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

Promises and pitfalls of preimplantation genetic testing for polygenic disorders: a narrative review

Preimplantation genetic testing for polygenic disorders (PGT-P) has been commercially available since 2019. PGT-P makes use of polygenic risk scores for conditions which are multifactorial and are significantly influenced by environmental and lifestyle factors. If current predictions are accurate, then absolute risk reductions range from about 0.02% to 10.1%, meaning that between 10 and 5,000 in vitro fertilization patients would need to be tested with PGT-P to prevent one offspring from becoming affected in the future, depending on the condition and the number of embryos available. Survey and interview data reveal that patients and the public have largely favorable views regarding the use of PGT-P for disease prevention; however, clinicians and professional organizations have many reservations. The use of PGT-P raises multiple social and ethical concerns including the need for adequate counseling, the setting of realistic expectations, the application of distributive justice, the impact of environmental and social determinants of health, and the potential exacerbation of health inequities. Clinicians expressed significant concerns relating to the cost of PGT-P, the potential time-consuming counseling for reproductive endocrinologists and genetic counselors, the intentional creation of supernumerary embryos, and patients' unrealistic expectations regarding "healthiest disease-free" embryos. Furthermore, current evidence lacks long-term outcome data and generalizability. Prior to offering PGT-P to patients, additional clinical validation studies are needed. Also, ethical and social considerations raised by PGT-P should be carefully delineated. Systemic practices to increase equitable access to unbiased genetic counseling and reproductive services would be desirable prior to the ethical implementation of PGT-P.

Driving Global Health equity and precision medicine through African genomic data

Significant gaps persist despite the progress in raising awareness of genomic diversity and including individuals of African ancestry in genomic research. African populations remain underrepresented in genomic studies despite their deep evolutionary history, demographic diversity, and unique genetic architecture for gene discovery. This underrepresentation constrains the portability of findings from other populations to African settings due to the poor predictive performance of genetic scores. Consequently, it hinders global efforts in translational research, slows the progression of genomic medicine, and worsens health disparities-a missed opportunity for precision medicine globally. However, genuine prioritisation and expansion of genomic data collection from individuals of African ancestry can drive more equitable health solutions that benefit all populations. In this review, we highlight the opportunities presented by African genomic diversity, the urgent need for larger datasets and biobanks with diverse phenotypes from African populations, and recent developments in African genomic research.

Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma

A glaucoma polygenic risk score (PRS) can effectively identify disease risk, but some individuals with high PRS do not develop glaucoma. Factors contributing to this resilience remain unclear. Using 4,658 glaucoma cases and 113,040 controls in a cross-sectional study of the UK Biobank, we investigated whether plasma metabolites enhanced glaucoma prediction and if a metabolomic signature of resilience in high-genetic-risk individuals existed. Logistic regression models incorporating 168 NMR-based metabolites into PRS-based glaucoma assessments were developed, with multiple comparison corrections applied. While metabolites weakly predicted glaucoma (Area Under the Curve = 0.579), they offered marginal prediction improvement in PRS-only-based models (p=0.004). We identified a metabolomic signature associated with resilience in the top glaucoma PRS decile, with elevated glycolysis-related metabolites-lactate (p=8.8E-12), pyruvate (p=1.9E-10), and citrate (p=0.02)-linked to reduced glaucoma prevalence. These metabolites combined significantly modified the PRS-glaucoma relationship (Pinteraction = 0.011). Higher total resilience metabolite levels within the highest PRS quartile corresponded to lower glaucoma prevalence (Odds Ratiohighest vs. lowest total resilience metabolite quartile=0.71, 95% Confidence Interval = 0.64-0.80). As pyruvate is a foundational metabolite linking glycolysis to tricarboxylic acid cycle metabolism and ATP generation, we pursued experimental validation for this putative resilience biomarker in a human-relevant Mus musculus glaucoma model. Dietary pyruvate mitigated elevated intraocular pressure (p=0.002) and optic nerve damage (p<0.0003) in Lmx1bV265D mice. These findings highlight the protective role of pyruvate-related metabolism against glaucoma and suggest potential avenues for therapeutic intervention.

Characterizing selection on complex traits through conditional frequency spectra

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of its frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insights into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

Spontaneous Coronary Artery Dissection and a Family History of Aortic Dissection: A Genetic Association Study

BACKGROUND

Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndrome or sudden cardiac death, primarily affecting relatively young women (median age, 51 years) without typical cardiovascular risk factors. SCAD has a genetic component, with genome-wide association studies identifying multiple risk loci. Thoracic aortic dissection (type A) shares some genetic overlap with SCAD, suggesting potential common predispositions.

METHODS

We performed genetic screening or whole-genome sequencing of 17 patients with SCAD (94% women) with a first- or second-degree relative (89% men) affected by aortic dissection (AD). We assessed rare variants in candidate genes and genome-wide using the American College of Medical Genetics and Genomics criteria. Polygenic risk scores were calculated to assess genetic risk for SCAD, fibromuscular dysplasia, AD, and abdominal aortic aneurysm in patients with SCAD, relatives with AD, and controls.

RESULTS

Whole-genome sequencing identified pathogenic or likely pathogenic variants in SMAD3, CBS, and COL3A1 in 3 SCAD cases. Additionally, 4 variants of uncertain significance were found in candidate genes. Polygenic risk scores for SCAD were significantly associated with increased odds of SCAD in probands versus controls (odds ratio, 1.79 [95% CI, 1.08-2.99]; P=0.024).

CONCLUSIONS

Our study supports a complex genetic landscape underlying SCAD, implicating rare monogenic pathogenic variants and polygenic risk. We identified pathogenic variants in patients with SCAD with a family history of AD, highlighting potential genetic links between these vascular disorders. The findings underscore the importance of genetic screening in patients with SCAD with a history of AD to identify individuals at risk and guide preventive strategies.

Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSxEAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSxEAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Predicting Genetic Risk for Impulsivity and Substance Use in Adolescence

