Clinical use of current polygenic risk scores may exacerbate health disparities

Polygenic embryo screening: are there potential maternal and fetal harms?

Polygenic embryo screening (PES) and its derivate the Embryo Health Score (EHS) have generated interest in both infertile and fertile populations due to their potential ability to select embryos with a reduced risk of disease and improved long-term health outcomes. Concerns have been raised regarding the potential harms of IVF itself, including possible epigenetic changes that may affect the health of the offspring in late adulthood, which are not fully captured in the EHS calculation. Knowledge of the potential impacts of the trophectoderm biopsy, which is a key component of the PES procedure, on the offsprings' health is limited by the heterogeneity of the population characteristics used in the published studies. Nonetheless, the literature suggests a possible increased risk of preterm delivery, birth defects and pre-eclampsia after trophectoderm biopsy. Overall, the risks of PES for prenatal and postnatal health remain uncertain, and further research is needed. Counselling patients regarding these risks before considering PES is important, to provide an understanding of the risks and benefits. This review aims to highlight some of these issues, the need for continued investigation in this area, and the importance of informed decision-making in the context of PES.

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

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

Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population

This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.

Advancements in the Genetics of Spontaneous Coronary Artery Dissection

PURPOSE OF REVIEW

Spontaneous coronary artery dissection (SCAD) is a significant cause of acute myocardial infarction that is increasingly recognized in young and middle-aged women. The etiology of SCAD is likely multifactorial and may include the interaction of environmental and individual factors. Here, we summarize the current understanding of the genetic factors contributing to the development of SCAD.

RECENT FINDINGS

The molecular findings underlying SCAD have been demonstrated to include a combination of rare DNA sequence variants with large effects, common variants contributing to a complex genetic architecture, and variants with intermediate impact. The genes associated with SCAD highlight the role of arterial cells and their extracellular matrix in the pathogenesis of the disease and shed light on the relationship between SCAD and other disorders, including fibromuscular dysplasia and connective tissue diseases. While up to 10% of affected individuals may harbor a rare variant with large effect, SCAD most often presents as a complex genetic condition. Analyses of larger and more diverse cohorts will continue to improve our understanding of risk susceptibility loci and will also enable consideration of the clinical utility of genetic testing strategies in the management of SCAD.

A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Birthweight, genetic risk, and gastrointestinal cancer incidence: a prospective cohort study

BACKGROUND

The epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce. The study aimed to prospectively assess the interactions and joint effects of birthweight and genetic risk levels on gastrointestinal cancer incidence in adulthood.

METHODS

A total of 254,997 participants were included in the UK Biobank study. We used multivariate restricted cubic splines and Cox regression models to estimate the hazard ratios (HRs) and 95% confidential intervals (CI) for the association between birthweight and gastrointestinal cancer risk, then constructed a polygenic risk score (PRS) to assess its interaction and joint effect with birthweight on the development of gastrointestinal cancer.

RESULTS

We documented 2512 incident cases during a median follow-up of 8.88 years. Compare with participants reporting a normal birthweight (2.5-4.5 kg), multivariable-adjusted HR of gastrointestinal cancer incidence for participants with high birthweight (≥4.5 kg) was 1.17 (95%CI: 1.01-1.36). Such association was remarkably observed in pancreatic cancer, with an HR of 1.82 (95%CI: 1.26-2.64). No statistically significant association was observed between low birth weight and gastrointestinal cancers. Participants with high birthweight and high PRS had the highest risk of gastrointestinal cancer (HR: 2.95, 95%CI: 2.19-3.96).

CONCLUSION

Our findings highlight that high birthweight is associated with a higher incidence of gastrointestinal cancer, especially for pancreatic cancer. Benefits would be obtained from birthweight control, particularly for individuals with a high genetic risk.KEY MESSAGESThe epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce.This cohort study of 254,997 adults in the United Kingdom found an association of high birthweight with the incidence of gastrointestinal cancer, especially for pancreatic cancer, and also found that participants with high birthweight and high polygenic risk score had the highest risk of gastrointestinal cancer.Our data suggests a possible effect of in utero or early life exposures on adulthood gastrointestinal cancer, especially for those with a high genetic risk.

The Ethics of Risk Prediction for Psychosis and Suicide Attempt in Youth Mental Health

OBJECTIVE

To identify potential clinical utility of polygenic risk scores (PRS) and exposomic risk scores (ERS) for psychosis and suicide attempt in youth and assess the ethical implications of these tools.

