Power and predictive accuracy of polygenic risk scores

Genetic background and multidomain interventions in mild cognitive impairment

BACKGROUND

The growing prevalence of dementia emphasizes the need for effective prevention strategies. Although the partial efficacy of multidomain interventions for dementia prevention has been demonstrated, understanding the characteristics of individuals who benefit most from these interventions is crucial for optimizing resource allocation. This study investigated the association between participants' genetic backgrounds and the effectiveness of multidomain interventions for preventing dementia.

METHODS

This study utilized data from the Japan-Multimodal Intervention Trial for the Prevention of Dementia (J-MINT), where older adults with mild cognitive impairment underwent 18 months of multidomain intervention. The intervention included exercise, nutrition, cognitive stimulation, social participation, and vascular risk management. Participants who completed the J-MINT intervention and had genetic data, including whole-genome sequencing (WGS), were analyzed. Using Japanese polygenic risk scores (PRSs) for Alzheimer's disease, participants were stratified into high- and low-genetic-risk groups. Cognitive composite score (CPS) improvement rates at 6-, 12-, and 18-months were compared between intervention and control groups, with subgroup analyses performed by age (< 75 and ≥ 75 years). Additionally, a comprehensive variant analysis using WGS was conducted to identify genetic signals potentially associated with the intervention's effectiveness.

RESULTS

Among 289 participants analyzed (168 aged < 75 years; 121 aged ≥ 75 years), 99 were classified into the high-risk PRS group (56 intervention, 43 control) and 190 into the low-risk PRS group (92 intervention, 98 control). For participants aged ≥ 75 years, no statistically significant differences in CPS improvement rates were observed between the intervention and control groups, regardless of PRS classification. However, in participants aged < 75, those in the high-risk PRS group showed significant CPS improvement at the 6-month follow-up. Additionally, analysis of 9,978,605 genetic variants identified two loci, ID3 and LMO1 (rs2067053 and rs201082658), with suggestive associations (P < 1 × 10⁻4) to reduced intervention effectiveness.

CONCLUSIONS

This study highlighted the utility of PRS in predicting cognitive improvement following multidomain interventions and identified genetic variants that may influence the intervention's effectiveness. The findings provide a valuable foundation for personalized dementia prevention strategies.

Using Genomics to Develop Personalized Cardiovascular Treatments

Advances in genomic technologies have significantly enhanced our understanding of both monogenic and polygenic etiologies of cardiovascular disease. In this review, we explore how the utilization of genomic information is bringing personalized medicine approaches to the forefront of cardiovascular disease management. We describe how genomic data can resolve diagnostic uncertainty, support cascade screening, and inform treatment strategies. We discuss how genome-wide association studies have identified thousands of genetic variants associated with polygenic cardiovascular diseases, and how integrating these insights into polygenic risk scores can enhance personalized risk prediction beyond traditional clinical algorithms. We detail how pharmacogenomics approaches leverage genotype information to guide drug selection and mitigate adverse events. Finally, we present the paradigm-shifting approach of gene therapy, which holds the promise of being a curative intervention for cardiovascular conditions.

PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits

Understanding gene-environment interaction (GxE) is crucial for deciphering the genetic architecture of human complex traits. However, current statistical methods for GxE inference face challenges in both scalability and interpretability. Here we introduce PIGEON-a unified statistical framework for quantifying polygenic GxE using a variance component analytical approach. Based on this framework, we outline the main objectives in GxE studies and introduce an estimation procedure that requires only summary statistics data as input. We demonstrate the effectiveness of PIGEON through theoretical and empirical analyses, including a quasi-experimental gene-by-education study of health outcomes and gene-by-sex interaction for 530 traits using UK Biobank. We also identify genetic interactors that explain the treatment effect heterogeneity in a clinical trial on smoking cessation. PIGEON suggests a path towards polygenic, summary statistics-based inference in future GxE studies.

Development of a Japanese polygenic risk score model for amyloid-β PET imaging in Alzheimer's disease

BACKGROUND

The use of polygenic risk scores (PRS) for predicting disease risk in Japanese populations, particularly for dementia and related phenotypes, remains markedly unexplored. The aim of this study was to bridge this gap by developing a novel PRS model designed to predict amyloid-β (Aβ) deposition utilizing positron emission tomography (PET) imaging data from a Japanese cohort.

METHODS

Using the polygenic risk score-continuous shrinkage (PRS-CS) algorithm, we calculated PRS based on significant single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) in this population. We applied a PRS calculation approach informed by Japanese genome-wide association studies (GWAS) summary statistics into a Japanese dementia cohort from Keio University.

RESULTS

Our findings revealed that a p-value threshold of pT < 0.1 optimally enhanced the predictive capability of the Japanese Aβ PET positivity risk model. Moreover, we demonstrated that distinguishing between the counts of APOE2 and APOE4 alleles in our calculations significantly elevated model performance, achieving an area under the curve (AUC) of 0.759. Remarkably, this predictive accuracy remained robust even when the pT was adjusted to be < 1.0 × 10- 5, maintaining an AUC of 0.735. This study validated the efficacy of the model in identifying individuals with a increased risk of amyloid pathology.

CONCLUSIONS

These findings highlight the potential of PRS as a noninvasive tool for early detection and risk stratification of AD, which could lead to enhanced clinical applications and interventions.

Assessing the causal effects of type 2 diabetes and obesity-related traits on COVID-19 severity

BACKGROUND

Type 2 diabetes (T2D) and obesity-related traits are highly comorbid with coronavirus disease 2019 (COVID-19), but their causal relationships with disease severity remain unclear. While recent Mendelian randomization (MR) studies suggest a causal link between obesity-related traits and COVID-19 severity, findings regarding T2D are inconsistent, particularly when adjusting for body mass index (BMI). This study aims to clarify these relationships.

