Multi-polygenic score approach to trait prediction

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Multiple polygenic risk scores can improve the prediction of systemic lupus erythematosus in Taiwan

OBJECTIVE

To identify new genetic variants associated with SLE in Taiwan and establish polygenic risk score (PRS) models to improve the early diagnostic accuracy of SLE.

METHODS

The study enrolled 2429 patients with SLE and 48 580 controls from China Medical University Hospital in Taiwan. A genome-wide association study (GWAS) and PRS analyses of SLE and other three SLE markers, namely ANA, anti-double-stranded DNA antibody (dsDNA) and anti-Smith antibody (Sm), were conducted.

RESULTS

Genetic variants associated with SLE were identified through GWAS. Some novel genes, which have been previously reported, such as RCC1L and EGLN3, were revealed to be associated with SLE in Taiwan. Multiple PRS models were established, and optimal cut-off points for each PRS were determined using the Youden Index. Combining the PRSs for SLE, ANA, dsDNA and Sm yielded an area under the curve of 0.64 for the optimal cut-off points. An analysis of human leucocyte antigen (HLA) haplotypes in SLE indicated that individuals with HLA-DQA1*01:01 and HLA-DQB1*05:01 were at a higher risk of being classified into the SLE group.

CONCLUSIONS

The use of PRSs to predict SLE enables the identification of high-risk patients before abnormal laboratory data were obtained or symptoms were manifested. Our findings underscore the potential of using PRSs and GWAS in identifying SLE markers, offering promise for early diagnosis and prediction of SLE.

Association of longitudinal trajectories of general and abdominal adiposity during middle age with mental health and well-being in late life: A prospective analysis

Single measures of adiposity markers, such as body mass index (BMI) and waist circumference (WC), are associated with adverse mental health outcomes; however, long-term patterns of adiposity and their health effects remain unclear. The current study assessed adiposity trajectories during a 14-year span beyond middle age and their relevance to mental well-being in late life, and the contribution of genetic and lifestyle factors to the trajectories. Based on a nationally representative sample with longitudinal anthropometric measures, adiposity trajectories were identified by latent mixture modeling, and logistic regression model was used to estimate their associations with mental well-being, with adjustment for confounders. Of the 3491 eligible participants included (mean [SD] age, 69.5 [8.9] years), five discrete BMI and four WC trajectory patterns were identified over 14 years. Compared with the low-stable BMI group (range, 22.8 to 22.9 kg/m²; representing stable healthy body weight), the high-stable group (range, 34.3 to 35.4 kg/m²; stable obese) was associated with increased risk of depression (odds ratio [OR], 1.63; 95 % CI, 1.28-2.07) and low subjective well-being (OR, 1.35; 95 % CI, 1.02-1.79). Compared with the low-stable WC group (range, 75 to 79 cm healthy WC), the high-increasing group (range, 114 to 121 cm) was associated with increased risk of depression (odds ratio [OR], 1.64; 95 % CI, 1.19-2.25) and low well-being (OR, 1.48; 95 % CI, 1.01-2.16). The adiposity trajectories, especially the high-stable/increasing groups, were driven by genetic factors in a dose-response manner, whereas the high/moderate-increasing groups were also behaviorally related. This longitudinal cohort study reveals that stably high trajectory patterns of central and general adiposity during middle age were associated with higher risk of depression and low well-being in late life. The findings indicate the importance of weight management beyond middle age, such as adherence to a healthy lifestyle, in promoting mental health and well-being.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure

AIMS

Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations.

METHODS AND RESULTS

Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF.

CONCLUSIONS

The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.

Ancestry Specific Polygenic Risk Score, Dietary Patterns, Physical Activity, and Cardiovascular Disease

It is unknown whether the impact of high diet quality and physical activity depends on the level of polygenic risk score (PRS) in different ancestries. Our cross-sectional study utilized de-identified data from 1987-2010 for self-reported European Americans (n = 6575) and African Americans (n = 1606). The high-risk PRS increased ASCVD risk by 59% (Risk Ratio (RR) = 1.59; 95% Confidence Interval:1.16-2.17) in the highest tertile for African Americans and by 15% (RR = 1.15; 1.13-1.30) and 18% (RR = 1.18; 1.04-1.35) in the second and highest tertiles compared to the lowest tertile in European Americans. Within the highest PRS tertiles, high physical activity-diet combinations (Dietary Approaches to Stop High Blood Pressure (DASH), Mediterranean, or Southern) reduced ASCVD risks by 9% (RR = 0.91; 0.85-0.96) to 15% (RR = 0.85; 0.80-0.90) in European Americans; and by 13% (RR = 0.87; 0.78-0.97) and 18% (RR = 0.82; 0.72-0.95) for DASH and Mediterranean diets, respectively, in African Americans. Top molecular pathways included fructose metabolism and catabolism linked to obesity, insulin resistance, and type 2 diabetes. Additional molecular pathways for African Americans were Vitamin D linked to depression and aging acceleration and death signaling associated with cancer. Effects of high diet quality and high physical activity can counterbalance the influences of genetically high-risk PRSs on ASCVD risk, especially in African Americans.