STUDY DESIGN

We conducted a narrative literature review of emerging findings on PRS and ERS for suicide and psychosis as well as a literature review on the ethics of PRS. We discuss the ethical implications of the emerging findings for the clinical potential of PRS and ERS.

RESULTS

Emerging evidence suggests that PRS and ERS may offer clinical utility in the relatively near future but that this utility will be limited to specific, narrow clinical questions, in contrast to the suggestion that population-level screening will have sweeping impact. Combining PRS and ERS might optimize prediction. This clinical utility would change the risk-benefit balance of PRS, and further empirical assessment of proposed risks would be necessary. Some concerns for PRS, such as those about counseling, privacy, and inequities, apply to ERS. ERS raise distinct ethical challenges as well, including some that involve informed consent and direct-to-consumer advertising. Both raise questions about the ethics of machine-learning/artificial intelligence approaches.

CONCLUSIONS

Predictive analytics using PRS and ERS may soon play a role in youth mental health settings. Our findings help educate clinicians about potential capabilities, limitations, and ethical implications of these tools. We suggest that a broader discussion with the public is needed to avoid overenthusiasm and determine regulations and guidelines for use of predictive scores.

Integration of Biomarker Polygenic Risk Score Improves Prediction of Coronary Heart Disease

There are several established biomarkers for coronary heart disease (CHD), including blood pressure, cholesterol, and lipoproteins. It is of high interest to determine how a combined polygenic risk score (PRS) of CHD-associated biomarkers (BioPRS) can further improve genetic prediction of CHD. We developed CHDBioPRS, combining BioPRS with PRS of CHD in the UK Biobank and tested it on FinnGen. We found that BioPRS was clearly predictive of CHD and that CHDBioPRS improved the standard CHD PRS. The largest effect was observed with early onset cases in FinnGen, with HRs above 2 per standard deviation of CHDBioPRS.

Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study

AIM

The influence of family history of diabetes, probably reflecting genetic and lifestyle factors, on the association of combined genetic and lifestyle risks with diabetes is unknown. We examined these associations.

METHODS

This cross-sectional study included 9,681 participants in the Tohoku Medical Megabank Community-based Cohort Study. A lifestyle score, which was categorized into ideal, intermediate, and poor lifestyles, was given. Family history was obtained through a self-reported questionnaire. A polygenic risk score (PRS) was constructed in the target data (n=1,936) using publicly available genome-wide association study summary statistics from BioBank Japan. For test data (n=7,745), we evaluated PRS performance and examined the associations of combined family history and genetic and lifestyle risks with diabetes. Diabetes was defined as non-fasting blood glucose ≥ 200 mmHg, HbA1c ≥ 6.5%, and/or self-reported diabetes treatment.

RESULTS

In test data, 467 (6.0%) participants had diabetes. Compared with a low genetic risk and an ideal lifestyle without a family history, the odds ratio (OR) was 3.73 (95% confidence interval [CI], 1.92-7.00) for a lower genetic risk and a poor lifestyle without a family history. Family history was significantly associated with diabetes (OR, 3.58 [95% CI, 1.73-6.98]), even in those with a low genetic risk and an ideal lifestyle. Even among participants who had an ideal lifestyle without a family history, a high genetic risk was associated with diabetes (OR, 2.49 [95% CI, 1.65-3.85]). Adding PRS to family history and conventional lifestyle risk factors improved the prediction ability for diabetes.

CONCLUSIONS

Our findings support the notion that a healthy lifestyle is important to prevent diabetes regardless of genetic risk.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study

BACKGROUND

As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD).

METHODS

The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure.

RESULTS

We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD.

CONCLUSIONS

As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Pruning and thresholding approach for methylation risk scores in multi-ancestry populations

Recent efforts have focused on developing methylation risk scores (MRS), a weighted sum of the individual's DNA methylation (DNAm) values of pre-selected CpG sites. Most of the current MRS approaches that utilize Epigenome-wide association studies (EWAS) summary statistics only include genome-wide significant CpG sites and do not consider co-methylation. New methods that relax the p-value threshold to include more CpG sites and account for the inter-correlation of DNAm might improve the predictive performance of MRS. We paired informed co-methylation pruning with P-value thresholding to generate pruning and thresholding (P+T) MRS and evaluated its performance among multi-ancestry populations. Through simulation studies and real data analyses, we demonstrated that pruning provides an improvement over simple thresholding methods for prediction of phenotypes. We demonstrated that European-derived summary statistics can be used to develop P+T MRS among other populations such as African populations. However, the prediction accuracy of P+T MRS may differ across multi-ancestry population due to environmental/cultural/social differences.