METHODS

We applied various MR methods to assess the causal effects of BMI-adjusted T2D (T2DadjBMI) and obesity-related traits (BMI, waist circumference, and waist-hip ratio) on COVID-19 severity. Genetic instruments were obtained from large-scale genome-wide association studies (GWAS), including 898K participants for T2D and 2M for COVID-19 severity. To address potential bias from sample overlap, we conducted large-scale simulations comparing MR results from overlapping and independent samples.

RESULTS

Our MR analysis identified a significant causal relationship between T2DadjBMI and increased COVID-19 severity (OR = 1.057, 95% CI = 1.012-1.105). Obesity-related traits were also causally associated with COVID-19 severity. Simulations confirmed that MR results remained robust to sample overlap, demonstrating consistency between overlapping and independent datasets.

CONCLUSIONS

These findings highlight the causal role of T2D and obesity-related traits in COVID-19 severity, emphasizing the need for targeted prevention and management strategies for high-risk populations. The robustness of our MR analysis, even in the presence of sample overlap, strengthens the reliability of these causal inferences.

Associations of polygenic risk score, environmental factors, and their interactions with the risk of schizophrenia spectrum disorders

BACKGROUND

Emerging evidence indicates that gene-environment interactions (GEIs) are important underlying mechanisms for the development of schizophrenia (SZ). We investigated the associations of polygenic risk score for SZ (PRS-SZ), environmental measures, and their interactions with case-control status and clinical phenotypes among patients with schizophrenia spectrum disorders (SSDs).

METHODS

The PRS-SZ for 717 SSD patients and 356 healthy controls (HCs) were calculated using the LDpred model. The Korea-Polyenvironmental Risk Score-I (K-PERS-I) and Early Trauma Inventory-Self Report (ETI-SR) were utilized as environmental measures. Logistic and linear regression analyses were performed to identify the associations of PRS-SZ and two environmental measures with case-control status and clinical phenotypes.

RESULTS

The PRS-SZ explained 8.7% of SZ risk. We found greater associations of PRS-SZ and total scores of the K-PERS-I with case-control status compared to the ETI-SR total score. A significant additive interaction was found between PRS-SZ and K-PERS-I. With the subdomains of the K-PERS-I and ETI-SR, we identified significant multiplicative or additive interactions of PRS-SZ and parental socioeconomic status (pSES), childhood adversity, and recent life events in association with case-control status. For clinical phenotypes, significant interactions were observed between PRS-SZ and the ETI-SR total score for negative-self and between PRS-SZ and obstetric complications within the K-PERS-I for negative-others.

CONCLUSIONS

Our findings suggest that the use of aggregate scores for genetic and environmental measures, PRS-SZ and K-PERS-I, can more accurately predict case-control status, and specific environmental measures may be more suitable for the exploration of GEIs.

Joint contribution of polygenic scores for depression and attention-deficit/hyperactivity disorder to youth suicidal ideation and attempt

Children presenting comorbid attention-deficit/hyperactivity disorder (ADHD) and depression symptoms have higher risks of later suicidal ideation and attempt. However, it is unclear to what extent this risk stems from individual differences in the genetic predisposition for ADHD and/or depression. We investigated the unique and combined contribution of genetic predisposition to ADHD and depression to suicidal ideation and attempt by early adulthood. Data were from two longitudinal population-based birth cohorts, the Quebec Longitudinal Study of Child Development and the Quebec Newborn Twin Study (total N = 1207). Genetic predisposition for ADHD and depression were measured using polygenic scores. Suicidal ideation and attempt by age 20 years were self-reported via questionnaires. Across the two cohorts, suicidal ideation and attempt were reported by 99 (8.2%) and 75 (6.1%) individuals, respectively. A higher polygenic score for depression was associated with significantly higher risk of suicidal ideation and attempt, while no significant associations were found for ADHD polygenic score. However, we found an interaction between polygenic scores for depression and ADHD in the association with suicide attempt (P = 0.012), but not suicidal ideation (P = 0.897). The association between polygenic score for depression and suicide attempt was significantly stronger for individuals with a higher polygenic score for ADHD. Individuals scoring ≥ 1-SD above the mean for both polygenic scores were at increased risk for suicide attempt compared to individuals with lower scores (OR 4.03, CI 1.64-9.90), as well as compared to individuals scoring ≥ 1-SD above the mean in only depression (OR 2.92, CI 1.01-8.50) or only ADHD (OR 4.88, CI 1.56-15.26) polygenic scores. Our findings suggest that genetic predisposition for ADHD and depression contributes to increase the risk of suicide attempt in a multiplicative, rather that additive, way. Our results contribute to our understanding of the etiology of suicide risk and may inform screening and risk stratification.

Examining the link between 179 lipid species and 7 diseases using genetic predictors

BACKGROUND

Genome-wide association studies of lipid species have identified several loci shared with various diseases, however, the relationship between lipid species and disease risk remains poorly understood. Here we investigated whether the plasma levels of lipid species are causally linked to disease risk.

METHODS

We built genetic predictors of 179 lipid species, measured in 7174 Finnish individuals, by utilising either 11 high-impact genomic loci or genome-wide polygenic scores (PGS). We assessed the impact of the lipid species on seven diseases by performing disease association across FinnGen (n = 500,348), UK Biobank (n = 420,531), and Generation Scotland (n = 20,032). We performed univariable Mendelian randomisation (MR) and multivariable MR (MVMR) analyses to examine whether lipid species impact disease risk independently of standard lipids.

FINDINGS

PGS explained >4% of the variance for 34 lipid species but variants outside the high-impact loci had only a marginal contribution. Variants within the high-impact loci showed association with all seven diseases. MVMR supported a causal role of ApoB in ischaemic heart disease after accounting for lipid species. Phosphatidylethanolamine-increasing LIPC variants seemed to lower age-related macular degeneration risk independently of HDL-cholesterol. MVMR suggested a protective effect of four lipid species containing arachidonic acid on cholelithiasis risk independently of Total Cholesterol.

