A cost-effectiveness analysis of lung cancer screening with low-dose computed tomography and a polygenic risk score

INTRODUCTION

Several studies have proved that Polygenic Risk Score (PRS) is a potential candidate for realizing precision screening. The effectiveness of low-dose computed tomography (LDCT) screening for lung cancer has been proved to reduce lung cancer specific and overall mortality, but the cost-effectiveness of diverse screening strategies remained unclear.

METHODS

The comparative cost-effectiveness analysis used a Markov state-transition model to assess the potential effect and costs of the screening strategies incorporating PRS or not. A hypothetical cohort of 300,000 heavy smokers entered the study at age 50-74 years and were followed up until death or age 79 years. The model was run with a cycle length of 1 year. All the transition probabilities were validated and the performance value of PRS was extracted from published literature. A societal perspective was adopted and cost parameters were derived from databases of local medical insurance bureau. Sensitivity analyses and scenario analyses were conducted.

RESULTS

The strategy incorporating PRS was estimated to obtain an ICER of CNY 156,691.93 to CNY 221,741.84 per QALY gained compared with non-screening with the initial start age range across 50-74 years. The strategy that screened using LDCT alone from 70-74 years annually could obtain an ICER of CNY 80,880.85 per QALY gained, which was the most cost-effective strategy. The introduction of PRS as an extra eligible criteria was associated with making strategies cost-saving but also lose the capability of gaining more LYs compared with LDCT screening alone.

CONCLUSION

The PRS-based conjunctive screening strategy for lung cancer screening in China was not cost-effective using the willingness-to-pay threshold of 1 time Gross Domestic Product (GDP) per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening for now. Further optimization of the screening modality can be useful to consider adoption of PRS and prospective evaluation remains a research priority.

A Longitudinal Study of Stress During Pregnancy, Children's Sleep and Polygenic Risk for Poor Sleep in the General Pediatric Population

Early life stress is robustly associated with poor sleep across life. Preliminary studies suggest that these associations may begin already in utero. Here, we study the longitudinal associations of prenatal psychosocial stress with sleep across childhood, and assess whether prenatal stress interacts with genetic liability for poor sleep.The study is embedded in the Generation R population-based birth cohort. Caregivers reported on prenatal psychosocial stress (life events, contextual, parental or interpersonal stressors) and on children's sleep at ages 2 months, 1.5, 2, 3 and 6 years. The study sample consisted of 4,930 children; polygenic risk scores for sleep traits were available in 2,063.Prenatal stress was consistently associated with more sleep problems across assessments. Effect sizes ranged from small (B = 0.21, 95%CI: 0.14;0.27) at 2 months to medium (B = 0.45, 95%CI: 0.38;0.53) at 2 years. Prenatal stress was moreover associated with shorter sleep duration at 2 months (Bhrs = -0.22, 95%CI: -0.32;-0.12) and at 2 years (Bhrs = -0.04, 95%CI -0.07; -0.001), but not at 3 years (Bhrs = 0.02, 95%CI: -0.02;0.06). Prenatal negative life events interacted with polygenic risk for insomnia to exacerbate sleep problems at 6 years (Binteraction = 0.07, 95%CI: 0.02;0.13).Psychosocial stress during pregnancy has negative associations with children's sleep that persist across childhood, and are exacerbated by genetic liability for insomnia. Associations with sleep duration were more pronounced in infancy and seem to attenuate with age. These findings highlight the role of the prenatal environment for developing sleep regulation, and could inform early intervention programs targeting sleep in children from high-risk pregnancies.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