Utilize polygenic risk score to enhance fracture risk estimation and improve the performance of FRAX in patients with osteoporosis

This study examined the use of polygenic risk scores (PGS) in combination with the Fracture Risk Assessment Tool (FRAX) to enhance fragility fractures risk estimation in osteoporosis patients. Analyzing data from over 57,000 participants, PGS improved fracture risk estimation, especially for individuals with intermediate to low risks, allowing personalized preventive strategies.

INTRODUCTION

Osteoporosis and fragility fractures are multifactorial, with contributions from both clinical and genetic determinants. However, whether using polygenic risk scores (PGS) may enhance the risk estimation of osteoporotic fracture in addition to Fracture Risk Assessment Tool (FRAX) remains unknown. This study investigated the collective association of PGS and FRAX with fragility fracture.

METHODS

We conducted a cohort study from the Taiwan Precision Medicine Initiative (TPMI) at Taichung Veterans General Hospital, Taiwan. Genotyping was performed to compute PGS associated with bone mineral density (BMD). Phenome-wide association studies were executed to pinpoint phenotypes correlated with the PGS. Logistic regression analysis was conducted to ascertain factors associated with osteoporotic fractures.

RESULTS

Among all 57,257 TPMI participants, 3744 (904 men and 2840 women, with a mean age of 66.7) individuals had BMD testing, with 540 (14.42%) presenting with fractures. The 3744 individuals who underwent BMD testing were categorized into four quartiles (Q1-Q4) based on PGS; 540 (14.42%) presented with fractures. Individuals with PGS-Q1 exhibited lower BMD, a higher prevalence of major fractures, and elevated FRAX-major and FRAX-hip than those with PGS-Q4. PGS was associated with major fractures after adjusting age, sex, and FRAX scores. Notably, the risk of major fractures (PGS-Q1 vs. Q4) was significantly higher in the subgroups of FRAX-major scores < 10% and 10-20%, but not in participants with a FRAX-major score ≧ 20%.

CONCLUSIONS

Our study highlights the potential of PGS to augment fracture risk estimation in conjunction with FRAX, particularly in individuals with middle to low risks. Incorporating genetic testing could empower physicians to tailor personalized preventive strategies for osteoporosis.

Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations

Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably.

WITHDRAWN: Genome-wide risk prediction of primary open-angle glaucoma across multiple ancestries

This manuscript has been withdrawn by medRxiv following a formal request by the QIMR Berghofer Medical Research Institute Research Integrity Office owing to lack of author consent.

Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel

BACKGROUND

Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women.

METHODS

We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel.

RESULTS

In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28).

CONCLUSIONS

Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

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.

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

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

Massive underrepresentation of Arabs in genomic studies of common disease

Arabs represent 5% of the world population and have a high prevalence of common disease, yet remain greatly underrepresented in genome-wide association studies, where only 1 in 600 individuals are Arab. We highlight the persistent and unaddressed underrepresentation of Arabs in genomic databases and discuss its impact on public health genomics and missed opportunities for biological discovery.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

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.

A longitudinal study of polygenic score and cognitive function decline considering baseline cognitive function, lifestyle behaviors, and diabetes among middle-aged and older US adults

BACKGROUND

Genomic study of cognition decline while considering baseline cognition and lifestyle behaviors is scarce. We aimed to evaluate the impact of a polygenic score for general cognition on cognition decline rate, while considering baseline cognition and lifestyle behaviors, among the general population and people with diabetes, a patient group commonly affected by cognition impairment.

METHODS

We tested associations of the polygenic score for general cognition with annual changing rates of cognition measures in 8 years of follow-up among 12,090 White and 3100 Black participants of the Health and Retirement Study (HRS), a nationally representative sample of adults aged 50 years and older in the USA. Cognition measures including word recall, mental status, and total cognitive score were measured biannually. To maximize sample size and length of follow-up, we treated the 2010 wave of survey as baseline, and follow-up data until 2018 were analyzed. Baseline lifestyle behaviors, APOE status, and measured cognition were sequentially adjusted. Given racial differences in polygenic score, all analyses were conducted by race.

