Polygenic risk scores: from research tools to clinical instruments

Assessment of cardiovascular disease risk: a 2023 update

PURPOSE OF REVIEW

The aim of this study was to highlight the current best practice for atherosclerotic cardiovascular disease (CVD) risk evaluation, including selective use of adjunctive tools for risk stratification [e.g. coronary artery calcium (CAC) scoring] and risk enhancement [e.g. lipoprotein(a) [Lp(a)], polygenic risk scoring (PRS)].

RECENT FINDINGS

New studies have evaluated the efficacy of various risk assessment tools. These studies demonstrate the role of Lp(a) as a risk-enhancing factor ready for more widespread use. CAC is the gold standard method of assessing subclinical atherosclerosis, enabling true risk stratification of patients, and informing net benefit assessment for initiating or titrating lipid-lowering therapy (LLT).

SUMMARY

Lp(a) concentration and CAC scoring, apart from the traditional risk factors, add the most value to the current CVD risk assessment approaches of all available tools, especially in terms of guiding LLT. In addition to new integrative tools such as the MESA CHD Risk Score and Coronary Age calculator, the future of risk assessment may include PRS and more advanced imaging techniques for atherosclerosis burden. Soon, polygenic risk scoring may be used to identify the age at which to begin CAC scoring, with CAC scores guiding preventive strategies.

Melanoma risk prediction based on a polygenic risk score and clinical risk factors

Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N  = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N  = 54 799) and its performance was tested using a cohort testing dataset ( N  = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.

Atopic Polygenic Risk Score Is Associated with Paradoxical Eczema Developing in Patients with Psoriasis Treated with Biologics

Biologic therapies for psoriasis can cause paradoxical eczema. The role of genetic factors in its pathogenesis is unknown. To identify risk variants, we conducted a GWAS of 3,212 patients with psoriasis, of whom 88 developed paradoxical eczema. Two lead SNPs reached genome-wide significance (P ≤ 5 × 10-8) for association with paradoxical eczema: rs192705221 (near UNC5B, P = 9.52 × 10-10) and rs72925168 (within SLC1A2, P = 1.66 × 10-9). Genome-wide significant SNPs from published GWAS were used to generate polygenic risk scores (PRSs) for atopic eczema, general atopic disease, or a combination, which were tested for association with paradoxical eczema. Improvement over a clinical risk model was assessed by the area under the curve. All three atopy polygenic risk scores were associated with paradoxical eczema (P < 0.05); polygenic risk score for a combination of atopic eczema and general atopic disease had the strongest association (OR = 1.83, 95% CI = 1.17-2.84, P = 0.0078). Including atopic polygenic risk scores in the multivariable model, which included age, sex, atopic background, and psoriatic arthritis history, increased the area under the curve from 0.671 to 0.681-0.686. Atopic genetic burden is associated with paradoxical eczema occurring in biologic-treated patients with psoriasis, indicating shared underlying mechanisms. Incorporating genetic risk may improve treatment outcome prediction models for psoriasis.

A Social and Ethical Framework for Providing Health Information Obtained from Combining Genetics and Fitness Tracking Data

In this paper we explore a new technological intersection in the "big data revolution": the integration of two forms of data, genetic data and fitness tracking data. For example, a small number of direct-to-consumer (DTC) genetic testing companies have recently begun offering customers the ability to link their fitness tracking data with their genetic profile to get personalized diet and exercise recommendations. In this paper we put forward four ethical considerations that should inform potential uses of this health information. Those considerations are: the heightened risks to privacy resulting from combining sensitive data sets; the poor quality of health information that is possible at present in the realm of precision DTC genomics; the limited usefulness of the recommendations; and finally, the cultural assumptions about health and personal responsibility that are embedded within fitness genetic testing and fitness tracking. To conclude, we offer some guidance on how the benefits and risks of returning this type of health information can be weighed.

Weight Loss Is a Strong Predictor of Memory Disorder Independent of Genetic Influences

BACKGROUND

Past studies identified a link between weight loss and dementia, but lacked consistent conclusions. We sought to establish this link by examining the weight change profiles before and after dementia diagnosis.

METHODS

Using data from the Health and Retirement Study (1996-2020), we examined 13,123 participants. We conducted a nested case-control analysis to assess differences in biennial weight change profile while controlling for BMI, longevity polygenic risk scores, and APOE gene variants.

RESULTS

Participants with a memory disorder lost weight (-0.63%) biennially, whereas those without a diagnosis did not (+0.013%, p-value < 0.0001). Our case-control study shows a significant difference (p-value < 0.01) in pre-dementia % weight changes between the cases (-0.29%) and controls (0.19%), but not in post-dementia weight changes. The weight loss group have the highest risk (OR = 2.01; p-value < 0.0001) of developing a memory disorder compared to the stable weight and weight gain groups. The observations hold true after adjusting for BMI, longevity polygenic risk scores, and APOE variant in a multivariable model.

CONCLUSIONS

We observe that weight loss in dementia is a physiological process independent of genetic factors associated with BMI and longevity. Pre-dementia weight loss may be an important prognostic criterion to assess a person's risk of developing a memory disorder.

Generalisation of genomic findings and applications of polygenic risk scores

Polygenic Risk Scores (PRS) (also known as polygenic scores, genetic risk scores or polygenic indexes) capture genetic contributions of a multitude of markers that characterise complex traits. Although their likely application to precision medicine remains to be established, promising advances have included their ability to stratify high risk individuals and targeted screening interventions. Current PRS have been mostly optimised for individuals of Northern European ancestries. If PRS are to become widespread as a tool for healthcare applications, more diverse populations and greater capacity for derived interventions need to be accomplished. In this editorial we aim to attract submissions from the research community that highlight current challenges in development of PRS applications at scale. We also welcome manuscripts that delve into the ethical, social and legal implications that the implementation of PRS may generate.

Genetics of cannabis use in opioid use disorder: A genome-wide association and polygenic risk score study

BACKGROUND

Individuals with an Opioid Use Disorder (OUD) have increased rates of cannabis use in comparison to the general population. Research on the short- and long-term impacts of cannabis use in OUD patients has been inconclusive. A genetic component may contribute to cannabis cravings.

AIMS

Identify genetic variants associated with cannabis use through Genome-wide Association Study (GWAS) methods and investigate a Polygenic Risk Score (PRS). In addition, we aim to identify any sex differences in effect size for genetic variants reaching or nearing genome-wide significance in the GWAS.

METHODS

The study outcomes of interest were: regular cannabis use (yes/no) (n = 2616), heaviness of cannabis use (n = 1293) and cannabis cravings (n = 836). Logistic and linear regressions were preformed, respectively, to test the association between genetic variants and each outcome, regular cannabis use and heaviness of cannabis use. GWAS summary statistics from a recent large meta-GWAS investigating cannabis use disorder were used to conduct PRS's. Findings are limited to a European ancestry sample.

