Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

Identification of women at high risk of postpartum psychiatric episodes: A population-based study quantifying relative and absolute risks following exposure to selected risk factors and genetic liability

BACKGROUND

We quantified relative and absolute risks of postpartum psychiatric episodes (PPE) following risk factors: Young age, past personal or family history of psychiatric disorders, and genetic liability.

METHODS

We conducted a register-based study using the iPSYCH2012 case-cohort sample. Exposures were personal history of psychiatric episodes prior to childbirth, being a young mother (giving birth before the age of 21.5 years), having a family history of psychiatric disorders, and a high (highest quartile) polygenic score (PGS) for major depression. PPE was defined within 12 months postpartum by prescription of psychotropic medication or in- and outpatient contact to a psychiatric facility. We included primiparous women born 1981-1999, giving birth before January 1st, 2016. We conducted Cox regression to calculate hazard ratios (HRs) of PPE, absolute risks were calculated using cumulative incidence functions.

RESULTS

We included 8174 primiparous women, and the estimated baseline PPE risk was 6.9% (95% CI 6.0%-7.8%, number of PPE cases: 2169). For young mothers with a personal and family history of psychiatric disorders, the absolute risk of PPE was 21.6% (95% CI 15.9%-27.8%). Adding information on high genetic liability to depression, the risk increased to 29.2% (95% CI 21.3%-38.4%) for PPE.

CONCLUSIONS

Information on prior personal and family psychiatric episodes as well as age may assist in estimating a personalized risk of PPE. Furthermore, additional information on genetic liability could add even further to this risk assessment.

Prenatal antidepressant exposure and the risk of decreased gestational age and lower birthweight: A polygenic score approach to investigate confounding by indication

INTRODUCTION

Prenatal antidepressant exposure has been associated with lower gestational age and birthweight. Yet, unmeasured residual confounding may inflate this association. We explored if maternal genetic liability for major depression explains part of the association of antidepressant use in pregnancy with lower gestational age and birthweight.

MATERIAL AND METHODS

We employed the maternal polygenic score (PGS) for major depression as a measure of genetic liability. We used generalised linear models to estimate the differences in gestational age and birthweight at each PGS quintile between children whose mothers continued antidepressant use during pregnancy (continuation group), children whose mothers discontinued antidepressant use during pregnancy (discontinuation group) and unexposed children.

RESULTS

After adjusting for confounders, we found significant differences in birthweight between PGS quintiles in the continuation and unexposed group. Yet, this relationship was not linear. Furthermore, at the lowest and highest PGS quintiles, the continuation group had significantly reduced mean gestational ages (adjusted β ranges: 1.7-4.5 days, p < 0.001-0.008) and lower mean birthweights (adjusted β ranges: 58.6-165.4 g, p = 0.001-0.008) than the discontinuation and unexposed groups.

CONCLUSION

We confirmed that antidepressant use in pregnancy was associated with small reductions in gestational age and birthweight but found that genetic liability for depression was not linearly associated with this risk. The causality of the observed associations could not be established due to the observational nature of the study. Residual confounding linked to the underlying disease was likely still present.

Polygenic liabilities and treatment trajectories in early-onset depression: a Danish register-based study

BACKGROUND

The clinical course of major depressive disorder (MDD) is heterogeneous, and early-onset MDD often has a more severe and complex clinical course. Our goal was to determine whether polygenic scores (PGSs) for psychiatric disorders are associated with treatment trajectories in early-onset MDD treated in secondary care.

METHODS

Data were drawn from the iPSYCH2015 sample, which includes all individuals born in Denmark between 1981 and 2008 who were treated in secondary care for depression between 1995 and 2015. We selected unrelated individuals of European ancestry with an MDD diagnosis between ages 10-25 (N = 10577). Seven-year trajectories of hospital contacts for depression were modeled using Latent Class Growth Analysis. Associations between PGS for MDD, bipolar disorder, schizophrenia, ADHD, and anorexia and trajectories of MDD contacts were modeled using multinomial logistic regressions.

RESULTS

We identified four trajectory patterns: brief contact (65%), prolonged initial contact (20%), later re-entry (8%), and persistent contact (7%). Relative to the brief contact trajectory, higher PGS for ADHD was associated with a decreased odds of membership in the prolonged initial contact (odds ratio = 1.06, 95% confidence interval = 1.01-1.11) and persistent contact (1.12, 1.03-1.21) trajectories, while PGS-AN was associated with increased odds of membership in the persistent contact trajectory (1.12, 1.03-1.21).