RESULTS

The polygenic score was significantly associated with annual changing rates of all cognition measures independent of lifestyle behaviors and APOE status. Together with age and sex, the polygenic score explained 29.9%, 15.9%, and 26.5% variances of annual changing rates of word recall, mental status, and total cognitive scores among Whites and explained 17.2%, 13.9%, and 18.7% variance of the three traits among Blacks. Among both White and Black participants, those in the top quartile of polygenic score had the three cognition measures increased annually, while those in the bottom quartile had the three cognition measures decreased annually. After further adjusting for the average cognition assessed in 3 visits around baseline, the polygenic score was still positively associated with annual changing rates of all cognition measures for White (P ≤ 2.89E - 19) but not for Black (P ≥ 0.07) participants. In addition, among participants with diabetes, physical activity offset the genetic susceptibility to decline of mental status (interaction P ≤ 0.01) and total cognitive scores (interaction P = 0.03).

CONCLUSIONS

Polygenic score predicted cognition changes in addition to measured cognition. Physical activity offset genetic risk for cognition decline among diabetes patients.

Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population

Polygenic risk scores (PRSs) have been studied for predicting human diseases, and various methods for PRS calculation have been developed. Most PRS studies to date have focused on European ancestry, and the performance of PRS has not been sufficiently assessed in East Asia. Herein, we evaluated the predictive performance of PRSs for East Asian populations under various conditions. Simulation studies using data from the Korean cohort, Health Examinees (HEXA), demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, we generated PRSs using real-world data from HEXA for ten diseases: asthma, breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We utilized the five previous PRS methods and genome-wide association study (GWAS) data from two biobank-scale datasets [European (UK Biobank) and East Asian (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that combines GWAS data from both ancestries, to generate a total of 110 PRS for ten diseases. Similar to the simulation results, SBayesRC showed better predictive performance for disease risk than the other methods. Furthermore, the East Asian GWAS data outperformed those from European ancestry for breast cancer, cataract, gastric cancer, and T2D, but neither of the two GWAS ancestries showed a significant advantage on PRS performance for the remaining six diseases. Based on simulation data and real data studies, it is expected that SBayesRC will offer superior performance for East Asian populations, and PRS generated using GWAS from non-East Asian may also yield good results.

Research on Health Disparities: Strategies and Findings From the Black Women's Health Study

The American Journal of Epidemiology has been a platform for findings from the Black Women's Health Study (BWHS) that are relevant to health disparities. Topics addressed have included methods of follow-up of a large cohort of Black women, disparities in health-care delivery, modifiable risk factors for health conditions that disproportionately affect Black women, associations with exposures that are highly prevalent in Black women, and methods for genetic research. BWHS papers have also highlighted the importance of considering social context, including perceived experiences of racism, in understanding health disparities. In the future, BWHS investigators will contribute to documentation of the role that structural racism plays in health disparities.

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.

Towards more accurate pharmacogenomic variant effect predictions

Tweetable abstract Accurate variant interpretation has become a key bottleneck for the translation of an individual's pharmacogenome into actionable recommendations. We recommend an integrated use of multiplexed assays, structure-based predictions and biobank data to develop more accurate effect predictors.

Predicting Heterogeneity in Patient Response to Morphine Treatment for Neonatal Opioid Withdrawal Syndrome

Infants with neonatal opioid withdrawal syndrome commonly receive morphine treatment to manage their withdrawal signs. However, the effectiveness of this pharmacotherapy in managing the infants' withdrawal signs vary widely. We sought to understand how information available early in infant monitoring can anticipate this treatment response, focusing on early modified Finnegan Neonatal Abstinence Scoring System (FNASS) scores, polygenic risk for opioid dependence (polygenic risk score (PRS)), and drug exposure. Using k-means clustering, we divided the 213 infants in our cohort into 3 groups based on their FNASS scores in the 12 hours before and after the initiation of pharmacotherapy. We found that these groups were pairwise significantly different for risk factors, including methadone exposure, and for in-hospital outcomes, including total morphine received, length of stay, and highest FNASS score. Whereas PRS was not predictive of receipt of treatment, PRS was pairwise significantly different between a subset of the groups. Using tree-based machine learning methods, we then constructed network graphs of the relationships among these groups, FNASS scores, PRS, drug exposures, and in-hospital outcomes. The resulting networks also showed meaningful connection between early FNASS scores and PRS, as well as between both of those and later in-hospital outcomes. These analyses present clinicians with the opportunity to better anticipate infant withdrawal progression and prepare accordingly, whether with expedited morphine treatment or non-pharmacotherapeutic alternative treatments.