A linear weighted combination of polygenic scores for a broad range of traits improves prediction of coronary heart disease

Polygenic scores (PGS) for coronary heart disease (CHD) are constructed using GWAS summary statistics for CHD. However, pleiotropy is pervasive in biology and disease-associated variants often share etiologic pathways with multiple traits. Therefore, incorporating GWAS summary statistics of additional traits could improve the performance of PGS for CHD. Using lasso regression models, we developed two multi-PGS for CHD: 1) multiPGSCHD, utilizing GWAS summary statistics for CHD, its risk factors, and other ASCVD as training data and the UK Biobank for tuning, and 2) extendedPGSCHD, using existing PGS for a broader range of traits in the PGS Catalog as training data and the Atherosclerosis Risk in Communities Study (ARIC) cohort for tuning. We evaluated the performance of multiPGSCHD and extendedPGSCHD in the Mayo Clinic Biobank, an independent cohort of 43,578 adults of European ancestry which included 4,479 CHD cases and 39,099 controls. In the Mayo Clinic Biobank, a 1 SD increase in multiPGSCHD and extendedPGSCHD was associated with a 1.66-fold (95% CI: 1.60-1.71) and 1.70-fold (95% CI: 1.64-1.76) increased odds of CHD, respectively, in models that included age, sex, and 10 PCs, whereas an already published PGS for CHD (CHD_PRSCS) increased the odds by 1.50 (95% CI: 1.45-1.56). In the highest deciles of extendedPGSCHD, multiPGSCHD, and CHD_PRSCS, 18.4%, 17.5%, and 16.3% of patients had CHD, respectively.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Ancestry Specific Polygenic Risk Score, Dietary Patterns, Physical Activity, and Cardiovascular Disease

Background

It is unknown whether the impact of high diet-quality and physical activity (PA) depends on the level of polygenic risk score (PRS) in different ancestries.

Objective

Determine the associations and interactions between high-risk PRSs, dietary patterns, and high PA with atherosclerotic cardiovascular disease (ASCVD) in European Americans (EAs) and African Americans (AAs). Another aim determined the molecular pathways of PRS-mapped genes and their relationships with dietary intake.

Methods

Cross-sectional analyses utilized de-identified data from 1987-2010 from 7-National Heart, Lung, and Blood Institute Candidate Gene Association Resource studies from the Database of Genotypes and Phenotypes studies for EAs (n=6,575) and AAs (n=1,606).

Results

The high-risk PRS increased ASCVD risk by 59% (Risk Ratio=1.59;95% Confidence Interval:1.16-2.17) in the highest tertile for AAs and by 15% (RR=1.15;1.13-1.30) and 18% (RR=1.18;1.04-1.35) in the second and highest tertiles compared to the lowest tertile in EAs. Within the highest PRS tertiles, high PA-diet combinations (Dietary Approaches to Stop High Blood Pressure (DASH), or Mediterranean, or Southern) reduced ASCVD risks by 9% (RR=0.91;0.85-0.96) to 15% (RR=0.85;0.80-0.90) in EAs; and by 13% (RR=0.87;0.78-0.97) and 18% (RR=0.82;0.72-0.95) for the DASH and Mediterranean diets, respectively in AAs. Top molecular pathways included fructose metabolism and catabolism linked to obesity, insulin resistance, and type 2 diabetes in both ancestries. Additional molecular pathways for AAs were Vitamin D linked to depression and aging acceleration; and death signaling associated with cancer.

Conclusions

Effects of high diet-quality and high PA can counterbalance the influences of genetically high-risk PRSs on ASCVD risk, especially in AAs.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