A Discussion of the Contemporary Prediction Models for Atrial Fibrillation

Atrial Fibrillation is a complex disease state with many environmental and genetic risk factors. While there are environmental factors that have been shown to increase an individual's risk of atrial fibrillation, it has become clear that atrial fibrillation has a genetic component that influences why some patients are at a higher risk of developing atrial fibrillation compared to others. This review will first discuss the clinical diagnosis of atrial fibrillation and the corresponding rhythm atrial flutter. We will then discuss how a patients' risk of stroke can be assessed by using other clinical co-morbidities. We will then review the clinical risk factors that can be used to help predict an individual patient's risk of atrial fibrillation. Many of the clinical risk factors have been used to create several different risk scoring methods that will be reviewed. We will then discuss how genetics can be used to identify individuals who are at higher risk for developing atrial fibrillation. We will discuss genome-wide association studies and other sequencing high-throughput sequencing studies. Finally, we will touch on how genetic variants derived from a genome-wide association studies can be used to calculate an individual's polygenic risk score for atrial fibrillation. An atrial fibrillation polygenic risk score can be used to identify patients at higher risk of developing atrial fibrillation and may allow for a reduction in some of the complications associated with atrial fibrillation such as cerebrovascular accidents and the development of heart failure. Finally, there is a brief discussion of how artificial intelligence models can be used to predict which patients will develop atrial fibrillation.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk

Background

Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects.

Methods

We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data.

Results

Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks.

Conclusions

Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis

BACKGROUND

COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability.

METHODS

We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2).

RESULTS

Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups.

CONCLUSIONS

Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.

PNPLA3 Genotype and Diabetes Identify Patients With Nonalcoholic Fatty Liver Disease at High Risk of Incident Cirrhosis

BACKGROUND & AIMS

Non-alcoholic fatty liver disease (NAFLD) can progress to cirrhosis and hepatic decompensation, but whether genetic variants influence the rate of progression to cirrhosis or are useful in risk stratification among patients with NAFLD is uncertain.

METHODS

We included participants from 2 independent cohorts, they Michigan Genomics Initiative (MGI) and UK Biobank (UKBB), who had NAFLD defined by elevated alanine aminotransferase (ALT) levels in the absence of alternative chronic liver disease. The primary predictors were genetic variants and metabolic comorbidities associated with cirrhosis. We conducted time-to-event analyses using Fine-Gray competing risk models.

RESULTS

We included 7893 and 46,880 participants from MGI and UKBB, respectively. In univariable analysis, PNPLA3-rs738409-GG genotype, diabetes, obesity, and ALT of ≥2× upper limit of normal were associated with higher incidence rate of cirrhosis in both MGI and UKBB. PNPLA3-rs738409-GG had additive effects with clinical risk factors including diabetes, obesity, and ALT elevations. Among patients with indeterminate fibrosis-4 (FIB4) scores (1.3-2.67), those with diabetes and PNPLA3-rs738409-GG genotype had an incidence rate of cirrhosis comparable to that of patients with high-risk FIB4 scores (>2.67) and 2.9-4.8 times that of patients with diabetes but CC/CG genotypes. In contrast, FIB4 <1.3 was associated with an incidence rate of cirrhosis significantly lower than that of FIB4 of >2.67, even in the presence of clinical risk factors and high-risk PNPLA3 genotype.

CONCLUSIONS

PNPLA3-rs738409 genotype and diabetes identified patients with NAFLD currently considered indeterminate risk (FIB4 1.3-2.67) who had a similar risk of cirrhosis as those considered high-risk (FIB4 >2.67). PNPLA3 genotyping may improve prognostication and allow for prioritization of intensive intervention.

