Polygenic risk scores: from research tools to clinical instruments

Crimson clues: advancing endometriosis detection and management with novel blood biomarkers

Endometriosis is an inflammatory condition affecting approximately 10% of the female-born population. Despite its prevalence, the lack of noninvasive biomarkers has contributed to an established global diagnostic delay. The intricate pathophysiology of this enigmatic disease may leave signatures in the blood, which, when detected, can be used as noninvasive biomarkers. This review provides an update on how investigators are utilizing the established disease pathways and innovative methodologies, including genome-wide association studies, next-generation sequencing, and machine learning, to unravel the clues left in the blood to develop blood biomarkers. Many blood biomarkers show promise in the discovery phase, but because of a lack of standardized and robust methodologies, they rarely progress to the development stages. However, we are now seeing biomarkers being validated with high diagnostic accuracy and improvements in standardization protocols, providing promise for the future of endometriosis blood biomarkers.

Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples

Externalizing behavior in early adolescence is associated with alcohol use in adolescence and early adulthood and these behaviors often emerge as part of a developmental sequence. This pattern can be the result of heterotypic continuity, in which different behaviors emerge over time based on an underlying shared etiology. In particular, there is largely a shared genetic etiology underlying externalizing and substance use behaviors. We examined whether polygenic risk for alcohol use disorder predicted (1) externalizing behavior in early adolescence and alcohol use in adolescence in the Early Steps Multisite sample and (2) externalizing behavior in adolescence and alcohol use in early adulthood in the Project Alliance 1 (PAL1) sample. We examined associations separately for African Americans and European Americans. When examining European Americans in the Early Steps sample, greater polygenic risk was associated with externalizing behavior in early adolescence. In European Americans in PAL1, we found greater polygenic risk was associated with alcohol use in early adulthood. Effects were largely absent in African Americans in both samples. Results imply that genetic predisposition for alcohol use disorder may increase risk for externalizing and alcohol use as these behaviors emerge developmentally.

Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity

The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.

Whole genome sequencing in clinical practice

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Cholesterol associated genetic risk score and acute coronary syndrome in Czech males

BACKGROUND

Despite a general decline in mean levels across populations, LDL-cholesterol levels remain a major risk factor for acute coronary syndrome (ACS). The APOB, LDL-R, CILP, and SORT-1 genes have been shown to contain variants that have significant effects on plasma cholesterol levels.

METHODS AND RESULTS

We examined polymorphisms within these genes in 1191 controls and 929 patients with ACS. Only rs646776 within SORT-1 was significantly associated with a risk of ACS (P < 0.05, AA vs. + G comparison; OR 1.21; 95% CI 1.01-1.45). With regard to genetic risk score (GRS), the presence of at least 7 alleles associated with elevated cholesterol levels was connected with increased risk (P < 0.01) of ACS (OR 1.26; 95% CI 1.06-1.52). Neither total mortality nor CVD mortality in ACS subjects (follow up-9.84 ± 3.82 years) was associated with the SNPs analysed or cholesterol-associated GRS.

CONCLUSIONS

We conclude that, based on only a few potent SNPs known to affect plasma cholesterol, GRS has the potential to predict ACS risk, but not ACS associated mortality.

Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles

Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we evaluated the predictive performance, risk stratification, and potential systemic health effects of tobacco use disorder (TUD) predisposing germline variants using a European- ancestry-derived polygenic score (PGS) in 24,202 participants from the multi-ancestry, hospital-based UCLA ATLAS biobank. Among genetically inferred ancestry groups (GIAs), TUD-PGS was significantly associated with TUD in European American (EA) (OR: 1.20, CI: [1.16, 1.24]), Hispanic/Latin American (HL) (OR:1.19, CI: [1.11, 1.28]), and East Asian American (EAA) (OR: 1.18, CI: [1.06, 1.31]) GIAs but not in African American (AA) GIA (OR: 1.04, CI: [0.93, 1.17]). Similarly, TUD-PGS offered strong risk stratification across PGS quantiles in EA and HL GIAs and inconsistently in EAA and AA GIAs. In a cross-ancestry phenome-wide association meta-analysis, TUD-PGS was associated with cardiometabolic, respiratory, and psychiatric phecodes (17 phecodes at P < 2.7E-05). In individuals with no history of smoking, the top TUD-PGS associations with obesity and alcohol-related disorders (P = 3.54E-07, 1.61E-06) persist. Mendelian Randomization (MR) analysis provides evidence of a causal association between adiposity measures and tobacco use. Inconsistent predictive performance of the TUD-PGS across GIAs motivates the inclusion of multiple ancestry populations at all levels of genetic research of tobacco use for equitable clinical translation of TUD-PGS. Phenome associations suggest that TUD-predisposed individuals may require comprehensive tobacco use prevention and management approaches to address underlying addictive tendencies.

Association between inflammatory bowel disease and Parkinson's disease: a prospective cohort study of 468,556 UK biobank participants

Introduction

Inflammatory Bowel Disease (IBD) and Parkinson's disease (PD) are both chronic, progressive disorders. As such, given the inconclusive results of extensive research on the association between IBD and PD, our study intends to examine this relationship further using the UK Biobank database.

Methods

We conducted a prospective cohort study using the Cox proportional hazards model, analyzing data from the UK Biobank to investigate the relationship between IBD and PD, following subjects until PD diagnosis, loss to follow up, death or study termination on 30 June, 2023.