RESULTS

No genome-wide significant associations were found. Rs1813412 (chromosome 17) for regular cannabis use and rs62378502 (chromosome 5) for heaviness of cannabis use were approaching genome-wide significance. Both these SNPs were nominally significant (p<0.05) within males and females, however sex did not modify the association. The PRS identified statistically significant association with cannabis cravings. The variance explained by all PRSs were less than 1.02x10-2.

CONCLUSION

This study provides promising results in understanding the genetic contribution to cannabis use in individuals living with OUD.

The Impact of Genomic Variation on Function (IGVF) Consortium

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (πc). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for πc differences related to hematological and metabolic traits (hematological fold-enrichment = 4.45, p = 2.15 × 10-7; metabolic fold-enrichment = 4.05, p = 4.01 × 10-6). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median πc = 0.15%, EAS-metabolic median πc = 0.18%) with the strongest πc difference with respect to respiratory traits (EAS-respiratory median πc = 0.50%; hematological-p = 2.26 × 10-3; metabolic-p = 3.48 × 10-3). In EUR, pairwise comparisons showed multiple πc differences related to the endocrine category (fold-enrichment = 5.83, p = 4.76 × 10-6), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median πc = 0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median πc = 0.50%; p = 1.19 × 10-4). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p = 2.18 × 10-4; EUR endocrine-gastrointestinal p = 6.80 × 10-4). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Development of risk prediction models for depression combining genetic and early life risk factors

Background

Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.

Methods

Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.

Results

In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.

Conclusion

We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.

Deep integrative models for large-scale human genomics

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

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms.

Mode of transport, genetic susceptibility, and incidence of coronary heart disease

BACKGROUND

Car use has been associated with higher risk of coronary heart disease (CHD). However, whether the associations of transport modes with CHD vary by genetic susceptibility to CHD are unknown. This study aims to investigate the associations of genetic susceptibility and modes of transport with incidence of CHD.

METHODS

We included 339,588 white British participants from UK Biobank with no history of CHD or stroke at baseline or within two years of follow-up (52.3% in work). Genetic susceptibility to CHD was quantified through weighted polygenic risk scores derived from 300 single-nucleotide polymorphisms related to CHD risk. Categories of transport mode included exclusive car use and alternatives to the car (e.g., walking, cycling and public transport), separately for non-commuting (e.g., getting about [n=339,588] excluding commuting for work), commuting (in the sub-set in work [n=177,370] who responded to the commuting question), and overall transport (transport mode for both commuting and non-commuting [n=177,370]). We used Cox regression with age as the underlying timescale to estimate hazard ratios (HR) of CHD (n=13,730; median 13.8-year follow-up) and tested the interaction between genetic susceptibility and travel modes with adjustment for confounders.

RESULTS

Compared to those using alternatives to the car, hazards of CHD were higher for exclusive use of cars for overall transport (HR: 1.16, 95% confidence interval (CI): 1.08-1.25), non-commuting (HR: 1.08, 95% CI: 1.04-1.12) and commuting (HR: 1.16, 95% CI: 1.09-1.23), after adjusting for confounders plus genetic susceptibility. HRs of CHD were 1.45 (95% CI: 1.38-1.52) and 2.04 (95% CI: 1.95-2.12) for the second and third tertile of genetic susceptibility to CHD, respectively, compared to the first. There was, in general, no strong evidence of interactions between genetic susceptibility and categories of overall, non-commuting and commuting transport. Estimated 10-year absolute risk of CHD was lower for the alternatives to the car across strata of genetic susceptibility, compared with exclusive use of cars for overall, non-commuting and commuting transport.

CONCLUSION

Exclusive use of cars was associated with a relatively higher risk of CHD across all strata of genetic susceptibility. Using alternatives to the car should be encouraged for prevention of CHD for the general population including individuals at high genetic risk.

The multifactorial nature of healthy brain ageing: Brain changes, functional decline and protective factors

As the global population faces a progressive shift towards a higher median age, understanding the mechanisms underlying healthy brain ageing has become of paramount importance for the preservation of cognitive abilities. The first part of the present review aims to provide a comprehensive look at the anatomical changes the healthy brain endures with advanced age, while also summarizing up to date findings on modifiable risk factors to support a healthy ageing process. Subsequently, we describe the typical cognitive profile displayed by healthy older adults, conceptualizing the well-established age-related decline as an impairment of four main cognitive factors and relating them to their neural substrate previously described; different cognitive trajectories displayed by typical Alzheimer's Disease patients and successful agers with a high cognitive reserve are discussed. Finally, potential effective interventions and protective strategies to promote cognitive reserve and defer cognitive decline are reviewed and proposed.

Gender-specific association of SLC19A1 and MTHFR genetic polymorphism with oxidative stress biomarkers and plasma folate levels in older adults

BACKGROUND

Plasma folate levels are closely related to antioxidant capacity and are regulated by folate pathway gene polymorphism. However, few studies have explored the gender-specific association of folate pathway gene polymorphism with oxidative stress biomarkers. The present study was designed to explore the gender-specific independent and combined impacts of solute carrier family 19 member 1 (SLC19A1) and methylenetetrahydrofolate reductase (MTHFR) genetic polymorphisms on oxidative stress biomarkers in older adults.

METHODS

A total of 401 subjects were recruited, including 145 males and 256 females. Demographic characteristics of the participants were collected by using a self-administered questionnaire. Fasting venous blood samples were taken for folate pathway gene genotyping, circulating lipids parameters and erythrocyte oxidative stress biomarkers measurement. The difference of genotype distribution and the Hardy-Weinberg equilibrium was calculated by the Chi-square test. The general linear model was applied to compare the plasma folate levels and erythrocyte oxidative stress biomarkers. Multiple linear regression was used to explore the correlation between genetic risk scores and oxidative stress biomarkers. Logistic regression was used to explore the association of genetic risk scores of folate pathway gene with folate deficiency.

RESULTS

The male subjects have lower plasma folate and HDL-C levels than the female ones, and the male carrying MTHFR rs1801133 (CC) or MTHFR rs2274976 (GA) genotypes have higher erythrocyte SOD activity. The plasma folate levels, erythrocyte SOD and GSH-PX activities were negatively correlated with genetic risk scores in the male subjects. A positive correlation between the genetic risk scores and folate deficiency was observed in the male subjects.

CONCLUSIONS

There was association between folate pathway gene polymorphism of Solute Carrier Family 19 Member 1 (SLC19A1) and Methylenetetrahydrofolate Reductase (MTHFR) with erythrocyte SOD and GSH-PX activities, and folate levels in male but not in female aging subjects. Genetic variant of genes involved in folate metabolism has strong impact on plasma folate levels in the male aging subjects. Our data demonstrated that there was a potential interaction of gender and its genetic background in affecting the body's antioxidant capacity and the risk of folate deficiency in aging subjects.

Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease

Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis

BACKGROUND

COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability.

METHODS

We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2).

RESULTS

Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups.

CONCLUSIONS

Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

AI-based multi-PRS models outperform classical single-PRS models

Polygenic risk scores (PRS) calculate the risk for a specific disease based on the weighted sum of associated alleles from different genetic loci in the germline estimated by regression models. Recent advances in genetics made it possible to create polygenic predictors of complex human traits, including risks for many important complex diseases, such as cancer, diabetes, or cardiovascular diseases, typically influenced by many genetic variants, each of which has a negligible effect on overall risk. In the current study, we analyzed whether adding additional PRS from other diseases to the prediction models and replacing the regressions with machine learning models can improve overall predictive performance. Results showed that multi-PRS models outperform single-PRS models significantly on different diseases. Moreover, replacing regression models with machine learning models, i.e., deep learning, can also improve overall accuracy.

Australian parental perceptions of genomic newborn screening for non-communicable diseases

Background: Newborn bloodspot screening (NBS) programs have improved neonatal healthcare since the 1960s. Genomic sequencing now offers potential to generate polygenic risk score (PRS) that could be incorporated into NBS programs, shifting the focus from treatment to prevention of future noncommunicable disease (NCD). However, Australian parents' knowledge and attitudes regarding PRS for NBS is currently unknown. Methods: Parents with at least one Australian-born child under 18 years were invited via social media platforms to complete an online questionnaire aimed at examining parents' knowledge of NCDs, PRS, and precision medicine, their opinions on receiving PRS for their child, and considerations of early-intervention strategies to prevent the onset of disease. Results: Of 126 participants, 90.5% had heard the term "non-communicable disease or chronic condition," but only 31.8% and 34.4% were aware of the terms "polygenic risk score" and "precision medicine" respectively. A large proportion of participants said they would consider screening their newborn to receive a PRS for allergies (77.9%), asthma (81.0%), cancer (64.8%), cardiovascular disease (65.7%), mental illness (56.7%), obesity (49.5%), and type 2 diabetes (66.7%). Additionally, participants would primarily consider diet and exercise as interventions for specific NCDs. Discussion: The results from this study will inform future policy for genomic NBS, including expected rate of uptake and interventions that parents would consider employing to prevent the onset of disease.

The association between polygenic risk scores for mental disorders and social cognition: A scoping review

People with mental disorders, such as psychosis or autism spectrum disorder (ASD), often present impairments in social cognition (SC), which may cause significant difficulties in real-world functioning. SC deficits are seen also in unaffected relatives, indicating a genetic substratum. The present review evaluated the evidence on the association between SC and the polygenic risk score (PRS), a single metric of the molecular genetic risk to develop a specific disorder. In July 2022, we conducted systematic searches in Scopus and PubMed following the PRISMA-ScR guidelines. We selected original articles written in English reporting results on the association between PRSs for any mental disorder and domains of SC either in people with mental disorders or controls. The search yielded 244 papers, of which 13 were selected for inclusion. Studies tested mainly PRSs for schizophrenia, ASD, and attention-deficit hyperactivity disorder. Emotion recognition was the most investigated domain of SC. Overall, evidence revealed that currently available PRSs for mental disorders do not explain variation in SC performances. To enhance the understanding of mechanisms underlying SC in mental disorders, future research should focus on the development of transdiagnostic PRSs, study their interaction with environmental risk factors, and standardize outcome measurement.

Heuristic Approach Uncovering Biological Significance of Gene-Lifestyle Interactions in Cardiometabolic Traits

BACKGROUND

Gene-lifestyle interaction studies using genome-wide association studies (GWAS) data contribute to a better understanding of individual responses to environmental exposures.

OBJECTIVES

Herein, we aimed at assessing the biological significance of overlapping genes reported in gene-lifestyle interaction studies in cardiometabolic health.

METHOD

A heuristic analysis of genes reporting significant interactions related to cardiometabolic traits was performed to determine the biological pathways common to the different traits.

RESULTS

A total of 873 genes were analyzed. Fine and condensed phenotypic solutions were obtained from overlapping genes common to more than one trait.

CONCLUSIONS

This study revealed significant metabolic pathways associated with the impact of gene-environment interactions on cardiometabolic risk. Graphical Abstract: Publicly available data in cloud-based repositories were used to perform enrichment analyses of genes previously described in GWAS studies that showed interaction with lifestyles. From the enriched pathways, cluster analysis was performed to group enriched metabolic disorders.

Expanding the application of haplotype-based genomic predictions to the wild: A case of antibody response against Teladorsagia circumcincta in Soay sheep

BACKGROUND

Genomic prediction of breeding values (GP) has been adopted in evolutionary genomic studies to uncover microevolutionary processes of wild populations or improve captive breeding strategies. While recent evolutionary studies applied GP with individual single nucleotide polymorphism (SNP), haplotype-based GP could outperform individual SNP predictions through better capturing the linkage disequilibrium (LD) between the SNP and quantitative trait loci (QTL). This study aimed to evaluate the accuracy and bias of haplotype-based GP of immunoglobulin (Ig) A (IgA), IgE, and IgG against Teladorsagia circumcincta in lambs of an unmanaged sheep population (Soay breed) based on Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian [BayesA, BayesB, BayesCπ, Bayesian Lasso (BayesL), and BayesR] methods.

RESULTS

The accuracy and bias of GPs using SNP, haplotypic pseudo-SNP from blocks with different LD thresholds (0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.00), or the combinations of pseudo-SNPs and non-LD clustered SNPs were obtained. Across methods and marker sets, higher ranges of genomic estimated breeding values (GEBV) accuracies were observed for IgA (0.20 to 0.49), followed by IgE (0.08 to 0.20) and IgG (0.05 to 0.14). Considering the methods evaluated, up to 8% gains in GP accuracy of IgG were achieved using pseudo-SNPs compared to SNPs. Up to 3% gain in GP accuracy for IgA was also obtained using the combinations of the pseudo-SNPs with non-clustered SNPs in comparison to fitting individual SNP. No improvement in GP accuracy of IgE was observed using haplotypic pseudo-SNPs or their combination with non-clustered SNPs compared to individual SNP. Bayesian methods outperformed GBLUP for all traits. Most scenarios yielded lower accuracies for all traits with an increased LD threshold. GP models using haplotypic pseudo-SNPs predicted less-biased GEBVs mainly for IgG. For this trait, lower bias was observed with higher LD thresholds, whereas no distinct trend was observed for other traits with changes in LD.

CONCLUSIONS

Haplotype information improves GP performance of anti-helminthic antibody traits of IgA and IgG compared to fitting individual SNP. The observed gains in the predictive performances indicate that haplotype-based methods could benefit GP of some traits in wild animal populations.

