychiatric disorders/traits on non-fatal suicide attempt and suicide death.

We used our genotyped European ancestry sample of 260 non-fatal suicide attempters, 317 suicide decedents and 874 non-psychiatric controls to test whether polygenic risk scores (PRSs) obtained from large GWASs for 22 suicide-related psychiatric disorders/traits were associated with suicidal behavior. Results were compared between non-fatal suicide attempt and suicide death in a sensitivity analysis.

PRSs for major depressive disorder, bipolar disorder, schizophrenia, ADHD, alcohol dependence, sensitivity to environmental stress and adversity, educational attainment, cognitive performance, and IQ were associated with suicidal behavior (Bonferroni-corrected p < 2.5 × 10−4). The polygenic effects of all 22 psychiatric disorders/traits had the same direction (p for binomial tests = 4.8 × 10−7) and were correlated (Spearman's ρ = 0.85) between non-fatal suicide attempters and suicide decedents.

We found that polygenic effects for major psychiatric disorders and diathesis-related traits including stress responsiveness and intellect/cognitive function contributed to suicidal behavior. While we found comparable polygenic architecture between non-fatal suicide attempters and suicide decedents based on correlations with PRSs of suicide-related psychiatric disorders/traits, our analyses are limited by small sample size resulting in low statistical power to detect difference between non-fatal suicide attempt and suicide death.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Youth who attempt suicide are more at risk for later mental disorders and suicide. However, little is known about their long-term socioeconomic outcomes.

We investigated associations between youth suicide attempts and adult economic and social outcomes.

Participants were drawn from the Quebec Longitudinal Study of Kindergarten Children (n = 2140) and followed up from ages 6 to 37 years. Lifetime suicide attempt was assessed at 15 and 22 years. Economic (employment earnings, retirement savings, welfare support, bankruptcy) and social (romantic partnership, separation/divorce, number of children) outcomes were assessed through data linkage with government tax return records obtained from age 22 to 37 years (2002–2017). Generalised linear models were used to test the association between youth suicide attempt and outcomes adjusting for background characteristics, parental mental disorders and suicide, and youth concurrent mental disorders.

By age 22, 210 youths (9.8%) had attempted suicide. In fully adjusted models, youth who attempted suicide had lower annual earnings (average last 5 years, US$ −4134, 95% CI −7950 to −317), retirement savings (average last 5 years, US$ −1387, 95% CI −2982 to 209), greater risk of receiving welfare support (risk ratio (RR) = 2.05, 95% CI 1.39 to 3.04) and were less likely to be married/cohabiting (RR = 0.82, 95% CI 0.73 to 0.93), compared with those who did not attempt suicide. Over a 40-year working career, the loss of individual earnings attributable to suicide attempts was estimated at US$98 384.

Youth who attempt suicide are at risk of poor adult socioeconomic outcomes. Findings underscore the importance of psychosocial interventions for young people who have attempted suicide to prevent long-term social and economic disadvantage.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

While childhood externalizing, internalizing and comorbid problems have been associated with suicidal risk, little is known about their specific associations with suicidal ideation and attempts. We examined associations between childhood externalizing, internalizing and comorbid problems and suicidal ideation (without attempts) and attempts by early adulthood, in males and females.

Participants were from the Quebec Longitudinal Study of Kindergarten Children, a population-based study of kindergarteners in Quebec from 1986 to 1988 and followed-up until 2005. We captured the co-development of teacher-rated externalizing and internalizing problems at age 6–12 using multitrajectories. Using the Diagnostic Interview Schedule administered at age 15 and 22, we identified individuals (1) who never experienced suicidal ideation/attempts, (2) experienced suicidal ideation but never attempted suicide and (3) attempted suicide.

The identified profiles were no/low problems (45%), externalizing (29%), internalizing (11%) and comorbid problems (13%). After adjusting for socioeconomic and familial characteristics, children with externalizing (OR 2.00, CI 1.39–2.88), internalizing (OR 2.34, CI 1.51–3.64) and comorbid (OR 3.29, CI 2.05–5.29) problems were at higher risk of attempting suicide (v. non-suicidal) by age 22 than those with low/no problems. Females with comorbid problems were at higher risk of attempting suicide than females with one problem. Childhood problems were not associated with suicidal ideation. Externalizing (OR 2.01, CI 1.29–3.12) and comorbid problems (OR 2.28, CI 1.29–4.03) distinguished individuals who attempted suicide from those who thought about suicide without attempting.

