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Background/Objectives: Metabolic syndrome (MetS) is a complex condition linking obesity, diabetes, and hypertension, representing a major challenge in clinical care. Its rising global prevalence, driven by urbanization, sedentary lifestyles, and dietary changes, underscores the need for effective management. This study aims to explore the genetic mechanisms behind MetS, including adiposity, inflammation, neurotransmitters, and β-cell function, to develop a prognostic tool for MetS risk. Methods: We genotyped 40 genetic variants across these pathways in 279 MetS patients and 397 healthy individuals. Using logistic regression, we evaluated the prognostic capability of a polygenic score model for MetS risk, both independently and with other factors like sex and age. Results: Logistic regression analysis identified 18 genetic variants significantly associated with MetS. The optimal predictive model used polygenic scores calculated with weights assigned to the 18 loci (AUC 82.5%, 95% CI 79.4–85.6%), with age and sex providing a minimal, non-significant improvement (AUC 83.3%, 95% CI 80.2–86.3%). The addition of the polygenic score significantly improved net reclassification (NRI = 1.03%, p = 3.42 × 10−50). Including all 40 variants did not enhance prediction (NRI = −0.11, p = 0.507). Conclusions: Polygenic scores could aid in predicting MetS risk and health outcomes, emphasizing the need for diagnostic tools tailored to specific populations. Additional research is warranted to corroborate these conclusions and explore the molecular mechanisms of MetS. [ABSTRACT FROM AUTHOR]

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

Background: Intraductal papillary mucinous neoplasms (IPMNs) are precursors to pancreatic cancer, but not all IPMNs progress to cancer. The objective of this study was to identify the germline genetic variants associated with IPMN clinical progression by conducting the first genome‐wide association study (GWAS) and computing a polygenic hazard score (PHS) in 338 patients with IPMN. Methods: The study population was divided into two subsets, and a Cox analysis adjusted for sex, age, cyst size at diagnosis, and the top 10 principal components was performed. A PHS was calculated using the genotypes of common variants associated with IPMN progression identified. Results: Eight loci with significant associations (p < 5 × 10−8) were identified, and the most significant was 7q21.11‐rs117620617 (hazard ratio, 16.35; 95% confidence interval, 6.93–38.60; p = 1.80 × 10−10). All variants were associated with inflammatory processes, suggesting that alleles that predispose to an inflammatory prone phenotype may promote progression. The PHS indicated a statistically significant association (hazard ratio, 18.05; 95% confidence interval, 7.96–45.80; p = 6.18 × 10−11) with IPMN progression among individuals who had the highest number of effect alleles (fourth quartile) compared with those who had the lowest number (first quartile). Conclusions: The current results study advance the understanding of individual predisposition to IPMN progression and underscore the potential use of genetics in the stratification of patients who have IPMN. This genome‐wide association study identifies eight loci associated with the progression of intraductal papillary mucinous neoplasms toward malignancy. The findings provide valuable insights into the genetic predisposition for intraductal papillary mucinous neoplasm progression, highlighting the role of inflammatory processes. [ABSTRACT FROM AUTHOR]

BackgroundIn the United States, ~50% of individuals who meet criteria for alcohol use disorder (AUD) during their lifetimes do not remit. We previously reported that a polygenic score for AUD (PGSAUD ) was positively associated with AUD severity as measured by DSM-5 lifetime criterion count, and AUD severity was negatively associated with remission. Thus, we hypothesized that PGSAUD would be negatively associated with remission.MethodsIndividuals of European (EA) and African ancestry (AA) from the Collaborative Study on the Genetics of Alcoholism (COGA) who met lifetime criteria for AUD, and two EA cohorts ascertained for studies of liver diseases and substance use disorders from the Indiana Biobank were included. In COGA, 12-month remission was defined as any period of ≥12 consecutive months without meeting AUD criteria except craving and was further categorized as abstinent and non-abstinent. In the Indiana Biobank, remission was defined based on ICD codes and could not be further distinguished as abstinent or non-abstinent. Sex and age were included as covariates. COGA analyses included additional adjustment for AUD severity, family history of remission, and AUD treatment history.ResultsIn COGA EA, PGSAUD was negatively associated with 12-month and non-abstinent remission (p ≤ 0.013, βs between -0.15 and -0.10) after adjusting for all covariates. In contrast to the COGA findings, PGSAUD was positively associated with remission (p = 0.004, β = 0.28) in the Indiana Biobank liver diseases cohort but not in the Indiana Biobank substance use disorder cohort (p = 0.17, β = 0.15).ConclusionsPGSAUD was negatively associated with 12-month and non-abstinent remission in COGA EA, independent of behavioral measures of AUD severity and family history of remission. The discrepant results in COGA and the Indiana Biobank could reflect different ascertainment strategies: the Indiana Biobank participants were older and had higher rates of liver disease, suggesting that these individuals remitted due to alcohol-related health conditions that manifested in later life.