Genetic Associations Between Stress-Related Disorders and Autoimmune Disease

Objective: Emerging evidence supports a bidirectional phenotypic association between stress-related disorders and autoimmune disease. However, the biological underpinnings remain unclear. Here, the authors examined whether and how shared genetics contribute to the observed phenotypic associations. Methods: Based on data from 4,123,631 individuals identified from Swedish nationwide registers, familial coaggregation of stress-related disorders (any disorder or posttraumatic stress disorder [PTSD]) and autoimmune disease were initially estimated in seven cohorts with different degrees of kinship. Polygenic risk score (PRS) analyses were then performed with individual-level genotyping data from 376,871 participants in the UK Biobank study. Finally, genetic correlation analyses and enrichment analyses were performed with genome-wide association study (GWAS) summary statistics. Results: Familial coaggregation analyses revealed decreasing odds of concurrence of stress-related disorders and autoimmune disease with descending kinship or genetic relatedness between pairs of relatives; adjusted odds ratios were 1.51 (95% CI=1.09–2.07), 1.28 (95% CI=0.97–1.68), 1.16 (95% CI=1.14–1.18), and 1.01 (95% CI=0.98–1.03) for monozygotic twins, dizygotic twins, full siblings, and half cousins, respectively. Statistically significant positive associations were observed between PRSs of stress-related disorders and autoimmune disease, as well as between PRSs of autoimmune disease and stress-related disorders. GWAS summary statistics revealed a genetic correlation of 0.26 (95% CI=0.14–0.38) between these two phenotypes and identified 10 common genes and five shared functional modules, including one module related to G-protein–coupled receptor pathways. Similar analyses performed for PTSD and specific autoimmune diseases (e.g., autoimmune thyroid disease) largely recapitulated the results of the main analyses. Conclusions: This study demonstrated familial coaggregation, genetic correlation, and common biological pathways between stress-related disorders and autoimmune disease.

risk prediction models with polygenic risk scores to refine cardiovascular prevention: the CoLaus|PsyCoLaus Study

BACKGROUND

Polygenic risk score (PRS) have potential to improve individual atherosclerotic cardiovascular disease (ASCVD) risk assessment.

AIMS

To determine whether a PRS combined with two clinical risk scores, the Systematic COronary Risk Evaluation 2 (SCORE2) and the Pooled Cohort Equation (PCE), improves prediction of ASCVD.

METHODS

Using a population-based European prospective cohort, with 6733 participants at baseline (2003-2006), the PRS presenting the best predictive accuracy was combined with SCORE2 and PCE to assess their joint performances for predicting ASCVD Discrimination, calibration, Cox proportional hazard regression and net reclassification index were assessed.

RESULTS

4,218 subjects (53% women; median age, 53.4 years), with 363 prevalent and incident ASCVD, were used to compare four PRSs. The metaGRS_CAD PRS presented the best predictive capacity (AUROC=0.77) and was used in the following analyses. 3,383 subjects (median follow-up of 14.4 years), with 190 first incident ASCVD, were employed to test ASCVD risk prediction. The changes in C statistic between SCORE2 and PCE models and those combining metaGRS_CAD with SCORE2 and PCE were 0.008 (95% CI, -0.00008-0.02, P =0.05), and 0.007 (95% CI, 0.005-0.01, P=0.03), respectively.Reclassification was improved for people at clinically-determined intermediate-risk for both clinical scores (NRI of 9.6% (95% CI, 0.3-18.8) and 12.0% (95%CI, 1.5-22.6) for SCORE2 and PCE, respectively).

CONCLUSION

Combining a PRS with clinical risk scores significantly improved the reclassification of risk for incident ASCVD for subjects in the clinically-determined intermediate-risk category. Introducing PRSs in clinical practice may refine cardiovascular prevention for subgroups of patients in whom prevention strategies are uncertain.

Constructing a polygenic risk score for childhood obesity using functional data analysis

Obesity is a highly heritable condition that affects increasing numbers of adults and, concerningly, of children. However, only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage longitudinal phenotypes. Novel functional data analysis (FDA) techniques are used to capitalize on longitudinal growth information from a cohort of children between birth and three years of age. In an ultra-high dimensional setting, hundreds of thousands of single nucleotide polymorphisms (SNPs) are screened, and selected SNPs are used to construct two polygenic risk scores (PRS) for childhood obesity using a weighting approach that incorporates the dynamic and joint nature of SNP effects. These scores are significantly higher in children with (vs. without) rapid infant weight gain-a predictor of obesity later in life. Using two independent cohorts, it is shown that the genetic variants identified in very young children are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in the cohort of young children. This provides an example of a successful application of FDA to GWAS. This application is complemented with simulations establishing that a deeply characterized sample can be just as, if not more, effective than a comparable study with a cross-sectional response. Overall, it is demonstrated that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes; and shows how FDA approaches can be used as an alternative to the traditional GWAS.

