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Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson’s disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5and DCC), synaptic structure and function genes (for example, PCLO, NCAM1and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n= 413,466), MD (n= 420,359), SZ (n= 320,404), and white matter FA (n= 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = −0.05 to −0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD= 0.83, p= 4.99e-7 and rSZ= 0.65, p= 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.

The relationship between psychotic disorders and cannabis use is heavily debated. Shared underlying genetic risk is one potential explanation. We investigated the genetic association between psychotic disorders (schizophrenia and bipolar disorder) and cannabis phenotypes (lifetime cannabis use and cannabis use disorder).

Executive function encompasses effortful cognitive processes that are particularly susceptible to aging. Functional brain networks supporting executive function—such as the frontoparietal control network and the multiple demand system—have been extensively investigated. However, it remains unclear how structural networks facilitate and constrain the dynamics of functional networks to contribute to aging-related executive function declines. We examined whether changes in structural network modal controllability—a network’s ability to facilitate effortful brain state transitions that support cognitive functions—are associated with changes in executive function cross-sectionally and longitudinally. Diffusion-weighted imaging and neuropsychological testing were conducted at two time points (Time 1: ages 56 to 66, N= 172; Time 2: ages 61 to 70, N= 267) in community-dwelling men from the Vietnam Era Twin Study of Aging. An executive function factor score was computed from six neuropsychological tasks. Structural networks constructed from white matter connectivity were used to estimate modal controllability in control network and multiple demand system. We showed that higher modal controllability in control network and multiple demand system was associated with better executive function at Time 2, after controlling for age, young adult general cognitive ability, and physical health status. Moreover, changes in executive function over a period of 5 to 6 years (Time 1-Time 2, N= 105) were associated with changes in modal controllability of the multiple demand system and weakly in the control network over the same time period. These findings suggest that changes in the ability of structural brain networks in facilitating effortful brain state transitions may be a key neural mechanism underlying aging-related executive function declines and cognitive aging.

Purpose: To evaluate how Canadian clinicians involved in trauma patient care and prescribing opioids perceive the use and effectiveness of strategies to prevent long-term opioid therapy following trauma. Barriers and facilitators to the implementation of these strategies were also assessed. Methods: We conducted a web-based cross-sectional survey. Potential participants were identified by trauma program managers and directors of the targeted departments in three Canadian provinces. We designed our questionnaire using standard health survey research methods. The questionnaire was administered between April 2021 and November 2021. Results: Our response rate was 47% (350/744), and 52% (181/350) of participants completed the entire survey. Most respondents (71%, 129/181) worked in teaching hospitals. Multimodal analgesia (93%, 240/257), nonsteroidal anti-inflammatory agents (77%, 198/257), and physical stimulation (75%, 193/257) were the strategies perceived to be the most frequently used. Several preventive strategies were perceived to be very effective by over 80% of respondents. Of these, some that were reported as not being frequently used were perceived to be among the most effective ones, including guidelines or protocols, assessing risk factors for opioid misuse, physical health follow-up by a professional, training for clinicians, patient education, and prescription monitoring systems. Staff shortages, time constraints, and organizational practices were identified as the main barriers to the implementation of the highest ranked preventive strategies. Conclusions: Several strategies to prevent long-term opioid therapy following trauma are perceived as being effective by those prescribing opioids in this population. Some of these strategies appear to be commonly used in everyday practice and others less so. Future research should focus on which preventive strategies should be given higher priority for implementation before assessing their effectiveness.