Background Adult height has been associated with handgrip strength, which is a surrogate marker of physical frailty. However, it is uncertain if this association is causative or due to confounding bias. Methods We evaluated pairwise associations among handgrip strength, adult height, and genetically determined height (using a polygenic score [PGS] for height in a mediation framework and a 2-sample Mendelian randomization approach) by means of a multivariable regression model using a prospective cohort of Chinese living in Singapore. We additionally evaluated pathway enrichments of height-related genes in relation to increased handgrip strength to discover common biological mechanisms underlying associations of genetically determined height with handgrip strength. Results Height PGS exhibited a positive association with handgrip strength at late life after adjusting for midlife body weight and other baseline exposures (cigarette smoking, education, and physical activity status, p  = 1.2 × 10−9). Approximately 66.4% of the total effect of height PGS on handgrip strength was mediated through adult height (βindirect-effect = 0.034, p indirect-effect = 1.4 × 10−40). Two-sample Mendelian randomization evaluations showed a consistent causal relationship between increased height and increased handgrip strength in late life (p between 6.6 × 10−4 and 3.9 × 10−18), with insignificant horizontal pleiotropic effects (p MR-Egger intercept = 0.853). Pathway analyses of genes related to both increased adult height and handgrip strength revealed enrichment in ossification and adipogenesis pathways (p adj between.034 and 6.8 × 10−4). Conclusions The study highlights a potentially causal effect between increased adult height and increased handgrip strength in late life, which may be explained by related biological processes underlying the preservation of muscle mass and strength in aging. [ABSTRACT FROM AUTHOR]

Plasma proteomics could enhance risk prediction for multiple diseases beyond conventional risk factors or polygenic scores (PS). To assess utility of proteomics for risk prediction of ischemic heart disease (IHD) compared with conventional risk factors and PS in Chinese and European populations. A nested case-cohort study measured plasma levels of 2923 proteins using Olink Explore panel in ~ 4000 Chinese adults (1976 incident IHD cases and 2001 sub-cohort controls). We used conventional and machine learning (Boruta) methods to develop proteomics-based prediction models of IHD, with discrimination assessed using area under the curve (AUC), C-statistics and net reclassification index (NRI). These were compared with conventional risk factors and PS in Chinese and in 37,187 Europeans. Overall, 446 proteins were associated with IHD (false discovery rate < 0.05) in Chinese after adjustment for conventional cardiovascular disease risk factors. Proteomic risk models alone yielded higher C-statistics for IHD than conventional risk factors or PS (0.855 [95%CI 0.841–0.868] vs. 0.845 [0.829–0.860] vs 0.553 [0.528–0.578], respectively). Addition of 446 proteins to PS improved C-statistics to 0.857 (0.843–0.871) and NRI by 109.1%; and addition to conventional risk factors improved C-statistics to 0.868 (0.854–0.882) and NRI by 86.9%. Boruta analysis identified 30 proteins accounting for ~ 90% of improvement in NRI for IHD conferred by all 2923 proteins. Similar proteomic panels yielded comparable improvements in risk prediction of IHD in Europeans. Plasma proteomics improved risk prediction of IHD beyond conventional risk factors and PS and could enhance precision medicine approaches for primary prevention of IHD. [ABSTRACT FROM AUTHOR]

Background and Aims This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. Methods Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. Results AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%–41.8%) vs. 29.3% (27.0%–31.5%) in UK Biobank, 36.5% (34.4%–38.5%) vs. 32.5% (30.4%–34.5%) in MGB, 41.8% (37.7%–45.9%) vs. 33.0% (28.9%–37.2%) in FHS, and 34.9% (28.8%–41.0%) vs. 28.9% (22.9%–35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P <.0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P <.0001), 0.856 vs. 0.818 in FHS (P <.0001), and 0.827 vs. 0.791 (P =.0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P =.0042) and All of Us (P =.049). Conclusions A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%–41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors. [ABSTRACT FROM AUTHOR]