What contribution can genetics make to predict the risk of Alzheimer's disease?

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although its etiology remains incompletely understood, genetic variants are important contributors. The prediction of AD risk through individual genetic variants is an important topic of research that may have individual and societal consequences when preventive treatments will become available. However, the genetic substratum of AD is heterogeneous. In addition to the extremely rare and fully penetrant pathogenic variants of the PSEN1, PSEN2 or APP genes causing autosomal dominant AD, a large spectrum of risk factors have been identified in complex forms, including the common risk factor APOEɛ4, which is associated with a moderate-to-high risk, common polymorphisms associated with a modest individual risk, and a plethora of rare variants in genes like SORL1, TREM2 or ABCA7 with moderate to high-magnitude effect. Understanding how these genetic factors contribute to AD risk in a given individual, in additional to non-genetic factors, remains a challenge. Over the last 10 years, age-related penetrance curves have progressively incorporated advances in the knowledge of AD genetics, from APOE to common polygenic components and, currently, SORL1 rare variants, which represents an important step towards precision medicine in AD. In this review, we present the complex genetic architecture of AD and we expose the prediction of AD risk according to its underlying genetic component.

Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk

OBJECTIVE

The 2-year risk of psychosis in persons who meet research criteria for a high-risk syndrome is about 15%-25%; improvements in risk prediction accuracy would benefit the development and implementation of preventive interventions. The authors sought to assess polygenic risk score (PRS) prediction of subsequent psychosis in persons at high risk and to determine the impact of adding the PRS to a previously validated psychosis risk calculator.

METHODS

Persons meeting research criteria for psychosis high risk (N=764) and unaffected individuals (N=279) were followed for up to 2 years. The PRS was based on the latest schizophrenia and bipolar genome-wide association studies. Variables in the psychosis risk calculator included stressful life events, trauma, disordered thought content, verbal learning, information processing speed, and family history of psychosis.

RESULTS

For Europeans, the PRS varied significantly by group and was higher in the psychosis converter group compared with both the nonconverter and unaffected groups, but was similar for the nonconverter group compared with the unaffected group. For non-Europeans, the PRS varied significantly by group; the difference between the converters and nonconverters was not significant, but the PRS was significantly higher in converters than in unaffected individuals, and it did not differ between nonconverters and unaffected individuals. The R2liability (R2 adjusted for the rate of disease risk in the population being studied, here assuming a 2-year psychosis risk between 10% and 30%) for Europeans varied between 9.2% and 12.3% and for non-Europeans between 3.5% and 4.8%. The amount of risk prediction information contributed by the addition of the PRS to the risk calculator was less than severity of disordered thoughts and similar to or greater than for other variables. For Europeans, the PRS was correlated with risk calculator variables of information processing speed and verbal memory.

CONCLUSIONS

The PRS discriminates psychosis converters from nonconverters and modestly improves individualized psychosis risk prediction when added to a psychosis risk calculator. The schizophrenia PRS shows promise in enhancing risk prediction in persons at high risk for psychosis, although its potential utility is limited by poor performance in persons of non-European ancestry.

Neuroticism polygenic risk score predicts 20-year burden of depressive symptoms for Whites but not Blacks

BACKGROUND

Black-White differences are reported in social, psychological, behavioral, medical, and biological correlates of depression. This study was conducted to compare Black and White older adults for the association between neuroticism polygenic risk score (N-PRS) and chronicity of depressive symptoms over 20 years.

METHODS

Data came from the Health and Retirement Study (HRS), 1990 - 2012, a nationally representative sample of Americans above age 50. Current analysis followed 9,249 individuals (7,924 Whites and 1,325 Blacks) for up to 22 years. Depressive symptoms were measured every two years between 1992 and 2012 using the 8-item Center for Epidemiological Studies-Depression Scale (CES-D-8). The independent variable was N-PRS. The dependent variable was average depressive symptoms between 1992 and 2012. Linear regression was used for data analysis.