CONCLUSIONS

We found significant associations between polygenic liabilities for psychiatric disorders and treatment trajectories in patients with secondary-treated early-onset MDD. These findings help elucidate the relationship between a patient's genetics and their clinical course; however, the effect sizes are small and therefore unlikely to have predictive value in clinical settings.

Descriptives and genetic correlates of eating disorder diagnostic transitions and presumed remission in the Danish registry

Objective

Eating disorders (EDs) are serious psychiatric disorders with an estimated 3.3 million healthy life-years lost worldwide yearly. Understanding the course of illness, diagnostic transitions and remission, and their associated genetic correlates could inform both ED etiology and treatment. The authors investigated occurrences of ED transitions and presumed remission and their genetic correlates as captured by polygenic scores (PGSs) in a large Danish register-based cohort.

Methods

The sample compromised of 10,565 individuals with a diagnosis of anorexia nervosa (AN), bulimia nervosa (BN), or eating disorder not otherwise specified (EDNOS) with at least two registered hospital contacts between 1995 and 2018. Based on medical records, occurrence of diagnostic transitions and periods of presumed remission were identified. Associations between 422 PGS and diagnostic transitions and presumed remission were evaluated using Cox proportional hazard models.

Results

A minority of ED cases (14.1%-23.1%) experienced a diagnostic transition. Presumed remission ranged between 86.9%-89.8%. Higher (one SD increase) PGS for major depressive disorder and multisite chronic pain were positively associated with transitioning from AN to either BN or EDNOS. Higher PGS on a measure of body fat percentage and financial difficulties were positively associated with presumed remission from AN. Higher PGS for mood swings was positively associated with presumed remission from EDNOS whereas higher PGS for health rating showed the opposite.

Conclusions

The authors found that most ED patients did not experience diagnostic transitions but were more likely to experience a period of presumed remission. Both diagnostic transitions and presumed remission have significant polygenic component.

Healthy lifestyle practice correlates with decreased obesity prevalence in individuals with high polygenic risk: TMM CommCohort study

Obesity and overweight, fundamental components of the metabolic syndrome, predispose individuals to lifestyle-related diseases. The extent to which adopting healthy lifestyles can reduce obesity risk, even in those with a high genetic risk, remains uncertain. Our aim was to assess the extent to which lifestyle modifications can improve outcomes in individuals with a high polygenic score (PGS) for obesity. We quantified the genetic risk of obesity using PGSs. Four datasets from the Tohoku Medical Megabank Community-Based Cohort (TMM CommCohort) were employed in the study. One dataset (n = 9958) was used to select the best model for calculating PGS. The remaining datasets (total n = 69,341) were used in a meta-analysis to validate the model and to evaluate associated risks. The odds ratio (OR) for obesity risk in the intermediate (11th-90th percentiles in the dataset) and high PGS categories (91st-100th) was 2.27 [95% confidence intervals: 2.12-2.44] and 4.83 [4.45-5.25], respectively, compared to that in the low PGS category (1st-10th). Trend analysis showed that an increase in leisure-time physical activity was significantly associated with reduced obesity risk across all genetic risk categories, representing an OR of 0.9 [0.87-0.94] even among individuals in the high PGS category. Similarly, sodium intake displayed a positive association with obesity across all genetic risk categories, yielding an OR of 1.24 [1.17-1.31] in the high PGS category. The risk of obesity was linked to the adoption of healthy lifestyles, even in individuals with high PGS. Our results may provide perspectives for integrating PGSs into preventive medicine.

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

Polygenic Risk of Mental Disorders and Subject-Specific School Grades

BACKGROUND

Education is essential for socioeconomic security and long-term mental health; however, mental disorders are often detrimental to the educational trajectory. Genetic correlations between mental disorders and educational attainment do not always align with corresponding phenotypic associations, implying heterogeneity in the genetic overlap.

METHODS

We unraveled this heterogeneity by investigating associations between polygenic risk scores for 6 mental disorders and fine-grained school outcomes: school grades in language and mathematics in ninth grade and high school, as well as educational attainment by age 25, using nationwide-representative data from established cohorts (N = 79,489).