Studying genetic contributions to substance initiation is crucial for identifying at-risk individuals and developing targeted prevention strategies. Investigating these factors during adolescence is vital, as this period is critical for brain development and represents an age of experimentation and initiation of substance use. Here we generate polygenic scores (PGSs), using data from the PGS catalog, across a range of substance use related traits to assess PGS in predicting i) measures of impulsivity taken from the UPPS-P questionnaire and ii) self-reported use of nicotine/tobacco, cannabis, alcohol and caffeine in early-mid adolescence. Repeat cross-sectional analyses across age bands (ages 9-10, 11-13, and 13-15) were conducted using the longitudinal Adolescent Brain Cognitive Development (ABCD) Study ® (total N=8,753; 55% female). Due to the large contribution of European-like (EUR-like) individuals in discovery samples, we performed ancestry stratified analysis in EUR-like (n=5,225), African (AFR-like; n=637) and ad-mixed (MIX-like; n=2,891) groups reflecting genetic similarity to continental ancestry groups. In the EUR-like group, PGS related to nicotine/tobacco were associated with greater impulsivity across all subscales of the UPPS-P at all ages. Analyses across ages 9-15 years old revealed PGS-impulsivity associations that: a) grew as the sample aged (e.g. Smoking Status PGS with Lack of Perseverance: 9-10 years-old: β=0.065, 11-13 years-old: β=0.11, 13-15 years-old: β=0.12) and b) others that diminished as the sample aged (e.g. Alcohol Consumption PGS with Sensation Seeking: 9-10 years-old: β=0.070, 11-13 years-old: β=0.062, 13-15 years-old: β=0.03). Evaluating the performance of PGS against self-reported substance use, PGS of nicotine/tobacco traits were associated with regular consumption of caffeine across ages. At ages 13-15, PGS of smoking traits were associated with cannabis and tobacco exposure (e.g., Smoking Initiation PGS and self-reported cannabis use, ΔR 2 =0.0094), in addition to weekly caffeine consumption. Across ages, nicotine/tobacco and alcohol PGS and regular energy drink consumption associations grew over time (e.g., Smoking Status PGS: 9-10 years-old: β=0.088, 11-13 years-old: β=0.24, 13-15 years-old: β=0.29). As with impulsivity, some PGS associations decreased over time (Alcohol Consumption PGS and self-reported alcohol use: 9-10 years-old: β=0.12, 11-13 years-old: β=0.11, 13-15 years-old: β=0.083). Replication of our EUR-like results in AFR-like and MIX-like sub-samples revealed a significant attenuation of effects, underscoring the importance of collecting genetic studies in larger ancestrally diverse cohorts. Our results highlight the dynamic relationship between genetic risk factors of substance use, trait impulsivity, and self-reported substance initiation throughout adolescence. Further, evidence here indicates caffeine consumption represents an early risk factor for problematic substance use in later life. Results support PGSs, in conjunction with larger phenotypic profiles, for identification of prevention efforts.

Polygenic Scores and Mood Disorder Onsets in the Context of Family History and Early Psychopathology

Importance

Bipolar disorder (BD) and major depressive disorder (MDD) aggregate within families, with risk often first manifesting as early psychopathology, including attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders.

Objective

To determine whether polygenic scores (PGS) are associated with mood disorder onset independent of familial high risk for BD (FHR-BD) and early psychopathology.

Design, Setting, and Participants

This cohort study used data from 7 prospective cohorts enriched in FHR-BD from Australia, Canada, the Netherlands, Spain, and the US. Participants with FHR-BD, defined as having at least 1 first-degree relative with BD, were compared with participants without FHR for any mood disorder. Participants were repeatedly assessed with variable follow-up intervals from July 1992 to July 2023. Data were analyzed from August 2023 to August 2024.

Exposures

PGS indexed genetic liability for MDD, BD, anxiety, neuroticism, subjective well-being, ADHD, self-regulation, and addiction risk factor. Semistructured diagnostic interviews with relatives established FHR-BD. ADHD or anxiety disorder diagnoses before mood disorder onset constituted early psychopathology.

Main Outcomes and Measures

The outcome of interest, mood disorder onset, was defined as a consensus-confirmed new diagnosis of MDD or BD. Cox regression examined associations of PGS, FHR-BD, ADHD, and anxiety with mood disorder onset. Kaplan-Meier curves and log-rank tests evaluated the probability of onset by PGS quartile and familial risk status.

Results

A total of 1064 participants (546 [51.3%] female; mean [SD] age at last assessment, 21.7 [5.1] years), including 660 with FHR-BD and 404 without FHR for any mood disorder, were repeatedly assessed for mental disorders. A total of 399 mood disorder onsets occurred over a variable mean (SD) follow-up interval of 6.3 (5.7) years. Multiple PGS were associated with onset after correcting for FHR-BD and early psychopathology, including PGS for ADHD (hazard ratio [HR], 1.19; 95% CI, 1.06-1.34), self-regulation (HR, 1.19; 95% CI, 1.06-1.34), neuroticism (HR, 1.18; 95% CI, 1.06-1.32), MDD (HR, 1.17; 95% CI, 1.04-1.31), addiction risk factor (HR, 1.16; 95% CI, 1.04-1.30), anxiety (HR, 1.15; 95% CI, 1.02-1.28), BD (HR, 1.14; 95% CI, 1.02-1.28), and subjective well-being (HR, 0.89; 95% CI, 0.79-0.99). High PGS for addiction risk factor, anxiety, BD, and MDD were associated with increased probability of onset in the control group. High PGS for ADHD and self-regulation increased rates of onset among participants with FHR-BD. PGS for self-regulation, ADHD, and addiction risk factors showed stronger associations with onsets of BD than MDD.

Conclusions and Relevance

In this cohort study, multiple PGS were associated with mood disorder onset independent of family history of BD and premorbid diagnoses of ADHD or anxiety. The association between PGS and mood disorder risk varied depending on family history status.

Aberrant gene expression prediction across human tissues

Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. To address this need, we compile an aberrant expression prediction benchmark covering 8.2 million rare variants from 633 individuals across 49 tissues. While not geared toward aberrant expression, the deleteriousness score CADD and the loss-of-function predictor LOFTEE show mild predictive ability (1-1.6% average precision). Leveraging these and further variant annotations, we next train AbExp, a model that yields 12% average precision by combining in a tissue-specific fashion expression variability with variant effects on isoforms and on aberrant splicing. Integrating expression measurements from clinically accessible tissues leads to another two-fold improvement. Furthermore, we show on UK Biobank blood traits that performing rare variant association testing using the continuous and tissue-specific AbExp variant scores instead of LOFTEE variant burden increases gene discovery sensitivity and enables improved phenotype predictions.

Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank

We develop a "block" LASSO (blockLASSO) approach for training polygenic scores (PGS) and demonstrate its use in All of Us (AoU) and the UK Biobank (UKB). blockLASSO utilizes the approximate block diagonal structure (due to chromosomal partition of the genome) of linkage disequilibrium (LD). The new implementation can be used for exploratory and methods research where repeated PGS training is necessary and expensive. For 11 different phenotypes, in two different biobanks, and across 5 different ancestry groups (African, American, East Asian, European, and South Asian) - we demonstrate that blockLASSO is generally as effective for training PGS as a (global) LASSO. Previous work has shown penalized regression methods produce competitive PGS to alternative approaches. It has been shown that some phenotypes are more/less polygenic than others. Using sparse algorithms, an accurate PGS can be trained for type 1 diabetes (T1D) using ∼ 100 single nucleotide variants (SNVs), but a PGS for body mass index (BMI) would need more than 10k SNVs. blockLASSO produces similar PGS for phenotypes while training with just a fraction of the variants per block. Within AoU (using only genetic information) block PGS for T1D reaches an AUC of 0 . 63 ± 0.02 and for BMI a correlation of 0 . 21 ± 0.01 , whereas a global LASSO approach which finds for T1D an AUC 0 . 65 ± 0.03 and BMI a correlation 0 . 19 ± 0.03 . This new block approach is more computationally efficient and scalable than naive global machine learning approaches and makes it ideal for exploratory methods investigations based on penalized regression.

