Polygenic Scores Research Articles

Explaining the Variability of Alzheimer Disease Fluid Biomarker Concentrations in Memory Clinic Patients Without Dementia.

Patients' comorbidities can affect Alzheimer disease (AD) blood biomarker concentrations. Because a limited number of factors have been explored to date, our aim was to assess the proportion of the variance in fluid biomarker levels explained by the clinical features of AD and by a large number of non-AD-related factors. MEMENTO enrolled 2,323 individuals with cognitive complaints or mild cognitive impairment in 26 French memory clinics. Baseline evaluation included clinical and neuropsychological assessments, brain MRI, amyloid-PET, CSF (optional), and blood sampling. Blood biomarker levels were determined using the Simoa-HDX analyzer. We performed linear regression analysis of the clinical features of AD (cognition, AD genetic risk score, and brain atrophy) to model biomarker concentrations. Next, we added covariates among routine biological tests, inflammatory markers, demographic and behavioral determinants, treatments, comorbidities, and preanalytical sample handling in final models using both stepwise selection processes and least absolute shrinkage and selection operator (LASSO). In total, 2,257 participants were included in the analysis (median age 71.7, 61.8% women, 55.2% with high educational levels). For blood biomarkers, the proportion of variance explained by clinical features of AD was 13.7% for neurofilaments (NfL), 11.4% for p181-tau, 3.0% for Aβ-42/40, and 1.4% for total-tau. In final models accounting for non-AD-related factors, the variance was mainly explained by age, routine biological tests, inflammatory markers, and preanalytical sample handling. In CSF, the proportion of variance explained by clinical features of AD was 24.8% for NfL, 22.3% for Aβ-42/40, 19.8% for total-tau, and 17.2% for p181-tau. In contrast to blood biomarkers, the largest proportion of variance was explained by cognition after adjustment for covariates. The covariates that explained the largest proportion of variance were also the most frequently selected with LASSO. The performance of blood biomarkers for predicting A+ and T+ status (PET or CSF) remained unchanged after controlling for drivers of variance. This comprehensive analysis demonstrated that the variance in AD blood biomarker concentrations was mainly explained by age, with minor contributions from cognition, brain atrophy, and genetics, conversely to CSF measures. These results challenge the use of blood biomarkers as isolated stand-alone biomarkers for AD.

Clinical features of prostate cancer by polygenic risk score.

Genome-wide association studies have identified more than 290 single nucleotide variants (SNVs) associated with prostate cancer. These SNVs can be combined to generate a Polygenic Risk Score (PRS), which estimates an individual's risk to develop prostate cancer. Identifying individuals at higher risk for prostate cancer using PRS could allow for personalized screening recommendations, improve current screening tools, and potentially result in improved survival rates, but more research is needed before incorporating them into clinical use. Our study aimed to investigate associations between PRS and clinical factors in affected individuals, including age of diagnosis, metastases, histology, International Society of Urological Pathology (ISUP) Grade Group (GG) and family history of prostate cancer, while taking into account germline genetic testing in known prostate cancer related genes. To evaluate the relationship between these clinical factors and PRS, a quantitative retrospective chart review of 250 individuals of European ancestry diagnosed with prostate cancer who received genetic counseling services at The Ohio State University's Genitourinary Cancer Genetics Clinic and a 72-SNV PRS through Ambry Genetics, was performed. We found significant associations between higher PRS and younger age of diagnosis (p = 0.002), lower frequency of metastases (p = 0.006), and having a first-degree relative diagnosed with prostate cancer (p = 0.024). We did not observe significant associations between PRS and ISUP GG, histology or a having a second-degree relative with prostate cancer. These findings provide insights into features associated with higher PRS, but larger multi-ancestral studies using PRS that are informative across populations are needed to understand its clinical utility.

Improving predictive accuracy in primary biliary cholangitis: Anew genetic risk score.

Genetic variants influence primary biliary cholangitis (PBC) risk. We established and tested an accurate polygenic risk score (PRS) using these variants. Data from two Italian cohorts (OldIT 444 cases, 901 controls; NewIT 255 cases, 579 controls) were analysed. The latest international genome-wide meta-analysis provided effect size estimates. The PRS, together with human leukocyte antigen (HLA) status and sex, was included in an integrated risk model. Starting from 46 non-HLA genes, 22 variants were selected. PBC patients in the OldIT cohort showed a higher risk score than controls: -.014 (interquartile range, IQR, -.023, .005) versus -.022 (IQR -.030, -.013) (p < 2.2 × 10-16). For genetic-based prediction, the area under the curve (AUC) was .72; adding sex increased the AUC to .82. Validation in the NewIT cohort confirmed the model's accuracy (.71 without sex, .81 with sex). Individuals in the top group, representing the highest 25%, had a PBC risk approximately 14 times higher than that of the reference group (lowest 25%; p < 10-6). The combination of sex and a novel PRS accurately discriminated between PBC cases and controls. The model identified a subset of individuals at increased risk of PBC who might benefit from tailored monitoring.