Childhood externalizing problems alone or combined with internalizing problems were associated with suicide attempts, but not ideation (without attempts), suggesting that these problems confer a specific risk for suicide attempts.

Low birth weight is associated with adult mental health, cognitive, and socioeconomic problems. However, the causal nature of these associations remains difficult to establish due to confounding. We aimed to estimate the contribution of birth weight to adult mental health, cognitive, and socioeconomic outcomes using two-sample Mendelian randomisation, an instrumental variable approach strengthening causal inference.

We used 48 independent single-nucleotide polymorphisms as genetic instruments for birth weight (N of the genome-wide association study, 264 498), and considered mental health (attention-deficit hyperactivity disorder [ADHD], autism spectrum disorders, bipolar disorder, major depressive disorders, obsessive-compulsive disorder, post-traumatic stress disorder [PTSD], schizophrenia, suicide attempt), cognitive (intelligence), and socioeconomic (educational attainment, income, social deprivation) outcomes. We performed a two-sample Mendelian randomisation using the random-effect Inverse Variance Weighing method as primary analysis, supplemented by a wide range of sensitivity analyses, including Egger regression, weighted median, and Pleiotropy Residual Sum and Outlier. Results were considered statistically significant after accounting for multiple testing using False Discovery Rate (q = 0.05).

After correction for multiple testing, we found evidence for a contribution of birth weight to ADHD (OR for 1 SD-unit decrease [~464 grams] in birth weight, 1.29; CI, 1.03–1.62), PTSD (OR = 1.69; CI = 1.06–2.71), and suicide attempt (OR = 1.39; CI = 1.05–1.84), as well as for intelligence (β= –0.07; CI= –0.13; –0.02), and socioeconomic outcomes, ie, educational attainment (β=−0.05; CI= –0.09; –0.01), income (β=−0.08; CI= –0.15; –0.02), and social deprivation (β=0.08; CI = 0.03; 0.13). However, no evidence was found for a contribution of birth weight to the other examined mental health outcomes. Results were consistent across main and sensitivity analyses.

These findings support that birthweight could be an important element on the causal pathway to mental health, cognitive and socioeconomic outcomes. Early interventions targeting birth weight may therefore have a positive impact on promoting mental health and improving socioeconomic outcomes.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 793396

Low birth weight is associated with adult mental health, cognitive and socioeconomic problems. However, the causal nature of these associations remains difficult to establish owing to confounding.

To estimate the contribution of birth weight to adult mental health, cognitive and socioeconomic outcomes using two-sample Mendelian randomisation, an instrumental variable approach strengthening causal inference.

We used 48 independent single-nucleotide polymorphisms as genetic instruments for birth weight (genome-wide association studies’ total sample: n = 264 498) and considered mental health (attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive–compulsive disorder, post-traumatic stress disorder (PTSD), schizophrenia, suicide attempt), cognitive (intelligence) and socioeconomic (educational attainment, income, social deprivation) outcomes.

We found evidence for a contribution of birth weight to ADHD (OR for 1 s.d. unit decrease (~464 g) in birth weight, 1.29; 95% CI 1.03–1.62), PTSD (OR = 1.69; 95% CI 1.06–2.71) and suicide attempt (OR = 1.39; 95% CI 1.05–1.84), as well as for intelligence (β = −0.07; 95% CI −0.13 to −0.02) and socioeconomic outcomes, i.e. educational attainment (β = −0.05; 95% CI −0.09 to −0.01), income (β = −0.08; 95% CI −0.15 to −0.02) and social deprivation (β = 0.08; 95% CI 0.03–0.13). However, no evidence was found for a contribution of birth weight to the other examined mental health outcomes. Results were consistent across a wide range of sensitivity analyses.

These findings support the hypothesis that birth weight could be an important element on the causal pathway to mental health, cognitive and socioeconomic outcomes.

Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.

Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.

All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.

The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

We aimed to identify groups of children presenting distinct perinatal adversity profiles and test the association between profiles and later risk of suicide attempt.