The polygenic and reactive nature of observed parenting

In Wertz et al. (2019), parents' polygenic scores of educational attainment (PGS-EA) predicted parental sensitive responses to the child's needs for support, as observed in a dyadic task (i.e., observed sensitivity). We aimed to replicate and expand these findings by combining longitudinal data, child genotype data and several polygenic scores in the Generation R Study. Mother-child dyads participated in two developmental periods, toddlerhood (14 months old; n = 648) and early childhood (3-4 years old, n = 613). Higher maternal PGS-EA scores predicted higher observed sensitivity in toddlerhood (b = 0.12, 95% CI 0.03, 0.20) and early childhood (b = 0.16, 95% CI 0.08, 0.24). Child PGS-EA was significantly associated with maternal sensitivity in early childhood (b = 0.11, 95% CI 0.02, 0.21), and the effect of maternal PGS-EA was no longer significant when correcting for child PGS-EA. A latent factor of PGSs based on educational attainment, intelligence (IQ) and income showed similar results. These polygenic scores might be associated with maternal cognitive and behavioral skills that help shape parenting. Maternal PGSs predicted observed sensitivity over and above the maternal phenotypes, showing an additional role for PGSs in parenting research. In conclusion, we replicated the central finding of Wertz et al. (2019) that parental PGS-EA partially explains parental sensitivity. Our findings may be consistent with evocative gene-environment correlation (rGE), emphasizing the dynamic nature of parenting behavior across time, although further research using family trios is needed to adequately test this hypothesis.

ARFID Genes and Environment (ARFID-GEN): study protocol

BACKGROUND

The Avoidant Restrictive Food Intake Disorder - Genes and Environment (ARFID-GEN) study is a study of genetic and environmental factors that contribute to risk for developing ARFID in children and adults.

METHODS

A total of 3,000 children and adults with ARFID from the United States will be included. Parents/guardians and their children with ARFID (ages 7 to 17) and adults with ARFID (ages 18 +) will complete comprehensive online consent, parent verification of child assent (when applicable), and phenotyping. Enrolled participants with ARFID will submit a saliva sample for genotyping. A genome-wide association study of ARFID will be conducted.

DISCUSSION

ARFID-GEN, a large-scale genetic study of ARFID, is designed to rapidly advance the study of the genetics of eating disorders. We will explicate the genetic architecture of ARFID relative to other eating disorders and to other psychiatric, neurodevelopmental, and metabolic disorders and traits. Our goal is for ARFID to deliver "actionable" findings that can be transformed into clinically meaningful insights.

TRIAL REGISTRATION

ARFID-GEN is a registered clinical trial: clinicaltrials.gov NCT05605067.

Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis

BACKGROUND AND HYPOTHESIS

Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease.

STUDY DESIGN

We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes.

STUDY RESULTS

All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05).

CONCLUSIONS

Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Associations of dietary, sociodemographic, and anthropometric factors with anemia among the Zhuang ethnic adults: a cross-sectional study in Guangxi Zhuang Autonomous Region, China

BACKGROUND

After decades of rapid economic development, anemia remains a significant public health challenge globally. This study aimed to estimate the associations of sociodemographic, dietary, and body composition factors with anemia among the Zhuang in Guangxi Zhuang Autonomous Region, China.

METHODS

Our study population from the baseline survey of the Guangxi ethnic minority Cohort Study of Chronic Diseases consisted of 13,465 adults (6,779 women and 6,686 men) aged 24-82 years. A validated interviewer-administered laptop-based questionnaire system was used to collect information on participants' sociodemographic, lifestyle, and dietary factors. Each participant underwent a physical examination, and hematological indices were measured. Least absolute shrinkage and selection operator (LASSO) regression was used to select the variables, and logistic regression was applied to estimate the associations of independent risk factors with anemia.

RESULTS

The overall prevalences of anemia in men and women were 9.63% (95% CI: 8.94-10.36%) and 18.33% (95% CI: 17.42─19.28%), respectively. LASSO and logistic regression analyses showed that age was positively associated with anemia for both women and men. For diet in women, red meat consumption for 5-7 days/week (OR = 0.79, 95% CI: 0.65-0.98, p = 0.0290) and corn/sweet potato consumption for 5-7 days/week (OR = 0.73, 95% CI: 0.55-0.96, p = 0.0281) were negatively associated with anemia. For men, fruit consumption for 5-7 days/week (OR = 0.75, 95% CI: 0.60-0.94, p = 0.0130) and corn/sweet potato consumption for 5-7 days/week (OR = 0.66, 95% CI: 0.46-0.91, p = 0.0136) were negatively correlated with anemia. Compared with a normal body water percentage (55-65%), a body water percentage below normal (< 55%) was negatively related to anemia (OR = 0.68, 95% CI: 0.53-0.86, p = 0.0014). Conversely, a body water percentage above normal (> 65%) was positively correlated with anemia in men (OR = 1.73, 95% CI: 1.38-2.17, p < 0.0001).

CONCLUSIONS

Anemia remains a moderate public health problem for premenopausal women and the elderly population in the Guangxi Zhuang minority region. The prevention of anemia at the population level requires multifaceted intervention measures according to sex and age, with a focus on dietary factors and the control of body composition.

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Don't miss the chance to reap the fruits of recent advances in behavioral genetics

In her target article, Burt revives a by now ancient debate on nature and nurture, and the ways to measure, disentangle, and ultimately trust one or the other of these forces. Unfortunately, she largely dismisses recent advances in behavior genetics and its huge potential in contributing to a better prediction and understanding of complex traits in social sciences.