Genetics: A Starting Point for the Prevention and the Treatment of Obesity

Obesity is a common, serious, and costly disease. More than 1 billion people worldwide are obese-650 million adults, 340 million adolescents, and 39 million children. The WHO estimates that, by 2025, approximately 167 million people-adults and children-will become less healthy because they are overweight or obese. Obesity-related conditions include heart disease, stroke, type 2 diabetes, and certain types of cancer. These are among the leading causes of preventable, premature death. The estimated annual medical cost of obesity in the United States was nearly $173 billion in 2019 dollars. Obesity is considered the result of a complex interaction between genes and the environment. Both genes and the environment change in different populations. In fact, the prevalence changes as the result of eating habits, lifestyle, and expression of genes coding for factors involved in the regulation of body weight, food intake, and satiety. Expression of these genes involves different epigenetic processes, such as DNA methylation, histone modification, or non-coding micro-RNA synthesis, as well as variations in the gene sequence, which results in functional alterations. Evolutionary and non-evolutionary (i.e., genetic drift, migration, and founder's effect) factors have shaped the genetic predisposition or protection from obesity in modern human populations. Understanding and knowing the pathogenesis of obesity will lead to prevention and treatment strategies not only for obesity, but also for other related diseases.

Polygenic risk scores in pharmacogenomics: opportunities and challenges-a mini review

Pharmacogenomics (PGx) aims at tailoring drug therapy by considering patient genetic makeup. While drug dosage guidelines have been extensively based on single gene mutations (single nucleotide polymorphisms) over the last decade, polygenic risk scores (PRS) have emerged in the past years as a promising tool to account for the complex interplay and polygenic nature of patients' genetic predisposition affecting drug response. Even though PRS research has demonstrated convincing evidence in disease risk prediction, the clinical utility and its implementation in daily care has yet to be demonstrated, and pharmacogenomics is no exception; usual endpoints include drug efficacy or toxicity. Here, we review the general pipeline in PRS calculation, and we discuss some of the remaining barriers and challenges that must be undertaken to bring PRS research in PGx closer to patient care. Besides the need in following reporting guidelines and larger PGx patient cohorts, PRS integration will require close collaboration between bioinformatician, treating physicians and genetic consultants to ensure a transparent, generalizable, and trustful implementation of PRS results in real-world medical decisions.

Polygenic Variants Linked to Oxidative Stress and the Antioxidant System Are Associated with Type 2 Diabetes Risk and Interact with Lifestyle Factors

Oxidative stress is associated with insulin resistance and secretion, and antioxidant systems are essential for preventing and managing type 2 diabetes (T2DM). This study aimed to explore the polygenic variants linked to oxidative stress and the antioxidant system among those associated with T2DM and the interaction of their polygenic risk scores (PRSs) with lifestyle factors in a large hospital-based cohort (n = 58,701). Genotyping, anthropometric, biochemical, and dietary assessments were conducted for all participants with an average body mass index of 23.9 kg/m². Genetic variants associated with T2DM were searched through genome-wide association studies in participants with T2DM (n = 5383) and without T2DM (n = 53,318). The Gene Ontology database was searched for the antioxidant systems and oxidative stress-related genes among the genetic variants associated with T2DM risk, and the PRS was generated by summing the risk alleles of selected ones. Gene expression according to the genetic variant alleles was determined on the FUMA website. Food components with low binding energy to the GSTA5 protein generated from the wildtype and mutated GSTA5_rs7739421 (missense mutation) genes were selected using in silico analysis. Glutathione metabolism-related genes, including glutathione peroxidase (GPX)1 and GPX3, glutathione disulfide reductase (GSR), peroxiredoxin-6 (PRDX6), glutamate-cysteine ligase catalytic subunit (GCLC), glutathione S-transferase alpha-5 (GSTA5), and gamma-glutamyltransferase-1 (GGT1), were predominantly selected with a relevance score of >7. The PRS related to the antioxidant system was positively associated with T2DM (ORs = 1.423, 95% CI = 1.22-1.66). The active site of the GASTA proteins having valine or leucine at 55 due to the missense mutation (rs7739421) had a low binding energy (<-10 kcal/mol) similarly or differently to some flavonoids and anthocyanins. The PRS interacted with the intake of bioactive components (specifically dietary antioxidants, vitamin C, vitamin D, and coffee) and smoking status (p < 0.05). In conclusion, individuals with a higher PRS related to the antioxidant system may have an increased risk of T2DM, and there is a potential indication that exogenous antioxidant intake may alleviate this risk, providing insights for personalized strategies in T2DM prevention.

Development and Replication of a Genome-Wide Polygenic Risk Score for Chronic Back Pain

Chronic back pain (CBP) is a complex heritable trait and a major cause of disability worldwide. We developed and validated a genome-wide polygenic risk score (PRS) for CBP using a large-scale GWAS based on UK Biobank participants of European ancestry (N = 265,000). The PRS showed poor overall predictive ability (AUC = 0.56 and OR = 1.24 per SD, 95% CI: 1.22-1.26), but individuals from the 99th percentile of PRS distribution had a nearly two-fold increased risk of CBP (OR = 1.82, 95% CI: 1.60-2.06). We validated the PRS on an independent TwinsUK sample, obtaining a similar magnitude of effect. The PRS was significantly associated with various ICD-10 and OPCS-4 diagnostic codes, including chronic ischemic heart disease (OR = 1.1, p-value = 4.8 × 10^-15), obesity, metabolism-related traits, spine disorders, disc degeneration, and arthritis-related disorders. PRS and environment interaction analysis with twelve known CBP risk factors revealed no significant results, suggesting that the magnitude of G × E interactions with studied factors is small. The limited predictive ability of the PRS that we developed is likely explained by the complexity, heterogeneity, and polygenicity of CBP, for which sample sizes of a few hundred thousand are insufficient to estimate small genetic effects robustly.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (π _c ). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for π _c differences related to hematological and metabolic traits (hematological fold-enrichment=4.45, p=2.15×10 ^-7 ; metabolic fold-enrichment=4.05, p=4.01×10 ^-6 ). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median π _c =0.15%, EAS-metabolic median π _c =0.18%) with the strongest π _c difference with respect to respiratory traits (EAS-respiratory median π _c =0.50%; Hematological-p=2.26×10 ^-3 ; Metabolic-p=3.48×10 ^-3 ). In EUR, pairwise comparisons showed multiple π _c differences related to the endocrine category (fold-enrichment=5.83, p=4.76×10 ^-6 ), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median π _c =0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median π _c =0.50%; p=1.19×10 ^-4 ). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p=2.18×10 ^-4 ; EUR endocrine-gastrointestinal p=6.80×10 ^-4 ). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

Inborn Errors of Immunity and Autoimmune Disease

Autoimmunity may be a manifestation of inborn errors of immunity, specifically as part of the subgroup of primary immunodeficiency known as primary immune regulatory disorders. However, although making a single gene diagnosis can have important implications for prognosis and management, picking patients to screen can be difficult, against a background of a high prevalence of autoimmune disease in the population. This review compares the genetics of common polygenic and rare monogenic autoimmunity, and explores the molecular mechanisms, phenotypes, and inheritance of autoimmunity associated with primary immune regulatory disorders, highlighting the emerging importance of gain-of-function and non-germline somatic mutations. A novel framework for identifying rare monogenic cases of common diseases in children is presented, highlighting important clinical and immunologic features that favor single gene disease and guides clinicians in selecting appropriate patients for genomic screening. In addition, there will be a review of autoimmunity in non-genetically defined primary immunodeficiency such as common variable immunodeficiency, and of instances where primary autoimmunity can result in clinical phenocopies of inborn errors of immunity.