Data were from the Québec Longitudinal Study of Child Development (QLSCD, N = 1623), and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5734). Exposures to 32 perinatal adversities (e.g. fetal, obstetric, psychosocial, and parental psychopathology) were modeled using latent class analysis, and associations with a self-reported suicide attempt by age 20 were investigated with logistic regression. We investigated to what extent childhood emotional and behavioral problems, victimization, and cognition explained the associations.

In both cohorts, we identified five profiles: No perinatal risk, Poor fetal growth, Socioeconomic adversity, Delivery complications, Parental mental health problems (ALSPAC only). Compared to children with No perinatal risk, children in the Poor fetal growth (pooled estimate QLSCD-ALSPAC, OR 1.89, 95% CI 1.04–3.44), Socioeconomic adversity (pooled-OR 1.42, 95% CI 1.08–1.85), and Parental mental health problems (OR 1.74, 95% CI 1.27–2.40), but not Delivery complications, profiles were more likely to attempt suicide. The proportion of this effect mediated by the putative mediators was larger for the Socioeconomic adversity profile compared to the others.

Perinatal adversities associated with suicide attempt cluster in distinct profiles. Suicide prevention may begin early in life and requires a multidisciplinary approach targeting a constellation of factors from different domains (psychiatric, obstetric, socioeconomic), rather than a single factor, to effectively reduce suicide vulnerability. The way these factors cluster together also determined the pathways leading to a suicide attempt, which can guide decision-making on personalized suicide prevention strategies.

Network analysis (NA) conceptualizes psychiatric disorders as complex dynamic systems of mutually interacting symptoms. Major depressive disorder (MDD) is a heterogeneous clinical condition, and very few studies to date have assessed putative changes in its psychopathological network structure in response to antidepressant (AD) treatment.

In this randomized trial with adult depressed outpatients (n = 151), we estimated Gaussian graphical models among nine core MDD symptom-domains before and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Networks were examined with the measures of cross-sectional and longitudinal structure and connectivity, centrality and predictability as well as stability and accuracy.

At baseline, the most connected MDD symptom-domains were fatigue–cognitive disturbance, whereas at week 8 they were depressed mood–suicidality. Overall, the most central MDD symptom-domains at baseline and week 8 were, respectively, fatigue and depressed mood; in contrast, the most peripheral symptom-domain across both timepoints was appetite/weight disturbance. Furthermore, the psychopathological network at week 8 was significantly more interconnected than at baseline, and they were also structurally dissimilar.

Our findings highlight the utility of focusing on the dynamic interaction between depressive symptoms to better understand how the treatment with ADs unfolds over time. In addition, depressed mood, fatigue, and cognitive/psychomotor disturbance seem to be central MDD symptoms that may be viable targets for novel, focused therapeutic interventions.

Suicide is a leading cause of mortality in youth, yet the course of suicide attempts is poorly documented. We explored the vulnerable transition from adolescence to emerging adulthood to identify group trajectories and risk factors.

The National Longitudinal Survey of Children and Youth is a prospective representative cohort of Canadian children. We followed participants aged 7–11 years in 1994–95 to age 23 (2008–09). We modelled self-reported past-year suicide attempts (ages 12 to 23 years) using growth mixture models. We analysed risk factors from self- and parent-report questionnaires at pre-adolescence (ages 10–11) and early adolescence (ages 12–13) using multinomial logistic regressions. Analyses were adjusted for sample non-response and attrition.

In 2233 participants answering questions on teen and adult suicide attempts, we identified three trajectories: never attempted (96.0%), adolescence-limited (2.0%) and persisting into adulthood (2.0%). Adolescent girls aged 12–13 with depression/anxiety symptoms, and with mothers experiencing depression had higher risks of adolescence-limited than never-attempted [relative risk RR 9.27 (95% confidence interval: 1.73–49.82); 2.03 (1.02–3.32), for each standard deviation increase; 1.07 (1.00–1.15); respectively]. Preteen ADHD symptoms increased the risk of attempts persisting into adulthood as compared to never-attempted [RR 2.05 (1.29–3.28) for each standard deviation increase]. Suicide death of schoolmate/acquaintance increased risks of an adulthood trajectory as compared to never-attempted and adolescence-limited [RR 8.41 (3.04–23.27) and 6.63 (1.29–34.06), respectively].

