OBJECTIVE: 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.
CONCLUSIONS: 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.

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.

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.

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.

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.

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.

UNASSIGNED: Coronary atherosclerotic burden and adverse coronary heart disease events are related phenotypes with likely shared genetic cause.
UNASSIGNED: 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.
UNASSIGNED: A 1-SD increase in PRSCHD was associated with multiple measures of CAD severity, including the log Gensini score (β, 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 (β, 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.
UNASSIGNED: 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.

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.

OBJECTIVE: 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: 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.

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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
UNASSIGNED: Ангедония характеризуется снижением способности предвосхищать, испытывать и/или усваивать удовольствие. Этот феномен имеет трансдиагностическую природу и является одним из ключевых симптомов расстройств настроения, шизофрении, аддикций и соматических состояний.
UNASSIGNED: Оценить генетическую архитектуру ангедонии и её перекрытие с другими психическими расстройствами и соматическими состояниями.
UNASSIGNED: Проведено исследование полногеномного поиска ассоциаций ангедонии на выборке из 4 520 человек из российской неклинической популяции. Используя доступную сводную статистику, мы рассчитали шкалы полигенного риска (polygenic risk scores, PRS), чтобы исследовать генетическую связь между ангедонией и другими психиатрическими или соматическими фенотипами.
UNASSIGNED: Не было идентифицировано ни одного варианта, достигшего полногеномного уровня значимости. PRS для депрессии, биполярного расстройства и шизофрении были значимо ассоциированы с ангедонией. И наоборот, не обнаружено значимых ассоциаций между PRS для тревожных расстройств и ангедонии, что хорошо согласуется с существующими клиническими данными. Ни один из PRS для соматических фенотипов не достиг уровня значимости после коррекции на множественные сравнения. При номинальном уровне значимости ассоциация с ангедонией выявлена для PRS ω-3 жирных кислот, сахарного диабета 2-го типа и болезни Крона.
UNASSIGNED: Ангедония имеет сложную полигенную архитектуру, в связи с чем её присутствие при соматических заболеваниях или нормальных состояниях может быть обусловлено генетической предрасположенностью к расстройствам настроения или шизофрении.