INTERPRETATION

Our study demonstrates how genetic predictors of lipid species can be utilised to gain insights into disease risk. We report potential links between lipid species and age-related macular degeneration and cholelithiasis risk, which can be explored for their utility in disease risk prediction and therapy.

FUNDING

The funders had no role in the study design, data analyses, interpretation, or writing of this article.

Polygenic scores of obesity in childhood based on summary statistics from adults versus children

The lack of polygenic scores (PGSs) developed for body mass index (BMI) in children may be problematic because the genetic architecture characterizing BMI changes throughout life. This study aims to describe the genetic susceptibility to obesity in children and to compare two PGSs based on data from adults and children and their association with BMI and discrimination of obesity. The study sample comprises 717 participants aged 4-13 years. Adult- and child-based PGSs were evaluated by examining (1) mean BMI across polygenic score risk categories, (2) the capacity to identify obesity with logistic regression, and (3) the linear association with BMI z-scores using linear regression. Increases in one standardized unit of adult-based PGS were related to a stronger increase in BMI z-score (β = 0.24-0.39) than PGS derived in children (β = 0.21-0.30). The association between obesity and the child score was higher (OR = 1.75-2.33) than that for the adult score (OR = 1.74-2.06) for the age group 4-7 years. The inverse was observed for the age group 8-13 years (ORchild 1.56-1.79 vs. ORadult 1.78-2.54). Both adult- and child-based PGSs show strong associations with BMI and risk of obesity, with the adult-based score standing out from 8 years old.

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are, on average, equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samles. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here, we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g., PRScs, SBayesR), the individual's genotype data, and a prior disorder probability (which can be specified flexibly, based on e.g., literature, small reference samples, or prior elicitation). The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning sample with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.

Influence and role of polygenic risk score in the development of 32 complex diseases

Background

The polygenic risk score (PRS) has been perceived as advantageous in predicting the risk of complex diseases compared to other measures. We aimed to systematically evaluate the influence of PRS on disease outcome and to explore its predictive value.

Methods

We comprehensively assessed the relationship between PRS and 32 complex diseases in the UK Biobank. We used Cox models to estimate the effects of PRS on the incidence risk. Then, we constructed prediction models to assess the clinical utility of PRS in risk prediction. For 16 diseases, we further compared the disease risk and prediction capability of PRS across early and late-onset cases.

Results

Higher PRS led to greater incident risk, with hazard ratio (HR) ranging from 1.07 (95% confidence interval (CI) = 1.06-1.08) for panic/anxiety disorder to 4.17 (95% CI = 4.03-4.31) for acute pancreatitis. This effect was more pronounced in early-onset cases for 12 diseases, increasing by 52.8% on average. Particularly, the early-onset risk of heart failure associated with PRS (HR = 3.02; 95% CI = 2.53-3.59) was roughly twice compared to the late-onset risk (HR = 1.48; 95% CI = 1.46-1.51). Compared to average PRS (20-80%), individuals positioned within the top 2.5% of the PRS distribution exhibited varying degrees of elevated risk, corresponding to a more than five times greater risk on average. PRS showed additional value in clinical risk prediction, causing an average improvement of 6.1% in prediction accuracy. Further, PRS demonstrated higher predictive accuracy for early-onset cases of 11 diseases, with heart failure displaying the most significant (37.5%) improvement when incorporating PRS into the prediction model (concordance index (C-index) = 0.546; standard error (SE) = 0.011 vs. C-index = 0.751; SE = 0.010, P = 2.47 × 10-12).

Conclusions

As a valuable complement to traditional clinical risk tools, PRS is closely related to disease risk and can further enhance prediction accuracy, especially for early-onset cases, underscoring its potential role in targeted prevention for high-risk groups.

Empowering genome-wide association studies via a visualizable test based on the regional association score

The genome-wide association studies identified genes associated with many diseases, but the identification and verification of disease variants are still challenging due to small effects and large number of individual variants. In this paper, we propose a powerful method that first quantifies the strength of regional associations at each single nucleotide polymorphism and converts these measures into time series data before using a change point detection algorithm to identify significant regions. In our extensive simulation study, the proposed method consistently demonstrates greater power than existing alternatives, achieving a relative increase of over 20% in challenging scenarios where true causal variants are sparse and multiple association regions exist at the same time, while maintaining a lower false positive rate.

From genetic disposition to academic achievement: The mediating role of non-cognitive skills across development

Genetic effects on academic achievement are likely to capture environmental, developmental, and psychological processes. How these processes contribute to translating genetic dispositions into observed academic achievement remains critically under-investigated. Here, we examined the role of non-cognitive skills-e.g., motivation, attitudes and self-regulation-in mediating education-associated genetic effects on academic achievement across development. Data were collected from 5,016 children enrolled in the Twins Early Development Study at ages 7, 9, 12, and 16, as well as their parents and teachers. We found that non-cognitive skills mediated polygenic score effects on academic achievement across development, and longitudinally, accounting for up to 64% of the total effects. Within-family analyses highlighted the contribution of non-cognitive skills beyond genetic, environmental and demographic factors that are shared between siblings, accounting for up to 83% of the total mediation effect, likely reflecting evocative/active gene-environment correlation. Our results underscore the role of non-cognitive skills in academic development in how children evoke and select experiences that align with their genetic propensity.

Genetic Correlates of Treatment-Resistant Depression

Importance

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable burdens. The etiological factors contributing to TRD are complex and not fully understood.

Objective

To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits and explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us (AoU) Research Program.

Design, Setting, and Participants

This study was a cohort design with observational data from participants in the AoU Research Program who have both electronic health records and genomic data. Data analysis was performed from March 27 to October 24, 2024.

Exposures

PGS for 61 unique traits from 7 domains.