Genetics in the diagnosis and treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) remain one of the leading causes of morbidity and mortality worldwide, with genetics being a major risk factor. Genetic cardiovascular disease can occur either because of single variant (Mendelian) or polygenic influences and has been linked to inherited cardiovascular conditions (ICC) such as arrhythmias, cardiomyopathies, dyslipidemias, and aortopathies which are significant factors leading to sudden cardiac death in young adults. Timely screening, diagnosis, and management of ICC can not only provide life-saving treatment to a patient, but also identify at-risk family members. The field of pharmacogenomics (PGx) helped to understand the variable action of medications such as clopidogrel, aspirin, warfarin, and statin according to genotype. Newer technologies such as multi-omics can combine data from multiple sources such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiome. These advancements can contribute to the development of polygenic prediction scores and precision medicine tailored to individual genotypes. Substantial strides have been made in genetic-based therapeutics, gene editing technologies, and drug delivery systems, which have significantly expanded treatment options for patients with acquired or inherited CVDs. Although variable, the country- and society-specific guidelines on genetic testing for ICC and PGx and treatment are being continuously updated to keep up with ongoing research in the field. Along with appropriate knowledge, other factors including cost and availability of genetic testing play a vital role in the usage by both physicians and patients. With the advent of newer genetic testing for CVDs, a key factor is the availability of genetic counselors (GCs) who are specifically trained in cardiovascular genomics. The current review provides a concise summary of the major influences of genetics in the diagnosis and treatment of CVDs.

Smoking timing, genetic susceptibility and the risk of incident type 2 diabetes: A cohort study from the UK Biobank.

To prospectively assess the association of smoking timing with the risk of type 2 diabetes (T2D) and examine whether smoking amount or genetic susceptibility might modify the relationship. A total of 294 815 participants without diabetes from the UK Biobank, including non-smokers and smokers with data on the time from waking to first cigarette, were included. Cox proportional hazards models were used to evaluate the association between smoking timing and the risk of incident T2D. During a median follow-up time of 12 years, a total of 9937 incident cases of T2D were documented. Compared with non-smokers, a shorter time from waking to first cigarette was significantly associated with a higher risk of incident T2D (P for trend < .001). In the fully adjusted model, the hazard ratios (HRs) (95% confidence interval) associated with smoking timing were 1.46 (1.17-1.81) for more than 2 hours, 1.51 (1.21-1.87) for 1-2 hours, 1.58 (1.34-1.85) for 30-60 minutes, 1.86 (1.57-2.21) for 5-15 minutes and 2.01 (1.60-2.54) for less than 5 minutes. We found that even among those who reported being light smokers, those with the shortest time from waking to first cigarette had a 105% higher risk of T2D with an HR of 2.05 (1.52-2.76), which was comparable with heavy smokers. The genetic risk score for T2D did not modify this association (P-interaction = .51). Our findings indicate that shorter time from waking to first cigarette is significantly associated with a higher risk of incident T2D.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores’ ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10−19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP).

To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥ 90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

Association of Cardiovascular Health with the Incidence of Venous Thromboembolism: A Prospective Study of 275,149 Participants from the UK Biobank.

The Life's Essential 8 (LE8) score, recently proposed by the American Heart Association, represents a new paradigm for evaluating cardiovascular health (CVH). We aimed to explore the association between CVH, estimated using LE8, and venous thromboembolism (VTE) incidence. A total of 275,149 participants were recruited from the UK Biobank and divided into high (LE8 score ≥ 80), moderate (LE8 score < 80 but ≥50), and low (LE8 score < 50) CVH groups. Restricted cubic spline analysis, the Kaplan-Meier method, and the Cox proportional hazards model were used to explore the association between CVH and VTE. The genetic predisposition to VTE was assessed with a polygenic risk score. Sensitivity analyses were performed to validate the results. During a median follow-up of 12.56 years, VTE developed in 506 (4.09%), 6069 (2.78%), and 720 (1.66%) participants with low, moderate, and high CVH levels, respectively. Compared with the low CVH group, participants in the moderate and high CVH groups had a 23% (HR, 0.77; 95% CI, 0.71-0.85) and 41% (0.59, 0.52-0.66) lower risk of VTE, respectively, after adjusting for demographic characteristics, medical history, socioeconomic status, and genetic predisposition. This association remained robust in multiple sensitivity analyses. Higher CVH levels led to a more pronounced reduction in the risk of VTE in females and could appreciably offset the genetic risk of VTE. Higher CVH levels were significantly associated with a lower incidence of VTE, encouraging efforts to increase LE8 scores in individuals.