Circulating Bilirubin Levels, but Not Their Genetic Determinants, Are Inversely Associated with Steatotic Liver Disease in Adolescents

Epidemiologic studies suggest that elevated plasma unconjugated bilirubin confer protection against steatotic liver disease (SLD) in adults. However, evidence supporting this protective role in adolescents remains limited. We aimed to assess the association between serum bilirubin levels and their genetic determinants in protecting against SLD in Chilean adolescents. We conducted a cross-sectional study with 704 adolescents aged 15.4 ± 1 years (52% girls) of the Chilean Growth and Obesity Cohort Study. Ultrasonography echogenicity was used to diagnose SLD. We measured Z-scores of body mass index (z-BMI), total bilirubin (TB), and the genetic determinants of bilirubin (including rs887829 genotypes of UGT1A1 and bilirubin polygenic scores). Multiple logistic regression models evaluated the associations between standardized TB and its genetic determinants with SLD. We found that 1-SD of standardized plasma TB was significantly associated with a 30% reduction in the likelihood of SLD after adjustment by sex, age, z-BMI, and ethnicity (OR = 0.7; 95% CI = 0.50-0.96; p = 0.03). No significant associations were found among the rs887829 genotypes, bilirubin polygenic scores, and SLD in logistic regression models adjusted by covariates. Increased circulating bilirubin levels are unlikely causally associated with protection against SLD, and the cross-sectional association could be due to unmeasured confounding.

Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications

Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 932-28,880). Our discovery analyses in participants of European ancestry recover previously reported pharmacogenetic signals at genome-wide significance level (APOE, LPA and SLCO1B1) and a novel rare variant association in GIMAP5 with HbA1c response to metformin. Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. We also found polygenic risk scores to predict drug response, though they explained less than 2% of the variance. In summary, we present an EHR-based framework to study the genetics of drug response and systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in 41,732 UK Biobank and 14,277 All of Us participants.

Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men

Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male-pattern baldness comes from individuals of European descent. Here, we examined a dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using the Men of African Descent and Carcinoma of the Prostate Array. We first tested how genetic predictions of baldness generalize from Europe to Africa and found that polygenic scores from European genome-wide association studies (GWASs) yielded area under the curve statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness generalized poorly from European to African populations. Subsequently, we conducted an African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for age at recruitment, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosome does not seem to have a large impact on baldness in African men. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, within the limits of statistical power, we did not find evidence that continental differences in the genetic architecture of baldness are due to Neanderthal introgression. While most loci that are associated with androgenetic alopecia do not have large integrative haplotype scores or fixation index statistics, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how population genetic differences contribute to the limited portability of polygenic predictions across ancestries.

Pyruvate and Related Energetic Metabolites Modulate Resilience Against High Genetic Risk for Glaucoma

A glaucoma polygenic risk score (PRS) can effectively identify disease risk, but some individuals with high PRS do not develop glaucoma. Factors contributing to this resilience remain unclear. Using 4,658 glaucoma cases and 113,040 controls in a cross-sectional study of the UK Biobank, we investigated whether plasma metabolites enhanced glaucoma prediction and if a metabolomic signature of resilience in high-genetic-risk individuals existed. Logistic regression models incorporating 168 NMR-based metabolites into PRS-based glaucoma assessments were developed, with multiple comparison corrections applied. While metabolites weakly predicted glaucoma (Area Under the Curve=0.579), they offered marginal prediction improvement in PRS-only-based models (P=0.004). We identified a metabolomic signature associated with resilience in the top glaucoma PRS decile, with elevated glycolysis-related metabolites-lactate (P=8.8E-12), pyruvate (P=1.9E-10), and citrate (P=0.02)-linked to reduced glaucoma prevalence. These metabolites combined significantly modified the PRS-glaucoma relationship (P interaction =0.011). Higher total resilience metabolite levels within the highest PRS quartile corresponded to lower glaucoma prevalence (Odds Ratio highest vs. lowest total resilience metabolite quartile =0.71, 95% Confidence Interval=0.64-0.80). As pyruvate is a foundational metabolite linking glycolysis to tricarboxylic acid cycle metabolism and ATP generation, we pursued experimental validation for this putative resilience biomarker in a human-relevant Mus musculus glaucoma model. Dietary pyruvate mitigated elevated intraocular pressure (P=0.002) and optic nerve damage (P<0.0003) in Lmx1b V265D mice. These findings highlight the protective role of pyruvate-related metabolism against glaucoma and suggest potential avenues for therapeutic intervention.

Polygenic risk scores for incident dementia in the Multi-Ethnic Study of Atherosclerosis

Over 75 Alzheimer's disease (AD) and dementia-associated variants have been identified through genome-wide association studies, but the utility of polygenic risk scores (PRS) for predicting AD and dementia in diverse and admixed populations remains unclear. We compared how PRS approaches differing in p -value thresholds, variant weights, and source ancestry perform in predicting dementia in 6,338 African American, Chinese, Hispanic, and White individuals from the Multi-Ethnic Study of Atherosclerosis. We tested clumping and thresholding (C+T) methods with varying parameters against Bayesian approaches (PRS-CS, PRS-CSx). We compared the ability of each method to predict incident dementia in all participants and in groups stratified by self-reported race/ethnicity. We additionally analyzed performance across groups stratified by estimated proportion of non-Finnish European (NFE)-like ancestry. Including more variants does not improve performance. The PRS based on C+T method with only 15 SNPs is more strongly associated with dementia (HR 5e-08 = 1.21, 95% CI: 1.11-1.31) than PRS derived from Bayesian models that include >800,000 SNPs (HR CSx = 1.13, 95% CI: 1.04-1.23), even in populations genetically dissimilar from the source data (HR lowNFE _ 5e-08 = 1.26, 95% CI: 1.08-1.47; HR lowNFE _ CSx = 1.13, 95% CI: 0.96-1.32). More selective PRS models using fewer SNPs may offer better AD prediction across diverse populations.

Genome-Wide Association Study for Resting Electrocardiogram in the Qatari Population Identifies 6 Novel Genes and Validates Novel Polygenic Risk Scores

BACKGROUND

Electrocardiography is one of the most valuable noninvasive diagnostic tools in determining the presence of many cardiovascular diseases. Genetic factors are important in determining ECG abnormalities and their link to cardiovascular diseases. Genome-wide association studies and polygenic risk scores (PRSs) have been conducted for various ECG traits such as QT interval and QRS duration. However, these studies mainly focused on cohorts of European descent.

METHODS

In this cohort study, genome-wide association studies for 6 ECG traits (RR, PR, corrected QT interval [QTc], QRS, JT, and P wave duration) were conducted in a Middle Eastern cohort from the Qatar Precision Health Institute, comprising 13 827 subjects with whole-genome sequence data. Middle Eastern PRSs were developed using clumping and thresholding, and their performance was compared with 26 published PRSs. Genetic predisposition to long QT syndrome was explored using rare variant analysis.