Main Outcomes and Measures

Logistic regressions to test if PGS was associated with treatment-resistant depression (TRD) compared with treatment-responsive major depressive disorder (trMDD). Cox proportional hazard model was used to determine if the progressions from MDD to TRD were associated with PGS.

Results

A total of 292 663 participants (median [IQR] age, 57 (41-69) years; 175 981 female [60.1%]) from the AoU Research Program were included in this analysis. In the discovery set (124 945 participants), 11 of the selected PGS were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia (odds ratio [OR], 1.11; 95% CI, 1.07-1.15) and specific neuroticism (OR, 1.11; 95% CI, 1.07-1.16) traits were associated with increased TRD risk, whereas higher education (OR, 0.88; 95% CI, 0.85-0.91) and intelligence (OR, 0.91; 95% CI, 0.88-0.94) scores were protective. The associations held across different TRD definitions (meta-analytic R2 >83%) and were consistent across 2 other independent sets within AoU (the whole-genome sequencing Diversity dataset, 104 388, and Microarray dataset, 63 330). Among 28 964 individuals followed up over time, 3854 developed TRD within a mean of 944 days (95% CI, 883-992 days). All 11 previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD.

Conclusions and Relevance

Results of this cohort study suggest that genetic predisposition related to neuroticism, cognitive function, and sleep patterns had a significant association with the development of TRD. These findings underscore the importance of considering psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance understanding of pathways leading to treatment resistance.

Mapping associations of polygenic scores with autistic and ADHD traits in a single city region

BACKGROUND

The genetic and environmental aetiology of autistic and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary spatially, but does this translate into variation in the association of specific common genetic variants?

METHODS

We mapped associations between polygenic scores for autism and ADHD and their respective traits in the Avon Longitudinal Study of Parents and Children (N = 4,255-6,165) across the area surrounding Bristol, UK, and compared them to maps of environments associated with the prevalence of autism and ADHD.

RESULTS

Our results suggest genetic associations vary spatially, with consistent patterns for autistic traits across polygenic scores constructed at different p-value thresholds. Patterns for ADHD traits were more variable across thresholds. We found that the spatial distributions often correlated with known environmental influences.

CONCLUSIONS

These findings shed light on the factors that contribute to the complex interplay between the environment and genetic influences in autistic and ADHD traits.

The effects of life experiences and polygenic risk for depression on the development of positive and negative cognitive biases across adolescence: The CogBIAS hypothesis

The Cognitive Bias (CogBIAS) hypothesis proposes that cognitive biases develop as a function of environmental influences (which determine the valence of biases) and the genetic susceptibility to those influences (which determines the potency of biases). The current study employed a longitudinal, polygenic-by-environment approach to examine the CogBIAS hypothesis. To this end, measures of life experiences and polygenic scores for depression were used to assess the development of memory and interpretation biases in a three-wave sample of adolescents (12-16 years) (N = 337). Using mixed effects modeling, three patterns were revealed. First, positive life experiences (PLEs) were found to diminish negative and enhance positive forms of memory and social interpretation biases. Second, and against expectation, negative life experiences and depression polygenic scores were not associated with any cognitive outcomes, upon adjusting for psychopathology. Finally, and most importantly, the interaction between high polygenic risk and greater PLEs was associated with a stronger positive interpretation bias for social situations. These results provide the first line of polygenic evidence in support of the CogBIAS hypothesis, but also extend this hypothesis by highlighting positive genetic and nuanced environmental influences on the development of cognitive biases across adolescence.

ASSOCIATIONS BETWEEN EPILEPSY-RELATED POLYGENIC RISK AND BRAIN MORPHOLOGY IN CHILDHOOD

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions.

Genomic instability in ovarian cancer: Through the lens of single nucleotide polymorphisms

Ovarian cancer (OC) is the deadliest gynecological malignancy among all female reproductive cancers. It is characterized by high mortality rate and poor prognosis. Genomic instability caused by mutations, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), microsatellite instability (MSI), and chromosomal instability (CIN) are associated with OC predisposition. SNPs, which are highly prevalent in the general population, show a greater relative risk contribution, particularly in sporadic cancers. Understanding OC etiology in terms of genetic basis can increase the use of molecular diagnostics and provide promising approaches for designing novel treatment modalities. This will help deliver personalized medicine to OC patients, which may soon be within reach. Given the pivotal impact of SNPs in cancers, the primary emphasis of this review is to shed light on their prevalence in key caretaker genes that closely monitor genomic integrity, viz., DNA damage response, repair, cell cycle checkpoints, telomerase maintenance, and apoptosis and their clinical implications in OC. We highlight the current challenges faced in different SNP-based studies. Various computational methods and bioinformatic tools employed to predict the functional impact of SNPs have also been comprehensively reviewed concerning OC research. Overall, this review identifies that variants in the DDR and HRR pathways are the most studied, implying their critical role in the disease. Conversely, variants in other pathways, such as NHEJ, MMR, cell cycle, apoptosis, telomere maintenance, and PARP genes, have been explored the least.

Clinical, Genomic, and Neurophysiological Correlates of Lifetime Suicide Attempts among Individuals with an Alcohol Use Disorder

Introduction

Research has identified multiple risk factors associated with suicide attempt (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder (AUD), despite their disproportionately higher rates of SA.

Methods

We examined lifetime SA in 4,068 individuals with an AUD from the Collaborative Study on the Genetics of Alcoholism (23% lifetime SA; 53% female; mean age: 38). We explored risk for lifetime SA across other clinical conditions ascertained from a clinical interview, polygenic scores for comorbid psychiatric problems, and neurocognitive functioning.

Results

Participants with an AUD who attempted suicide had greater rates of trauma exposure, major depressive disorder, post-traumatic stress disorder, other substance use disorders (SUDs), and suicidal ideation. Polygenic scores for SA, depression, and PTSD were associated with increased odds of reporting an SA (ORs = 1.22-1.44). Participants who reported an SA also had decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences relative to those who did not, but differences were small.