RESULTS

High polygenic liability of attention-deficit/hyperactivity disorder was associated with lower grades in language and mathematics, whereas high polygenic risk of anorexia nervosa or bipolar disorder was associated with higher grades in language and mathematics. Associations between polygenic risk and school grades were mixed for schizophrenia and major depressive disorder and neutral for autism spectrum disorder.

CONCLUSIONS

Polygenic risk scores for mental disorders are differentially associated with language and mathematics school grades.

Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations

Key Points

Background

CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.

Methods

This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.

Results

The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.

Conclusions

In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period

Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genome-wide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received ≥2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs.

Postpartum and non-postpartum depression: a population-based matched case-control study comparing polygenic risk scores for severe mental disorders

It remains inconclusive whether postpartum depression (PPD) and depression with onset outside the postpartum period (MDD) are genetically distinct disorders. We aimed to investigate whether polygenic risk scores (PGSs) for major mental disorders differ between PPD cases and MDD cases in a nested case-control study of 50,057 women born from 1981 to 1997 in the iPSYCH2015 sample in Demark. We identified 333 women with first-onset postpartum depression (PPD group), who were matched with 993 women with first-onset depression diagnosed outside of postpartum (MDD group), and 999 female population controls. Data on genetics and depressive disorders were retrieved from neonatal biobanks and the Psychiatric Central Research Register. PGSs were calculated from both individual-level genetic data and meta-analysis summary statistics from the Psychiatric Genomics Consortium. Conditional logistic regression was used to calculate the odds ratio (OR), accounting for the selection-related reproductive behavior. After adjustment for covariates, higher PGSs for severe mental disorders were associated with increased ORs of both PPD and MDD. Compared with MDD cases, MDD PGS and attention-deficit/hyperactivity disorder PGS were marginally but not statistically higher for PPD cases, with the OR of PPD versus MDD being 1.12 (95% CI: 0 .97-1.29) and 1.11 (0.97-1.27) per-standard deviation increase, respectively. The ORs of PPD versus MDD did not statistically differ by PGSs of bipolar disorder, schizophrenia, or autism spectrum disorder. Our findings suggest that relying on PGS data, there was no clear evidence of distinct genetic make-up of women with depression occurring during or outside postpartum, after taking the selection-related reproductive behavior into account.

Ensembled best subset selection using summary statistics for polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L 0 L 2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

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.

Deep integrative models for large-scale human genomics

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

Polygenic Risk and Episode Polarity Among Individuals With Bipolar Disorder

OBJECTIVE

The authors investigated associations between polygenic liabilities for bipolar disorder, major depression, and schizophrenia and episode polarity among individuals with bipolar disorder.

METHODS

The sample consisted of 2,705 individuals diagnosed with bipolar disorder at Danish psychiatric hospitals between January 1995 and March 2017. DNA was obtained from dried blood spots collected at birth as part of routine screening. Polygenic risk scores (PRSs) for bipolar disorder, major depression, and schizophrenia were generated using a meta-PRS method combining internally and externally trained components. Associations between PRS and polarity at first episode, polarity at any episode, and number of episodes with a given polarity were evaluated for each disorder-specific PRS using logistic and negative binominal regressions adjusted for the other two PRSs, age, sex, genotype platform, and five ancestral principal components.

RESULTS

PRS for bipolar disorder was positively associated with any manic episodes (odds ratio=1.23, 95% CI=1.09-1.38). PRS for depression was positively associated with any depressive (odds ratio=1.11, 95% CI=1.01-1.23) and mixed (odds ratio=1.15, 95% CI=1.03-1.28) episodes and negatively associated with any manic episodes (odds ratio=0.76, 95% CI=0.69-0.84). PRS for schizophrenia was positively associated with any manic episodes (odds ratio=1.13, 95% CI=1.01-1.27), but only when psychotic symptoms were present (odds ratio for psychotic mania: 1.27, 95% CI=1.05-1.54; odds ratio for nonpsychotic mania: 1.06, 95% CI=0.93-1.20). These patterns were similar for first-episode polarity and for the number of episodes within each pole.

CONCLUSIONS

PRSs for bipolar disorder, major depression, and schizophrenia are associated with episode polarity and psychotic symptoms in a congruent manner among individuals with bipolar disorder.