RESULTS

In the pooled sample, higher N-PRS was associated with higher average depressive symptoms over the 20-year follow up period [b=0.01, 95%CI=0.00 to 0.04], net of all covariates. We also found an interaction between race and N-PRS [b=-0.02, 95%CI=-0.03 to 0.00], suggesting a stronger effect of N-PRS on 20-year average depressive symptoms for Whites than Blacks. Based on our race-specific linear regression models, higher N-PRS was associated with higher depressive symptoms from 1992 to 2012 for Whites [b=0.01, 95%CI=0.01 to 0.02] but not Blacks [b=0.00, 95%CI=-0.02 to 0.02].

CONCLUSION

Black and White older adults may differ in the salience of the existing N-PRS for depressive symptoms, which better reflects the burden of depression for Whites than Blacks. This may be because the existing PRSs are derived from mostly or exclusively White samples, limiting their applicability in other race groups. Racial variation in psychosocial, clinical, and biological correlates of depression needs further research.

Schizophrenia Polygenic Risk Score as a Predictor of Antipsychotic Efficacy in First-Episode Psychosis

OBJECTIVE

Pharmacogenomic studies of antipsychotics have typically examined effects of individual polymorphisms. By contrast, polygenic risk scores (PRSs) derived from genome-wide association studies (GWAS) can quantify the influence of thousands of common alleles of small effect in a single measure. The authors examined whether PRSs for schizophrenia were predictive of antipsychotic efficacy in four independent cohorts of patients with first-episode psychosis (total N=510).

METHOD

All study subjects received initial treatment with antipsychotic medication for first-episode psychosis, and all were genotyped on standard single-nucleotide polymorphism (SNP) arrays imputed to the 1000 Genomes Project reference panel. PRS was computed based on the results of the large-scale schizophrenia GWAS reported by the Psychiatric Genomics Consortium. Symptoms were measured by using total symptom rating scales at baseline and at week 12 or at the last follow-up visit before dropout.

RESULTS

In the discovery cohort, higher PRS significantly predicted higher symptom scores at the 12-week follow-up (controlling for baseline symptoms, sex, age, and ethnicity). The PRS threshold set at a p value <0.01 gave the strongest result in the discovery cohort and was used to replicate the findings in the other three cohorts. Higher PRS significantly predicted greater posttreatment symptoms in the combined replication analysis and was individually significant in two of the three replication cohorts. Across the four cohorts, PRS was significantly predictive of adjusted 12-week symptom scores (pooled partial r=0.18; 3.24% of variance explained). Patients with low PRS were more likely to be treatment responders than patients with high PRS (odds ratio=1.91 in the two Caucasian samples).

CONCLUSIONS

Patients with higher PRS for schizophrenia tended to have less improvement with antipsychotic drug treatment. PRS burden may have potential utility as a prognostic biomarker.

Predictors and Implications of Accelerated Cognitive Aging

Aging is a major risk factor for both normal and pathological cognitive decline. However, individuals vary in their rate of age-related decline. We developed an easily interpretable composite measure of cognitive age, and related both the level of cognitive age and cognitive slope to sociodemographic, genetic, and disease indicators and examine its prediction of dementia transition. Using a sample of 19,594 participants from the Health and Retirement Study, cognitive age was derived from a set of performance tests administered at each wave. Our findings reveal different conclusions as they relate to levels versus slopes of cognitive age, with more pronounced differences by sex and race/ethnicity for absolute levels of cognitive decline rather than for rates of declines. We also find that both level and slope of cognitive age are inversely related to education, as well as increased for persons with APOE ε4 and/or diabetes. Finally, results show that the slope in cognitive age predicts subsequent dementia among non-demented older adults. Overall, our study suggests that this measure is applicable to cross-sectional and longitudinal studies on cognitive aging, decline, and dementia with the goal of better understanding individual differences in cognitive decline.

