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BACKGROUND: Effective clinical implementation of polygenic risk testing for glaucoma relies on healthcare professionals' attitudes and knowledge of the test. Given the emerging applications of the test, it will likely impact a range of healthcare professionals and will require competency in polygenic risk scores concepts for all those involved in patient care. To our knowledge, this is the first study to assess healthcare professionals' views towards polygenic testing for glaucoma. METHODS: An online cross-sectional questionnaire was distributed to healthcare professionals via relevant professional organisations in Australia. The questionnaire assessed experience and confidence with genetic testing, glaucoma and genetic knowledge, recommendations for the tests, and factors affecting the decision. RESULTS: A total of 94 participants completed the questionnaire. The sample was composed of ophthalmologists (36%), optometrists (21%), orthoptists (17%), general practitioners (16%) and clinical geneticists/genetic counsellors (10%). Although familiarity with polygenic risk scores for glaucoma was low overall (11%), the majority reported a positive attitude towards recommending testing based on known risk factors such as family history (91%) and older age (57%). Over 95% indicated that ophthalmologists would be the most appropriate group to order polygenic risk testing and communicate results. The majority felt they would benefit from more training on polygenic risk scores (93%). CONCLUSIONS: Our findings indicated that multiple groups of healthcare professionals were neither familiar nor confident with the concept of glaucoma polygenic risk testing, and identified training and education needs to support the implementation of testing into clinical practice.

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Our study aimed to identify sweetness preference-associated single-nucleotide polymorphisms (SNPs), characterize the related genetic loci, and develop SNP-based polygenic risk scores (PRS) to analyze their associations with obesity. For genotyping, we utilized a pooled genome-wide association study (GWAS) dataset of 18,499 females and 10,878 males. We conducted genome-wide association analyses, functional annotation, and employed the weighted method to calculate the levels of PRS from 677 sweetness preference-related SNPs. We used Cox proportional hazards modeling with time-varying covariates to estimate age-adjusted and multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for obesity incidence. We also tested the correlation between PRS and environmental factors, including smoking and dietary components, on obesity. Our results showed that in males, the TT genotype of rs4861982 significantly increased obesity risk compared to the GG genotype in the Health Professionals Follow-up Study (HPFS) cohort (HR = 1.565; 95% CI, 1.122-2.184; p = 0.008) and in the pooled analysis (HR = 1.259; 95% CI, 1.030-1.540; p = 0.025). Protein tyrosine phosphatase receptor type O (PTPRO) was identified as strongly associated with sweetness preference, indicating a positive correlation between sweetness preference and obesity risk. Moreover, each 10 pack-year increment in smoking was significantly associated with an increased risk of obesity in the HPFS cohort (HR = 1.024; 95% CI, 1.000-1.048) in males but not in females. In conclusion, significant associations between rs4861982, sweetness preference, and obesity were identified, particularly among males, where environmental factors like smoking are also correlated with obesity risk.

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BACKGROUND: The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized. METHODS AND RESULTS: We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene-environment interactions between genome-wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome-wide PRSs for systolic BP and 4 for genome-wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P < 2 × 10-4). We found evidence for gene-dependent influence of lifestyle factors such as cardiorespiratory fitness, dietary patterns, and tobacco exposure, as well as biomarkers such as serum cholesterol, creatinine, and alkaline phosphatase on BP. CONCLUSIONS: Individuals that are genetically susceptible to high BP may be more vulnerable to common acquired risk factors for hypertension, but these effects appear to be modifiable. The gene-dependent influence of several common acquired risk factors indicates the potential of genetic data combined with lifestyle assessments in risk stratification, and gene-environment-informed risk modeling in the prevention and management of hypertension.

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BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function, and a cure for this devastating disease remains elusive. This study aimed to identify pre-disposing genetic, phenotypic, and exposure-related factors for amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential. METHODS: Utilizing data from the UK (United Kingdom) Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates. RESULTS: Both PRSs modestly predicted ALS diagnosis but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved the prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a fourfold higher ALS risk (95% CI [2.04, 7.73]) versus those in the 40%-60% range. DISCUSSION: By leveraging UK Biobank data, our study uncovers pre-disposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.