Shifting the trajectory of therapeutic development for neurological and psychiatric disorders

Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.

British South Asian ancestry participants views of pharmacogenomics clinical implementation and research: a thematic analysis

BACKGROUND

South Asian ancestry populations are underrepresented in genomic studies and therapeutics trials. British South Asians suffer from multi-morbidity leading to polypharmacy. Our objective was to elucidate British South Asian ancestry community perspectives on pharmacogenomic implementation and sharing pharmacogenomic clinical data for research.

METHODS

Four focus groups were conducted (9-12 participants in each). Two groups were mixed gender, while one group was male only and one was female only. Simultaneous interpretation was available to participants in Urdu and Bengali. Focus groups were recorded and abridged transcription and thematic analysis were undertaken.

RESULTS

There were 42 participants, 64% female. 26% were born in the UK or Europe. 52% were born in Bangladesh and 17% in Pakistan. 36% reported university level education. Implementation of pharmacogenomics was perceived to be beneficial to individuals but pose a risk of overburdening resource limited systems. Pharmacogenomic research was perceived to be beneficial to the community, with concerns about data privacy and misuse. Data sharing was desirable if the researchers did not have a financial stake, and benefits would be shared. Trust was the key condition for the acceptability of both clinical implementation and research. Trust was linked with medication compliance. Education, outreach, and communication facilitate trust.

CONCLUSIONS (SIGNIFICANCE AND IMPACT OF THE STUDY)

Pharmacogenomics implementation with appropriate education and communication has the potential to enhance trust and contribute to increased medication compliance. Trust drives data sharing, which would enable enhanced representation in research. Representation in scientific evidence base could cyclically enhance trust and compliance.

Genetic distance informs polygenic score predictive accuracy

Polygenic scores (PGSs) aggregate the effects of variants across the genome to estimate genetic liability, but have lower performance in external study populations. A new study by Ding et al. has applied a novel framework to estimate the individual-level predictive accuracy of PGSs, and demonstrates that performance reduction occurs linearly with genetic distance.

Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D

Vitamin D status-a complex trait influenced by environmental and genetic factors-is tightly associated with skin colour and ancestry. Yet very few studies have investigated the genetic underpinnings of vitamin D levels across diverse ancestries, and the ones that have, relied on small sample sizes, resulting in inconclusive results. Here, we conduct genome-wide association studies (GWAS) of 25 hydroxyvitamin D (25OHD)-the main circulating form of vitamin D-in 442,435 individuals from four broad genetically-determined ancestry groups represented in the UK Biobank: European (N = 421,867), South Asian (N = 9,983), African (N = 8,306) and East Asian (N = 2,279). We identify a new genetic determinant of 25OHD (rs146759773) in individuals of African ancestry, which was not detected in previous analysis of much larger European cohorts due to low minor allele frequency. We show genome-wide significant evidence of dominance effects in 25OHD that protect against vitamin D deficiency. Given that key events in the synthesis of 25OHD occur in the skin and are affected by pigmentation levels, we conduct GWAS of 25OHD stratified by skin colour and identify new associations. Lastly, we test the interaction between skin colour and variants associated with variance in 25OHD levels and identify two loci (rs10832254 and rs1352846) whose association with 25OHD differs in individuals of distinct complexions. Collectively, our results provide new insights into the complex relationship between 25OHD and skin colour and highlight the importance of diversity in genomic studies. Despite the much larger rates of vitamin D deficiency that we and others report for ancestry groups with dark skin (e.g., South Asian), our study highlights the importance of considering ancestral background and/or skin colour when assessing the implications of low vitamin D.

The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people

BACKGROUND

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

METHODS

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

RESULTS

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity.

CONCLUSIONS

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Polygenic Background Modifies Risk of Coronary Artery Disease Among Individuals With Heterozygous Familial Hypercholesterolemia

Background

Heterozygous familial hypercholesterolemia (HeFH) is a monogenic disorder characterized by increased circulating low-density lipoprotein cholesterol and accelerated atherosclerosis. Even among this high-risk group, prior studies note considerable variability in risk of coronary artery disease (CAD).

Objectives

The purpose of this study was to evaluate the cumulative impact of many common DNA variants-as quantified by a polygenic score-on incident CAD among individuals carrying a HeFH variant.

Methods

We analyzed data from a prospective cohort study of 1,315 individuals who carried a HeFH variant and 1,315 matched family noncarriers derived from a nationwide screening program in the Netherlands, with subsequent replication in 151,009 participants of the UK Biobank.