Negative Urgency Mediates the Relation between Genetically-Influenced Serotonin Functioning and Alcohol Problems

Serotonin (5-HT) functioning is associated with alcohol problems. However, the mechanisms underlying this association remain unclear. The current study tested whether five separate dimensions of impulsivity (UPPS-P) mediated the relation between a polygenic score indexing 5-HT functioning and alcohol problems and whether any of these paths were moderated by age. Results showed that a 5-HT polygenic score predicted alcohol problems indirectly through negative urgency, but not any other facet of impulsivity. The 5-HT polygenic score also directly predicted alcohol problems. No age moderation was found. Findings suggest that negative urgency might be one important mechanism underlying the relation between genetically-influenced 5-HT functioning and alcohol problems. However, genetically-influenced 5-HT functioning likely influences alcohol problems through additional mechanisms. More broadly, results suggest that the previously observed transdiagnostic nature of 5-HT functioning on diverse types of psychopathology might be, in part, explained by its effect on negative urgency.

Polygenic Risk Score associated with specific symptom dimensions in first-episode psychosis

Recent Genome-Wide Association Studies (GWAS) have provided evidence for the involvement of a number of genetic variants in schizophrenia (SCZ). The objective of the current study was to examine the association between these variants and symptom dimensions, evaluated prospectively over a period of 24months, in a clinically well-characterized sample of individuals (n=241) with first-episode psychosis (FEP). The genetic variants were analyzed collectively as captured through a Polygenic Risk Score (PRS), calculated for each individual. At each evaluation time point (baseline, 1, 2, 6 and 24months), correlation analysis was conducted with PRS and symptom dimension scores assessed by the Positive and Negative Syndrome Scale (PANSS). We also examined the association of PRS with global symptom rating, depression, anxiety, social and occupational functioning as measured by widely used and well validated scales. At baseline, significant positive correlation was observed between PRS and the general psychopathology dimension of the PANSS but no associations were observed with the positive or negative symptom dimensions. Anxiety, assessed using the Hamilton Anxiety Rating Scale, was also significantly correlated with the PRS. No significant correlation was observed with other symptom dimensions or with the PANSS score at the later evaluations. These results provide novel evidence of an association between general psychopathology and PRS in young people with first episode psychosis. They also demonstrate that it is important to note the dynamic changes of symptoms over time when trying to refine the relationship between genetic factors and phenotypes.

The Impact of Peer Substance Use and Polygenic Risk on Trajectories of Heavy Episodic Drinking Across Adolescence and Emerging Adulthood

BACKGROUND

Heavy episodic drinking is developmentally normative among adolescents and young adults, but is linked to adverse consequences in later life, such as drug and alcohol dependence. Genetic and peer influences are robust predictors of heavy episodic drinking in youth, but little is known about the interplay between polygenic risk and peer influences as they impact developmental patterns of heavy episodic drinking.

METHODS

Data were from a multisite prospective study of alcohol use among adolescents and young adults with genome-wide association data (n = 412). Generalized linear mixed models were used to characterize the initial status and slopes of heavy episodic drinking between age 15 and 28. Polygenic risk scores (PRS) were derived from a separate genome-wide association study for alcohol dependence and examined for their interaction with substance use among the adolescents' closest friends in predicting the initial status and slopes of heavy episodic drinking.

RESULTS

Close friend substance use was a robust predictor of adolescent heavy episodic drinking, even after controlling for parental knowledge and peer substance use in the school. PRS were predictive of the initial status and early patterns of heavy episodic drinking in males, but not in females. No interaction was detected between PRS and close friend substance use for heavy episodic drinking trajectories in either males or females.

CONCLUSIONS

Although substance use among close friends and genetic influences play an important role in predicting heavy episodic drinking trajectories, particularly during the late adolescent to early adult years, we found no evidence of interaction between these influences after controlling for other social processes, such as parental knowledge and broader substance use among other peers outside of close friends. The use of longitudinal models and accounting for multiple social influences may be crucial for future studies focused on uncovering gene-environment interplay. Clinical implications are also discussed.