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Objective: Eating disorders (EDs) are serious psychiatric disorders with an estimated 3.3 million healthy life-years lost worldwide yearly. Understanding the course of illness, diagnostic transitions and remission, and their associated genetic correlates could inform both ED etiology and treatment. The authors investigated occurrences of ED transitions and presumed remission and their genetic correlates as captured by polygenic scores (PGSs) in a large Danish register-based cohort. Methods: The sample compromised of 10,565 individuals with a diagnosis of anorexia nervosa (AN), bulimia nervosa (BN), or eating disorder not otherwise specified (EDNOS) with at least two registered hospital contacts between 1995 and 2018. Based on medical records, occurrence of diagnostic transitions and periods of presumed remission were identified. Associations between 422 PGS and diagnostic transitions and presumed remission were evaluated using Cox proportional hazard models. Results: A minority of ED cases (14.1%-23.1%) experienced a diagnostic transition. Presumed remission ranged between 86.9%-89.8%. Higher (one SD increase) PGS for major depressive disorder and multisite chronic pain were positively associated with transitioning from AN to either BN or EDNOS. Higher PGS on a measure of body fat percentage and financial difficulties were positively associated with presumed remission from AN. Higher PGS for mood swings was positively associated with presumed remission from EDNOS whereas higher PGS for health rating showed the opposite. Conclusions: The authors found that most ED patients did not experience diagnostic transitions but were more likely to experience a period of presumed remission. Both diagnostic transitions and presumed remission have significant polygenic component.

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Spondyloarthropathies (SpA), including ankylosing spondylitis (AS) and psoriatic arthritis (PsA), have been shown to have a substantial genetic predisposition based on heritability estimates derived from family studies and genome-wide association studies (GWAS). GWAS have uncovered numerous genetic loci associated with susceptibility to SpA, with significant associations to human leukocyte antigen (HLA) genes, which are major genetic risk factors for both AS and PsA. Specific loci differentiating PsA from cutaneous-only psoriasis have been identified, though these remain limited. Further research with larger sample sizes is necessary to identify more PsA-specific genetic markers. Current research focuses on translating these genetic insights into clinical applications. For example, polygenic risk scores are showing promise for the classification of disease risk and diagnosis and future research should focus on refining these risk assessment tools to improve clinical outcomes for individuals with SpA. Addressing these challenges will help integrate genetic testing into patients care and impact clinical practice.

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Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

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In bipolar disorders, abnormalities of sleep patterns and of circadian rhythms of activity are observed during mood episodes, but also persist during euthymia. Shared vulnerabilities between mood disorders and abnormalities of sleep patterns and circadian rhythms of activity have been suggested. This exploratory study investigated the association between polygenic risk scores for bipolar disorder and major depressive disorder, actigraphy estimates of sleep patterns, and circadian rhythms of activity in a sample of 62 euthymic individuals with bipolar disorder. The polygenic risk score - bipolar disorder and polygenic risk score - major depressive disorder were calculated for three stringent thresholds of significance. Data reduction was applied to aggregate actigraphy measures into dimensions using principal component analysis. A higher polygenic risk score - major depressive disorder was associated with more fragmented sleep, while a higher polygenic risk score - bipolar disorder was associated with a later peak of circadian rhythms of activity. These results remained significant after adjustment for age, sex, bipolar disorder subtype, body mass index, current depressive symptoms, current tobacco use, and medications prescribed at inclusion, but not after correction for multiple testing. In conclusion, the genetic vulnerabilities to major depression and to bipolar disorder might be associated with different abnormalities of sleep patterns and circadian rhythms of activity. The results should be replicated in larger and independent samples.

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Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from a small cohort of post-mortem control and PD brain tissue. We found a significant association of oligodendrocytes (ODCs) and oligodendrocyte precursor cells (OPCs) with PD-linked risk loci and report several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria. In an independent PD cohort with clinical measures (681 cases and 549 controls), polygenic risk scores derived from the dysregulated genes significantly predicted Montreal Cognitive Assessment (MoCA)-, and Beck Depression Inventory-II (BDI-II)-scores but not motor impairment (UPDRS-III). We extended our analysis of clinical outcome prediction by incorporating differentially expressed genes from three separate datasets that were previously published by different laboratories. In the first dataset from the anterior cingulate cortex, we identified an association between ODCs and BDI-II. In the second dataset obtained from the substantia nigra (SN), OPCs displayed an association with UPDRS-III. In the third dataset from the SN region, a distinct subtype of OPCs, labeled OPC_ADM, exhibited an association with UPDRS-III. Intriguingly, the OPC_ADM cluster also demonstrated a significant increase in PD samples. These results suggest that by expanding our focus to glial cells, we can uncover region-specific molecular pathways associated with PD symptoms.