Conclusions

Overall, individuals with an AUD who report lifetime SA experience greater levels of trauma, have more severe comorbidities, and carry increased polygenic risk for other psychiatric problems. Our results demonstrate the need to further investigate SAs in the presence of SUDs.

Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.

Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer and these loci have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs); PRSs were calculated using a pruning-thresholding-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23) using the 40%-60% percentile group as the reference, and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLong's test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.

A multi-omics Mendelian randomization study identifies new therapeutic targets for alcohol use disorder and problem drinking

Integrating proteomic and transcriptomic data with genetic architectures of problematic alcohol use and alcohol consumption behaviours can advance our understanding and help identify therapeutic targets. We conducted systematic screens using genome-wise association study data from ~3,500 cortical proteins (N = 722) and ~6,100 genes in 8 canonical brain cell types (N = 192) with 4 alcohol-related outcomes (N ≤ 537,349), identifying 217 cortical proteins and 255 cell-type genes associated with these behaviours, with 36 proteins and 37 cell-type genes being new. Although there was limited overlap between proteome and transcriptome targets, downstream neuroimaging revealed shared neurophysiological pathways. Colocalization with independent genome-wise association study data further prioritized 16 proteins, including CAB39L and NRBP1, and 12 cell-type genes, implicating mechanisms such as mTOR signalling. In addition, genes such as SAMHD1, VIPAS39, NUP160 and INO80E were identified as having favourable neuropsychiatric profiles. These findings provide insights into the genetic landscapes governing problematic alcohol use and alcohol consumption behaviours, highlighting promising therapeutic targets for future research.

Case-control association study between polygenic risk score and COVID-19 severity in a Russian population using low-pass genome sequencing

The course of COVID-19 is highly variable, with genetics playing a significant role. Through large-scale genetic association studies, a link between single nucleotide polymorphisms and disease susceptibility and severity was established. However, individual single nucleotide polymorphisms identified thus far have shown modest effects, indicating a polygenic nature of this trait, and individually have limited predictive performance. To address this limitation, we investigated the performance of a polygenic risk score model in the context of COVID-19 severity in a Russian population. A genome-wide polygenic risk score model including information from over a million common single nucleotide polymorphisms was developed using summary statistics from the COVID-19 Host Genetics Initiative consortium. Low-coverage sequencing (5x) was performed for ~1000 participants, and polygenic risk score values were calculated for each individual. A multivariate logistic regression model was used to analyse the association between polygenic risk score and COVID-19 outcomes. We found that individuals in the top 10% of the polygenic risk score distribution had a markedly elevated risk of severe COVID-19, with adjusted odds ratio of 2.9 (95% confidence interval: 1.8-4.6, p-value = 4e-06), and more than four times higher risk of mortality from COVID-19 (adjusted odds ratio = 4.3, p-value = 2e-05). This study highlights the potential of polygenic risk score as a valuable tool for identifying individuals at increased risk of severe COVID-19 based on their genetic profile.

Polygenic and Polyenvironment Interplay in Schizophrenia-Spectrum Disorder and Affective Psychosis; the EUGEI First Episode Study

BACKGROUND

Multiple genetic and environmental risk factors play a role in the development of both schizophrenia-spectrum disorders and affective psychoses. How they act in combination is yet to be clarified.

METHODS

We analyzed 573 first episode psychosis cases and 1005 controls, of European ancestry. Firstly, we tested whether the association of polygenic risk scores for schizophrenia, bipolar disorder, and depression (PRS-SZ, PRS-BD, and PRS-D) with schizophrenia-spectrum disorder and affective psychosis differed when participants were stratified by exposure to specific environmental factors. Secondly, regression models including each PRS and polyenvironmental measures, including migration, paternal age, childhood adversity and frequent cannabis use, were run to test potential polygenic by polyenvironment interactions.

RESULTS

In schizophrenia-spectrum disorder vs controls comparison, PRS-SZ was the strongest genetic predictor, having a nominally larger effect in nonexposed to strong environmental factors such as frequent cannabis use (unexposed vs exposed OR 2.43 and 1.35, respectively) and childhood adversity (3.04 vs 1.74). In affective psychosis vs controls, the relative contribution of PRS-D appeared to be stronger in those exposed to environmental risk. No evidence of interaction was found between any PRS with polyenvironmental score.

CONCLUSIONS

Our study supports an independent role of genetic liability and polyenvironmental risk for psychosis, consistent with the liability threshold model. Whereas schizophrenia-spectrum disorders seem to be mostly associated with polygenic risk for schizophrenia, having an additive effect with well-replicated environmental factors, affective psychosis seems to be a product of cumulative environmental insults alongside a higher genetic liability for affective disorders.

Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine

Integrating clinical knowledge into AI remains challenging despite numerous medical guidelines and vocabularies. Medical codes, central to healthcare systems, often reflect operational patterns shaped by geographic factors, national policies, insurance frameworks, and physician practices rather than the precise representation of clinical knowledge. This disconnect hampers AI in representing clinical relationships, raising concerns about bias, transparency, and generalizability. Here, we developed a resource of 67,124 clinical vocabulary embeddings derived from a clinical knowledge graph tailored to electronic health record vocabularies, spanning over 1.3 million edges. Using graph transformer neural networks, we generated clinical vocabulary embeddings that provide a new representation of clinical knowledge by unifying seven medical vocabularies. These embeddings were validated through a phenotype risk score analysis involving 4.57 million patients from Clalit Healthcare Services, effectively stratifying individuals based on survival outcomes. Inter-institutional panels of clinicians evaluated the embeddings for alignment with clinical knowledge across 90 diseases and 3,000 clinical codes, confirming their robustness and transferability. This resource addresses gaps in integrating clinical vocabularies into AI models and training datasets, paving the way for knowledge-grounded population and patient-level models.

Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review

This scoping review aims to identify and evaluate the landscape of Polygenic Risk Score (PRS)-based methods for genomic prediction from 2013 to 2023, highlighting their advancements, key concepts, and existing gaps in knowledge, research, and technology. Over the past decade, various PRS-based methods have emerged, each employing different statistical frameworks aimed at enhancing prediction accuracy, processing speed and memory efficiency. Despite notable advancements, challenges persist, including unrealistic assumptions regarding sample sizes and the polygenicity of traits necessary for accurate predictions, as well as limitations in exploring hyper-parameter spaces and considering environmental interactions. We included studies focusing on PRS-based methods for risk prediction that underwent methodological evaluations using valid approaches and released computational tools/software. Additionally, we restricted our selection to studies involving human participants that were published in English language. This review followed the standard protocol recommended by Joanna Briggs Institute Reviewer's Manual, systematically searching Ovid MEDLINE, Ovid Embase, Scopus and Web of Science databases. Additionally, searches included grey literature sources like pre-print servers such as bioRxiv, and articles recommended by experts to ensure comprehensive and diverse coverage of relevant records. This study identified 34 studies detailing 37 genomic prediction methods, the majority of which rely on linkage disequilibrium (LD) information and necessitate hyper-parameter tuning. Nine methods integrate functional/gene annotation, while 12 are suitable for cross-ancestry genomic prediction, with only one considering gene-environment (GxE) interaction. While some methods require individual-level data, most leverage summary statistics, offering flexibility. Despite progress, challenges remain. These include computational complexity and the need for large sample sizes for high prediction accuracy. Furthermore, recent methods exhibit varying effectiveness across traits, with absolute accuracies often falling short of clinical utility. Transferability across ancestries varies, influenced by trait heritability and diversity of training data, while handling admixed populations remains challenging. Additionally, the absence of standard error measurements for individual PRSs, crucial in clinical settings, underscores a critical gap. Another issue is the lack of customizable graphical visualization tools among current software packages. While genomic prediction methods have advanced significantly, there is still room for improvement. Addressing current challenges and embracing future research directions will lead to the development of more universally applicable, robust, and clinically relevant genomic prediction tools.

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

GFPrint™: A machine learning tool for transforming genetic data into clinical insights

The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint™ is a proprietary streaming algorithm designed to meet that need. By extracting the most relevant functional features, GFPrint™ transforms high-dimensional, noisy genetic sequencing data into an embedded representation, allowing unsupervised models to create data clusters that can be re-mapped to the original clinical information. Ultimately, this allows the identification of genes and pathways relevant to disease onset and progression. GFPrint™ has been tested and validated using two cancer genomic datasets publicly available. Analysis of the TCGA dataset has identified panels of genes whose mutations appear to negatively influence survival in non-metastatic colorectal cancer (15 genes), epidermoid non-small cell lung cancer (167 genes) and pheochromocytoma (313 genes) patients. Likewise, analysis of the Broad Institute dataset has identified 75 genes involved in pathways related to extracellular matrix reorganization whose mutations appear to dictate a worse prognosis for breast cancer patients. GFPrint™ is accessible through a secure web portal and can be used in any therapeutic area where the genetic profile of patients influences disease evolution.

Shared genetic architecture and causality between autism spectrum disorder and irritable bowel syndrome, multisite pain, and fatigue

Autism spectrum disorder (ASD) often co-occurs with functional somatic syndromes (FSS), such as irritable bowel syndrome (IBS), multisite pain, and fatigue. However, the underlying genetic mechanisms and causality have not been well studied. Using large-scale genome-wide association study (GWAS) data, we investigated the shared genetic architecture and causality between ASD and FSS. Specifically, we first estimated genetic correlations and then conducted a multi-trait analysis of GWAS (MTAG) to detect potential novel genetic variants for single traits. Afterwards, polygenic risk scores (PRS) of ASD were derived from GWAS and MTAG to examine the associations with phenotypes in the large Dutch Lifelines cohort. Finally, we performed Mendelian randomization (MR) to evaluate the causality. We observed positive genetic correlations between ASD and FSS (IBS: rg = 0.27, adjusted p = 2.04 × 10-7; multisite pain: rg = 0.13, adjusted p = 1.10 × 10-3; fatigue: rg = 0.33, adjusted p = 5.21 × 10-9). Leveraging these genetic correlations, we identified 3 novel genome-wide significant independent loci for ASD by conducting MTAG, mapped to NEDD4L, MFHAS1, and RP11-10A14.4. PRS of ASD derived from both GWAS and MTAG were associated with ASD and FSS in Lifelines, and MTAG-derived PRS showed a bigger effect size, larger explained variance, and smaller p-values. We did not observe significant causality using MR. Our study found genetic associations between ASD and FSS, specifically with IBS, multisite pain, and fatigue. These findings suggest that a shared genetic architecture may partly explain the co-occurrence between ASD and FSS. Further research is needed to investigate the causality between ASD and FSS due to current limited statistical power of the GWASs.

Inclusiveness of the All of Us Research Program improves polygenic risk scores and fosters genomic medicine for all

Kidenya and Mboowa discuss the current state of genomic inclusiveness in medicine. They champion the efforts of the All of Us Research Program to broaden diversity in population genomics and reduce disparities across different ancestries.

Telomere length and cognitive changes in 7,877 older UK adults of European ancestry

Background

Telomere length (TL) has been linked to cognitive function, decline and dementia. This study aimed to explore whether both measured TL and genetic disposition for TL predict dimensions of cognitive performance in a longitudinal sample of older UK adults.

Methods

We analysed data from PROTECT study participants aged ≥50 years without a dementia diagnosis, who had completed longitudinal cognitive testing. We calculated polygenic scores for telomere length (PGS-TL) for 7,877 participants and measured relative telomere length (RTL) in a subgroup of 846 participants using DNA extracted from saliva samples collected within 6 months either side of their baseline cognitive testing. Latent growth models were used to examine whether RTL and PGS-TL predict both baseline and longitudinal changes in cognitive performance (4 time-points, annually).