Polygenic risk scores for cardiovascular risk prediction: moving towards implementation into clinical practice?

Polygenic risk scores for cardiovascular risk prediction: moving towards implementation into clinical practice?

Interpreting population- and family-based genome-wide association studies in the presence of confounding.

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Genetics of IgA nephrology: risks, mechanisms, and therapeutic targets.

IgA nephropathy (IgAN) is a genetically complex multifactorial trait. Over the past decade, population-based genome-wide association studies (GWAS) have identified more than 30 IgAN risk loci, providing novel perspectives on both the epidemiology of the disease and its underlying molecular mechanisms. In addition, the association between IgAN and galactose-deficient IgA1 (Gd-IgA1) presented another avenue for genetic exploration due to the heritability of the elevated serum Gd-IgA1 levels. These endeavors also yielded and enabled refinement of polygenic risk scores, which may help identify specific groups of individuals at significantly increased risks, leading to stratifications of medical treatments. In this review, we aim to explore the existing evidence for genetic causation in IgAN. We summarize the state of genetic research in IgAN and how it has led to the reformulation of the new pathogenesis model and novel therapeutic targets.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.

Impact of Family History and Germline Genetic Risk Single Nucleotide Polymorphisms on Long-Term Outcomes of Favorable-Risk Prostate Cancer.

Family history and germline genetic risk single nucleotide polymorphisms (SNPs) have been separately shown to stratify lifetime risk of prostate cancer. Here, we evaluate the combined prognostic value of family history of prostate and other related cancers and germline risk SNPs among patients with favorable-risk prostate cancer. A total of 1367 participants from the prospective Health Professionals Follow-up Study diagnosed with low- or favorable intermediate-risk prostate cancer from 1986 to 2017 underwent genome-wide SNP genotyping. Multivariable Cox regression was used to estimate the association between family history, specific germline risk variants, and a 269 SNP polygenic risk score with prostate cancer‒specific death. Family history of prostate, breast, and/or pancreatic cancer was observed in 489 (36%) participants. With median follow-up from diagnosis of 14.9 years, participants with favorable-risk prostate cancer with a positive family history had a significantly higher risk of prostate cancer‒specific death (HR 1.95, 95% CI 1.15-3.32, P = .014) compared to those without any family history. The rs2735839 (19q13) risk allele was associated with prostate cancer‒specific death (HR 1.81 per risk allele, 95% CI 1.04-3.17, P = .037), whereas the polygenic risk score was not. Combined family history and rs2735839 risk allele were each associated with an additive risk of prostate cancer‒specific death (HR 1.78 per risk factor, 95% CI 1.25-2.53, P = .001). Family history of prostate, breast, or pancreatic cancer and/or a 19q13 germline risk allele are associated with an elevated risk of prostate cancer‒specific death among favorable-risk patients. These findings have implications for how family history and germline genetic risk SNPs should be factored into clinical decision-making around favorable-risk prostate cancer.

Beyond the AJR: Coronary Calcium Remains the Best Risk Predictor for Coronary Heart Disease for Now, but the Polygenic Risk Score Is Fast Becoming a Contender Among Young Adults.

Beyond the AJR: Coronary Calcium Remains the Best Risk Predictor for Coronary Heart Disease for Now, but the Polygenic Risk Score Is Fast Becoming a Contender Among Young Adults.

Polygenic Risk Scores Driving Clinical Change in Glaucoma.

Glaucoma is a clinically heterogeneous disease and the world's leading cause of irreversible blindness. Therapeutic intervention can prevent blindness but relies on early diagnosis, and current clinical risk factors are limited in their ability to predict who will develop sight-threatening glaucoma. The high heritability of glaucoma makes it an ideal substrate for genetic risk prediction, with the bulk of risk being polygenic in nature. Here, we summarize the foundations of glaucoma genetic risk, the development of polygenic risk prediction instruments, and emerging opportunities for genetic risk stratification. Although challenges remain, genetic risk stratification will significantly improve glaucoma screening and management.