Arfid Genes and Environment (ARFID-GEN): Study Protocol

Background

The Avoidant Restrictive Food Intake Disorder Genes and Environment (ARFID-GEN) study is a study of genetic and environmental factors that contribute to risk for developing ARFID in children and adults.

Methods

A total of 3,000 children and adults with ARFID from the United States will be included. Parents/guardians and their children with ARFID (ages 7 to 17) and adults with ARFID (ages 18+) will complete comprehensive online consent, parent verification of child assent (when applicable), and phenotyping. Enrolled participants with ARFID will submit a saliva sample for genotyping. A genome-wide association study of ARFID will be conducted.

Discussion

ARFID-GEN, a large-scale genetic study of ARFID, is designed to rapidly advance the study of the genetics of eating disorders. We will explicate the genetic architecture of ARFID relative to other eating disorders and to other psychiatric, neurodevelopmental, and metabolic disorders and traits. Our goal is for ARFID to deliver "actionable" findings that can be transformed into clinically meaningful insights.

Trial registration

ARFID-GEN is a registered clinical trial: clinicaltrials.gov NCT05605067.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

AI-based multi-PRS models outperform classical single-PRS models

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.

The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study

BACKGROUND

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).

METHODS

We used genome-wide association summary statistics from individuals with European ancestry from the Psychiatric Genomics Consortium, UK Biobank, and International Cannabis Consortium. We estimated heritability, polygenicity, and discoverability of each phenotype. We performed genome-wide and local genetic correlations. Shared loci were identified and mapped to genes, which were tested for functional enrichment. Shared genetic liabilities to psychotic disorders and cannabis phenotypes were explored using causal analyses and polygenic scores, using the Norwegian Thematically Organized Psychosis cohort.

FINDINGS

Psychotic disorders were more heritable than cannabis phenotypes and more polygenic than cannabis use disorder. We observed positive genome-wide genetic correlations between psychotic disorders and cannabis phenotypes (range 0·22-0·35) with a mixture of positive and negative local genetic correlations. Three to 27 shared loci were identified for the psychotic disorder and cannabis phenotype pairs. Enrichment of mapped genes implicated neuronal and olfactory cells as well as drug-gene targets for nicotine, alcohol, and duloxetine. Psychotic disorders showed a causal effect on cannabis phenotypes, and lifetime cannabis use had a causal effect on bipolar disorder. Of 2181 European participants from the Norwegian Thematically Organized Psychosis cohort applied in polygenic risk score analyses, 1060 (48·6%) were females and 1121 (51·4%) were males (mean age 33·1 years [SD 11·8]). 400 participants had bipolar disorder, 697 had schizophrenia, and 1044 were healthy controls. Within this sample, polygenic scores for cannabis phenotypes predicted psychotic disorders independently and improved prediction beyond the polygenic score for the psychotic disorders.

INTERPRETATION

A subgroup of individuals might have a high genetic risk of developing a psychotic disorder and using cannabis. This finding supports public health efforts to reduce cannabis use, particularly in individuals at high risk or patients with psychotic disorders. Identified shared loci and their functional implications could facilitate development of novel treatments.

FUNDING

US National Institutes of Health, the Research Council Norway, the South-East Regional Health Authority, Stiftelsen Kristian Gerhard Jebsen, EEA-RO-NO-2018-0535, European Union's Horizon 2020 Research and Innovation Programme, the Marie Skłodowska-Curie Actions, and University of Oslo Life Science.

The hermeneutics of recovery: Facilitating dialogue between African and Western mental health frameworks

The widespread use of faith-based and traditional healing for mental disorders within African contexts is well known. However, normative responses tend to fall within two camps: on one hand, those oriented towards the biomedical model of psychiatry stress the abuses and superstition of such healing, whilst critics adopting a more 'local' perspective have fundamentally challenged the universalist claims of biomedical diagnostic categories and psychiatric treatments. What seemingly emerges is a dichotomy between those who endorse more 'universalist' or 'relativist' approaches as an analytical lens to the challenges of the diverse healing strands within African contexts. In this article, we draw upon the resources of philosophy and existing empirical work to challenge the notion that constructive dialogue cannot be had between seemingly incommensurable healing practices in global mental health. First, we suggest the need for much-needed conceptual clarity to explore the hermeneutics of meaning, practice, and understanding, in order to forge constructive normative pathways of dialogue between seemingly incommensurable values and conceptual schemas around mental disorder and healing. Second, we contextualise the complex motives to emphasise difference amongst health practitioners within a competitive healing economy. Finally, we appeal to the notion of recovery as discovery as a fruitful conceptual framework which incorporates dialogue, comparative evaluation, and cross-cultural enrichment across divergent conceptualisations of mental health.