Results

The results show that IBD had no effect on the risk of PD (HR: 1.356, 95% CI: 0.941-1.955, p = 0.103), and the effect remained consistent in specific Crohn's disease, ulcerative colitis or unclassified IBD populations. In addition, after sensitivity analysis using propensity matching scores and excluding patients diagnosed with PD 5 or 10 years after baseline, IBD had no effect on the risk of PD. However, in the subgroup analysis, we found that in females (HR: 1.989, 95% CI: 1.032-3.835, p = 0.040), the polygenic risk score was highest (HR: 2.476, 95% CI: 1.401-4.374, p = 0.002), and having ulcerative colitis without hypertension (HR: 2.042, 95% CI: 1.128-3.697, p = 0.018) was associated with an increased risk of PD.

Conclusion

In conclusion, over an average follow-up period of 13.93 years, we found no significant association between IBD and PD.

Methylation profiles at birth linked to early childhood obesity

Childhood obesity represents a significant global health concern and identifying risk factors is crucial for developing intervention programs. Many 'omics' factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

Joint Association of Polygenic Risk and Social Determinants of Health with Coronary Heart Disease in the United States

Background

The joint effects of polygenic risk and social determinants of health (SDOH) on coronary heart disease (CHD) in the United States are unknown.

Methods

In 67,256 All of Us (AoU) participants with available SDOH data, we ascertained self-reported race/ethnicity and calculated a polygenic risk score for CHD (PRS CHD ). We used 90 SDOH survey questions to develop an SDOH score for CHD (SDOH CHD ). We assessed the distribution of SDOH CHD across self-reported races and US states. We tested the joint association of SDOH CHD and PRS CHD with CHD in regression models that included clinical risk factors.

Results

SDOH CHD was highest in self-reported black and Hispanic people. Self-reporting as black was associated with higher odds of CHD but not after adjustment for SDOH CHD . Median SDOH CHD values varied by US state and were associated with heart disease mortality. A 1-SD increase in SDOH CHD was associated with CHD (OR=1.36; 95% CI, 1.29 to 1.46) and incident CHD (HR=1.73; 95% CI, 1.27 to 2.35) in models that included PRS CHD and clinical risk factors. Among people in the top 20% of PRS CHD , CHD prevalence was 4.8% and 7.8% in the bottom and top 20% of SDOH CHD , respectively.

Conclusions

Increased odds of CHD in self-reported black people are likely due to higher SDOH burden. SDOH and PRS were independently associated with CHD in the US. Our findings emphasize the need to consider both PRS and SDOH for equitable disease risk assessment.

Genetic variants and effect modifiers of QT interval prolongation in patients with sickle cell disease

BACKGROUND

Sickle cell disease (SCD) is a common inherited blood disorder among African Americans (AA), with premature mortality which has been associated with prolongation of the heart rate-corrected QT interval (QTc), a known risk factor for sudden cardiac death. Although numerous genetic variants have been identified as contributors to QT interval prolongation in the general population, their impact on SCD patients remains unclear. This study used an unweighted polygenic risk score (PRS) to validate the previously identified associations between SNPs and QTc interval in SCD patients, and to explore possible interactions with other factors that prolong QTc interval in AA individuals with SCD.

METHODS

In SCD patients, candidate genetic variants associated with the QTc interval were genotyped. To identify any risk SNPs that may be correlated with QTc interval prolongation, linear regression was employed, and an unweighted PRS was subsequently constructed. The effect of PRS on the QTc interval was evaluated using linear regression, while stratification analysis was used to assess the influence of serum alanine transaminase (ALT), a biomarker for liver disease, on the PRS effect. We also evaluated the PRS with the two subcomponents of QTc, the QRS and JTc intervals.

RESULTS

Out of 26 candidate SNPs, five risk SNPs were identified for QTc duration under the recessive model. For every unit increase in PRS, the QTc interval prolonged by 4.0 ms (95% CI: [2.0, 6.1]; p-value: <0.001) in the additive model and 9.4 ms in the recessive model (95% CI: [4.6, 14.1]; p-value: <0.001). Serum ALT showed a modification effect on PRS-QTc prolongation under the recessive model. In the normal ALT group, each PRS unit increased QTc interval by 11.7 ms (95% CI: [6.3, 17.1]; p-value: 2.60E-5), whereas this effect was not observed in the elevated ALT group (0.9 ms; 95% CI: [-7.0, 8.8]; p-value: 0.823).

CONCLUSION

Several candidate genetic variants are associated with QTc interval prolongation in SCD patients, and serum ALT acts as a modifying factor. The association of a CPS1 gene variant in both QTc and JTc duration adds to NOS1AP as evidence of involvement of the urea cycle and nitric oxide metabolism in cardiac repolarization in SCD. Larger replication studies are needed to confirm these findings and elucidate the underlying mechanisms.

Polygenic risk score model for renal cell carcinoma in the Korean population and relationship with lifestyle-associated factors

BACKGROUND

The polygenic risk score (PRS) is used to predict the risk of developing common complex diseases or cancers using genetic markers. Although PRS is used in clinical practice to predict breast cancer risk, it is more accurate for Europeans than for non-Europeans because of the sample size of training genome-wide association studies (GWAS). To address this disparity, we constructed a PRS model for predicting the risk of renal cell carcinoma (RCC) in the Korean population.