Background: The clinical course of major depressive disorder (MDD) is heterogeneous, and early-onset MDD often has a more severe and complex clinical course. Our goal was to determine whether polygenic scores (PGSs) for psychiatric disorders are associated with treatment trajectories in early-onset MDD treated in secondary care. Methods: Data were drawn from the iPSYCH2015 sample, which includes all individuals born in Denmark between 1981 and 2008 who were treated in secondary care for depression between 1995 and 2015. We selected unrelated individuals of European ancestry with an MDD diagnosis between ages 10–25 (N = 10577). Seven-year trajectories of hospital contacts for depression were modeled using Latent Class Growth Analysis. Associations between PGS for MDD, bipolar disorder, schizophrenia, ADHD, and anorexia and trajectories of MDD contacts were modeled using multinomial logistic regressions. Results: We identified four trajectory patterns: brief contact (65%), prolonged initial contact (20%), later re-entry (8%), and persistent contact (7%). Relative to the brief contact trajectory, higher PGS for ADHD was associated with a decreased odds of membership in the prolonged initial contact (odds ratio = 1.06, 95% confidence interval = 1.01–1.11) and persistent contact (1.12, 1.03–1.21) trajectories, while PGS-AN was associated with increased odds of membership in the persistent contact trajectory (1.12, 1.03–1.21). Conclusions: We found significant associations between polygenic liabilities for psychiatric disorders and treatment trajectories in patients with secondary-treated early-onset MDD. These findings help elucidate the relationship between a patient's genetics and their clinical course; however, the effect sizes are small and therefore unlikely to have predictive value in clinical settings. [ABSTRACT FROM AUTHOR]

Summary Background/Objectives Subjects/Methods Results Conclusions Few studies have examined the putative mediating role of eating behaviours linking genetic susceptibility and body weight. The goal of this study was to investigate the extent to which two polygenic scores (PGSs) for body mass index (BMI), based on child and adult data, predicted BMI through over‐eating and fussy eating across childhood.The study sample involved 692 participants from a birth cohort study. Height and weight were measured on six occasions between ages 6 and 13 years. Over‐eating and fussy eating behaviours were assessed five times between ages 2 and 6 years. Longitudinal growth curve mediation analysis was used to estimate the contributions of the PGSs to BMI z‐scores mediated by over‐eating and fussy eating.Both PGSs predicted BMI z‐scores (PGSchild: β = 0.26, 95% CI: 0.19–0.33; PGSadult: β = 0.34, 95% CI: 0.27–0.41). Over‐eating significantly mediated these associations, but this mediation decreased over time from 6 years (PGSchild: 18.0%, 95% CI: 3.1–32.9, p‐value = 0.018; PGSadult: 14.2%, 95% CI: 2.8–25.5, p‐value = 0.014) to 13 years (PGSchild: 11.4%, 95% CI: −0.4‐23.1, p‐value = 0.057; PGSadult: 6.2%, 95% CI: 0.4–12.0, p‐value = 0.037). Fussy eating did not show any mediation.Our results support the view that appetite is key to translating genetic susceptibility into changes in body weight. [ABSTRACT FROM AUTHOR]

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

Familial hypercholesterolemia (FH) is the most significant inherited risk factor for coronary heart disease (CHD). Current guidelines focus on monogenic FH, but the polygenic form is more common and less understood. This study aimed to assess the clinical utility of an 8-SNP LDLC polygenic score in a central Romanian cohort. The cohort included 97 healthy controls and 125 patients with premature (P)CHD. The weighted LDLC polygenic risk score (wPRS) was analyzed for associations with relevant phenotypic traits, PCHD risk, and clinical FH diagnosis. The wPRS positively correlated with LDLC and DLCN scores, and LDLC concentrations could be predicted by wPRS. A trend of increasing LDLC and DLCN scores with wPRS deciles was observed. A +1 SD increase in wPRS was associated with a 36% higher likelihood of having LDLC > 190 mg/dL and increases in LDLC (+0.20 SD), DLCN score (+0.16 SD), and BMI (+0.15 SD), as well as a decrease in HDLC (−0.14 SD). Although wPRS did not predict PCHD across the entire spectrum of values, individuals above the 90th percentile were three times more likely to have PCHD compared to those within the 10th or 20th percentiles. Additionally, wPRS > 45th percentile identified "definite" clinical FH (DLCN score > 8) with 100% sensitivity and 45% specificity. The LDLC polygenic score correlates with key phenotypic traits, and individuals with high scores are more likely to have PCHD. Implementing this genetic tool may enhance risk prediction and patient stratification. These findings, the first of their kind in Romania, are consistent with the existing literature. [ABSTRACT FROM AUTHOR]