Genetic Determinants of the Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.

Analysis of ancestry-specific polygenic risk score and diet composition in type 2 diabetes

BACKGROUND

Carbohydrate and protein dietary proportions have been debated as to whether higher or lower levels are optimal for diabetes metabolic control.

OBJECTIVE

The objective of this study was to investigate the associations, interactions, and mediational relationships between a polygenic risk score (PRS), carbohydrate and protein intake, and physical activity level on type 2 diabetes (T2DM) by genetic ancestry, in European Americans and African Americans. A secondary objective examined the biological pathways associated with the PRS-linked genes and their relationships to dietary intake.

METHODS

We performed a cross-sectional study in 9,393 participants: 83.3% European Americans and 16.7% African Americans from 7-NHLBI Care studies obtained from the database of Genotypes and Phenotypes. The main outcome was T2DM. Carbohydrate and protein intake derived from food frequency questionnaires were calculated as percent calories. Data were analyzed using multivariable generalized estimation equation models to derive odds ratios (OR) and 95% confidence intervals (CI). Ancestry-specific PRSs were constructed using joint-effects Summary Best Linear Unbiased Estimation in the train dataset and replicated in the test dataset. Mediation analysis was performed using VanderWeele's method.

RESULTS

The PRS in the highest tertile was associated with higher risk of T2DM in European Americans (OR = 1.25;CI = 1.03-1.51) and African Americans (OR = 1.54;1.14-2.09). High carbohydrate and low protein intake had lower risks of T2DM when combined with the PRS after adjusting for covariates. In African Americans, high physical activity combined with the high PRS and high protein diet was associated with a 28% lower incidence of T2DM when compared to low physical activity. In mediational models in African Americans, the PRS-T2DM association was mediated by protein intake in the highest tertile by 55%. The top PRS tertile had the highest magnitude of risks with metabolic factors that were significantly associated with T2DM, especially in European Americans. We found metabolic pathways associated with the PRS-linked genes that were related to insulin/IGF and ketogenesis/ketolysis that can be activated by moderate physical activity and intermittent fasting for better T2DM control.

CONCLUSIONS

Clinicians may want to consider diets with a higher portion of carbohydrates than protein, especially when the burden of high-risk alleles is great in patients with T2DM. In addition, clinicians and other medical professionals may want to emphasize the addition of physical activity as part of treatment regimen especially for African Americans. Given the metabolic pathways we identified, moderate physical activity and intermittent fasting should be explored. Researchers may want to consider longitudinal or randomized clinical trials to determine the predictive ability of different dietary patterns to inhibit T2DM in the presence of obesity and an elevated PRS.

Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Validation of a polygenic risk score for Frailty in the Lothian Birth Cohort and English Longitudinal Study of Ageing

Frailty is a complex trait. Twin studies and a high-powered Genome Wide Association Study (GWAS) conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from this GWAS to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples - the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67-84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085-0.21) and 1.6% (β = 0.14, 95%CI, 0.10-0.17) of the variance, respectively, at age ~68/~70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~68/~70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.

Response to erenumab assessed by Headache Impact Test-6 is modulated by genetic factors and arterial hypertension: An explorative cohort study

BACKGROUND AND PURPOSE

Response predictors to erenumab (ERE) in migraine patients would benefit their clinical management. We investigate associations between patients' clinical characteristics and polymorphisms at calcitonin receptor-like receptor (CALCRL) and receptor activity-modifying protein 1 (RAMP1) genes and response to ERE treatment measured as clinically meaningful improvement on the Headache Impact Test-6 (HIT-6) score.

METHODS

This post hoc analysis of a prospective, multicenter, investigator-initiated study involves 110 migraine patients starting ERE 70 mg/month. Demographics, medical history, and migraine-related burden measured by HIT-6 score were collected during 3 months before and after ERE start. Selected polymorphic variants of CALCRL and RAMP1 genes were determined using real-time polymerase chain reaction. Logistic regression models identified independent predictors for response to ERE, defined as HIT-6 score improvement ≥ 8 points (HIT-6 responders [HIT-6 RESP] vs. HIT-6 nonresponders).

RESULTS

At Month 3, 58 (52.7%) patients were HIT-6 RESP. Comorbid hypertension predicted a lower probability of being HIT-6 RESP (odds ratio [OR] = 0.160, 95% confidence interval [CI] = 0.047-0.548, p = 0.003). Compared to major alleles, minor alleles CALCRL rs6710852G and RAMP rs6431564G conferred an increased probability of being HIT-6 RESP (for each G allele: OR = 2.82, 95% CI = 1.03-7.73, p = 0.043; OR = 2.10, 95% CI = 1.05-4.22, p = 0.037). RAMP1 rs13386048A and RAMP1 rs12465864G decreased this probability (for each rs13386048A, OR = 0.53, 95% CI = 0.28-0.98, p = 0.042; for each rs12465864G, OR = 0.32, 95% CI = 0.13-0.75, p = 0.009). A genetic risk score based on the presence and number of identified risk alleles was independently associated with HIT-6 RESP (OR = 0.49, 95% CI = 0.33-0.72, p = 0.0003), surviving Bonferroni correction.