RESULTS

Using GWAS analysis, we identified 43 Korean-specific variants and calculated the PRS. Subsequent to plotting receiver operating characteristic (ROC) curves, we selected the 31 best-performing variants to construct an optimal PRS model. The resultant PRS model with 31 variants demonstrated a prediction rate of 77.4%. The pathway analysis indicated that the identified non-coding variants are involved in regulating the expression of genes related to cancer initiation and progression. Notably, favorable lifestyle habits, such as avoiding tobacco and alcohol, mitigated the risk of RCC across PRS strata expressing genetic risk.

CONCLUSION

A Korean-specific PRS model was established to predict the risk of RCC in the underrepresented Korean population. Our findings suggest that lifestyle-associated factors influencing RCC risk are associated with acquired risk factors indirectly through epigenetic modification, even among individuals in the higher PRS category.

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.

Subset scanning for multi-trait analysis using GWAS summary statistics

MOTIVATION

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge.

RESULTS

To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression.

AVAILABILITY AND IMPLEMENTATION

Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Induced Pluripotent Stem Cells as a Possible Approach for Exploring the Pathophysiology of Polycystic Ovary Syndrome (PCOS)

Polycystic ovary syndrome (PCOS) is the most prevalent endocrine condition among women with pleiotropic sequelae possessing reproductive, metabolic, and psychological characteristics. Although the exact origin of PCOS is elusive, it is known to be a complex multigenic disorder with a genetic, epigenetic, and environmental background. However, the pathogenesis of PCOS, and the role of genetic variants in increasing the risk of the condition, are still unknown due to the lack of an appropriate study model. Since the debut of induced pluripotent stem cell (iPSC) technology, the ability of reprogrammed somatic cells to self-renew and their potential for multidirectional differentiation have made them excellent tools to study different disease mechanisms. Recently, researchers have succeeded in establishing human in vitro PCOS disease models utilizing iPSC lines from heterogeneous PCOS patient groups (iPSCPCOS). The current review sets out to summarize, for the first time, our current knowledge of the implications and challenges of iPSC technology in comprehending PCOS pathogenesis and tissue-specific disease mechanisms. Additionally, we suggest that the analysis of polygenic risk prediction based on genome-wide association studies (GWAS) could, theoretically, be utilized when creating iPSC lines as an additional research tool to identify women who are genetically susceptible to PCOS. Taken together, iPSCPCOS may provide a new paradigm for the exploration of PCOS tissue-specific disease mechanisms.

Analysis of comprehensive biomolecules in critically ill patients via bioinformatics technologies

Each patient with a critical illness such as sepsis and severe trauma has a different genetic background, comorbidities, age, and sex. Moreover, pathophysiology changes dynamically over time even in the same patient. Therefore, individualized treatment is necessary to account for heterogeneity in patient backgrounds. Recently, the analysis of comprehensive biomolecular information using clinical specimens has revealed novel molecular pathological classifications called subtypes. In addition, comprehensive biomolecular information using clinical specimens has enabled reverse translational research, which is a data-driven approach to the identification of drug target molecules. The development of these methods is expected to visualize the heterogeneity of patient backgrounds and lead to personalized therapy.

Principles and methods for transferring polygenic risk scores across global populations

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.

Diet-microbiota associations in gastrointestinal research: a systematic review

Interactions between diet and gastrointestinal microbiota influence health status and outcomes. Evaluating these relationships requires accurate quantification of dietary variables relevant to microbial metabolism, however current dietary assessment methods focus on dietary components relevant to human digestion only. The aim of this study was to synthesize research on foods and nutrients that influence human gut microbiota and thereby identify knowledge gaps to inform dietary assessment advancements toward better understanding of diet-microbiota interactions. Thirty-eight systematic reviews and 106 primary studies reported on human diet-microbiota associations. Dietary factors altering colonic microbiota included dietary patterns, macronutrients, micronutrients, bioactive compounds, and food additives. Reported diet-microbiota associations were dominated by routinely analyzed nutrients, which are absorbed from the small intestine but analyzed for correlation to stool microbiota. Dietary derived microbiota-relevant nutrients are more challenging to quantify and underrepresented in included studies. This evidence synthesis highlights advancements needed, including opportunities for expansion of food composition databases to include microbiota-relevant data, particularly for human intervention studies. These advances in dietary assessment methodology will facilitate translation of microbiota-specific nutrition therapy to practice.

Replacing device-measured sedentary time with physical activity is associated with lower risk of coronary heart disease regardless of genetic risk

BACKGROUND

Excess sedentary time (ST) is recognized as an important modifiable risk factor for coronary heart disease (CHD). However, whether the associations of genetic susceptibility with CHD incidence can be modified by replacing wearable-device-measured ST with physical activity (PA) is unknown.

OBJECTIVES

To examine the associations of wearable-device-measured ST replaced by PA with incident CHD across strata of genetic susceptibility.

METHODS

This study included 77,500 White British (57% female) with valid wrist-worn accelerometry and without prevalent CHD/stroke from UK Biobank. Genetic susceptibility to CHD was quantified through weighted polygenic risk scores for CHD based on 300 single-nucleotide polymorphisms. Wrist-worn accelerometer data were used to derive ST, light PA, and moderate-to-vigorous PA (MVPA).