RESULTS

Seventy-four independent loci were obtained with genome-wide significance across the 6 traits (P<5×10-8). Of the 74 loci, 67 (90.5%) were previously reported, and 7 loci (9.5%) were novel and contained 6 genes: STAC and CSMD1 for PR, ANK1 and NCOA2 for QRS, LSP1 for QTc, and MKLN1 for P wave duration. All 26 published PRSs showed good performance in our cohort. PGS002276 showed the best performance for QTc (R2=0.059, P=4.83×10-185), PGS002166 showed the best performance for QRS (R2=0.024, P=1.53×10-75), and PGS000905 showed the best performance for PR (R2=0.053, P=2.57×10-165). Some of these PRSs were associated with cardiovascular diseases. For example, PGS003500, a QTc PRS, was significantly associated with cardiomyopathy (odds ratio per 1 SD=1.58 [95% CI, 1.23-2.01]; P=2.42×10-4). Middle Eastern PRSs substantially outperformed published PRSs and did not perform well in the UK Biobank data. Ten pathogenic variants, including 3 that are specific to Qatari individuals, were observed in 17 long QT syndrome genes and were carried by 19 individuals. The QTc average was larger for mutation carriers (415.6±23.5 versus 402.3±18.5 in noncarriers). Five-year follow-up data did not show a significant change in ECG patterns, regardless of mutation status and PRS values. Four of 2302 individuals had prolonged QTc intervals over the 2 time points.

CONCLUSIONS

In this first genome-wide association study for ECG traits in the Middle East using whole-genome sequence data, 7 novel loci (6 genes) were identified. Published PRSs performed well, but newly developed Middle Eastern-specific PRSs performed the best. Novel variants in long QT syndrome genes were observed for the first time in Qatari individuals. Follow-up data did not show significant changes in ECG patterns.

Polygenic Risk Score Added to Conventional Case Finding to Identify Undiagnosed Chronic Obstructive Pulmonary Disease

Importance

Chronic obstructive pulmonary disease (COPD) is often undiagnosed. Although genetic risk plays a significant role in COPD susceptibility, its utility in guiding spirometry testing and identifying undiagnosed cases is unclear.

Objective

To determine whether a COPD polygenic risk score (PRS) enhances the identification of undiagnosed COPD beyond a case-finding questionnaire (eg, the Lung Function Questionnaire) using conventional risk factors and respiratory symptoms.

Design, Setting, and Participants

This cross-sectional analysis of participants 35 years or older who reported no history of physician-diagnosed COPD was conducted using data from 2 observational studies: the community-based Framingham Heart Study (FHS) and the COPD-enriched Genetic Epidemiology of COPD (COPDGene) study.

Exposures

Modified Lung Function Questionnaire (mLFQ) scores and COPD PRS.

Main Outcomes and Measures

The primary outcome was spirometry-defined moderate to severe COPD (forced expiratory volume in the first second of expiration/forced vital capacity [FEV1/FVC] <0.7 and FEV1 [percent predicted] <80%). The performance of logistic models was assessed using the PRS, mLFQ score, and PRS plus mLFQ score for predicting spirometry-defined COPD.

Results

Among 3385 FHS participants (median age, 52.0 years; 45.9% male) and 4095 COPDGene participants (median age, 56.8 years; 55.5% male) who reported no history of COPD, 160 (4.7%) FHS and 775 (18.9%) COPDGene participants had spirometry-defined COPD. Adding the PRS to the mLFQ score significantly improved the area under the curve from 0.78 to 0.84 (P < .001) in FHS, 0.69 to 0.72 (P = .04) in COPDGene non-Hispanic African American, and 0.75 to 0.78 (P < .001) in COPDGene non-Hispanic White participants. At a risk threshold for spirometry referral of 10%, the addition of the PRS to the mLFQ score correctly reclassified 13.8% (95% CI, 6.6%-21.0%) of COPD cases in FHS, but not in COPDGene.

Conclusions and Relevance

A COPD PRS enhances the identification of undiagnosed COPD beyond a conventional case-finding approach in the general population. Further research is needed to assess its impact on COPD diagnosis and outcomes.

Precision medicine for obesity: current evidence and insights for personalization of obesity pharmacotherapy

Obesity is a chronic and complex disease associated with increased morbidity, mortality, and financial burden. It is expected that by 2030 one of two people in the United States will have obesity. The backbone for obesity management continues to be lifestyle interventions, consisting of calorie deficit diets and increased physical activity levels, however, these interventions are often insufficient to achieve sufficient and maintained weight loss. As a result, multiple patients require additional interventions such as antiobesity medications or bariatric interventions in order to achieve clinically significant weight loss and improvement or resolution of obesity-associated comorbidities. Despite the recent advances in the field of obesity pharmacotherapy that have resulted in never-before-seen weight loss outcomes, comorbidity improvement, and even reduction in cardiovascular mortality, there is still a significant interindividual variability in terms of response to antiobesity medications, with a subset of patients not achieving a clinically significant weight loss. Currently, the trial-and-error paradigm for the selection of antiobesity medications results in increased costs and risks for developing side effects, while also reduces engagement in weight management programs for patients with obesity. The implementation of a precision medicine framework to the selection of antiobesity medications might help reduce heterogeneity and optimize weight loss outcomes by identifying unique subsets of patients, or phenotypes, that have a better response to a specific intervention. The detailed study of energy balance regulation holds promise, as actionable behavioral and physiologic traits could help guide antiobesity medication selection based on previous mechanistic studies. Moreover, the rapid advances in genotyping, multi-omics, and big data analysis might hold the key to discover additional signatures or phenotypes that might respond better to a certain intervention and might permit the widespread adoption of a precision medicine approach for obesity management.

Genetic and epigenetic factors shape phenotypes and outcomes in systemic lupus erythematosus - focus on juvenile-onset systemic lupus erythematosus

PURPOSE OF REVIEW

Systemic lupus erythematosus (SLE) is a severe autoimmune/inflammatory disease. Patients with juvenile disease-onset and those of non-European ancestry are most severely affected. While the exact pathophysiology remains unknown, common and rare gene variants in the context of environmental exposure and epigenetic alterations are involved. This manuscript summarizes the current understanding of genetic and epigenetic contributors to SLE risk, manifestations and outcomes.

RECENT FINDINGS

Though SLE is a mechanistically complex disease, we are beginning to understand the impact of rare and common gene variants on disease expression and associated outcomes. Recent trans -ancestral and multigenerational studies suggest that differential genetic and environmental impacts shape phenotypic variability between age-groups and ancestries. High genetic burden associates with young age at disease-onset, organ involvement, and severity. Additional epigenetic impact contributes to disease-onset and severity, including SLE-phenotypes caused by rare single gene variants. Studies aiming to identify predictors of organ involvement and disease outcomes promise future patient stratification towards individualized treatment and care.

SUMMARY

An improved understanding of genetic variation and epigenetic marks explain phenotypic differences between age-groups and ancestries, promising their future exploitation for diagnostic, prognostic and therapeutic considerations.