In adults, polygenic scores (PGSs) of telomere length (TL) alleles explain about 4.5% of the variance in TL, as measured by quantitative polymerase chain reaction (qPCR). Yet, these PGSs strongly infer a causal role of telomeres in aging‐related diseases. To better understand the determinants of TL through the lifespan, it is essential to examine to what extent these PGSs explain TL in newborns. This study investigates the effect of PGSs on TL in both newborns and their parents, with TL measured by Southern blotting and expressed in base‐pairs (bp). Additionally, the study explores the impact of PGSs related to transmitted or non‐transmitted alleles on TL in newborns. For parents and newborns, the PGS effects on TL were 172 bp (p = 2.03 × 10−15) and 161 bp (p = 3.06 × 10−8), explaining 6.6% and 5.2% of the TL variance, respectively. The strongest PGS effect was shown for maternally transmitted alleles in newborn girls, amounting to 214 bp (p = 3.77 × 10−6) and explaining 7.8% of the TL variance. The PGS effect of non‐transmitted alleles was 56 bp (p = 0.0593) and explained 0.6% of the TL variance. Our findings highlight the importance of TL genetics in understanding early‐life determinants of TL. They point to the potential utility of PGSs composed of TL alleles in identifying susceptibility to aging‐related diseases from birth and reveal the presence of sexual dimorphism in the effect of TL alleles on TL in newborns. Finally, we attribute the higher TL variance explained by PGSs in our study to TL measurement by Southern blotting. [ABSTRACT FROM AUTHOR]

Context The hypothalamic-pituitary-gonadal axis's transient activity in infancy, i.e, minipuberty, is considered crucial for male reproductive function. Historically, minipuberty has been considered a passive response triggered by the withdrawal of placental steroids at birth. However, given its potential link to adult reproductive function, we hypothesize that minipuberty is a partially genetically regulated process, suggesting a link between the genetic architecture of reproductive hormone concentrations across lifespan. Objective To investigate the association of UK Biobank Study-based polygenic scores (PGS) of adult total testosterone (T) and sex hormone-binding globulin (SHBG) concentrations with trajectories of reproductive hormones concentrations in male infants. Design Prospective, longitudinal birth cohort (The COPENHAGEN Minipuberty Study, 2016-2018, ClinTrial: NCT02784184). Individual PGSs in male infants derived from published literature were calculated for total T and SHBG. The associations with mean SD scores (SDS) of reproductive hormone concentrations in infancy were tested. Setting Population-based. Patients or other participants Healthy, male, term, singleton newborns were followed with repeated clinical examinations including blood sampling during a 1-year follow-up (n = 109). Main outcome measures Circulating reproductive hormone concentrations. Results T-PGSadult were significant associated with mean T-SDSinfancy, mean SHBG-SDSinfancy, and mean LH-SDSinfancy (P =.02, <.001 and.03, with r2 = 0.05, 0.21 and 0.04, respectively). SHBG-PGSadult was significantly associated with mean SHBG-SDSinfancy (P <.001, r2 = 0.18). T-PGSadult explained 5% and 21% of the phenotypic variation in infancy of mean T-SDSinfancy and SHBG-SDSinfancy, respectively. Conclusion Our findings suggest that the genetic architecture underlying total T and SHBG in adults also associates with hormone concentrations and their trajectories during infancy. [ABSTRACT FROM AUTHOR]

ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Abstract The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

Abstract Background The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. Methods Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. Results The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2 ASRS = 61.11%, R2 ASRS + PGS=61.69%), the WURS by 0.61pp (R2 WURS = 77.33%, R2 WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2 ASRS + WURS=80.84%, R2 ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2 family = 28.06%, R2 family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. Conclusion We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined.