RESULTS

Reallocation of 60 min/day of ST into the same amount of MVPA was associated with approximately 9% lower relative risk of CHD for all participants and across strata of genetic risk: replacement of 1 min/day of ST associated with <1% lower relative risk of CHD. No evidence of interaction (p: 0.784) was found between genetic risk and ST for CHD risk. Reallocating 60 min/day of ST into the same MVPA time was associated with greater absolute CHD risk reductions at high genetic risk (0.27%) versus low genetic risk (0.15%).

CONCLUSIONS

Replacing any amount of ST with an equal amount of MVPA time is associated with a lower relative risk of CHD, irrespective of genetic susceptibility to CHD. Reductions in CHD absolute risk for replacing ST with MVPA are greater at high genetic risk versus low genetic risk.

The accuracy of polygenic score models for anthropometric traits and Type II Diabetes in the Native Hawaiian Population

Polygenic scores (PGS) are promising in stratifying individuals based on the genetic susceptibility to complex diseases or traits. However, the accuracy of PGS models, typically trained in European- or East Asian-ancestry populations, tend to perform poorly in other ethnic minority populations, and their accuracies have not been evaluated for Native Hawaiians. Using body mass index, height, and type-2 diabetes as examples of highly polygenic traits, we evaluated the prediction accuracies of PGS models in a large Native Hawaiian sample from the Multiethnic Cohort with up to 5,300 individuals. We evaluated both publicly available PGS models or genome-wide PGS models trained in this study using the largest available GWAS. We found evidence of lowered prediction accuracies for the PGS models in some cases, particularly for height. We also found that using the Native Hawaiian samples as an optimization cohort during training did not consistently improve PGS performance. Moreover, even the best performing PGS models among Native Hawaiians would have lowered prediction accuracy among the subset of individuals most enriched with Polynesian ancestry. Our findings indicate that factors such as admixture histories, sample size and diversity in GWAS can influence PGS performance for complex traits among Native Hawaiian samples. This study provides an initial survey of PGS performance among Native Hawaiians and exposes the current gaps and challenges associated with improving polygenic prediction models for underrepresented minority populations.

Stakeholder Perception of the Implementation of Genetic Risk Testing for Twelve Multifactorial Diseases

Genome-wide association studies have been employed to develop numerous risk prediction models using polygenic risk scores (PRSs) for multifactorial diseases. However, healthcare providers lack confidence in their understanding of PRS risk stratification for multifactorial diseases, which underscores the need to assess the readiness of PRSs for clinical use. To address this issue, we surveyed the perceptions of healthcare providers as stakeholders in the clinical implementation of genetic-based risk prediction for multifactorial diseases. We conducted a web-based study on the need for risk prediction based on genetic information and the appropriate timing of testing for 12 multifactorial diseases. Responses were obtained from 506 stakeholders. Positive perceptions of genetic risk testing were found for adult-onset chronic diseases. As per participant opinion, testing for adult-onset diseases should be performed after the age of 20 years, whereas testing for psychiatric and allergic disorders that manifest during childhood should be performed from birth to 19 years of age. The stakeholders recognized the need for genetic risk testing for diseases that develop in adulthood, believing that the appropriate testing time is after maturity. This study contributes to the discussion on the clinical implementation of the PRS for genetic risk prediction of multifactorial diseases.

Machine Learning to Advance Human Genome-Wide Association Studies

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Genetic landscape of Parkinson's disease and related diseases in Luxembourg

Objectives

To explore the genetic architecture of PD in the Luxembourg Parkinson's Study including cohorts of healthy people and patients with Parkinson's disease (PD) and atypical parkinsonism (AP).

Methods

809 healthy controls, 680 PD and 103 AP were genotyped using the Neurochip array. We screened and validated rare single nucleotide variants (SNVs) and copy number variants (CNVs) within seven PD-causing genes (LRRK2, SNCA, VPS35, PRKN, PARK7, PINK1 and ATP13A2). Polygenic risk scores (PRSs) were generated using the latest genome-wide association study for PD. We then estimated the role of common variants in PD risk by applying gene-set-specific PRSs.

Results

We identified 60 rare SNVs in seven PD-causing genes, nine of which were pathogenic in LRRK2, PINK1 and PRKN. Eleven rare CNVs were detected in PRKN including seven duplications and four deletions. The majority of PRKN SNVs and CNVs carriers were heterozygous and not differentially distributed between cases and controls. The PRSs were significantly associated with PD and identified specific molecular pathways related to protein metabolism and signal transduction as drivers of PD risk.

Conclusion

We performed a comprehensive genetic characterization of the deep-phenotyped individuals of the Luxembourgish Parkinson's Study. Heterozygous SNVs and CNVs in PRKN were not associated with higher PD risk. In particular, we reported novel digenic variants in PD related genes and rare LRRK2 SNVs in AP patients. Our findings will help future studies to unravel the genetic complexity of PD.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

BACKGROUND

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.

RESULTS

In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.