Do polygenic indices capture "direct" effects on child externalizing behavior problems? Within-family analyses in two longitudinal birth cohorts

Failures of self-control can manifest as externalizing behaviors (e.g., aggression, rule-breaking) that have far-reaching negative consequences. Researchers have long been interested in measuring children's genetic risk for externalizing behaviors to inform efforts at early identification and intervention. Drawing on data from the Environmental Risk Longitudinal Twin Study (N = 862 twins) and the Millennium Cohort Study (N = 2,824 parent-child trios), two longitudinal cohorts from the UK, we leveraged molecular genetic data and within-family designs to test for genetic associations with externalizing behavior that are not affected by common sources of environmental influence. We found that a polygenic index (PGI) calculated from genetic variants discovered in previous studies of self-controlled behavior in adults captures direct genetic effects on externalizing problems in children and adolescents when evaluated with rigorous within-family designs (β's = 0.13-0.19 across development). The externalizing behavior PGI can usefully augment psychological studies of the development of self-control.

Multifactorial influences on childhood insomnia: Genetic, socioeconomic, brain development and psychopathology insights

Insomnia is the most prevalent sleep disturbance during childhood and can result in extensively detrimental effects. Children's insomnia involves a complex interplay of biological, neurodevelopmental, social-environmental, and behavioral variables, yet remains insufficiently addressed. This study aimed to investigate the multifactorial etiology of childhood insomnia from its genetic architecture and social-environmental variables to its neural instantiation and the relationship to mental health. This cohort study uses 4340 participants at baseline and 2717 participants at 2-year follow-up from the Adolescent Brain Cognitive Development (ABCD) Study. We assessed the joint effects of polygenic risk score (PRS) and socioeconomic status (SES) on insomnia symptoms and then investigated the underlying neurodevelopmental mechanisms. Structural equation model (SEM) was applied to investigate the directional relationships among these variables. SES and PRS affected children's insomnia symptoms independently and additively (SES: β = -0.089, P = 1.91 × 10-8; PRS: β = 0.041, P = 0.008), which was further indirectly mediated by the deviation of inferior precentral sulcus (β = 0.0027, P = 0.0071). SEM revealed that insomnia (β = 0.457, P < 0.001) and precentral development (β = -0.039, P = 0.009) significantly mediated the effect of SES_PRS (accumulated risks of PRS and SES) on psychopathology symptoms. Furthermore, baseline insomnia symptoms, SES_PRS, and precentral deviation significantly predicted individual total psychopathology syndromes (r = 0.346, P < 0.001). These findings suggest the additive effects of genetic and socioenvironmental factors on childhood insomnia via precentral development and highlight potential targets in early detection and intervention for childhood insomnia.

Cardiovascular Risk Predicts White Matter Hyperintensities, Brain Atrophy and Treatment Resistance in Major Depressive Disorder: Role of Genetic Liability

INTRODUCTION

Depressive disorders are a leading cause of global disease burden, particularly with the challenge of treatment-resistant depression (TRD). Research points to a complex bidirectional relationship between cardiovascular (CV) risk factors and TRD, with CV risk negatively impacting brain structure and potentially influencing antidepressant resistance. Moreover, the association between depression and the genetic vulnerability to cardiovascular disease suggests a shared pathophysiological process between the two. This study investigates the mediating role of brain structural alterations in the relationship between CV and cerebrovascular (CeV) risk and treatment resistance in depression.

METHODS

We assessed 165 inpatients with Major depressive disorder. Each patient's CV risk was assessed via the QRISK 3 calculator. For a subset of patients, CV and CeV disease polygenic risk scores (PRS) were obtained. All patients underwent a 3 T MRI scan, and white matter hyperintensities estimates and indicators of brain trophic state were obtained.

RESULTS

Both CV risk and CV disease PRSs are associated with treatment resistance status, white matter hyperintensities, and indicators of brain atrophy. Mediation analyses suggested that CV-induced brain alterations might underlie the relation between CV genetic and phenotypic risk and antidepressant treatment resistance.

CONCLUSION

These results underscore the need to explore cardiovascular risk management as part of treatment strategies for depression, pointing toward a shared pathophysiological process linking heart and brain health in treatment-resistant depression.

Nuclear regulatory disturbances precede and predict the development of Type-2 diabetes in Asian populations

To identify biomarkers and pathways to Type-2 diabetes (T2D), a major global disease, we completed array-based epigenome-wide association in whole blood in 5,709 Asian people. We found 323 Sentinel CpGs (from 314 genetic loci) that predict future T2D. The CpGs reveal coherent, nuclear regulatory disturbances in canonical immune activation pathways, as well as metabolic networks involved in insulin signalling, fatty acid metabolism and lipid transport, which are causally linked to development of T2D. The CpGs have potential clinical utility as biomarkers. An array-based composite Methylation Risk Score (MRS) is predictive for future T2D (RR: 5.2 in Q4 vs Q1; P=7x10 -25 ), and is additive to genetic risk. Targeted methylation sequencing revealed multiple additional CpGs predicting T2D, and synthesis of a sequencing-based MRS that is strongly predictive for T2D (RR: 8.3 in Q4 vs Q1; P=1.0x10 -11 ). Importantly, MRS varies between Asian ethnic groups, in a way that explains a large fraction of the difference in T2D risk between populations. We thus provide new insights into the nuclear regulatory disturbances that precede development of T2D, and reveal the potential for sequence-based DNA methylation markers to inform risk stratification in diabetes prevention.

Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions

PURPOSE OF REVIEW

To review the genetics of type 1 diabetes (T1D) and T1D genetic risk scores, focusing on their development, research and clinical applications, and future directions.

RECENT FINDINGS

More than 90 genetic loci have been linked to T1D risk, with approximately half of the genetic risk attributable to the human leukocyte antigen (HLA) locus, along with non-HLA loci that have smaller effects to disease risk. The practical use of T1D genetic risk scores simplifies the complex genetic information, within the HLA and non-HLA regions, by combining the additive effect and interactions of single nucleotide polymorphisms (SNPs) associated with risk. Genetic risk scores have proven to be useful in various aspects, including classifying diabetes (e.g., distinguishing between T1D vs. neonatal, type 2 or other diabetes types), predicting the risk of developing T1D, assessing the prognosis of the clinical course (e.g., determining the risk of developing insulin dependence and glycemic control), and research into the heterogeneity of diabetes (e.g., atypical diabetes). However, there are gaps in our current knowledge including the specific sets of genes that regulate transition between preclinical stages of T1D, response to disease modifying therapies, and other outcomes of interest such as persistence of beta cell function. Several T1D genetic risk scores have been developed and shown to be valuable in various contexts, from classification of diabetes to providing insights into its etiology and predicting T1D risk across different stages of T1D. Further research is needed to develop and validate T1D genetic risk scores that are effective across all populations and ancestries. Finally, barriers such as cost, and training of medical professionals have to be addressed before the use of genetic risk scores can be incorporated into routine clinical practice.

Does Age Modify the Relation Between Genetic Predisposition to Glaucoma and Various Glaucoma Traits in the UK Biobank?