The PANcreatic Disease ReseArch (PANDoRA) consortium: Ten years' experience of association studies to understand the genetic architecture of pancreatic cancer

Pancreatic cancer has an incidence that almost matches its mortality. Only a small number of risk factors and 33 susceptibility loci have been identified. so Moreover, the relative rarity of pancreatic cancer poses significant hurdles for research aimed at increasing our knowledge of the genetic mechanisms contributing to the disease. Additionally, the inability to adequately power research questions prevents small monocentric studies from being successful. Several consortia have been established to pursue a better understanding of the genetic architecture of pancreatic cancers. The Pancreatic disease research (PANDoRA) consortium is the largest in Europe. PANDoRA is spread across 12 European countries, Brazil and Japan, bringing together 29 basic and clinical research groups. In the last ten years, PANDoRA has contributed to the discovery of 25 susceptibility loci, a feat that will be instrumental in stratifying the population by risk and optimizing preventive strategies.

Polygenic scores for estimated glomerular filtration rate in a population of general adults and elderly - comparative results from the KORA and AugUR study

BACKGROUND

Polygenic scores (PGSs) combining genetic variants found to be associated with creatinine-based estimated glomerular filtration rate (eGFR_crea) have been applied in various study populations with different age ranges. This has shown that PGS explain less eGFR_crea variance in the elderly. Our aim was to understand how differences in eGFR variance and the percentage explained by PGS varies between population of general adults and elderly.

RESULTS

We derived a PGS for cystatin-based eGFR (eGFR_cys) from published genome-wide association studies. We used the 634 variants known for eGFR_crea and the 204 variants identified for eGFR_cys to calculate the PGS in two comparable studies capturing a general adult and an elderly population, KORA S4 (n = 2,900; age 24-69 years) and AugUR (n = 2,272, age ≥ 70 years). To identify potential factors determining age-dependent differences on the PGS-explained variance, we evaluated the PGS variance, the eGFR variance, and the beta estimates of PGS association on eGFR. Specifically, we compared frequencies of eGFR-lowering alleles between general adult and elderly individuals and analyzed the influence of comorbidities and medication intake. The PGS for eGFR_crea explained almost twice as much (R² = 9.6%) of age-/sex adjusted eGFR variance in the general adults compared to the elderly (4.6%). This difference was less pronounced for the PGS for eGFR_cys (4.7% or 3.6%, respectively). The beta-estimate of the PGS on eGFR_crea was higher in the general adults compared to the elderly, but similar for the PGS on eGFR_cys. The eGFR variance in the elderly was reduced by accounting for comorbidities and medication intake, but this did not explain the difference in R²-values. Allele frequencies between general adult and elderly individuals showed no significant differences except for one variant near APOE (rs429358). We found no enrichment of eGFR-protective alleles in the elderly compared to general adults.

CONCLUSIONS

We concluded that the difference in explained variance by PGS was due to the higher age- and sex-adjusted eGFR variance in the elderly and, for eGFR_crea, also by a lower PGS association beta-estimate. Our results provide little evidence for survival or selection bias.

Genetic Evaluation and Screening in Cardiomyopathies: Opportunities and Challenges for Personalized Medicine

Cardiomyopathy is a major cause of heart failure caused by abnormalities of the heart muscles that make it harder for it to fill or eject blood. With technological advances, it is important for patients and families to understand that there are potential monogenic etiologies of cardiomyopathy. A multidisciplinary approach to clinical genetic screening for cardiomyopathies involving genetic counseling and clinical genetic testing is beneficial for patients and families. With early identification of inherited cardiomyopathy, patients can initiate guideline-directed medical therapies earlier, resulting in a greater likelihood of improving prognoses and health outcomes. Identifying impactful genetic variants will also allow for cascade testing to determine at-risk family members through clinical (phenotype) screening and risk stratification. Addressing genetic variants of uncertain significance and causative variants that may change in pathogenicity is also important to consider. This review will dive into the clinical genetic testing approaches for the various cardiomyopathies, the significance of early detection and treatment, the value of family screening, the personalized treatment process associated with genetic evaluation, and current strategies for clinical genetic testing outreach.

Analysis of ancestry-specific polygenic risk score and diet composition in type 2 diabetes

BACKGROUND

Carbohydrate and protein dietary proportions have been debated as to whether higher or lower levels are optimal for diabetes metabolic control.

OBJECTIVE

The objective of this study was to investigate the associations, interactions, and mediational relationships between a polygenic risk score (PRS), carbohydrate and protein intake, and physical activity level on type 2 diabetes (T2DM) by genetic ancestry, in European Americans and African Americans. A secondary objective examined the biological pathways associated with the PRS-linked genes and their relationships to dietary intake.

METHODS

We performed a cross-sectional study in 9,393 participants: 83.3% European Americans and 16.7% African Americans from 7-NHLBI Care studies obtained from the database of Genotypes and Phenotypes. The main outcome was T2DM. Carbohydrate and protein intake derived from food frequency questionnaires were calculated as percent calories. Data were analyzed using multivariable generalized estimation equation models to derive odds ratios (OR) and 95% confidence intervals (CI). Ancestry-specific PRSs were constructed using joint-effects Summary Best Linear Unbiased Estimation in the train dataset and replicated in the test dataset. Mediation analysis was performed using VanderWeele's method.

RESULTS

The PRS in the highest tertile was associated with higher risk of T2DM in European Americans (OR = 1.25;CI = 1.03-1.51) and African Americans (OR = 1.54;1.14-2.09). High carbohydrate and low protein intake had lower risks of T2DM when combined with the PRS after adjusting for covariates. In African Americans, high physical activity combined with the high PRS and high protein diet was associated with a 28% lower incidence of T2DM when compared to low physical activity. In mediational models in African Americans, the PRS-T2DM association was mediated by protein intake in the highest tertile by 55%. The top PRS tertile had the highest magnitude of risks with metabolic factors that were significantly associated with T2DM, especially in European Americans. We found metabolic pathways associated with the PRS-linked genes that were related to insulin/IGF and ketogenesis/ketolysis that can be activated by moderate physical activity and intermittent fasting for better T2DM control.