CONCLUSIONS

Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Genetic counseling for pre-implantation genetic testing of monogenic disorders (PGT-M)

Pre-implantation genetic testing (PGT) is a vital tool in preventing chromosomal aneuploidies and other genetic disorders including those that are monogenic in origin. It is performed on embryos created by intracytoplasmic sperm injection (ICSI). Genetic counseling in the area of assisted reproductive technology (ART) has also evolved along with PGT and is considered an essential and integral part of Reproductive Medicine. While PGT has the potential to prevent future progeny from being affected by genetic conditions, genetic counseling helps couples understand and adapt to the medical, psychological, familial and social implications of the genetic contribution to disease. Genetic counseling is particularly helpful for couples with recurrent miscarriages, advanced maternal age, a partner with a chromosome translocation or inversion, those in a consanguineous marriage, and those using donor gametes. Partners with a family history of genetic conditions including hereditary cancer, late onset neurological diseases and with a carrier status for monogenic disorders can benefit from genetic counseling when undergoing PGT for monogenic disorders (PGT-M). Genetic counseling for PGT is useful in cases of Mendelian disorders, autosomal dominant and recessive conditions and sex chromosome linked disorders and for the purposes of utilizing HLA matching technology for creating a savior sibling. It also helps in understanding the importance of PGT in cases of variants of uncertain significance (VUS) and variable penetrance. The possibilities and limitations are discussed in detail during the sessions of genetic counseling.

A genome-wide association, polygenic risk score and sex study on opioid use disorder treatment outcomes

Opioid use disorder continues to be a health concern with a high rate of opioid related deaths occurring worldwide. Medication Assisted Treatments (MAT) have been shown to reduce opioid withdrawal, cravings and opioid use, however variability exists in individual's treatment outcomes. Sex-specific differences have been reported in opioid use patterns, polysubstance use and health and social functioning. Candidate gene studies investigating methadone dose as an outcome have identified several candidate genes and only five genome-wide associations studies have been conducted for MAT outcomes. This study aimed to identify genetic variants associated with MAT outcomes through genome-wide association study (GWAS) and test the association between genetic variants previously associated with methadone dose through a polygenic risk score (PRS). Study outcomes include: continued opioid use, relapse, methadone dose and opioid overdose. No genome-wide significance SNPs or sex-specific results were identified. The PRS identified statistically significant results (p < 0.05) for the outcome of methadone dose (R2 = 3.45 × 10-3). No other PRS was statistically significant. This study provides evidence for association between a PRS and methadone dose. More research on the PRS to increase the variance explained is needed before it can be used as a tool to help identify a suitable methadone dose within this population.

Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living

The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.

Schizophrenia polygenic risk score in psychosis proneness

Schizophrenia (SZ) is a complex disorder with a highly polygenic inheritance. It can be conceived as the extreme expression of a continuum of traits that are present in the general population often broadly referred to as schizotypy. However, it is still poorly understood how these traits overlap genetically with the disorder. We investigated whether polygenic risk for SZ is associated with these disorder-related phenotypes (schizotypy, psychotic-like experiences, and subclinical psychopathology) in a sample of 253 non-clinically identified participants. Polygenic risk scores (PRSs) were constructed based on the latest SZ genome-wide association study using the PRS-CS method. Their association with self-report and interview measures of SZ-related traits was tested. No association with either schizotypy or psychotic-like experiences was found. However, we identified a significant association with the Motor Change subscale of the Comprehensive Assessment of At-Risk Mental States (CAARMS) interview. Our results indicate that the genetic overlap of SZ with schizotypy and psychotic-like experiences is less robust than previously hypothesized. The relationship between high PRS for SZ and motor abnormalities could reflect neurodevelopmental processes associated with psychosis proneness and SZ.

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events

A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 360k Finns spanning up to 50 years of individuals' lifetimes. Individuals with a high genetic generalized epilepsy PRS (PRSGGE) in FinnGen had an increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.55 per PRSGGE standard deviation [SD]) across their lifetime and after unspecified seizure events. Effect sizes of epilepsy PRSs were comparable to effect sizes in clinically curated data supporting our EHR-derived epilepsy diagnoses. Within 10 years after an unspecified seizure, the GGE rate was 37% when PRSGGE > 2 SD compared to 5.6% when PRSGGE < -2 SD. The effect of PRSGGE was even larger on GGE subtypes of idiopathic generalized epilepsy (IGE) (HR 2.1 per SD PRSGGE). We further report significantly larger effects of PRSGGE on epilepsy in females and in younger age groups. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). We found PRSGGE specifically associated with GGE in comparison with >2000 independent diseases while PRSNAFE was also associated with other diseases than NAFE such as back pain. Here, we show that epilepsy specific PRSs have good discriminative ability after a first seizure event i.e. in circumstances where the prior probability of epilepsy is high outlining a potential to serve as biomarkers for an epilepsy diagnosis.

Research progress and challenges of preimplantation genetic testing for polygenic diseases

Preimplantation genetic testing is an important part in assisted reproductive technology, which can block the intergenerational inheritance of a single gene or chromosomal diseases. Preimplantation genetic testing for polygenic disease risk (PGT-P) is one of the latest developments in the field. With the development of artificial intelligence and genetic detection technology, PGT-P can be used to analyze genetic material, calculate polygenic risk scores and convert these into incidence probability. Embryos with relatively low incidence probability can be screened for transfer, in order to reduce the possibility that the offspring suffers from the disease in the future. This has significant clinical and social significance. At present, PGT-P has been applied clinically and made phased progress at home and abroad. But as a developing technology, PGT-P still has some technical limitations as unstable results, environmental influences and racial differences cannot be ruled out. From the ethical perspective, if the screening indications are not strictly regulated, it is likely to cause new social problems. In this paper, we review the technical details and recent progress in PGT-P, and discuss the prospects of its future development, especially how to establish a complete and suitable screening model for Chinese population.