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BACKGROUND: Coronary atherosclerotic burden and adverse coronary heart disease events are related phenotypes with likely shared genetic cause. METHODS: We analyzed 6021 patients with available coronary angiography, genotyping, and exome sequencing data. We tested for associations of polygenic risk scores for coronary heart disease (PRSCHD) with multiple measures of coronary artery disease (CAD) severity. We assessed the interplay between PRSCHD and pathogenic/likely pathogenic variants in 3 familial hypercholesterolemia genes. We performed mediation analyses to explore whether CAD severity mediated the association of PRSCHD with prevalent coronary heart disease and incident myocardial infarction. RESULTS: A 1-SD increase in PRSCHD was associated with multiple measures of CAD severity, including the log Gensini score (ß, 0.31 [95% CI, 0.28-0.33]). Carrying a pathogenic/likely pathogenic familial hypercholesterolemia variant was associated with a higher log Gensini score after adjustment for PRSCHD (ß, 0.21 [95% CI, 0.03-0.38]). PRSCHD was associated with incident myocardial infarction over a mean follow-up of 9.2 years (hazard ratio, 1.20 [95% CI, 1.13-1.27]; P=5×10-10), and the Gensini score mediated 90% of this association. CONCLUSIONS: PRSCHD was associated with multiple measures of CAD severity. The association of PRSCHD with incident myocardial infarction was almost fully mediated by CAD severity, indicating a considerable genetic overlap between the 2 phenotypes.

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Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

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RESEARCH QUESTION: What are the perspectives of preimplantation genetic testing (PGT) patients in Belgium on the ethics of PGT for polygenic risk scoring (PGT-P)? DESIGN: In-depth interviews (18 in total, 10 couples, 8 women, n = 28) were performed with patients who had undergone treatment with PGT for monogenic/single-gene defects (PGT-M) or chromosomal structural rearrangements (PGT-SR) between 2017 and 2019 in Belgium. Participants were asked about their own experiences with PGT-M/SR and about their viewpoints on PGT-P, including their own interest and their ideas on its desirability, scope and consequences. Inductive content analysis was used to analyse the interviews. RESULTS: Participants stated that their experiences with PGT-M/SR had been physically, psychologically and practically difficult. Most participants stated that, partly because of these difficulties, they did not see the added value of knowing the risk scores of embryos via PGT-P. Many participants worried that PGT-P could lead to additional anxieties, responsibilities and complex choices in reproduction and parenthood. They argued that not everything should be controlled and felt that PGT-P, especially non-medical and broad screening, was going too far. With regards to the clinical implementation of PGT-P, participants in general preferred PGT-P to be limited to people with a serious polygenic family history and wanted embryo selection decisions to be made by healthcare professionals. CONCLUSIONS: This study shows that individuals with experience of PGT-M/SR saw PGT-P as different from PGT-M/SR. They had various ethical concerns with regards to PGT-P, especially regarding broadly offering PGT-P. These stakeholder viewpoints need to be considered regarding potential PGT-P implementation and guidelines.

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Recent advances in psychiatric genetics have enabled the use of polygenic risk scores (PRS) to estimate genetic risk for psychiatric disorders. However, the potential use of PRS in child and adolescent psychiatry has raised concerns. This study provides an in-depth examination of attitudes among child and adolescent psychiatrists (CAP) regarding the use of PRS in psychiatry. We conducted semi-structured interviews with U.S.-based CAP (n = 29) who possess expertise in genetics. The majority of CAP indicated that PRS have limited clinical utility in their current form and are not ready for clinical implementation. Most clinicians stated that nothing would motivate them to generate PRS at present; however, some exceptions were noted (e.g., parent/family request). Clinicians spoke to challenges related to ordering, interpreting, and explaining PRS to patients and families. CAP raised concerns regarding the potential for this information to be misinterpreted or misused by patients, families, clinicians, and outside entities such as insurance companies. Finally, some CAP noted that PRS may lead to increased stigmatization of psychiatric disorders, and at the extreme, could be used to support eugenics. As PRS testing increases, it will be critical to examine CAP and other stakeholders' views to ensure responsible implementation of this technology.