BackgroundResearchers have identified genetic and neural risk factors for externalizing behaviors. However, it has not yet been determined if genetic liability is conferred in part through associations with more proximal neurophysiological risk markers.MethodsParticipants from the Collaborative Study on the Genetics of Alcoholism, a large, family-based study of alcohol use disorders were genotyped and polygenic scores for externalizing (EXT PGS) were calculated. Associations with target P3 amplitude from a visual oddball task (P3) and broad endorsement of externalizing behaviors (indexed via self-report of alcohol and cannabis use, and antisocial behavior) were assessed in participants of European (EA; N = 2851) and African ancestry (AA; N = 1402). Analyses were also stratified by age (adolescents, age 12-17 and young adults, age 18-32).ResultsThe EXT PGS was significantly associated with higher levels of externalizing behaviors among EA adolescents and young adults as well as AA young adults. P3 was inversely associated with externalizing behaviors among EA young adults. EXT PGS was not significantly associated with P3 amplitude and therefore, there was no evidence that P3 amplitude indirectly accounted for the association between EXT PGS and externalizing behaviors.ConclusionsBoth the EXT PGS and P3 amplitude were significantly associated with externalizing behaviors among EA young adults. However, these associations with externalizing behaviors appear to be independent of each other, suggesting that they may index different facets of externalizing.

Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations. [ABSTRACT FROM AUTHOR]

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS‐C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue‐specific enrichment analysis indicate that the protective effect of PGS‐C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high‐cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS‐C. [ABSTRACT FROM AUTHOR]

Background: An increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer's disease (LOAD) pathology. Due to the increased permeability of the blood-brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it. Methods: We used a genome-wide association study (GWAS) of 90 different plasma proteins (n = 17,747) to create polygenic scores (PGSs) in an independent discovery (cases = 1,852 and controls = 1,990) and replication (cases = 799 and controls = 778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases = 21,982, controls = 41,944) as an outcome to explore possible causal relationships between the two. Results: We identified four plasma protein level PGSs that were significantly associated (FDR-adjusted p < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR/95%CI = 0.945/0.906-0.984, p = 0.005). Conclusion: Our results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a complex disorder that is influenced by multiple genetic risk factors. There is a significant heterogeneity in PD presentation, both pathologically and clinically. Some of the most common and important symptoms affecting the patient are cognitive impairment and dementia. However, the genetic and biological basis underlying the differences in cognitive profiles, including the development of dementia in PD, is not yet well understood. Understanding the role of genes in cognitive outcomes is crucial for effective patient counseling and treatment. Research on familial PD has discovered more than 20 genes that can cause the disease. The identified genes responsible for familial cases of PD are LRRK2, PARK7, PINK1, PRKN, or SNCA gene, although theremay be other genes that also contribute. Additionally, some of these genes may also play a role in cases that were previously thought to be sporadic. Currently, numerous well-described genes increase the risk of cognitive decline in PD, each with varying levels of penetrance. The aim of this review is to identify the relevant genetic factors that contribute to differences in cognition. We discuss the genes thatmay affect cognition and the challenges in establishing a clear genetic diagnostic and prognostic assessment. This article aims to demonstrate the complexity of the genetic background of cognition in PD and to present the different types of genotype changes that can impact cognition through various neurobiological mechanisms. [ABSTRACT FROM AUTHOR]

This article discusses the importance and complexity of reference intervals for thyroid-stimulating hormone (TSH) levels. Reference intervals are established based on a sample of healthy individuals and are used to determine whether an individual's TSH levels are within the normal range. However, factors such as age, ethnicity, sex, iodine status, and pregnancy can affect the reference intervals for TSH. The article also highlights the impact of genetic factors on TSH reference intervals and suggests that personalized treatment based on more accurate reference intervals may improve patient outcomes. However, further research is needed to explore these possibilities. [Extracted from the article]

Background: Serum thyroid-stimulating hormone (TSH) measurement is the diagnostic cornerstone for primary thyroid dysfunction. There is high inter-individual but limited intra-individual variation in TSH concentrations, largely due to genetic factors. The currently used wide population-based reference intervals may lead to inappropriate management decisions. Methods: A polygenic score (PGS) including 59 genetic variants was used to calculate genetically determined TSH reference ranges in a thyroid disease-free cohort (n = 6,834). Its effect on reclassification of diagnoses was investigated when compared to using population-based reference ranges. Next, results were validated in a second independent population-based thyroid disease-free cohort (n = 3,800). Potential clinical implications were assessed in a third independent population-based cohort including individuals without thyroid disease (n = 26,321) as well as individuals on levothyroxine (LT4) treatment (n = 1,132). Results: PGS was a much stronger predictor of individual TSH concentrations than FT4 (total variance in TSH concentrations explained 9.2–11.1% vs. 2.4–2.7%, respectively) or any other nongenetic factor (total variance in TSH concentrations explained 0.2–1.8%). Genetically determined TSH reference ranges differed significantly between PGS quartiles in all cohorts, while the differences in FT4 concentrations were absent or only minor. Up to 24.7–30.1% of individuals, previously classified as having subclinical hypo- and hyperthyroidism when using population-based TSH reference ranges, were reclassified as euthyroid when genetically determined TSH reference ranges were applied. Individuals in the higher PGS quartiles had a higher probability of being prescribed LT4 treatment compared to individuals from the lower PGS quartiles (3.3% in Q1 vs. 5.2% in Q4, Pfor trend =1.7 × 10−8). Conclusions: Individual genetic profiles have the potential to personalize TSH reference ranges, with large effects on reclassification of diagnosis and LT4 prescriptions. As the currently used PGS can only predict approximately 10% of inter-individual variation in TSH concentrations, it should be further improved when more genetic variants determining TSH concentrations are identified in future studies. [ABSTRACT FROM AUTHOR]