CONCLUSIONS

Clinicians may want to consider diets with a higher portion of carbohydrates than protein, especially when the burden of high-risk alleles is great in patients with T2DM. In addition, clinicians and other medical professionals may want to emphasize the addition of physical activity as part of treatment regimen especially for African Americans. Given the metabolic pathways we identified, moderate physical activity and intermittent fasting should be explored. Researchers may want to consider longitudinal or randomized clinical trials to determine the predictive ability of different dietary patterns to inhibit T2DM in the presence of obesity and an elevated PRS.

The interplay of family history of depression and early trauma: associations with lifetime and current depression in the German national cohort (NAKO)

Introduction

Family history of depression and childhood maltreatment are established risk factors for depression. However, how these factors are interrelated and jointly influence depression risk is not well understood. The present study investigated (i) if childhood maltreatment is associated with a family history of depression (ii) if family history and childhood maltreatment are associated with increased lifetime and current depression, and whether both factors interact beyond their main effects, and (iii) if family history affects lifetime and current depression via childhood maltreatment.

Methods

Analyses were based on a subgroup of the first 100,000 participants of the German National Cohort (NAKO), with complete information (58,703 participants, mean age = 51.2 years, 53% female). Parental family history of depression was assessed via self-report, childhood maltreatment with the Childhood Trauma Screener (CTS), lifetime depression with self-reported physician's diagnosis and the Mini-International Neuropsychiatric Interview (MINI), and current depressive symptoms with the depression scale of the Patient Health Questionnaire (PHQ-9). Generalized linear models were used to test main and interaction effects. Mediation was tested using causal mediation analyses.

Results

Higher frequencies of the childhood maltreatment measures were found in subjects reporting a positive family history of depression. Family history and childhood maltreatment were independently associated with increased depression. No statistical interactions of family history and childhood maltreatment were found for the lifetime depression measures. For current depressive symptoms (PHQ-9 sum score), an interaction was found, with stronger associations of childhood maltreatment and depression in subjects with a positive family history. Childhood maltreatment was estimated to mediate 7%-12% of the effect of family history on depression, with higher mediated proportions in subjects whose parents had a depression onset below 40 years. Abuse showed stronger associations with family history and depression, and higher mediated proportions of family history effects on depression than neglect.

Discussion

The present study confirms the association of childhood maltreatment and family history with depression in a large population-based cohort. While analyses provide little evidence for the joint effects of both risk factors on depression beyond their individual effects, results are consistent with family history affecting depression via childhood maltreatment to a small extent.

Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits

The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R ² value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.

Fast and accurate Bayesian polygenic risk modeling with variational inference

The advent of large-scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction with single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods use a multiple linear regression framework to infer joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, most existing Bayesian approaches employ Markov chain Monte Carlo (MCMC) algorithms, which are computationally inefficient and do not scale favorably to higher dimensions, for posterior inference. Here, we introduce variational inference of polygenic risk scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes variational inference techniques to approximate the posterior distribution for the effect sizes. Our experiments with 36 simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that VIPRS is consistently competitive with the state-of-the-art in prediction accuracy while being more than twice as fast as popular MCMC-based approaches. This performance advantage is robust across a variety of genetic architectures, SNP heritabilities, and independent GWAS cohorts. In addition to its competitive accuracy on the "White British" samples, VIPRS showed improved transferability when applied to other ethnic groups, with up to 1.7-fold increase in R2 among individuals of Nigerian ancestry for low-density lipoprotein (LDL) cholesterol. To illustrate its scalability, we applied VIPRS to a dataset of 9.6 million genetic markers, which conferred further improvements in prediction accuracy for highly polygenic traits, such as height.

Ethnic disparities in fracture risk assessment using polygenic scores

Whether the PGS developed using data from European ancestry is predictive of fracture risk for minorities remains unclear. This study demonstrated that PGSs based on common BMD-related genetic variants discovered in the European ancestry cohort are predictive of fracture risk in people of Asian but not African ancestry.

PURPOSE

Large-scale genome-wide association studies (GWAS) on bone mineral density (BMD) have been conducted predominantly in European cohorts. Genetic models based on common variants associated with BMD have been evaluated using almost exclusively European data, which could potentially exacerbate health disparities due to different linkage disequilibrium among different ethnic groups.

METHODS

UK Biobank (UKB) is a large-scale population-based observational study starting in 2006 that recruited 502,617 individuals aged between 40 and 69 years with genotypic and phenotypic data available. Based on the summary statistics of two GWAS studies of femoral neck BMD and total body BMD, we derived four PGSs and assessed the association between each PGS and prevalent/incident fractures within each ethnic group separately using Multivariate logistic regressions and Cox proportional hazard models. All models were adjusted for age, sex, and the first four principal components.

RESULTS

We assessed four PGSs derived from European cohorts. Significant associations were observed between PGSs and fracture in European and Asian cohorts but not in the African cohort. Of all four PGSs, [Formula: see text] performed the best. A standard deviation decreases in [Formula: see text] were associated with an increased hazard ratio (HR) of 1.24 (1.22-1.27), 1.28 (0.83-1.99), and 1.34 (1.10-1.64) in European, African, and Asian ancestry, respectively. A low BMD-related PGS is associated with up to 2.35- and 4.31-fold increased fracture risk in European and Asian populations.

CONCLUSIONS

These results showed that PGSs based on common BMD-related genetic variants discovered in the European ancestry cohort are predictive of fracture risk in people of Asian but not African ancestry.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort

OBJECTIVE

Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes.

RESEARCH DESIGN AND METHODS

Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants.

RESULTS

We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race.

CONCLUSIONS

Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Double Mutations in a Patient with Early-Onset Alzheimer's Disease in Korea: An APP Val551Met and a PSEN2 His169Asn

The etiology of early-onset Alzheimer's disease (EOAD) is associated with alterations in the production of amyloid beta (Aβ) species caused by mutations in the APP, PSEN1, and PSEN2 genes. Mutations affect intra- or inter-molecular interactions and processes between the γ-secretase complex and amyloid precursor protein (APP), leading to the aberrant sequential cleavage of Aβ species. A 64-year-old woman presented with progressive memory decline, mild right hippocampal atrophy, and a family history of Alzheimer's dementia (AD). Whole exome sequencing was performed to evaluate AD-related gene mutations, which were verified by Sanger sequencing. A mutation-caused structural alteration of APP was predicted using in silico prediction programs. Two AD-related mutations, in APP (rs761339914; c.G1651A; p.V551M) and PSEN2 (rs533813519; c.C505A; p.H169N), were identified. The APP Val551Met mutation in the E2 domain may influence APP homodimerization through changes in intramolecular interactions between adjacent amino acids, altering Aβ production. The second mutation was PSEN2 His169Asn mutation, which was previously reported in five EOAD patients from Korea and China, with a relatively high frequency in the East Asian population. According to a previous report, the presenilin 2 protein was predicted to result in a major helical torsion by PSEN2 His169Asn mutation. Notably, the co-existence of APP Val551Met and PSEN2 His169Asn may induce a synergistic effect by both mutations. Future functional studies are needed to clarify the pathological effects of these double mutations.