Results

Despite identification and lipid management within the Dutch screening program, 84 (6.4%) of HeFH variant carriers developed CAD as compared to 45 (3.4%) of matched family members (median follow-up 10.2 years, HR 1.88, 95% CI: 1.31-2.70). Among HeFH variant carriers, a polygenic score was associated with CAD with an effect size similar to low-density lipoprotein cholesterol - HR of 1.35 (95% CI: 1.07-1.70) and 1.41 (95% CI: 1.17-1.70) per standard deviation increase, respectively. When compared to noncarriers, CAD risk increased from 1.24-fold (95% CI: 0.64-2.34) to 3.37-fold (95% CI: 2.11-5.36) across quintiles of the polygenic score. A similar risk gradient, 1.36-fold (95% CI: 0.65-2.85) to 2.88-fold (95% CI: 1.59-5.20), was observed in 429 carriers in the UK Biobank.

Conclusions

In 2 cohort studies involving 1,744 individuals with genetically confirmed HeFH - the largest study to date - risk of CAD varied according to polygenic background, in some cases approaching the risk observed in noncarriers.

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.

Polygenic Propensity for Longevity, APOE-ε4 Status, Dementia Diagnosis, and Risk for Cause-Specific Mortality: A Large Population-Based Longitudinal Study of Older Adults

To deepen the understanding of genetic mechanisms influencing mortality risk, we investigated the impact of genetic predisposition to longevity and APOE-ε4, on all-cause mortality and specific causes of mortality. We further investigated the mediating effects of dementia on these relationships. Using data on 7 131 adults aged ≥50 years (mean = 64.7 years, standard deviation [SD] = 9.5) from the English Longitudinal Study of Aging, genetic predisposition to longevity was calculated using the polygenic score approach (PGSlongevity). APOE-ε4 status was defined according to the absence or presence of ε4 alleles. The causes of death were ascertained from the National Health Service central register, which was classified into cardiovascular diseases, cancers, respiratory illness, and all other causes of mortality. Of the entire sample, 1 234 (17.3%) died during an average 10-year follow-up. One-SD increase in PGSlongevity was associated with a reduced risk for all-cause mortality (hazard ratio [HR] = 0.93, 95% confidence interval [CI]: 0.88-0.98, p = .010) and mortalities due to other causes (HR = 0.81, 95% CI: 0.71-0.93, p = .002) in the following 10 years. In gender-stratified analyses, APOE-ε4 status was associated with a reduced risk for all-cause mortality and mortalities related to cancers in women. Mediation analyses estimated that the percent excess risk of APOE-ε4 on other causes of mortality risk explained by the dementia diagnosis was 24%, which increased to 34% when the sample was restricted to adults who were aged ≤75 years old. To reduce the mortality rate in adults who are aged ≥50 years old, it is essential to prevent dementia onset in the general population.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health

Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.

Interplay Of Serum Bilirubin and Tobacco Smoking with Lung and Head and Neck Cancers in a Diverse, EHR-linked Los Angeles Biobank

Background

Bilirubin is a potent antioxidant with a protective role in many diseases. We examined the relationships between serum bilirubin (SB) levels, tobacco smoking (a known cause of low SB), and aerodigestive cancers, grouped as lung cancers (LC) and head and neck cancers (HNC).

Methods

We examined the associations between SB, LC, and HNC using data from 393,210 participants from a real-world, diverse, de-identified data repository and biobank linked to the UCLA Health system. We employed regression models, propensity score matching, and polygenic scores to investigate the associations and interactions between SB, tobacco smoking, LC, and HNC.

Results

Current tobacco smokers showed lower SB (-0.04mg/dL, 95% CI: [-0.04, -0.03]), compared to never-smokers. Lower SB levels were observed in HNC and LC cases (-0.10 mg/dL, [-0.13, -0.09] and - 0.09 mg/dL, CI [-0.1, -0.07] respectively) compared to cancer-free controls with the effect persisting after adjusting for smoking. SB levels were inversely associated with HNC and LC risk (ORs per SD change in SB: 0.64, CI [0.59,0.69] and 0.57, CI [0.43,0.75], respectively). Lastly, a polygenic score (PGS) for SB was associated with LC (OR per SD change of SB-PGS: 0.71, CI [0.67, 0.76]).