CONCLUSIONS

Response to ERE was associated with comorbid hypertension and specific allelic variants in CALCRL and RAMP1 genes. Results require confirmation in future studies.

The clinical utility of polygenic risk scores for combined hyperlipidemia

PURPOSE OF REVIEW

Combined hyperlipidemia is the most common lipid disorder and is strongly polygenic. Given its prevalence and associated risk for atherosclerotic cardiovascular disease, this review describes the potential for utilizing polygenic risk scores for risk prediction and management of combined hyperlipidemia.

RECENT FINDINGS

Different diagnostic criteria have led to inconsistent prevalence estimates and missed diagnoses. Given that individuals with combined hyperlipidemia have risk estimates for incident coronary artery disease similar to individuals with familial hypercholesterolemia, early identification and therapeutic management of those affected is crucial. With diagnostic criteria including traits such apolipoprotein B, low-density lipoprotein cholesterol, and triglyceride, polygenic risk scores for these traits strongly associate with combined hyperlipidemia and could be used in combination for clinical risk prediction models and developing specific treatment plans for patients.

SUMMARY

Polygenic risk scores are effective tools in risk prediction of combined hyperlipidemia, can provide insight into disease pathophysiology, and may be useful in managing and guiding treatment plans for patients. However, efforts to ensure equitable polygenic risk score performance across different genetic ancestry groups is necessary before clinical implementation in order to prevent the exacerbation of racial disparities in the clinic.

Genetic and Geographical Associations With Six Dimensions of Psychotic Experiences in Adolesence

BACKGROUND AND HYPOTHESIS

Large-scale epidemiological and genetic research have shown that psychotic experiences in the community are risk factors for adverse physical and psychiatric outcomes. We investigated the associations of six types of specific psychotic experiences and negative symptoms assessed in mid-adolescence with well-established environmental and genetic risk factors for psychosis.

STUDY DESIGN

Fourteen polygenic risk scores (PRS) and nine geographical environmental variables from 3590 participants of the Twins Early Development Study (mean age 16) were associated with paranoia, hallucinations, cognitive disorganization, grandiosity, anhedonia, and negative symptoms scales. The predictors were modeled using LASSO regularization separately (Genetic and Environmental models) and jointly (GE model).

STUDY RESULTS

In joint GE models, we found significant genetic associations of negative symptoms with educational attainment PRS (β = -.07; 95% CI = -0.12 to -0.04); cognitive disorganization with neuroticism PRS (β = .05; 95% CI = 0.03-0.08); paranoia with MDD (β = .07; 95% CI = 0.04-0.1), BMI (β = .05; 95% CI = 0.02-0.08), and neuroticism PRS (β = .05; 95% CI = 0.02-0.08). From the environmental measures only family SES (β = -.07, 95% CI = -0.10 to -0.03) and regional education levels (β = -.06; 95% CI = -0.09 to -0.02) were associated with negative symptoms.

CONCLUSIONS

Our findings advance understanding of how genetic propensity for psychiatric, cognitive, and anthropometric traits, as well as environmental factors, together play a role in creating vulnerability for specific psychotic experiences and negative symptoms in mid-adolescence.

Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition

BACKGROUND

Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear.

METHODS

We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities.

RESULTS

The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from r_g = -0.101 for smoking initiation to r_g = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from r_g = -0.161 for educational attainment to r_g = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure.

CONCLUSIONS

These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.

Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis

BACKGROUND

Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown.

METHODS

We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set.

RESULTS

The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%).

CONCLUSIONS

We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.

Statistical Methods for Disease Risk Prediction with Genotype Data

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan

Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits.

Associations between psychiatric polygenic risk scores and general and specific psychopathology symptoms in childhood and adolescence between and within dizygotic twin pairs

BACKGROUND

Although polygenic risk scores (PRS) predict psychiatric problems, these associations might be attributable to indirect pathways including population stratification, assortative mating, or dynastic effects (mediation via parental environments). The goal of this study was to examine whether PRS-psychiatric symptom associations were attributable to indirect versus direct pathways.

METHODS

The sample consisted of 3,907 dizygotic (DZ) twin pairs. In childhood, their parents rated them on 98 symptoms. In adolescence (n = 2,393 DZ pairs), both the parents and the twins rated themselves on 20 symptoms. We extracted one general and seven specific factors from the childhood data, and one general and three specific factors from the adolescent data. We then regressed each general factor model onto ten psychiatric PRS simultaneously. We first conducted the regressions between individuals (β) and then within DZ twin pairs (β_w ), which controls for indirect pathways.