Results

In the whole sample, we did not observe significant associations between either measure of telomere length and initial or longitudinal changes in cognitive performance. Stratifying by median age, in older adults (≥ ∼62 years), longer baseline RTL showed a nominal association with poorer baseline verbal reasoning performance (n = 423, M intercept = 47.58, B = -1.05, p = .011) and PGS-TL was associated with performance over time (n = 3,939; slope factor, M slope = 3.23, B = -0.45, p = .001; slope 2 factor, M slope 2 = 0.21, B = 0.13, p = .002).

Conclusion

Our findings suggest either the absence of a significant relationship between telomere length (RTL and PGS-TL) and cognitive performance (baseline and change over time), or possibly a weak age-dependent and domain-specific relationship, in older adults of European ancestry. More research is needed in representative and ancestrally diverse samples over a longer assessment period. Alternative biological ageing indicators may still provide utility in the early detection of individuals at risk for cognitive decline (e.g., pace-of ageing epigenetic clocks).

Gene - maltreatment interplay in adult ADHD symptoms: main role of a gene-environment correlation effect in a Brazilian population longitudinal study

Childhood maltreatment correlates with attention-deficit/hyperactivity disorder (ADHD) in previous research. The interaction between ADHD genetic predisposition and maltreatment's impact on ADHD symptom risk remains unclear. We aimed to elucidate this relationship by examining the interplay between a polygenic score for ADHD (ADHD-PGS) and childhood maltreatment in predicting ADHD symptoms during young adulthood. Using data from the 2004 Pelotas (Brazil) birth cohort comprising 4231 participants, we analyzed gene-environment interaction (GxE) and correlation (rGE). We further explored rGE mechanisms through mediation models. ADHD symptoms were assessed at age 18 via self-report (Adult Self Report Scale - ASRS) and mother-reports (Strength and Difficulties Questionnaire - SDQ). The ADHD-PGS was derived from published ADHD GWAS meta-analysis. Physical and psychological child maltreatment was gauged using the Parent-Child Conflict Tactics Scale (CTSPC) at ages 6 and 11, with a mean score utilized as a variable. The ADHD-PGS exhibited associations with ADHD symptoms on both ASRS (β = 0.53; 95% CI: 0.03; 1.03, p = 0.036), and SDQ (β = 0.20; 95% CI: 0.08; 0.32, p = 0.001) scales. The total mean maltreatment score was associated with ADHD symptoms using both scales [(βASRS = 0.51; 95% CI: 0.26;0.77) and (βSDQ = 0.24; 95% CI: 0.18;0.29)]. The ADHD-PGS was associated with total mean maltreatment scores (β = 0.09; 95% CI: 0.01; 0.17; p = 0.030). Approximately 47% of the total effect of ADHD-PGS on maltreatment was mediated by ADHD symptoms at age 6. No evidence supported gene-environment interaction in predicting ADHD symptoms. Our findings underscore the significant roles of genetics and childhood maltreatment as predictors for ADHD symptoms in adulthood, while also indicating a potential evocative mechanism through gene-environment correlation.

Methodologies underpinning polygenic risk scores estimation: a comprehensive overview

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

Immunocyte phenotype and breast cancer risk: A Mendel randomization analysis

BACKGROUND

Breast cancer remains a significant global health challenge. Understanding its etiological factors, particularly the role of immune system components, is crucial. This study leverages Mendelian randomization (MR) to investigate the causal relationship between various immune cell features and the risk of developing breast cancer.

METHODS

Utilizing two-sample MR analysis, we examined 731 immune cell features across 7 groups for their potential causal links to breast cancer. We analyzed genome-wide association studies (GWAS) data of 257,730 Europeans, comprising 17,389 cases and 240,341 controls, focusing on 24,133,589 single nucleotide polymorphisms (SNPs). Instrumental variables (IVs) were selected based on genetic associations, with rigorous statistical methods employed, including inverse variance weighting (IVW) and weighted median-based estimation.

RESULTS

Our analysis identified 20 immunophenotypes with significant causal associations with breast cancer risk. Notably, contain B cell, mature T cell, T + B + NK (TBNK) cells, regulatory T (Treg) cell, Classic dendritic cells (cDCs), Monocyte, and Myeloid cell group features displayed positive or negative correlations with breast cancer. For instance, specific B cell phenotypes were found to have both positive and negative causal relationships with breast cancer. Additionally, reverse MR analysis revealed no significant causal effects of breast cancer on these immune characteristics.

CONCLUSIONS

This study underscores the complex interplay between various immune cell phenotypes and breast cancer risk. The identified immunophenotypes could be potential biomarkers or targets for future therapeutic interventions. Our findings contribute to a deeper understanding of the immunological dimensions of breast cancer etiology.

Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes

BACKGROUND

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.

METHODS

We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts.

RESULTS

PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9).

CONCLUSIONS

Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

The effect of heavy smoking on retirement risk: A mendelian randomisation analysis

BACKGROUND AND AIMS

The extent to which heavy smoking and retirement risk are causally related remains to be determined. To overcome the endogeneity of heavy smoking behaviour, we employed a novel approach by exploiting the genetic predisposition to heavy smoking, as measured with a polygenic risk score (PGS), in a Mendelian Randomisation approach.

METHODS

8164 participants (mean age 68.86 years) from the English Longitudinal Study of Ageing had complete data on smoking behaviour, employment and a heavy smoking PGS. Heavy smoking was indexed as smoking at least 20 cigarettes a day. A time-to-event Mendelian Randomization (MR) analysis, using a complementary log-log (cloglog) link function, was employed to model the retirement risk.

RESULTS

Our results show that being a heavy smoker significantly increases the risk of retirement (β = 1.324, standard error = 0.622, p < 0.05). Results were robust to a battery of checks and a placebo analysis considering the never-smokers.