In half the participants attempting suicide, attempts continued into adulthood. We stress the need for preventive strategies in early adolescence and differential clinical/educational interventions as identified for each trajectory.

Poor cognitive abilities and low intellectual quotient (IQ) are associated with an increased risk of suicide attempts and suicide mortality. However, knowledge of how this association develops across the life-course is limited. Our study aims to establish whether individuals who died by suicide by mid-adulthood are distinguishable by their child-to-adolescence cognitive trajectories.

Participants were from the 1958 British Birth Cohort and were assessed for academic performance at ages 7, 11, and 16 and intelligence at 11 years. Suicides occurring by September 2012 were identified from linked national death certificates. We compared mean mathematics and reading abilities and rate of change across 7–16 years for individuals who died by suicide v. those still alive, with and without adjustment for potential early-life confounding factors. Analyses were based on 14 505 participants.

Fifty-five participants (48 males) had died by suicide by age 54 years. While males who died by suicide did not differ from participants still alive in reading scores at age 7 [effect size (g) = −0.04, p = 0.759], their reading scores had a less steep improvement up to age 16 compared to other participants. Adjustments for early-life confounding factors explained these differences. A similar pattern was observed for mathematics scores. There was no difference between individuals who died by suicide v. participants still alive on intelligence at 11 years.

While no differences in tests of academic performance and IQ were observed, individuals who died by suicide had a less steep improvement in reading abilities over time compared to same-age peers.

Heritability of antisocial behaviour is estimated at approximately 50% and involves multiple genes.

To investigate the cumulative genetic effects of 116 single nucleotide polymorphisms mapping to 11 candidate serotonergic genes and antisocial behaviours, in adolescence and in early adulthood.

Participants were 410 male members of the Quebec Longitudinal Study of Kindergarten Children, a population-based cohort followed up prospectively from age 6 to age 23. The serotonergic genes were selected based on known physiological processes and prior associations with antisocial behaviours. Antisocial behaviours were self-reported and assessed by using semi-structured interviews in adolescence and in adulthood.

Cumulative, haplotype-based contributions of serotonergic genes conferring risk and protection for antisocial behaviours were detected by using multilocus genetic profile risk scores (MGPRSs) and multilocus genetic profile protection scores (MGPPSs). Cumulatively, haplotype-based MGPRSs and MGPPSs contributed to 9.6, 8.5 and 15.2% of the variance in general delinquency in adolescence, property/violent crimes in early adulthood and physical partner violence in early adulthood, respectively.

This study extends previous research by showing a cumulative effect of multiple haplotypes conferring risk and protection to antisocial behaviours in adolescence and early adulthood. The findings further support the relevance of concomitantly considering multiple serotonergic polymorphisms to better understand the genetic aetiology of antisocial behaviours. Future studies should investigate the interplay between risk and protective haplotype-based multilocus genetic profile scores with the environment.

I.O.-M. holds a Canada Research Chair in the developmental origins of vulnerability and resilience.

Cerebral cavernous malformation (CCM) is a form of intracranial vascular disease that may arise sporadically or be dominantly inherited. Linkage studies have revealed genetic heterogeneity among the dominantly inherited forms suggesting the existence of at least three loci called CCM1, CCM2 and CCM3.

In the present study, we screened five families with dominantly inherited CCM for CCM1 gene mutations with denaturing high performance liquid chromatography (DHPLC). Then, we performed linkage analysis and haplotyping on these five families using highly polymorphic markers at the candidate CCM loci.

None of the five families tested with DHPLC were found to have mutations in the CCM1 gene. Based on haplotyping, we identified three families segregating alleles for CCM2, while two families segregated alleles for CCM3. Using linkage analysis, we could confirm that one family (IFCAS-1) had a positive Lod score of 2.03 (p<0.0001) at the CCM2 locus using marker D7S678.

The present study is the first one to replicate linkage at the CCM2 locus and provides a fifth family identified as such. It also supports the concept of genetic heterogeneity in CCM, identifying four other families that showed no mutations in the CCM1 gene.