RESULTS

In childhood, the PRS for ADHD predicted general psychopathology (β = 0.09, 95% CI: [0.06, 0.12]; β_w  = 0.07 [0.01, 0.12]). Furthermore, the PRS for ADHD predicted specific inattention (β = 0.04 [0.00, 0.08]; β_w  = 0.09 [0.01, 0.17]) and specific hyperactivity (β = 0.07 [0.04, 0.11]; β_w  = 0.09 [0.01, 0.16]); the PRS for schizophrenia predicted specific learning (β = 0.08 [0.03, 0.13]; β_w  = 0.19 [0.08, 0.30]) and specific inattention problems (β = 0.05 [0.01, 0.09]; β_w  = 0.10 [0.02, 0.19]); and the PRS for neuroticism predicted specific anxiety (β = 0.06 [0.02, 0.10]; β_w  = 0.06 [0.00, 0.12]). Overall, the PRS-general factor associations were similar between individuals and within twin pairs, whereas the PRS-specific factors associations amplified by 84% within pairs.

CONCLUSIONS

This implies that PRS-psychiatric symptom associations did not appear attributable to indirect pathways such as population stratification, assortative mating, or mediation via parental environments. Rather, genetics appeared to directly influence symptomatology.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

The sleep EEG envelope is a novel, neuronal firing-based human biomarker

Sleep EEG reflects voltage differences relative to a reference, while its spectrum reflects its composition of various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations, while its spectrum reflects the rhythmicity of the occurrence of these oscillations. The sleep EEG spectrum is known to relate to demographic, psychological and clinical characteristics, but the envelope spectrum has been rarely studied. In study 1, we demonstrate in human invasive data from cortex-penetrating microelectrodes and subdural grids that the sleep EEG envelope spectrum reflects neuronal firing. In study 2, we demonstrate that the scalp EEG envelope spectrum is stable within individuals. A multivariate learning algorithm could predict age (r = 0.6) and sex (r = 0.5) from the EEG envelope spectrum. With age, oscillations shifted from a 4-5 s rhythm to faster rhythms. Our results demonstrate that the sleep envelope spectrum is a promising biomarker of demographic and disease-related phenotypes.

Research Review: A guide to computing and implementing polygenic scores in developmental research

The increasing availability of genotype data in longitudinal population- and family-based samples provides opportunities for using polygenic scores (PGS) to study developmental questions in child and adolescent psychology and psychiatry. Here, we aim to provide a comprehensive overview of how PGS can be generated and implemented in developmental psycho(patho)logy, with a focus on longitudinal designs. As such, the paper is organized into three parts: First, we provide a formal definition of polygenic scores and related concepts, focusing on assumptions and limitations. Second, we give a general overview of the methods used to compute polygenic scores, ranging from the classic approach to more advanced methods. We include recommendations and reference resources available to researchers aiming to conduct PGS analyses. Finally, we focus on the practical applications of PGS in the analysis of longitudinal data. We describe how PGS have been used to research developmental outcomes, and how they can be applied to longitudinal data to address developmental questions.

Gene-environment interaction using polygenic scores: Do polygenic scores for psychopathology moderate predictions from environmental risk to behavior problems?

The DNA revolution has energized research on interactions between genes and environments (GxE) by creating indices of G (polygenic scores) that are powerful predictors of behavioral traits. Here, we test the extent to which polygenic scores for attention-deficit/hyperactivity disorder and neuroticism moderate associations between parent reports of their children's environmental risk (E) at ages 3 and 4 and teacher ratings of behavior problems (hyperactivity/inattention, conduct problems, emotional symptoms, and peer relationship problems) at ages 7, 9 and 12. The sampling frame included up to 6687 twins from the Twins Early Development Study. Our analyses focused on relative effect sizes of G, E and GxE in predicting behavior problems. G, E and GxE predicted up to 2%, 2% and 0.4%, respectively, of the variance in externalizing behavior problems (hyperactivity/inattention and conduct problems) across ages 7, 9 and 12, with no clear developmental trends. G and E predictions of emotional symptoms and peer relationship problems were weaker. A quarter (12 of 48) of our tests of GxE were nominally significant (p = .05). Increasing the predictive power of G and E would enhance the search for GxE.