Association between lipoprotein(a), LPA genetic risk score, aortic valve disease, and subsequent major adverse cardiovascular events.

Cohort studies have demonstrated associations between calcific aortic valve disease (CAVD) and Lp(a). As Lp(a) is almost entirely genetically determined, in this study, we aim to determine whether Lp(a), when predicted from genetic data, is associated with CAVD and major adverse cardiovascular events (MACEs). Patients undergoing coronary angiography between January 2012 and May 2013 were invited to participate in the study. Of 752 analysable participants, 446 had their Lp(a) measured and 703 had a calculable LPA genetic risk score (GRS). The primary outcomes were the presence of CAVD at baseline and MACE over a 7-year follow-up. The GRS explained 45% of variation in Lp(a). After adjustment for cardiac risk factors and coronary artery disease (CAD), the odds of CAVD increased with increasing Lp(a) [odds ratio (OR) 1.039 per 10-unit increase, 95% confidence interval (CI) 1.022-1.057, P < 0.001] and GRS (OR 1.054 per 10-unit increase, 95% CI 1.024-1.086; P < 0.001). Lipoprotein(a) and the GRS as continuous variables were not associated with subsequent MACEs. A dichotomized GRS (>54) was associated with MACE, but this relationship became non-significant when CAD classification was added into the model (OR 1.333, 95% CI 0.927-1.912; P = 0.12). An LPA GRS can explain 45% of variation in Lp(a) levels, and both Lp(a) and the GRS are associated with CAVD. An elevated GRS is associated with future cardiac events in a secondary risk setting, but, if the CAD status is known, it does not provide additional prognostic information.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence.

The 'diabetic bone paradox' suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence

The ‘diabetic bone paradox’ suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

Motor Complications in Parkinson's Disease: Results from 3343 Patients Followed for up to 12 Years.

Motor complications are well recognized in Parkinson's disease (PD), but their reported prevalence varies and functional impact has not been well studied. To quantify the presence, severity, impact and associated factors for motor complications in PD. Analysis of three large prospective cohort studies of recent-onset PD patients followed for up to 12 years. The MDS-UPDRS part 4 assessed motor complications and multivariable logistic regression tested for associations. Genetic risk score (GRS) for Parkinson's was calculated from 79 single nucleotide polymorphisms. 3343 cases were included (64.7% male). Off periods affected 35.0% (95% CI 33.0, 37.0) at 4-6 years and 59.0% (55.6, 62.3) at 8-10 years. Dyskinesia affected 18.5% (95% CI 16.9, 20.2) at 4-6 years and 42.1% (38.7, 45.5) at 8-10 years. Dystonia affected 13.4% (12.1, 14.9) at 4-6 years and 22.8% (20.1, 25.9) at 8-10 years. Off periods consistently caused greater functional impact than dyskinesia. Motor complications were more common among those with higher drug doses, younger age at diagnosis, female gender, and greater dopaminergic responsiveness (in challenge tests), with associations emerging 2-4 years post-diagnosis. Higher Parkinson's GRS was associated with early dyskinesia (0.026 ≤ P ≤ 0.050 from 2 to 6 years). Off periods are more common and cause greater functional impairment than dyskinesia. We confirm previously reported associations between motor complications with several demographic and medication factors. Greater dopaminergic responsiveness and a higher genetic risk score are two novel and significant independent risk factors for the development of motor complications.

Genetic predisposition for negative affect predicts mental health burden during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic was accompanied by an increase in mental health challenges including depression, stress, loneliness, and anxiety. Common genetic variants can contribute to the risk for psychiatric disorders and may present a risk factor in times of crises. However, it is unclear to what extent polygenic risk played a role in the mental health response to the COVID-19 pandemic. In this study, we investigate whether polygenic scores (PGSs) for mental health-related traits can distinguish between four resilience-vulnerability trajectories identified during the COVID-19 pandemic and associated lockdowns in 2020/21. We used multinomial regression in a genotyped subsample (n = 1316) of the CovSocial project. The most resilient trajectory characterized by the lowest mental health burden and the highest recovery rates served as the reference group. Compared to this most resilient trajectory, a higher value on the PGS for the well-being spectrum decreased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.3%). Conversely, a higher value on the PGS for neuroticism increased the odds for individuals to be in one of the more vulnerable trajectories (adjusted R-square = 0.2%). Latent change in mental health burden extracted from the resilience-vulnerability trajectories was not associated with any PGS. Although our findings support an influence of PGS on mental health during COVID-19, the small added explained variance suggests limited utility of such genetic markers for the identification of vulnerable individuals in the general population.