CONCLUSIONS

Overall, our findings support a causal pathway from heavy smoking to earlier retirement.

The GenoPred pipeline: a comprehensive and scalable pipeline for polygenic scoring

MOTIVATION

Polygenic scoring is an approach for estimating an individual's likelihood of a given outcome. Polygenic scores are typically calculated from genome-wide association study (GWAS) summary statistics and individual-level genotype data for the target sample. Going from genotype to interpretable polygenic scores involves many steps and there are many methods available, limiting the accessibility of polygenic scores for research and clinical application. Additional challenges exist for studies in ancestrally diverse populations. We have implemented the leading polygenic scoring methodologies within an easy-to-use pipeline called GenoPred.

RESULTS

Here, we present the GenoPred pipeline, an easy-to-use, high-performance, reference-standardized, and reproducible workflow for polygenic scoring. It requires minimal inputs and offers various configuration options to cater to a range of use cases. GenoPred implements a comprehensive set of analyses, including genotype and GWAS quality control, target sample ancestry inference, polygenic score file generation using a range of leading methods, and target sample scoring. GenoPred standardizes the polygenic scoring process using reference genetic data, providing interpretable polygenic scores. The pipeline is applicable to GWAS and targets data from any population within the reference, facilitating studies of diverse ancestry. GenoPred is a Snakemake pipeline with associated Conda software environments, ensuring reproducibility. We apply the pipeline to UK Biobank data demonstrating the pipeline's simplicity, efficiency, and performance. The GenoPred pipeline provides a novel resource for polygenic scoring, integrating a range of complex processes within an easy-to-use framework. GenoPred widens access to the leading polygenic scoring methodology and their application to studies of diverse ancestry.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at https://github.com/opain/GenoPred.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

HiCMC: High-Efficiency Contact Matrix Compressor

BACKGROUND

Chromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data.

RESULTS

By modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms the state-of-the-art method CMC by approximately 8% and the other state-of-the-art methods cooler, LZMA, and bzip2 by over 50% across multiple cell lines and contact matrix resolutions. In addition, HiCMC integrates domain-specific information into the compressed bitstreams that it generates, and this information can be used to speed up downstream analyses.

CONCLUSION

HiCMC is a novel compression approach that utilizes intrinsic properties of contact matrix, such as compartments and domains. It allows for a better compression in comparison to the state-of-the-art methods. HiCMC is available at https://github.com/sXperfect/hicmc .

Capturing biomarkers associated with Alzheimer disease subtypes using data distribution characteristics

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.

Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort

Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991-2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants' postal codes to objective, registry-based exposures. Not the genome (R 2 = -0.007 [-0.026-0.010]), but the general exposome (R 2 = 0.047 [0.015-0.076]) and especially the specific exposome (R 2 = 0.702 [0.637-0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction.

Forensic height estimation using polygenic score in Korean population

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

Dietary and genetic determinants of non-alcoholic fatty liver disease in coronary heart disease patients

PURPOSE

A healthy diet reduces the risk of non-alcoholic fatty liver disease (NAFLD) in the general population, especially in individuals who are genetically predisposed to NAFLD. Little is known in patients who suffered from a myocardial infarction (MI). We examined the interaction between diet quality and genetic predisposition in relation to NAFLD in post-MI patients.

METHODS

We included 3437 post-MI patients from the Alpha Omega Cohort. Diet quality was assessed with adherence to the Dutch Healthy Diet index 2015 (DHD15-index). A weighted genetic risk score (GRS) for NAFLD was computed using 39 genetic variants. NAFLD prevalence was predicted using the Fatty Liver Index. Prevalence ratios (PR) with 95% confidence intervals of DHD15-index and GRS in relation to NAFLD were obtained with multivariable Cox proportional hazards models. The interaction between DHD15-index and GRS in relation to NAFLD was assessed on an additive and multiplicative scale.

RESULTS

Patients had a mean age of 69 (± 5.5) years, 77% was male and 20% had diabetes. The DHD15-index ranged from 28 to 120 with a mean of 73. Patients with higher diet quality were less likely to suffer from NAFLD, with a PR of 0.76 (0.62, 0.92) for the upper vs lower quintile of DHD15-index. No association between the GRS and NAFLD prevalence was found (PR of 0.92 [0.76, 1.11]). No statistically significant interaction between the DHD15-index and GRS was observed.

CONCLUSION

In Dutch post-MI patients, adherence to the Dutch dietary guidelines was associated with a lower prevalence of NAFLD, as assessed by the FLI. This association was present regardless of genetic predisposition in this older aged cohort.

Increased BMI associated with decreased breastfeeding initiation in Million Veteran Program participants

Background

Breastfeeding has been associated with maternal and infant health benefits but has been inversely associated with body mass index (BMI) prepartum. Breastfeeding and BMI are both linked to socioeconomic factors.

Methods

Data from parous female participants with available breastfeeding information from the Million Veteran Program cohort was included. BMI at enrollment and earliest BMI available were extracted, and polygenic scores (PGS) for BMI were calculated. We modeled breastfeeding for one month or more as a function of BMI at enrollment; earliest BMI where available pre-pregnancy; and PGS for BMI. We conducted Mendelian randomization for breastfeeding initiation using PGS as an instrumental variable.

Results

A higher BMI predicted a lower likelihood of breastfeeding for one month or more in all analyses. A +5 kg/m 2 BMI pre-pregnancy was associated with a 24% reduced odds of breastfeeding, and a +5 kg/m 2 genetically predicted BMI was associated with a 17% reduced odds of breastfeeding.

Conclusions

BMI predicts a lower likelihood of breastfeeding for one month or longer. Given the high success of breastfeeding initiation regardless of BMI in supportive environments as well as potential health benefits, patients with elevated BMI may benefit from additional postpartum breastfeeding support.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.