Background: High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. Methods: One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b= 0, 500, 1000, and 2000 s/mm2using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b= 2000 s/mm2via extrapolation based on mono-exponential decay, using b= 0 and b= 500 s/mm2(sDWI500) and b= 0, b= 500 s/mm2, and b= 1000 s/mm2(sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). Results: Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500,sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. Conclusion: sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n= 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n= 33,735). We found brain volumetric phenotypes share substantial genetic variants (74–96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1and ARL17Awas associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Recent genome‐wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n= 105,318), bipolar disorder (BIP, n= 413,466), DEP (n= 450,619), attention‐deficit hyperactivity disorder (ADHD, n= 53,293), and MOOD (n= 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg= 0.10–0.62). Of 10.4 K genomic variants influencing MOOD, 4 K–9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty‐three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene‐set, “synapse organization.” The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.amjmed.2006.07.033 Byline: Krista M. Dale (a)(c)(d), C. Michael White (a)(c)(d), Nickole N. Henyan (a)(c)(d), Jeffrey Kluger (b)(c), Craig I. Coleman (a)(c)(d) Keywords: Creatine kinase; HMG CoA reductase inhibitor; Safety; Statin; Transaminase Abstract: Higher intensity statin therapy reduces cardiovascular events more than lower intensity therapy, but the safety impact of higher intensity therapy is unknown. We performed a meta-analysis of randomized controlled trials comparing higher versus lower intensity therapy on liver and muscle safety. Author Affiliation: (a) The University of Connecticut Schools of Pharmacy, Hartford, Conn. (b) The University of Connecticut Schools of Medicine, Hartford, Conn. (c) Storrs and Farmington; and the Divisions of Cardiology, Hartford, Conn. (d) Drug Information, Hartford Hospital, Hartford, Conn.

Objective: Pharmacy-managed medication assistance programs (MAPs) allow patients without insurance coverage to receive medications free of charge. Improved access to medications may provide an avenue for these patients to positively [...]

Abstract:Kenneth M. Wilson has argued that Augustine innovated the concept of “demonic faith” and that his influence has caused interpreters to misconstrue the diatribe found in Jas 2:18–20. Wilson reads the interlocutor’s speech as continuing through verse 19 and concludes that the statement about the demons believing does not come from the author of James. I respond to this reading by critiquing Wilson’s assertions about the ancient rhetorical form diatribe, clarifying the meaning of the verb πιστεύω, tracing the argument of the passage and showing how verse 19 fits within it, and giving attention to larger thematic resonances in the epistle. Of particular importance is a passage about two types of wisdom (3:13–18); one type, labeled demonic, provides an interpretive parallel for the statement in 2:19 that “the demons believe.” I seek to advance the conversation about the unity of the epistle by demonstrating how the themes of faith and wisdom resonate throughout chapters 1–3. In the Jacobean perspective, there are genuine forms of faith and wisdom that are gifts from above as well as deficient forms of each that are connected with the demonic realm. In this light, it makes sense to read the statement about the demons believing as part of the author’s argument rather than an objection from the interlocutor.

Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. Understanding the genetic architecture of this comorbidity could be improved by focusing on intermediate traits that show positive genetic correlation with the disorders. Thus, we aimed to characterize the shared vs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD) –beyond genetic correlation, and boost discovery for jointly-associated loci. Summary statistics for MOOD (a binary measure of the tendency to report frequent mood swings), AC (number of standard drinks over a typical consumption week) and AUD GWASs (Ns > 200,000) were analyzed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD. To do so, we used a newly established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly associated genomic loci, which were mapped to genes and biological functions. MOOD was highly polygenic (10.4k single nucleotide polymorphisms, SNPs, SD = 2k) compared to AC (4.9k SNPs, SD = 0.6k) and AUD (4.3k SNPs, SD = 2k). The polygenic overlap of MOOD and AC was twice that of MOOD and AUD (98% vs. 49%), with opposite genetic correlation (−0.2 vs. 0.23), as confirmed in independent samples. MOOD&AUD associated SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 jointly associated loci, fifteen were novel for MOOD, AC and AUD. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated partly shared biological underpinnings, yet, clearly distinct functional patterns between MOOD&AC and MOOD&AUD, suggesting new mechanisms for the comorbidity of AUD with mood disorders.

Background: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. Methods: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry—the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. Results: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43–15.16] in ProtecT, 7.07 [6.58–7.60] in African ancestry, 10.31 [9.58–11.11] in Asian ancestry, and 11.18 [10.34–12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11–0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15–0.22) and 0.26 (0.19–0.33), respectively. Conclusions: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Study objective: Awareness of bonchiectasis on the part of clinicians has been low in recent years, although it was previously wen recognized. We believe that bronchiectasis is underdiagnosed, and that [...]

Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainly due to overlapping symptoms and high rates of comorbidity. Despite this, genetic correlation reported for these disorders is low and non-significant. Here we aimed to better characterize the genetic architecture of these disorders utilizing recent large genome-wide association studies (GWAS). We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 controls) and BD (20,352 cases and 31,358 controls) applying the conditional/conjunctional false discovery rate (condFDR/conjFDR) statistical framework that increases the power to detect novel phenotype-specific and shared loci by leveraging the combined power of two GWAS. We observed cross-trait polygenic enrichment for ADHD conditioned on associations with BD, and vice versa. Leveraging this enrichment, we identified 19 novel ADHD risk loci and 40 novel BD risk loci at condFDR <0.05. Further, we identified five loci jointly associated with ADHD and BD (conjFDR < 0.05). Interestingly, these five loci show concordant directions of effect for ADHD and BD. These results highlight a shared underlying genetic risk for ADHD and BD which may help to explain the high comorbidity rates and difficulties in differentiating between ADHD and BD in the clinic. Improving our understanding of the underlying genetic architecture of these disorders may aid in the development of novel stratification tools to help reduce these diagnostic difficulties.

The Adolescent Brain Cognitive Development (ABCD) Study®is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N= 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N= 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N= 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Background: We previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. Materials and methods: Genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. Results: CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings. Conclusion: We identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Background: Clinical variables—age, family history, genetics—are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death. Methods: Genotype/phenotype data were obtained from a nested case–control Cohort of Swedish Men (n= 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests. Results: Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3–84.1) and 81.4 (75.4–86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78–4.17]), family history (HR 1.72 [1.46–2.03]), alcohol (HR 1.74 [1.40–2.15]), diabetes (HR 0.53 [0.37–0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99–2.97]), family history (HR 1.73 [1.48–2.03]), alcohol (HR 1.45 [1.19–1.76]), diabetes (HR 0.62 [0.42–0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p&lt; 10–15). Conclusions: PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Background: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). Materials and method: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. Results: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Conclusions: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50(hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69–1.77] to 2.41 [2.40–2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50of the Discovery-SNP model increased from 1.05 [0.93–1.18] to 2.19 [2.16–2.23]. HR98/50of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70–1.85] and 1.73 [1.71–1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Schizophrenia is a severe psychiatric disorder with considerable morbidity and mortality. Although the past two decades have seen limited improvement in the treatment of schizophrenia, research into the genetic causes of this condition has made important advances that offer new insights into the aetiology of schizophrenia. This Review summarizes the evidence for a polygenic architecture of schizophrenia that involves a large number of risk alleles across the whole range of population frequencies. These genetic risk loci implicate biological processes related to neurodevelopment, neuronal excitability, synaptic function and the immune system in the pathogenesis of schizophrenia. Mathematical models also suggest a substantial overlap between schizophrenia and psychiatric, behavioural and cognitive traits, a situation that has implications for understanding its clinical epidemiology, psychiatric nosology and pathobiology. Looking ahead, further genetic discoveries are expected to lead to clinically relevant predictive approaches for identifying high-risk individuals, improved diagnostic accuracy, increased yield from drug development programmes and improved stratification strategies to address the heterogeneous disease course and treatment responses observed among affected patients.

To identify barriers and facilitators for the use of augmentative and alternative communication (AAC) and voice restorative strategies for adult patients with an advanced airway in the intensive care unit (ICU).

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders associated with cognitive impairment, which is considered a major determinant of functional outcome. Despite this, the etiology of the cognitive impairment is poorly understood, and no satisfactory cognitive treatments exist. Increasing evidence indicates that genetic risk for SCZ may contribute to cognitive impairment, whereas the genetic relationship between BD and cognitive function remains unclear. Here, we combined large genome-wide association study data on SCZ (n= 82,315), BD (n= 51,710), and general intelligence (n= 269,867) to investigate overlap in common genetic variants using conditional false discovery rate (condFDR) analysis. We observed substantial genetic enrichment in both SCZ and BD conditional on associations with intelligence indicating polygenic overlap. Using condFDR analysis, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loci associated with both SCZ and intelligence, and 12 loci associated with both BD and intelligence at conjunctional FDR < 0.01. Among these loci, 20 are novel for SCZ, and four are novel for BD. Most SCZ risk alleles (61 of 75, 81%) were associated with poorer cognitive performance, whereas most BD risk alleles (9 of 12, 75%) were associated with better cognitive performance. A gene set analysis of the loci shared between SCZ and intelligence implicated biological processes related to neurodevelopment, synaptic integrity, and neurotransmission; the same analysis for BD was underpowered. Altogether, the study demonstrates that both SCZ and BD share genetic influences with intelligence, albeit in a different manner, providing new insights into their genetic architectures.