Interplay of ADHD Polygenic Liability With Birth-Related, Somatic, and Psychosocial Factors in ADHD: A Nationwide Study

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD) is a multifactorial neurodevelopmental disorder, yet the interplay between ADHD polygenic risk scores (PRSs) and other risk factors remains relatively unexplored. The authors investigated associations, confounding, and interactions of ADHD PRS with birth-related, somatic, and psychosocial factors previously associated with ADHD.

METHODS

Participants included a random general population sample (N=21,578) and individuals diagnosed with ADHD (N=13,697) from the genotyped Danish iPSYCH2012 case cohort, born between 1981 and 2005. The authors derived ADHD PRSs and identified 24 factors previously associated with ADHD using national registers. Logistic regression was used to estimate associations of ADHD PRS with each risk factor in the general population. Cox models were used to evaluate confounding of risk factor associations with ADHD diagnosis by ADHD PRS and parental psychiatric history, and interactions between ADHD PRS and each risk factor.

RESULTS

ADHD PRS was associated with 12 of 24 risk factors (odds ratio range, 1.03-1.30), namely, small gestational age, infections, traumatic brain injury, and most psychosocial risk factors. Nineteen risk factors were associated with ADHD diagnosis (odds ratio range, 1.20-3.68), and adjusting for ADHD PRS and parental psychiatric history led to only minor attenuations. Only the interaction between ADHD PRS and maternal autoimmune disease survived correction for multiple testing.

CONCLUSIONS

Higher ADHD PRS in the general population is associated with small increases in risk for certain birth-related and somatic ADHD risk factors, and broadly to psychosocial adversity. Evidence of gene-environment interaction was limited, as was confounding by ADHD PRS and family psychiatric history on ADHD risk factor associations. This suggests that the majority of the investigated ADHD risk factors act largely independently of current ADHD PRS to increase risk of ADHD.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Genetic and psychosocial influence on the association between early childhood infections and later psychiatric disorders

To evaluate the influence of extensive genetic and psychosocial confounding on the association between early childhood infection and five major psychiatric disorders METHODS: A case-cohort study including participants from the Danish iPSYCH2012 sample, a case-cohort sample where all cases born between May 1, 1981, and December 31, 2005, diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar affective disorder (BIP), Major Depressive Disorder (MDD) or schizophrenia (SCZ), were identified and pooled with a representative sample (subcohort) of the Danish population. We used Cox proportional hazards regression customized to the case-cohort setup to calculate hazard ratios of outcome with 95% confidence intervals (CIs), following exposure to early childhood infection before the age of 5 years for ADHD and ASD, and before the age of 10 years for BIP, MDD, and SCZ. To evaluate psychosocial confounding we included sex, calendar period, sibling infections, urbanicity, parental socio-economic status, parental mental health information, and polygenic risk scores for all five disorders, as covariates. To estimate how liability for psychiatric disorders measured through the PRS influenced the risk of early childhood infection, we calculated odds ratios (ORs) with 95% CIs, using logistic regression RESULTS: Early childhood infection was associated with ADHD, ASD, MDD, and SCZ with number of childhood infections increasing the hazard. The HR was still significant in the model with full adjustments after 1 infection for ADHD (HR 1.29, 95% CI: 1.19-1.41), ASD (HR 1.28, 95% CI: 1.18-1.40), MDD (HR 1.23, 95% CI: 1.14-1.33), and SCZ (HR 1.21, 95% CI: 1.07-1.36), but not for BIP (HR1.17, 95% CI: 0.96-1.42). Probands exposed to sibling infections, but not own infection had an absolute risk of ADHD, BIP, MDD, and SCZ that closely approached the absolute risk for individuals exposed to own infections. We found evidence of gene-environment correlation with higher PRS of MDD and to some extent SCZ increasing the risk of infections and higher PRS of BIP associated with significantly decreased risk CONCLUSION: Early childhood infection is significantly associated with ADHD, ASD, MDD, and SCZ and not explained by genetic or psychosocial confounding. Although we found evidence of gene-environment correlation, it had minor impact on the results.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood

In a multi-stage analysis of 52,436 individuals aged 17-90 across diverse cohorts and biobanks, we train, test, and evaluate a polygenic risk score (PRS) for hypertension risk and progression. The PRS is trained using genome-wide association studies (GWAS) for systolic, diastolic blood pressure, and hypertension, respectively. For each trait, PRS is selected by optimizing the coefficient of variation (CV) across estimated effect sizes from multiple potential PRS using the same GWAS, after which the 3 trait-specific PRSs are combined via an unweighted sum called "PRSsum", forming the HTN-PRS. The HTN-PRS is associated with both prevalent and incident hypertension at 4-6 years of follow up. This association is further confirmed in age-stratified analysis. In an independent biobank of 40,201 individuals, the HTN-PRS is confirmed to be predictive of increased risk for coronary artery disease, ischemic stroke, type 2 diabetes, and chronic kidney disease.