Polygenic scores in biomedical research

Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

Gene-by-peer-environment interaction effects on cigarette, alcohol, and marijuana use among US high school students of European Ancestry

Research has shown that adolescents' substance use behavior is determined not only by individual characteristics but also by peer environments, and an emerging literature in social genomics has also found that individual genotypes moderate peer effects on egos' substance use. However, the previous literature on genetic by peer environment (GxPE) interaction effects is limited by the use of genetic measures with limited power and a lack of focus on causality. Based on a sample of about 4000 adolescents of European Ancestry from the National Longitudinal Study of Adolescent to Adult Health, this study utilizes polygenic scores to examine GxPE interactions between ego's genetics and peers' cigarette, alcohol, and marijuana use. The results show peers' cigarette and marijuana use positively affect ego's substance use, and peer effects are stronger when the ego is genetically predisposed to substance use. However, genetic propensities toward risk tolerance are found to weaken the peer effects on the ego's marijuana use. Overall, our findings provide new evidence for the existence of GxPE effects on adolescent substance use and reveal the multidimensional nature of GxPE effects.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

The actions and interactions of family genetic risk scores for alcohol use disorder and major depression on the risk for these two disorders

We know little about how genetic risk factors for two disorders jointly act and interact in predisposing to illness. Therefore, in the Swedish population, born 1970-1990 (n = 2,116,082) and followed through 2015, we examine, using additive Cox models, the impact of the family genetic risk scores (FGRS) for alcohol use disorder (AUD) and major depression (MD), their interaction with each other and with the relevant comorbid disorder on risk for AUD and MD. FGRS scores are constructed using rates of illness in first-fourth degree relatives. FGRS for AUD and MD interacted in predicting of both disorders and one FRGS (e.g., for AUD) interacted with the phenotype of MD to predict that disorder (e.g., AUD). These FGRS interactions were not substantially attenuated by adding interactions with the disorders. These results replicated across sexes. In predicting risk for a given disorder, we rarely consider genetic liabilities for other disorders. But such effects were here significant and interactive. Furthermore, the primary disorder genetic risk interacts with comorbid disorders. The pathways to risk for disorders from their and other disorders' genetic liability may be more complex than commonly considered.

Using DNA to predict behaviour problems from preschool to adulthood

BACKGROUND

One goal of the DNA revolution is to predict problems in order to prevent them. We tested here if the prediction of behaviour problems from genome-wide polygenic scores (GPS) can be improved by creating composites across ages and across raters and by using a multi-GPS approach that includes GPS for adult psychiatric disorders as well as for childhood behaviour problems.

METHOD

Our sample included 3,065 genotyped unrelated individuals from the Twins Early Development Study who were assessed longitudinally for hyperactivity, conduct, emotional problems, and peer problems as rated by parents, teachers, and children themselves. GPS created from 15 genome-wide association studies were used separately and jointly to test the prediction of behaviour problems composites (general behaviour problems, externalising, and internalising) across ages (from age 2 to 21) and across raters in penalised regression models. Based on the regression weights, we created multi-trait GPS reflecting the best prediction of behaviour problems. We compared GPS prediction to twin heritability using the same sample and measures.

RESULTS

Multi-GPS prediction of behaviour problems increased from <2% of the variance for observed traits to up to 6% for cross-age and cross-rater composites. Twin study estimates of heritability, although to a lesser extent, mirrored patterns of multi-GPS prediction as they increased from <40% to 83%.

CONCLUSIONS

The ability of GPS to predict behaviour problems can be improved by using multiple GPS, cross-age composites and cross-rater composites, although the effect sizes remain modest, up to 6%. Our approach can be used in any genotyped sample to create multi-trait GPS predictors of behaviour problems that will be more predictive than polygenic scores based on a single age, rater, or GPS.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood

BACKGROUND

Polygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance.

METHODS

We computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor.

RESULTS

Seven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology.

CONCLUSIONS

The results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.

Genome-wide pleiotropy analysis identifies novel blood pressure variants and improves its polygenic risk scores

Systolic and diastolic blood pressure (S/DBP) are highly correlated modifiable risk factors for cardiovascular disease (CVD). We report here a bidirectional Mendelian Randomization (MR) and horizontal pleiotropy analysis of S/DBP summary statistics from the UK Biobank (UKB)-International Consortium for Blood Pressure (ICBP) (UKB-ICBP) BP genome-wide association study and construct a composite genetic risk score (GRS) by including pleiotropic variants. The composite GRS captures greater (1.11-3.26 fold) heritability for BP traits and increases (1.09- and 2.01-fold) Nagelkerke's R² for hypertension and CVD. We replicated 118 novel BP horizontal pleiotropic variants including 18 novel BP loci using summary statistics from the Million Veteran Program (MVP) study. An additional 219 novel BP signals and 40 novel loci were identified after a meta-analysis of the UKB-ICBP and MVP summary statistics but without further independent replication. Our study provides further insight into BP regulation and provides a novel way to construct a GRS by including pleiotropic variants for other complex diseases.