Conclusions

Low SB levels are associated with an increased risk of both HNC and LC, independent of the effect of tobacco smoking. Additionally, tobacco smoking demonstrated a strong interaction with SB on LC risk. Lastly, genetically predicted low SB (using a polygenic score) is negatively associated with LC. These findings suggest that SB could serve as a potential early and low-cost biomarker for LC and HNC. The interaction with tobacco smoking suggests that smokers with lower bilirubin could likely be at higher risk for LC compared to never smokers, suggesting the utility of SB in risk stratification for patients at risk for LC. Lastly, the results of the polygenic score analyses suggest potential shared biological pathways between the genetic control of SB and the risk of LC development.

Ancestry-specific regulatory and disease architectures are likely due to cell-type-specific gene-by-environment interactions

Multi-ancestry genome-wide association studies (GWAS) have highlighted the existence of variants with ancestry-specific effect sizes. Understanding where and why these ancestry-specific effects occur is fundamental to understanding the genetic basis of human diseases and complex traits. Here, we characterized genes differentially expressed across ancestries (ancDE genes) at the cell-type level by leveraging single-cell RNA-seq data in peripheral blood mononuclear cells for 21 individuals with East Asian (EAS) ancestry and 23 individuals with European (EUR) ancestry (172K cells); then, we tested if variants surrounding those genes were enriched in disease variants with ancestry-specific effect sizes by leveraging ancestry-matched GWAS of 31 diseases and complex traits (average N = 90K and 267K in EAS and EUR, respectively). We observed that ancDE genes tend to be cell-type-specific, to be enriched in genes interacting with the environment, and in variants with ancestry-specific disease effect sizes, suggesting the impact of shared cell-type-specific gene-by-environment (GxE) interactions between regulatory and disease architectures. Finally, we illustrated how GxE interactions might have led to ancestry-specific MCL1 expression in B cells, and ancestry-specific allele effect sizes in lymphocyte count GWAS for variants surrounding MCL1. Our results imply that large single-cell and GWAS datasets in diverse populations are required to improve our understanding on the effect of genetic variants on human diseases.

Microsatellites used in forensics are in regions enriched for trait-associated variants

The 20 short tandem repeat (STR) loci of the combined DNA index system (CODIS) are the basis of the vast majority of forensic genetics in the United States. One argument for permissive rules about the collection of CODIS genotypes is that the CODIS loci are thought to contain little information about ancestry or traits. However, in the past 20 years, a growing field has identified hundreds of thousands of genotype-trait associations. Here, we conduct a survey of the landscape of such associations surrounding the CODIS loci as compared with non-CODIS STRs. Although this study cannot establish or quantify associations between CODIS genotypes and phenotypes, we find that the regions around the CODIS loci are enriched for both known pathogenic variants (> 90th percentile) and for trait-associated SNPs identified in genome-wide association studies (GWAS) (≥ 95th percentile in 10kb and 100kb flanking regions), compared with other random sets of autosomal tetranucleotide-repeat STRs.

Profiling the inflammatory bowel diseases using genetics, serum biomarkers, and smoking information

Crohn's disease (CD) and ulcerative colitis (UC) are two etiologically related yet distinctive subtypes of the inflammatory bowel diseases (IBD). Differentiating CD from UC can be challenging using conventional clinical approaches in a subset of patients. We designed and evaluated a novel molecular-based prediction model aggregating genetics, serum biomarkers, and tobacco smoking information to assist the diagnosis of CD and UC in over 30,000 samples. A joint model combining genetics, serum biomarkers and smoking explains 46% (42-50%, 95% CI) of phenotypic variation. Despite modest overlaps with serum biomarkers, genetics makes unique contributions to distinguishing IBD subtypes. Smoking status only explains 1% (0-6%, 95% CI) of the phenotypic variance suggesting it may not be an effective biomarker. This study reveals that molecular-based models combining genetics, serum biomarkers, and smoking information could complement current diagnostic strategies and help classify patients based on biologic state rather than imperfect clinical parameters.

Genetic Prediction of Lifetime Risk of Fracture

CONTEXT

Fragility fracture is a significant public health problem because it is associated with increased mortality. We want to find out whether the risk of fracture can be predicted from the time of birth.

OBJECTIVE

To examine the association between a polygenic risk score (PRS) and lifetime fracture risk.

METHODS

This population-based prospective study involved 3515 community-dwelling individuals aged 60+ years who have been followed for up to 20 years. Femoral neck bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry. A PRS was created by summing the weighted number of risk alleles for each single nucleotide polymorphism using BMD-associated coefficients. Fragility fractures were radiologically ascertained, whereas mortality was ascertained through a state registry. Residual lifetime risk of fracture (RLRF) was estimated by survival analysis.