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Early-Life Injuries and the Development of Attention-Deficit/Hyperactivity Disorder

Objective: To estimate phenotypic and familial association between early-life injuries and attention-deficit/hyperactivity disorder (ADHD) and the genetic contribution to the association using polygenic risk score for ADHD (PRS-ADHD) and genetic correlation analyses. Methods: Children born in Denmark between 1995-2010 (n = 786,543) were followed from age 5 years until a median age of 14 years (interquartile range: 10-18 years). Using ICD-10 diagnoses, we estimated hazard ratios (HRs) and absolute risks of ADHD by number of hospital/emergency ward-treated injuries by age 5. In a subset of ADHD cases and controls born 1995 to 2005 who had genetic data available (n = 16,580), we estimated incidence rate ratios (IRRs) for the association between PRS-ADHD and number of injuries before age 5 and the genetic correlation between ADHD and any injury before age 5. Results: Injuries were associated with ADHD (HR = 1.61; 95% CI, 1.55-1.66) in males (HR = 1.59; 1.53-1.65) and females (HR = 1.65; 1.54-1.77), with a dose-response relationship with number of injuries. The absolute ADHD risk by age 15 was 8.4% (3+ injuries) vs 3.1% (no injuries). ADHD was also associated with injuries in relatives, with a stronger association in first- than second-degree relatives. PRS-ADHD was marginally associated with the number of injuries in the general population (IRR = 1.06; 1.00-1.14), with a genetic correlation of 0.53 (0.21-0.85). Conclusions: Early-life injuries in individuals and their relatives were associated with a diagnosis of ADHD. However, even in children with the most injuries, more than 90% were not diagnosed with ADHD by age 15. Despite a low positive predictive value and that the impact of unmeasured factors such as parental behavior remains unclear, results indicate that the association is partly explained by genetics, suggesting that early-life injuries may represent or herald early behavioral manifestations of ADHD.

Evaluating the interrelations between the autism polygenic score and psychiatric family history in risk for autism

Psychiatric family history or a high autism polygenic risk score (PRS) have been separately linked to autism spectrum disorder (ASD) risk. The study aimed to simultaneously consider psychiatric family history and individual autism genetic liability (PRS) in autism risk. We performed a case-control study of all Denmark singleton births, May 1981-December 2005, in Denmark at their first birthday and a known mother. Cases were diagnosed with ASD before 2013 and controls comprised a random sample of 30,000 births without ASD, excluding persons with non-Denmark-born parents, missing ASD PRS, non-European ancestry. Adjusted odds ratios (aOR) were estimated for ASD by PRS decile and by psychiatric history in parents or full siblings (8 mutually-exclusive categories) using logistic regression. Adjusted ASD PRS z-score least-squares means were estimated by psychiatric family history category. ASD risk (11,339 ASD cases; 20,175 controls) from ASD PRS was not substantially altered after accounting for psychiatric family history (e.g., ASD PRS 10th decile aOR: 2.35 (95% CI 2.11-2.63) before vs 2.11 (95% CI 1.91-2.40) after adjustment) nor from psychiatric family history after accounting for ASD PRS (e.g., ASD family history aOR: 6.73 (95% CI 5.89-7.68) before vs 6.32 (95% CI 5.53-7.22) after adjustment). ASD risk from ASD PRS varied slightly by psychiatric family history. While ASD risk from psychiatric family history was not accounted for by ASD PRS and vice versa, risk overlap between the two factors will likely increase as measures of genetic risk improve. The two factors are best viewed as complementary measures of family-based autism risk. LAY SUMMARY: Autism risk from a history of mental disorders in the immediate family was not explained by a measure of individual genetic risk (autism polygenic risk score) and vice versa. That is, genetic risk did not appear to overlap family history risk. As genetic measures for autism improve then the overlap in autism risk from family history versus genetic factors will likely increase, but further study may be needed to fully determine the components of risk and how they are inter-related between these key family factors. Meanwhile, the two factors may be best viewed as complementary measures of autism family-based risk.