Advancing the communication of genetic risk for cardiometabolic diseases: a critical interpretive synthesis

BACKGROUND

Genetics play an important role in risk for cardiometabolic diseases-including type 2 diabetes, cardiovascular disease and obesity. Existing research has explored the clinical utility of genetic risk tools such as polygenic risk scores-and whether interventions communicating genetic risk information using these tools can impact on individuals' cognitive appraisals of disease risk and/or preventative health behaviours. Previous systematic reviews suggest mixed results. To expand current understanding and address knowledge gaps, we undertook an interpretive, reflexive method of evidence synthesis-questioning the theoretical basis behind current interventions that communicate genetic risk information and exploring how the effects of genetic risk tools can be fully harnessed for cardiometabolic diseases.

METHODS

We obtained 189 records from a combination of database, website and grey literature searches-supplemented with reference chaining and expert subject knowledge within the review team. Using pre-defined critical interpretive synthesis methods, quantitative and qualitative evidence was synthesised and critiqued alongside theoretical understanding from surrounding fields of behavioural and social sciences.

FINDINGS

Existing interventions communicating genetic risk information focus predominantly on the "self", targeting individual-level cognitive appraisals, such as perceived risk and perceived behavioural control. This approach risks neglecting the role of contextual factors and upstream determinants that can reinforce individuals' interpretations of risk. It also assumes target populations to embody an "ascetic subject of compliance"-the idea of a patient who strives to comply diligently with professional medical advice, logically and rationally adopting any recommended lifestyle changes. We developed a synthesising argument-"beyond the ascetic subject of compliance"-grounded in three major limitations of this perspective: (1) difficulty applying existing theories/models to diverse populations, (2) the role of familial variables and (3) the need for a life course perspective.

CONCLUSIONS

Interventions communicating genetic risk information should account for wider influences that can affect individuals' responses to risk at different levels-including through interactions with their family systems, socio-cultural environments and wider health provision.

PROTOCOL REGISTRATION

PROSPERO CRD42021289269.

A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.

Power of inclusion: Enhancing polygenic prediction with admixed individuals

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care

Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.

Adherence to a healthy lifestyle, genetic susceptibility to abdominal obesity, cardiometabolic risk markers, and risk of coronary heart disease

BACKGROUND

Little is known about whether the association between genetic susceptibility to high waist-to-hip ratio (WHR), a measure of abdominal obesity, and incident coronary heart disease (CHD) is modified by adherence to a healthy lifestyle.

OBJECTIVES

To explore the interplay of genetic susceptibility to high WHR and adherence to a healthy lifestyle on incident CHD.

METHODS

This study included 282,316 white British individuals from the UK Biobank study. Genetic risk for high WHR was estimated in the form of weighted polygenic risk scores (PRSs), calculated based on 156 single-nucleotide polymorphisms. Lifestyle scores were calculated based on 5 healthy lifestyle factors: regular physical activity, no current smoking, a healthy diet, <3 times/wk of alcohol consumption and 7-9 h/d of sleep. Incident CHD (n = 11,635) was accrued over a median 13.8 y of follow-up, and 12 individual cardiovascular disease risk markers assessed at baseline.

RESULTS

Adhering to a favorable lifestyle (4-5 healthy factors) was associated with a 25% (hazard ratio: 0.75, 95% confidence interval: 0.70, 0.81) lower hazard of CHD compared with an unfavorable lifestyle (0-1 factor), independent of PRS for high WHR. Estimated 12-y absolute risk of CHD was lower for a favorable lifestyle at high genetic risk (1.73%) and medium genetic risk (1.67%) than for an unfavorable lifestyle at low genetic risk (2.08%). Adhering to a favorable lifestyle was associated with healthier levels of cardiovascular disease risk markers (except random glucose and high-density lipoprotein), independent of PRS for high WHR.

CONCLUSIONS

Individuals who have high or medium genetic risk of abdominal obesity but adhere to a healthy lifestyle may have a lower risk of developing CHD, compared with those who have low genetic risk and an unhealthy lifestyle. Future clinical trials of lifestyle modification could be implemented for individuals at high genetic risk of abdominal obesity for the primary prevention of CHD events.

Sex differences in the genetic and molecular mechanisms of coronary artery disease

Sex differences in coronary artery disease (CAD) presentation, risk factors and prognosis have been widely studied. Similarly, studies on atherosclerosis have shown prominent sex differences in plaque biology. Our understanding of the underlying genetic and molecular mechanisms that drive these differences remains fragmented and largely understudied. Through reviewing genetic and epigenetic studies, we identified more than 40 sex-differential candidate genes (13 within known CAD loci) that may explain, at least in part, sex differences in vascular remodeling, lipid metabolism and endothelial dysfunction. Studies with transcriptomic and single-cell RNA sequencing data from atherosclerotic plaques highlight potential sex differences in smooth muscle cell and endothelial cell biology. Especially, phenotypic switching of smooth muscle cells seems to play a crucial role in female atherosclerosis. This matches the known sex differences in atherosclerotic phenotypes, with men being more prone to lipid-rich plaques, while women are more likely to develop fibrous plaques with endothelial dysfunction. To unravel the complex mechanisms that drive sex differences in CAD, increased statistical power and adjustments to study designs and analysis strategies are required. This entails increasing inclusion rates of women, performing well-defined sex-stratified analyses and the integration of multi-omics data.

Impact of intrinsic and extrinsic risk factors on early-onset lung disease in cystic fibrosis

BACKGROUND

Although respiratory pathology is known to develop in young children with cystic fibrosis (CF), the determinants of early-onset lung disease have not been elucidated.