Purpose

Glaucoma polygenic risk scores could guide glaucoma public health screening initiatives. We investigated how age influences the relationship between a multitrait glaucoma polygenic risk score (mtGPRS) and primary open-angle glaucoma indicators, including intraocular pressure (IOP), retinal structure, and glaucoma prevalence.

Methods

We analyzed UK Biobank participants with demographic and genetic data, assessing IOP (n = 118,153), macular retinal nerve fiber layer thickness (mRNFL; n = 42,132), macular ganglion cell inner plexiform layer thickness (mGCIPL; n = 42,042), and prevalent glaucoma status (8982 cases among 192,283 participants). An mtGPRS was constructed using 2673 genetic variants. We used multivariable linear regression to assess how age modifies the relationship between mtGPRS and glaucoma traits (IOP, mRFNL, and mGCIPL) and multivariable logistic regression for prevalent glaucoma risk. We analyzed age quartiles (Q1 = <51, Q2 = 51-57, Q3 = 58-62, and Q4 = ≥63 years) - glaucoma trait interaction tests with the Wald test. All analyses were adjusted for confounders, including nonlinear age effects.

Results

Age significantly modified the relationship between the mtGPRS and IOP (Pinteraction = 2.7e-27). Mean IOP differences (millimeters of mercury [mm Hg]) per standard deviation (SD) of mtGPRS were 0.95, 1.02, 1.18, and 1.24 across age quartiles. Similar trends were observed for glaucoma risk (odds ratio per SD of mtGPRS = 2.38, 2.57, 2.80, and 2.75; Pinteraction = 1.0e-06). Relationships between mtGPRS and inner retinal thickness (mRNFL and mGCIPL) across age strata were inconsistently modified by age (Pinteraction ≥ 0.01).

Conclusions

With increasing age, an mtGPRS was a better predictor of higher IOP and glaucoma prevalence. It is useful to consider chronological age with genetic information in designing glaucoma screening strategies.

A meta-analysis of genome-wide studies of resilience in the German population

Resilience is the capacity to adapt to stressful life events. As such, this trait is associated with physical and mental functions and conditions. Here, we aimed to identify the genetic factors contributing to shape resilience. We performed variant- and gene-based meta-analyses of genome-wide association studies from six German cohorts (N = 15822) using the 11-item version of the Resilience Scale (RS-11) as outcome measure. Variant- and gene-level results were combined to explore the biological context using network analysis. In addition, we conducted tests of correlation between RS-11 and the polygenic scores (PGSs) for 12 personality and mental health traits in one of these cohorts (PROCAM-2, N = 3879). The variant-based analysis found no signals associated with resilience at the genome-wide level (p < 5 × 10-8), but suggested five genomic loci (p < 1 × 10-5). The gene-based analysis identified three genes (ROBO1, CIB3 and LYPD4) associated with resilience at genome-wide level (p < 2.48 × 10-6) and 32 potential candidates (p < 1 × 10-4). Network analysis revealed enrichment of biological pathways related to neuronal proliferation and differentiation, synaptic organization, immune responses and vascular homeostasis. We also found significant correlations (FDR < 0.05) between RS-11 and the PGSs for neuroticism and general happiness. Overall, our observations suggest low heritability of resilience. Large, international efforts will be required to uncover the genetic factors that contribute to shape trait resilience. Nevertheless, as the largest investigation of the genetics of resilience in general population to date, our study already offers valuable insights into the biology potentially underlying resilience and resilience's relationship with other personality traits and mental health.

Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits

Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene-environment and gene-gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Effects of Genetic Risk and Lifestyle Habits on Gout: A Korean Cohort Study

BACKGROUND

Gout is a type of inflammatory arthritis caused by monosodium urate crystal deposits, and the prevalence of this condition has been increasing. This study aimed to determine the combined effects of genetic risk factors and lifestyle habits on gout, using data from a Korean cohort study. Identifying high-risk individuals in advance can help prevent gout and its associated disorders.

METHODS

We analyzed data from the Korean Genome and Epidemiology Study-Urban Health Examinees cohort (KoGES-HEXA). Genetic information of the participants was collected at baseline, and gout cases were identified based on patient statements. The polygenic risk score (PRS) was calculated using nine independent genome-wide association study datasets, and lifestyle factors and metabolic syndrome status were measured for each participant using the KoGES. Logistic regression models were used to estimate the odds ratios (ORs) for gout in relation to genetic risk, lifestyle habits, and metabolic health status, after adjusting for age and sex.

RESULTS

Among 44,605 participants, 617 were diagnosed with gout. Gout was associated with older age, higher body mass index, and higher prevalence of hypertension, diabetes, and hypertriglyceridemia. High PRS, unfavorable lifestyle habits, and poor metabolic profiles were significantly associated with an increased risk of gout. Compared with that in the low-genetic-risk and healthy lifestyle group or ideal metabolic profile group, the risk of gout was increased in the high-genetic-risk plus unfavorable lifestyle (OR, 3.64; 95% confidence interval [CI], 2.32-6.03) or poor metabolic profile (OR, 7.78; 95% CI, 4.61-13.40) group. Conversely, adherence to favorable lifestyle habits significantly reduced gout risk, especially in high-genetic-risk groups.

CONCLUSION

Genetic predisposition and unhealthy lifestyle habits significantly increase the risk of gout. Promoting healthy lifestyle habits is crucial to prevent the development of gout, particularly in individuals with high genetic susceptibility.

Social Determinants of Health and Lifestyle Risk Factors Modulate Genetic Susceptibility for Women's Health Outcomes

Women's health conditions are influenced by both genetic and environmental factors. Understanding these factors individually and their interactions is crucial for implementing preventative, personalized medicine. However, since genetics and environmental exposures, particularly social determinants of health (SDoH), are correlated with race and ancestry, risk models without careful consideration of these measures can exacerbate health disparities. We focused on seven women's health disorders in the All of Us Research Program: breast cancer, cervical cancer, endometriosis, ovarian cancer, preeclampsia, uterine cancer, and uterine fibroids. We computed polygenic risk scores (PRSs) from publicly available weights and tested the effect of the PRSs on their respective phenotypes as well as any effects of genetic risk on age at diagnosis. We next tested the effects of environmental risk factors (BMI, lifestyle measures, and SDoH) on age at diagnosis. Finally, we examined the impact of environmental exposures in modulating genetic risk by stratified logistic regressions for different tertiles of the environment variables, comparing the effect size of the PRS. Of the twelve sets of weights for the seven conditions, nine were significantly and positively associated with their respective phenotypes. None of the PRSs was associated with different ages at diagnoses in the time-to-event analyses. The highest environmental risk group tended to be diagnosed earlier than the low and medium-risk groups. For example, the cases of breast cancer, ovarian cancer, uterine cancer, and uterine fibroids in highest BMI tertile were diagnosed significantly earlier than the low and medium BMI groups, respectively). PRS regression coefficients were often the largest in the highest environment risk groups, showing increased susceptibility to genetic risk. This study's strengths include the diversity of the All of Us study cohort, the consideration of SDoH themes, and the examination of key risk factors and their interrelationships. These elements collectively underscore the importance of integrating genetic and environmental data to develop more precise risk models, enhance personalized medicine, and ultimately reduce health disparities.