Assisted reproductive technologies at the nexus of fertility treatment and disease prevention

Assisted reproductive technology (ART) refers to processing gametes in vitro and usually involves in vitro fertilization. Originally developed for the treatment of infertility, culture of human embryos in vitro also provides an opportunity to screen embryos for inherited genetic disorders of the nuclear and mitochondrial genomes. Progress in identifying causative genetic variants has massively increased the scope of preimplantation genetic testing in preventing genetic disorders. However, because ART procedures are not without risk of adverse maternal and child outcomes, careful consideration of the balance of risks and benefits is warranted. Further research on early human development will help to minimize risks while maximizing the benefits of ART.

Bacterial community modifies host genetics effect on early childhood caries

Background

By age five approximately one-fifth of children have early childhood caries (ECC). Both the oral microbiome and host genetics are thought to influence susceptibility. Whether the oral microbiome modifies genetic susceptibility to ECC has not been tested. We test whether the salivary bacteriome modifies the association of a polygenic score (PGS, a score derived from genomic data that summarizes genetic susceptibility to disease) for primary tooth decay on ECC in the Center for Oral Health Research in Appalachia 2 longitudinal birth cohort.

Methods

Children were genotyped using the Illumina Multi-Ethnic Genotyping Array and underwent annual dental examinations. We constructed a PGS for primary tooth decay using weights from an independent, genome-wide association meta-analysis. Using Poisson regression, we tested for associations between the PGS (high versus low) and ECC incidence, adjusting for demographic characteristics (n=783). An incidence-density sampled subset of the cohort (n=138) had salivary bacteriome data at 24- months of age. We tested for effect modification of the PGS on ECC case status by salivary bacterial community state type (CST).

Results

By 60-months, 20.69% of children had ECC. High PGS was not associated with an increased rate of ECC (incidence-rate ratio:1.09 (95% confidence interval (CI): 0.83, 1.42)). However, having a cariogenic salivary bacterial CST at 24-months was associated with ECC (odds ratio (OR): 7.48 (95%CI: 3.06, 18.26)), which was robust to PGS adjustment. An interaction existed between the salivary bacterial CST and the PGS on the multiplicative scale (P= 0.04). The PGS was associated with ECC (OR: 4.83 (95% CI: 1.29, 18.17)) only among individuals with a noncariogenic salivary bacterial CST (n=70).

Conclusions

Genetic causes of caries may be harder to detect when not accounting for cariogenic oral microbiomes. As certain salivary bacterial CSTs increased ECC-risk across genetic-risk strata, preventing colonization of cariogenic microbiomes would be universally beneficial.

Height-Related Polygenic Variants Are Associated with Metabolic Syndrome Risk and Interact with Energy Intake and a Rice-Main Diet to Influence Height in KoGES

Adult height is inversely related to metabolic syndrome (MetS) risk, but its genetic impacts have not been revealed. The present study aimed to examine the hypothesis that adult height-related genetic variants interact with lifestyle to influence adult height and are associated with MetS risk in adults aged >40 in Korea during 2010–2014. Participants were divided into short stature (SS; control) and tall stature (TS; case) by the 85th percentile of adult height. The genetic variants linked to adult height were screened from a genome-wide association study in a city hospital-based cohort (n = 58,701) and confirmed in Ansan/Ansung plus rural cohorts (n = 13,783) among the Korean Genome and Epidemiology Study. Genetic variants that interacted with each other were identified using the generalized multifactor dimensionality reduction (GMDR) analysis. The interaction between the polygenic risk score (PRS) of the selected genetic variants and lifestyles was examined. Adult height was inversely associated with MetS, cardiovascular diseases, and liver function. The PRS, including zinc finger and BTB domain containing 38 (ZBTB38)_rs6762722, polyadenylate-binding protein-interacting protein-2B (PAIP2B)_rs13034890, carboxypeptidase Z (CPZ)_rs3756173, and latent-transforming growth factor beta-binding protein-1 (LTBP1)_rs4630744, was positively associated with height by 1.29 times and inversely with MetS by 0.894 times after adjusting for covariates. In expression quantitative trait loci, the gene expression of growth/differentiation factor-5 (GDF5)_rs224331, non-SMC condensin I complex subunit G (NCAPG)_rs2074974, ligand-dependent nuclear receptor corepressor like (LCORL)_rs7700107, and insulin-like growth factor-1 receptor (IGF1R)_rs2871865 was inversely linked to their risk allele in the tibial nerve and brain. The gene expression of PAIP2B_rs13034890 and a disintegrin and metalloproteinase with thrombospondin motifs-like-3 (ADAMTSL3)_rs13034890 was positively related to it. The PRS was inversely associated with MetS, hyperglycemia, HbA1c, and white blood cell counts. The wild type of GDF5_rs224331 (Ala276) lowered binding energy with rugosin A, D, and E (one of the hydrolyzable tannins) but not the mutated one (276Ser) in the in-silico analysis. The PRS interacted with energy intake and rice-main diet; PRS impact was higher in the high energy intake and the low rice-main diet. In conclusion, the PRS for adult height interacted with energy intake and diet patterns to modulate height and was linked to height and MetS by modulating their expression in the tibial nerve and brain.

Validation of a polygenic risk score for Frailty in the Lothian Birth Cohort and English Longitudinal Study of Ageing

Frailty is a complex trait. Twin studies and a high-powered Genome Wide Association Study (GWAS) conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from this GWAS to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples - the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67-84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085-0.21) and 1.6% (β = 0.14, 95%CI, 0.10-0.17) of the variance, respectively, at age ~68/~70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~68/~70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.