OBJECTIVE

We aimed to determine the impact of potential intrinsic and extrinsic risk factors during the first 3 years of life, testing the hypothesis that both contribute significantly to early-onset CF lung disease.

DESIGN

We studied 104 infants born during 2012-2017, diagnosed through newborn screening by age 3 months, and evaluated comprehensively to 36 months of age. Lung disease manifestations were quantified with a new scoring system known as CFELD for Cystic Fibrosis Early-onset Lung Disease. The variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene were determined and categorized. Whole genome sequencing was performed on each subject and the data transformed to polygenic risk scores (PRS) that aggregate variants associated with lung function. Extrinsic factors included socioeconomic status (SES) indicators and environmental experiences such as exposures to smoking, pets, and daycare.

RESULTS

We found by univariate analysis that CFTR genotype and genetic modifiers aggregated by the PRS method were significantly associated with early-onset CF lung disease. Ordinal logistic regression analysis demonstrated that high and stable SES (maternal education ≥community college, stable 2-parent home, and not receiving Medicaid) and better growth (weight-for-age and height-for-age z-scores) reduced risks, while exposure to smoking and daycare ≥20 h/week increased the risk of CFELD severity.

CONCLUSIONS

Extrinsic, modifiable determinants are influential early and potentially as important as the intrinsic risk factors in the onset of CF lung disease.

Perceived benefits and barriers to implementing precision preventive care: Results of a national physician survey

Polygenic risk scores (PRS) may improve risk-stratification in preventive care. Their clinical implementation will depend on primary care physicians' (PCPs) uptake. We surveyed PCPs in a national physician database about the perceived clinical utility, benefits, and barriers to the use of PRS in preventive care. Among 367 respondents (participation rate 96.3%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p < 0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and insurance discrimination concerns (22%) as extreme barriers. Latent class analysis identified 3 subclasses of respondents. Skeptics (n = 83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n = 134, 36.5%) and enthusiasts (n = 150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS. Overall results suggest that PCPs generally endorse using PRS to guide medical decision-making about preventive care, and barriers identified suggest interventions to address their needs and concerns.

Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children

Importance

Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown.

Objective

To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children.

Design, Setting, and Participants

This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023.

Exposure

Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline.

Main Outcomes and Measures

Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding.

Results

The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations.

Conclusions and Relevance

Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

BACKGROUND

Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.

METHOD

To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.

RESULTS

We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease).

CONCLUSION

We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions

Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.

Adverse Childhood Events, Mood and Anxiety Disorders, and Substance Dependence: Gene x Environment Effects and Moderated Mediation

Background

Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear.

Methods

We examined associations among ACEs, mood/anxiety disorders, and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/White). We generated latent variables for each phenotype and modeled direct and indirect effects of ACEs on substance dependence, mediated by mood/anxiety disorders (forward or "self-medication" model) and of ACEs on mood/anxiety disorders, mediated by substance dependence (reverse or "substance-induced" model). In a sub-sample, we also generated polygenic scores for substance dependence and mood/anxiety disorder factors, which we tested as moderators in the mediation models.

Results

Although there were significant indirect effects in both directions, mediation by mood/anxiety disorders (forward model) was greater than by substance dependence (reverse model). Greater genetic risk for substance dependence was associated with a weaker direct effect of ACEs on substance dependence in both the African- and European-ancestry groups (i.e., gene-environment interaction) and a weaker indirect effect in European-ancestry individuals (i.e., moderated mediation).

Conclusion

We found greater evidence that substance dependence results from self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence who are more likely to develop a dependence diagnosis, ACEs exert less of an effect in promoting that outcome. Following exposure to ACEs, multiple pathways lead to mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification

Introduction

Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.

Methods

Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n  =  1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.

Results

adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.

Discussion

Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Adverse Childhood Events, Mood and Anxiety Disorders, and Substance Dependence: Gene X Environment Effects and Moderated Mediation

Background

Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear.

Methods

We examined associations among ACEs, mood/anxiety disorders, and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/White). We generated latent variables for each phenotype and modeled direct and indirect effects of ACEs on substance dependence, mediated by mood/anxiety disorders (forward or "self-medication" model) and of ACEs on mood/anxiety disorders, mediated by substance dependence (reverse or "substance-induced" model). In a sub-sample, we also generated polygenic scores for substance dependence and mood/anxiety disorder factors, which we tested as moderators in the mediation models.

Results

Although there were significant indirect effects in both directions, mediation by mood/anxiety disorders (forward model) was greater than by substance dependence (reverse model). Greater genetic risk for substance dependence was associated with a weaker direct effect of ACEs on substance dependence in both the African- and European-ancestry groups (i.e., gene-environment interaction) and a weaker indirect effect in European-ancestry individuals (i.e., moderated mediation).

Conclusion

We found greater evidence that substance dependence results from self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence who are more likely to develop a dependence diagnosis, ACEs exert less of an effect in promoting that outcome. Following exposure to ACEs, multiple pathways lead to mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

Multi-system diseases and death trajectory of metabolic dysfunction-associated fatty liver disease: findings from the UK Biobank

BACKGROUND

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly defined condition encompassing hepatic steatosis and metabolic dysfunction. However, the relationship between MAFLD and multi-system diseases remains unclear, and the time-dependent sequence of these diseases requires further clarification.