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BACKGROUND: Associations between metabolic status and metabolic changes with the risk of cardiovascular outcomes have been reported. However, the role of genetic susceptibility underlying these associations remains unexplored. We aimed to examine how metabolic status, metabolic transitions, and genetic susceptibility collectively impact cardiovascular outcomes and all-cause mortality across diverse body mass index (BMI) categories. METHODS: In our analysis of the UK Biobank, we included a total of 481,576 participants (mean age: 56.55; male: 45.9%) at baseline. Metabolically healthy (MH) status was defined by the presence of < 3 abnormal components (waist circumstance, blood pressure, blood glucose, triglycerides, and high-density lipoprotein cholesterol). Normal weight, overweight, and obesity were defined as 18.5 ≤ BMI < 25 kg/m2, 25 ≤ BMI < 30 kg/m2, and BMI ≥ 30 kg/m2, respectively. Genetic predisposition was estimated using the polygenic risk score (PRS). Cox regressions were performed to evaluate the associations of metabolic status, metabolic transitions, and PRS with cardiovascular outcomes and all-cause mortality across BMI categories. RESULTS: During a median follow-up of 14.38 years, 31,883 (7.3%) all-cause deaths, 8133 (1.8%) cardiovascular disease (CVD) deaths, and 67,260 (14.8%) CVD cases were documented. Among those with a high PRS, individuals classified as metabolically healthy overweight had the lowest risk of all-cause mortality (hazard ratios [HR] 0.70; 95% confidence interval [CI] 0.65, 0.76) and CVD mortality (HR 0.57; 95% CI 0.50, 0.64) compared to those who were metabolically unhealthy obesity, with the beneficial associations appearing to be greater in the moderate and low PRS groups. Individuals who were metabolically healthy normal weight had the lowest risk of CVD morbidity (HR 0.54; 95% CI 0.51, 0.57). Furthermore, the inverse associations of metabolic status and PRS with cardiovascular outcomes and all-cause mortality across BMI categories were more pronounced among individuals younger than 65 years (Pinteraction < 0.05). Additionally, the combined protective effects of metabolic transitions and PRS on these outcomes among BMI categories were observed. CONCLUSIONS: MH status and a low PRS are associated with a lower risk of adverse cardiovascular outcomes and all-cause mortality across all BMI categories. This protective effect is particularly pronounced in individuals younger than 65 years. Further research is required to confirm these findings in diverse populations and to investigate the underlying mechanisms involved.

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BACKGROUND: Anhedonia is characterized by a reduced ability to anticipate, experience, and/or learn about pleasure. This phenomenon has a transdiagnostic nature and is one of the key symptoms of mood disorders, schizophrenia, addictions, and somatic conditions. AIM: To evaluate the genetic architecture of anhedonia and its overlap with other mental disorders and somatic conditions. METHODS: We performed a genome-wide association study of anhedonia on a sample of 4,520 individuals from a Russian non-clinical population. Using the available summary statistics, we calculated polygenic risk scores (PRS) to investigate the genetic relationship between anhedonia and other psychiatric or somatic phenotypes. RESULTS: No variants with a genome-wide significant association were identified. PRS for major depression, bipolar disorder, and schizophrenia were significantly associated with anhedonia. Conversely, no significant associations were found between PRS for anxiety and anhedonia, which aligns well with existing clinical evidence. None of the PRS for somatic phenotypes attained a significance level after correction for multiple comparisons. A nominal significance for the anhedonia association was determined for omega-3 fatty acids, type 2 diabetes mellitus, and Crohn's disease. CONCLUSION: Anhedonia has a complex polygenic architecture, and its presence in somatic diseases or normal conditions may be due to a genetic predisposition to mood disorders or schizophrenia.

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AIMS/HYPOTHESIS: Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes. METHODS: We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose. RESULTS: We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone. CONCLUSIONS/INTERPRETATION: Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

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Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

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BACKGROUND: Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual "omics" signature that distinguishes subjects with varying cognitive profiles. RESULTS: We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging. CONCLUSIONS: These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

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