RESULTS

The mortality-adjusted RLRF for women and men was 36% (95% CI, 34%-39%) and 21% (18%-24%), respectively. Individuals with PRS > 4.24 (median) had a greater risk (1.2-fold in women and 1.1-fold in men) than the population average risk. For hip fracture, the average RLRF was 10% (95% CI, 8%-12%) for women and ∼5% (3%-7%) for men; however, the risk was significantly increased by 1.5-fold and 1.3-fold for women and men with high PRS, respectively.

CONCLUSION

A genetic profiling of BMD-associated genetic variants is associated with the residual lifetime risk of fracture, suggesting the potential for incorporating the polygenic risk score in personalized fracture risk assessment.

Clinical utility of polygenic scores for cardiometabolic disease in Arabs

Arabs account for 5% of the world population and have a high burden of cardiometabolic disease, yet clinical utility of polygenic risk prediction in Arabs remains understudied. Among 5399 Arab patients, we optimize polygenic scores for 10 cardiometabolic traits, achieving a performance that is better than published scores and on par with performance in European-ancestry individuals. Odds ratio per standard deviation (OR per SD) for a type 2 diabetes score was 1.83 (95% CI 1.74-1.92), and each SD of body mass index (BMI) score was associated with 1.18 kg/m2 difference in BMI. Polygenic scores associated with disease independent of conventional risk factors, and also associated with disease severity-OR per SD for coronary artery disease (CAD) was 1.78 (95% CI 1.66-1.90) for three-vessel CAD and 1.41 (95% CI 1.29-1.53) for one-vessel CAD. We propose a pragmatic framework leveraging public data as one way to advance equitable clinical implementation of polygenic scores in non-European populations.

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations

Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models may misrepresent or provide less accurate treatment recommendations for underrepresented populations, contributing to health disparities. This study introduces an adaptable machine learning toolkit that integrates multiple existing methodologies and novel techniques to enhance the prediction accuracy for underrepresented populations in genomic datasets. By leveraging machine learning techniques, including gradient boosting and automated methods, coupled with novel population-conditional re-sampling techniques, our method significantly improves the phenotypic prediction from single nucleotide polymorphism (SNP) data for diverse populations. We evaluate our approach using the UK Biobank, which is composed primarily of British individuals with European ancestry, and a minority representation of groups with Asian and African ancestry. Performance metrics demonstrate substantial improvements in phenotype prediction for underrepresented groups, achieving prediction accuracy comparable to that of the majority group. This approach represents a significant step towards improving prediction accuracy amidst current dataset diversity challenges. By integrating a tailored pipeline, our approach fosters more equitable validity and utility of statistical genetics methods, paving the way for more inclusive models and outcomes.

Evaluating genomic polygenic risk scores for childhood acute lymphoblastic leukemia in Latinos

The utility of polygenic risk score (PRS) models has not been comprehensively evaluated for childhood acute lymphoblastic leukemia (ALL), the most common type of cancer in children. Previous PRS models for ALL were based on significant loci observed in genome-wide association studies (GWASs), even though genomic PRS models have been shown to improve prediction performance for a number of complex diseases. In the United States, Latino (LAT) children have the highest risk of ALL, but the transferability of PRS models to LAT children has not been studied. In this study, we constructed and evaluated genomic PRS models based on either non-Latino White (NLW) GWAS or a multi-ancestry GWAS. We found that the best PRS models performed similarly between held-out NLW and LAT samples (PseudoR2 = 0.086 ± 0.023 in NLW vs. 0.060 ± 0.020 in LAT), and can be improved for LAT if we performed GWAS in LAT-only (PseudoR2 = 0.116 ± 0.026) or multi-ancestry samples (PseudoR2 = 0.131 ± 0.025). However, the best genomic models currently do not have better prediction accuracy than a conventional model using all known ALL-associated loci in the literature (PseudoR2 = 0.166 ± 0.025), which includes loci from GWAS populations that we could not access to train genomic PRS models. Our results suggest that larger and more inclusive GWASs may be needed for genomic PRS to be useful for ALL. Moreover, the comparable performance between populations may suggest a more oligogenic architecture for ALL, where some large effect loci may be shared between populations. Future PRS models that move away from the infinite causal loci assumption may further improve PRS for ALL.

Revealing polygenic pleiotropy using genetic risk scores for asthma

In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

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.