METHODS

After propensity score matching, 163,303 MAFLD subjects and 163,303 matched subjects were included in the community-based UK Biobank study. The International Classification of Diseases, Tenth Revision (ICD-10), was used to reclassify medical conditions into 490 and 16 specific causes of death. We conducted a disease trajectory analysis to map the key pathways linking MAFLD to various health conditions, providing an overview of their interconnections.

RESULTS

Participants aged 59 (51-64) years, predominantly males (62.5%), were included in the study. During the 12.9-year follow-up period, MAFLD participants were found to have a higher risk of 113 medical conditions and eight causes of death, determined through phenome-wide association analysis using Cox regression models. Temporal disease trajectories of MAFLD were established using disease pairing, revealing intermediary diseases such as asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These trajectories primarily resulted in acute myocardial infarction, disorders of fluid, electrolyte, and acid-base balance, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding death trajectories of MAFLD, malignant neoplasms, cardiovascular diseases, and respiratory system deaths were the main causes, and organ failure, infective disease, and internal environment disorder were the primary end-stage conditions. Disease trajectory analysis based on the level of genetic susceptibility to MAFLD yielded consistent results.

CONCLUSIONS

Individuals with MAFLD have a risk of a number of different medical conditions and causes of death. Notably, these diseases and potential causes of death constitute many pathways that may be promising targets for preventing general health decline in patients with MAFLD.

Associations between the Genetic Heritability of Dyslipidemia and Dietary Patterns in Korean Adults Based on Sex Differences

Dyslipidemia can be defined as an abnormality in serum lipid levels that is substantially linked to genetic variations and lifestyle factors, such as diet patterns, and has distinct sex-specific characteristics. We aimed to elucidate the genetic impact of dyslipidemia according to sex and explore the associations between genetic variants and dietary patterns in large-scale population-based cohorts. After performing genome-wide association studies (GWASs) in male, female, and entire cohorts, significant single nucleotide polymorphisms (SNPs) were identified in the three groups, and genetic risk scores (GRSs) were calculated by summing the risk alleles from the selected SNPs. After adjusting for confounding variables, the risk of dyslipidemia was 2.013-fold and 2.535-fold higher in the 3rd quartile GRS group in the male and female cohorts, respectively, than in the 1st quartile GRS group. While instant noodle and soft drink intake were significantly associated with GRS related to hyperlipidemia in male cohorts, coffee consumption was substantially related to GRS related to hyperlipidemia in female cohorts. Considering the influence of genetic factors and dietary patterns, the findings of this study suggest the potential for implementing sex-specific strategic interventions to avoid dyslipidemia.

Transforming global approaches to chronic disease prevention and management across the lifespan: integrating genomics, behavior change, and digital health solutions

Chronic illnesses are a major threat to global population health through the lifespan into older age. Despite world-wide public health goals, there has been a steady increase in chronic and non-communicable diseases (e.g., cancer, cardiovascular and metabolic disorders) and strong growth in mental health disorders. In 2010, 67% of deaths worldwide were due to chronic diseases and this increased to 74% in 2019, with accelerated growth in the COVID-19 era and its aftermath. Aging and wellbeing across the lifespan are positively impacted by the presence of effective prevention and management of chronic illness that can enhance population health. This paper provides a short overview of the journey to this current situation followed by discussion of how we may better address what the World Health Organization has termed the "tsunami of chronic diseases." In this paper we advocate for the development, validation, and subsequent deployment of integrated: 1. Polygenic and multifactorial risk prediction tools to screen for those at future risk of chronic disease and those with undiagnosed chronic disease. 2. Advanced preventive, behavior change and chronic disease management to maximize population health and wellbeing. 3. Digital health systems to support greater efficiencies in population-scale health prevention and intervention programs. It is argued that each of these actions individually has an emerging evidence base. However, there has been limited research to date concerning the combined population-level health effects of their integration. We outline the conceptual framework within which we are planning and currently conducting studies to investigate the effects of their integration.

Founder population-specific weights yield improvements in performance of polygenic risk scores for Alzheimer disease in the Midwestern Amish

Alzheimer disease (AD) is the most common type of dementia and is estimated to affect 6 million Americans. Risk for AD is multifactorial, including both genetic and environmental risk factors. AD genomic research has generally focused on identification of risk variants. Using this information, polygenic risk scores (PRSs) can be calculated to quantify an individual's relative disease risk due to genetic factors. The Amish are a founder population descended from German and Swiss Anabaptist immigrants. They experienced a genetic bottleneck after arrival in the United States, making their genetic architecture different from the broader European ancestry population. Prior work has demonstrated the lack of transferability of PRSs across populations. Here, we compared the performance of PRSs derived from genome-wide association studies (GWASs) of Amish individuals to those derived from a large European ancestry GWAS. Participants were screened for cognitive impairment with further evaluation for AD. Genotype data were imputed after collection via Illumina genotyping arrays. The Amish individuals were split into two groups based on the primary site of recruitment. For each group, GWAS was conducted with account for relatedness and adjustment for covariates. PRSs were then calculated using weights from the other Amish group. PRS models were evaluated with and without covariates. The Amish-derived PRSs distinguished between dementia status better than the European-derived PRS in our Amish populations and demonstrated performance improvements despite a smaller training sample size. This work highlighted considerations for AD PRS usage in populations that cannot be adequately described by basic race/ethnicity or ancestry classifications.