Association of GABRG3, GABRB3, HTR2A Gene Variants with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental and neurobehavioral disorder characterized by impaired social communication, repetitive and restricted patterns of behavior, activity, or interest, and altered emotional processing. Reported prevalence is 4 times higher in men and it has increased in recent years. Immunological, environmental, epigenetic, and genetic factors play a role in the pathophysiology of autism. Many neurochemical pathways and neuroanatomical events are effective in determining the disease. It is still unclear how the main symptoms of autism occur because of this complex and heterogeneous situation. In this study, we focused on gamma amino butyric acid (GABA) and serotonin, which are thought to contribute to the etiology of autism; it is aimed to elucidate the mechanism of the disease by investigating variant changes in the GABA receptor subunit genes GABRB3, GABRG3 and the HTR2A gene, which encodes one of the serotonin receptors. 200 patients with ASD between the ages of 3-9 and 100 healthy volunteers were included in the study. Genomic DNA isolation was performed from peripheral blood samples taken from volunteers. Genotyping was performed using the RFLP method with PCR specific for specific variants. Data were analyzed with SPSS v25.0 program. According to the data obtained in our study; In terms of HTR2A (rs6313 T102C) genotypes, the homozygous C genotype carrying frequency in the patient group and the homozygous T genotype carrying frequency in the GABRG3 (rs140679 C/T) genotypes were found to be significantly higher in the patient group compared to the control group (*p: 0.0001, p: 0.0001). It was determined that the frequency of individuals with homozygous genotype was significantly higher in the patient group compared to the control group and having homozygous genotypes increased the disease risk approximately 1.8 times. In terms of GABRB3 (rs2081648 T/C) genotypes, it was determined that there was no statistically significant difference in the frequency of carrying homozygous C genotype in the patient group compared to the control group (p: 0.36). According to the results of our study, we think that the HTR2A (rs6313 T102C) polymorphism is effective in modulating the empathic and autistic characteristics of individuals, and that the HTR2A (rs6313 T102C) polymorphism is more distributed in the post-synaptic membranes in individuals with a higher number of C alleles. We believe that this situation can be attributed to the spontaneous stimulatory distribution of the HTR2A gene in the postsynaptic membranes because of T102C transformation. In genetically based autism cases, carrying the point mutation in the rs6313 variant of the HTR2A gene and the C allele and the point mutation in the rs140679 variant of the GABRG3 gene and accordingly carrying the T allele provide a predisposition to the disease.

Polygenic Scores and Onset of Major Mood or Psychotic Disorders Among Offspring of Affected Parents

OBJECTIVE

Family history is an established risk factor for mental illness. The authors sought to investigate whether polygenic scores (PGSs) can complement family history to improve identification of risk for major mood and psychotic disorders.

METHODS

Eight cohorts were combined to create a sample of 1,884 participants ages 2-36 years, including 1,339 offspring of parents with mood or psychotic disorders, who were prospectively assessed with diagnostic interviews over an average of 5.1 years. PGSs were constructed for depression, bipolar disorder, anxiety, attention deficit hyperactivity disorder (ADHD), schizophrenia, neuroticism, subjective well-being, p factor, and height (as a negative control). Cox regression was used to test associations between PGSs, family history of major mental illness, and onsets of major mood and psychotic disorders.

RESULTS

There were 435 onsets of major mood and psychotic disorders across follow-up. PGSs for neuroticism (hazard ratio=1.23, 95% CI=1.12-1.36), schizophrenia (hazard ratio=1.15, 95% CI=1.04-1.26), depression (hazard ratio=1.11, 95% CI=1.01-1.22), ADHD (hazard ratio=1.10, 95% CI=1.00-1.21), subjective well-being (hazard ratio=0.90, 95% CI=0.82-0.99), and p factor (hazard ratio=1.14, 95% CI=1.04-1.26) were associated with onsets. After controlling for family history, neuroticism PGS remained significantly positively associated (hazard ratio=1.19, 95% CI=1.08-1.31) and subjective well-being PGS remained significantly negatively associated (hazard ratio=0.89, 95% CI=0.81-0.98) with onsets.

CONCLUSIONS

Neuroticism and subjective well-being PGSs capture risk of major mood and psychotic disorders that is independent of family history, whereas PGSs for psychiatric illness provide limited predictive power when family history is known. Neuroticism and subjective well-being PGSs may complement family history in the early identification of persons at elevated risk.

The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS into clinical diagnosis is anticipated to increase the population-level screening benefits.

PURPOSE

This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures.

METHODS

Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment.

RESULTS

For each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. Out of four studied PGSs, [Formula: see text] (HR: 1.03; 95%CI 1.01-1.05, p = 0.001) had the weakest and the [Formula: see text] (HR: 1.17; 95%CI 1.15-1.19, p < 0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] improved the reclassification of fracture by 1.2% (95% CI, 1.0 to 1.3%), 0.2% (95% CI, 0.1 to 0.3%), 1.4% (95% CI, 1.3 to 1.5%), and 2.2% (95% CI, 2.1 to 2.4%), respectively.

CONCLUSIONS

Our findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.7-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.

Primary care physician use of patient race and polygenic risk scores in medical decision-making

PURPOSE

The use of patient race in medicine is controversial for its potential either to exacerbate or address health disparities. Polygenic risk scores (PRSs) have emerged as a tool for risk stratification models used in preventive medicine. We examined whether PRS results affect primary care physician (PCP) medical decision-making and whether that effect varies by patient race.

METHODS

Using an online survey with a randomized experimental design among PCPs in a national database, we ascertained decision-making around atherosclerotic cardiovascular disease prevention and prostate cancer screening for case scenario patients who were clinically identical except for randomized reported race.

RESULTS

Across 369 PCPs (email open rate = 10.8%, partial completion rate = 93.7%), recommendations varied with PRS results in expected directions (low-risk results, no available PRS results, and high-risk results). Still, physicians randomized to scenarios with Black patients were more likely to recommend statin therapy than those randomized to scenarios with White patients (odds ratio = 1.74, 95% CI = 1.16-2.59, P = .007) despite otherwise identical clinical profiles and independent of PRS results. Similarly, physicians were more likely to recommend prostate cancer screening for Black patients than for White patients (odds ratio = 1.58, 95% CI = 1.06-2.35, P = .025) despite otherwise identical clinical and genetic profiles.

CONCLUSION

Despite advances in precision risk stratification, physicians will likely continue to use patient race implicitly or explicitly in medical decision-making.

Could interventions on physical activity mitigate genomic liability for obesity? Applying the health disparity framework in genetically informed studies

Polygenic scores (PGS) are now commonly available in longitudinal cohort studies, leading to their integration into epidemiological research. In this work, our aim is to explore how polygenic scores can be used as exposures in causal inference-based methods, specifically mediation analyses. We propose to estimate the extent to which the association of a polygenic score indexing genetic liability to an outcome could be mitigated by a potential intervention on a mediator. To do this this, we use the interventional disparity measure approach, which allows us to compare the adjusted total effect of an exposure on an outcome, with the association that would remain had we intervened on a potentially modifiable mediator. As an example, we analyse data from two UK cohorts, the Millennium Cohort Study (MCS, N = 2575) and the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 3347). In both, the exposure is genetic liability for obesity (indicated by a PGS for BMI), the outcome is late childhood/early adolescent BMI, and the mediator and potential intervention target is physical activity, measured between exposure and outcome. Our results suggest that a potential intervention on child physical activity can mitigate some of the genetic liability for childhood obesity. We propose that including PGSs in a health disparity measure approach, and causal inference-based methods more broadly, is a valuable addition to the study of gene-environment interplay in complex health outcomes.