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10- 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10- 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10- 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10- 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 - 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10- 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10- 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41-1.13) of African ancestry individuals and 6.4% (95% CI 5.6-7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.

Evaluation of Polygenic Risk Score for Prediction of Childhood Onset and Severity of Asthma

Asthma is a common complex disease with susceptibility defined through an interplay of genetic and environmental factors. Responsiveness to asthma treatment varies between individuals and is largely determined by genetic variability. The polygenic score (PGS) approach enables an individual risk of asthma and respective response to drug therapy. PGS models could help to predict the individual risk of asthma using 26 SNPs of drug pathway genes involved in the metabolism of glucocorticosteroids (GCS), and beta-2-agonists, antihistamines, and antileukotriene drugs associated with the response to asthma treatment within GWAS were built. For PGS, summary statistics from the Trans-National Asthma Genetic Consortium GWAS meta-analysis, and genotype data for 882 individuals with asthma/controls from the Volga-Ural region, were used. The study group was comprised of Russian, Tatar, Bashkir, and mixed ethnicity individuals with asthma (N = 378) aged 2-18 years. and individuals without features of atopic disease (N = 504) aged 4-67 years from the Volga-Ural region. The DNA samples for the study were collected from 2000 to 2021. The drug pathway genes' PGS revealed a higher odds for childhood asthma risk (p = 2.41 × 10-12). The receiver operating characteristic (ROC) analysis showed an Area Under the Curve, AUC = 0.63. The AUC of average significance for moderate-to-severe and severe asthma was observed (p = 5.7 × 10-9, AUC = 0.64). Asthma drug response pathway gene variant PGS models may contribute to the development of modern approaches to optimise asthma diagnostics and treatment.

Characterizing the genetic architecture of drug response using gene-context interaction methods

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

Mapping the relative accuracy of cross-ancestry prediction

The overwhelming majority of participants in genome-wide association studies (GWAS) have European (EUR) ancestry, and polygenic scores (PGS) derived from EURs often perform poorly in non-EURs. Previous studies suggest that between-ancestry differences in allele frequencies and linkage disequilibrium are significant contributors to the poor portability of PGS in cross-ancestry prediction. We hypothesize that the portability of (local) PGS varies significantly over the genome. Therefore, we develop a method, MC-ANOVA, to estimate the loss of accuracy in cross-ancestry prediction attributable to allele frequency and linkage disequilibrium differences between ancestries. Using data from the UK Biobank we develop PGS relative accuracy (RA) maps quantifying the local portability of EUR-derived PGS in non-EUR ancestries. We report substantial variability in RA along the genome, suggesting that even in ancestries with low overall RA of EUR-derived effects (e.g., African), there are regions with high RA. We substantiate our findings using six complex traits, which show that EUR-derived effects from regions where MC-ANOVA predicts high RA also have high empirical RA in real PGS. We provide software implementing MC-ANOVA and RA maps for several non-EUR ancestries. These maps can be used to interpret similarities and differences in GWAS results between groups and to improve cross-ancestry prediction.

Conceptual frameworks for the integration of genetic and social epidemiology in complex diseases

Uncovering the root causes of complex diseases requires complex approaches, yet many studies continue to isolate the effects of genetic and social determinants of disease. Epidemiologic efforts that under-utilize genetic epidemiology methods and findings may lead to incomplete understanding of disease. Meanwhile, genetic epidemiology studies are often conducted without consideration of social and environmental context, limiting the public health impact of genomic discoveries. This divide endures despite shared goals and increases in interdisciplinary data due to a lack of shared theoretical frameworks and differing language. Here, we demonstrate that bridging epidemiological divides does not require entirely new ways of thinking. Existing social epidemiology frameworks including Ecosocial theory and Fundamental Cause Theory, can both be extended to incorporate principles from genetic epidemiology. We show that genetic epidemiology can strengthen, rather than detract from, efforts to understand the impact of social determinants of health. In addition to presenting theoretical synergies, we offer practical examples of how genetics can improve the public health impact of epidemiology studies across the field. Ultimately, we aim to provide a guiding framework for trainees and established epidemiologists to think about diseases and complex systems and foster more fruitful collaboration between genetic and traditional epidemiological disciplines.

The Geneticization of Education and Its Bioethical Implications

The day has arrived that genetic tests for educational outcomes are available to the public. Today parents and students alike can send off a sample of blood or saliva and receive a 'genetic report' for a range of characteristics relevant to education, including intelligence, math ability, reading ability, and educational attainment. DTC availability is compounded by a growing "precision education" initiative, which proposes the application of DNA tests in schools to tailor educational curricula to children's genomic profiles. Here I argue that these happenings are a strong signal of the geneticization of education; the process by which educational abilities and outcomes come to be examined, understood, explained, and treated as primarily genetic characteristics. I clarify what it means to geneticize education, highlight the nature and limitations of the underlying science, explore both real and potential downstream bioethical implications, and make proposals for mitigating negative impacts.

Improving GWAS performance in underrepresented groups by appropriate modeling of genetics, environment, and sociocultural factors

Genome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, "White and Asian" (WA), and "Any Other Asian" (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities resulting in consistent patterns of clustering used to train a support vector machine (SVM). The SVM model was utilized to reassign n = 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We then leverage these samples to assess GWAS performance and PGS development. We further include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnull and GWASenv. We show that PGS performance derived from environmentally adjusted GWAS yields comparable prediction to PGS models developed with an order of magnitude larger training dataset (R 2=0.021 vs 0.026). Models with 7 - 8 environmental covariates double the variance explained by PGS alone. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.

Methylation Clocks Do Not Predict Age or Alzheimer's Disease Risk Across Genetically Admixed Individuals

Epigenetic clocks that quantify rates of aging from DNA methylation patterns across the genome have emerged as a potential biomarker for risk of age-related diseases, like Alzheimer's disease (AD), and environmental and social stressors. However, methylation clocks have not been validated in genetically diverse cohorts. Here we evaluate a set of methylation clocks in 621 AD patients and matched controls from African American, Hispanic, and white cohorts. The clocks are less accurate at predicting age in genetically admixed individuals, especially those with substantial African ancestry, than in the white cohort. The clocks also do not consistently identify age acceleration in admixed AD cases compared to controls. Methylation QTL (meQTL) commonly influence CpGs in clocks, and these meQTL have significantly higher frequencies in African genetic ancestries. Our results demonstrate that methylation clocks often fail to predict age and AD risk beyond their training populations and suggest avenues for improving their portability.

Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies

Family-based studies provide a unique opportunity to characterize genetic risks of diseases in the presence of population structure, assortative mating, and indirect genetic effects. We propose a novel framework, PGS-TRI, for the analysis of polygenic scores (PGS) in case-parent trio studies for estimation of the risk of an index condition associated with direct effects of inherited PGS, indirect effects of parental PGS, and gene-environment interactions. Extensive simulation studies demonstrate the robustness of PGS-TRI in the presence of complex population structure and assortative mating compared to alternative methods. We apply PGS-TRI to multi-ancestry trio studies of autism spectrum disorders (Ntrio = 1,517) and orofacial clefts (Ntrio = 1,904) to establish the first transmission-based estimates of risk associated with pre-defined PGS for these conditions and other related traits. For both conditions, we further explored offspring risk associated with polygenic gene-environment interactions, and direct and indirect effects of genetically predicted levels of gene expression and metabolite traits.

Systemic lupus erythematosus genetics: insights into pathogenesis and implications for therapy

Systemic lupus erythematosus (SLE) is a prime example of how the interplay between genetic and environmental factors can trigger systemic autoimmunity, particularly in young women. Although clinical disease can take years to manifest, risk is established by the unique genetic makeup of an individual. Genome-wide association studies have identified almost 200 SLE-associated risk loci, yet unravelling the functional effect of these loci remains a challenge. New analytic tools have enabled researchers to delve deeper, leveraging DNA sequencing and cell-specific and immune pathway analysis to elucidate the immunopathogenic mechanisms. Both common genetic variants and rare non-synonymous mutations can interact to increase SLE risk. Notably, variants strongly associated with SLE are often located in genome super-enhancers that regulate MHC class II gene expression. Additionally, the 3D conformations of DNA and RNA contribute to genome regulation and innate immune system activation. Improved therapies for SLE are urgently needed and current and future knowledge from genetic and genomic research should provide new tools to facilitate patient diagnosis, enhance the identification of therapeutic targets and optimize testing of agents.

Heterogeneous genetic architectures of prostate cancer susceptibility in sub-Saharan Africa

Men of African descent have the highest prostate cancer incidence and mortality rates, yet the genetic basis of prostate cancer in African men has been understudied. We used genomic data from 3,963 cases and 3,509 controls from Ghana, Nigeria, Senegal, South Africa and Uganda to infer ancestry-specific genetic architectures and fine-map disease associations. Fifteen independent associations at 8q24.21, 6q22.1 and 11q13.3 reached genome-wide significance, including four new associations. Intriguingly, multiple lead associations are private alleles, a pattern arising from recent mutations and the out-of-Africa bottleneck. These African-specific alleles contribute to haplotypes with odds ratios above 2.4. We found that the genetic architecture of prostate cancer differs across Africa, with effect size differences contributing more to this heterogeneity than allele frequency differences. Population genetic analyses reveal that African prostate cancer associations are largely governed by neutral evolution. Collectively, our findings emphasize the utility of conducting genetic studies that use diverse populations.

Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors

Background

The risk for heart failure (HF) is increased among cancer survivors, but predicting individual HF risk is difficult. Polygenic risk scores (PRS) for HF prediction summarize the combined effects of multiple genetic variants specific to the individual.

Objectives

The aim of this study was to compare clinical HF prediction models with PRS in both cancer and noncancer populations.

Methods

Cancer and HF diagnoses were identified using International Classification of Diseases-10th Revision codes. HF risk was calculated using the ARIC (Atherosclerosis Risk in Communities) HF score (ARIC-HF). The PRS for HF (PRS-HF) was calculated according to the Global Biobank Meta-analysis Initiative. The predictive performance of the ARIC-HF and PRS-HF was compared using the area under the curve (AUC) in both cancer and noncancer populations.

Results

After excluding 2,644 participants with HF prior to consent, 440,813 participants without cancer (mean age 57 years, 53% women) and 43,720 cancer survivors (mean age 60 years, 65% women) were identified at baseline. Both the ARIC-HF and PRS-HF were significant predictors of incident HF after adjustment for chronic kidney disease, overall health rating, and total cholesterol. The PRS-HF performed poorly in predicting HF among cancer (AUC: 0.552; 95% CI: 0.539-0.564) and noncancer (AUC: 0.561; 95% CI: 0.556-0.566) populations. However, the ARIC-HF predicted incident HF in the noncancer population (AUC: 0.804; 95% CI: 0.800-0.808) and provided acceptable performance among cancer survivors (AUC: 0.748; 95% CI: 0.737-0.758).

Conclusions

The prediction of HF on the basis of conventional risk factors using the ARIC-HF score is superior compared to the PRS, in cancer survivors, and especially among the noncancer population.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

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

BACKGROUND

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

OBJECTIVE AND RATIONALE

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

SEARCH METHODS

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

OUTCOMES

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

WIDER IMPLICATIONS

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

Causal interpretations of family GWAS in the presence of heterogeneous effects

Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or average treatment effects; ATEs) of alleles, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. This feature will matter if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in linkage disequilibrium patterns. At a single locus, family-based GWAS can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores (PGSs), however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate of the LATE for any subset or weighted average of families. In practice, the potential biases of a family-based GWAS are likely smaller than those that can arise from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, their causal interpretation is less straightforward than has been widely appreciated.

Polygenic risk score portability for common diseases across genetically diverse populations

BACKGROUND

Polygenic risk scores (PRS) derived from European individuals have reduced portability across global populations, limiting their clinical implementation at worldwide scale. Here, we investigate the performance of a wide range of PRS models across four ancestry groups (Africans, Europeans, East Asians, and South Asians) for 14 conditions of high-medical interest.

METHODS

To select the best-performing model per trait, we first compared PRS performances for publicly available scores, and constructed new models using different methods (LDpred2, PRS-CSx and SNPnet). We used 285 K European individuals from the UK Biobank (UKBB) for training and 18 K, including diverse ancestries, for testing. We then evaluated PRS portability for the best models in Europeans and compared their accuracies with respect to the best PRS per ancestry. Finally, we validated the selected PRS models using an independent set of 8,417 individuals from Biobank of the Americas-Genomelink (BbofA-GL); and performed a PRS-Phewas.

RESULTS

We confirmed a decay in PRS performances relative to Europeans when the evaluation was conducted using the best-PRS model for Europeans (51.3% for South Asians, 46.6% for East Asians and 39.4% for Africans). We observed an improvement in the PRS performances when specifically selecting ancestry specific PRS models (phenotype variance increase: 1.62 for Africans, 1.40 for South Asians and 0.96 for East Asians). Additionally, when we selected the optimal model conditional on ancestry for CAD, HDL-C and LDL-C, hypertension, hypothyroidism and T2D, PRS performance for studied populations was more comparable to what was observed in Europeans. Finally, we were able to independently validate tested models for Europeans, and conducted a PRS-Phewas, identifying cross-trait interplay between cardiometabolic conditions, and between immune-mediated components.

CONCLUSION

Our work comprehensively evaluated PRS accuracy across a wide range of phenotypes, reducing the uncertainty with respect to which PRS model to choose and in which ancestry group. This evaluation has let us identify specific conditions where implementing risk-prioritization strategies could have practical utility across diverse ancestral groups, contributing to democratizing the implementation of PRS.

Asthma-Genomic Advances Toward Risk Prediction

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.