Background: We used a polygenic score for externalizing behavior (extPGS) and structural MRI to examine potential pathways from genetic liability to conduct problems via the brain across the adolescent transition. Methods: Three annual assessments of child conduct problems, attention‐deficit/hyperactivity problems, and internalizing problems were conducted across across 9–13 years of age among 4,475 children of European ancestry in the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®). Results: The extPGS predicted conduct problems in each wave (R2 = 2.0%–2.9%). Bifactor models revealed that the extPRS predicted variance specific to conduct problems (R2 = 1.7%–2.1%), but also variance that conduct problems shared with other measured problems (R2 =.8%–1.4%). Longitudinally, extPGS predicted levels of specific conduct problems (R2 = 2.0%), but not their slope of change across age. The extPGS was associated with total gray matter volume (TGMV; R2 =.4%) and lower TGMV predicted both specific conduct problems (R2 = 1.7%–2.1%) and the variance common to all problems in each wave (R2 = 1.6%–3.1%). A modest proportion of the polygenic liability specific to conduct problems in each wave was statistically mediated by TGMV. Conclusions: Across the adolescent transition, the extPGS predicted both variance specific to conduct problems and variance shared by all measured problems. The extPGS also was associated with TGMV, which robustly predicted conduct problems. Statistical mediation analyses suggested the hypothesis that polygenic variation influences individual differences in brain development that are related to the likelihood of conduct problems during the adolescent transition, justifying new research to test this causal hypothesis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques. Methods: Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556–1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated. Results: All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks. Conclusions: These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest. [ABSTRACT FROM AUTHOR]

Insulin resistance (IR) and beta cell dysfunction are the major drivers of type 2 diabetes (T2D). Genome-Wide Association Studies (GWAS) on IR have been predominantly conducted in European populations, while Middle Eastern populations remain largely underrepresented. We conducted a GWAS on the indices of IR (HOMA2-IR) and beta cell function (HOMA2-%B) in 6,217 nondiabetic individuals from the Qatar Biobank (QBB; Discovery cohort; n = 2170, Replication cohort; n = 4047) with and without body mass index (BMI) adjustment. We also developed polygenic scores (PGS) for HOMA2-IR and compared their performance with a previously derived PGS for HOMA-IR (PGS003470). We replicated 11 loci that have been previously associated with HOMA-IR and 24 loci that have been associated with HOMA-%B, at nominal statistical significance. We also identified a novel locus associated with beta cell function near VEGFC gene, tagged by rs61552983 (P = 4.38 × 10-8). Moreover, our best performing PGS (Q-PGS4; Adj R² = 0.233 ± 0.014; P = 1.55 x 10-3) performed better than PGS003470 (Adj R² = 0.194 ± 0.014; P = 5.45 x 10-2) in predicting HOMA2-IR in our dataset. This is the first GWAS on HOMA2 and the first GWAS conducted in the Middle East focusing on IR and beta cell function. Herein, we report a novel locus in VEGFC that is implicated in beta cell dysfunction. Inclusion of under-represented populations in GWAS has potentials to provide important insights into the genetic architecture of IR and beta cell function. [ABSTRACT FROM AUTHOR]

Background: The inclusion of biomarkers could improve diagnostic accuracy of attention-deficit/hyperactivity disorder (ADHD). One potential biomarker is the ADHD polygenic score (PGS), a measure of genetic liability for ADHD. This study aimed to investigate if the ADHD PGS can provide additional information alongside ADHD rating scales and examination of family history of ADHD to distinguish between ADHD cases and controls. Methods: Polygenic scores were calculated for 576 adults with ADHD and 530 ethnically matched controls. ADHD PGS was used alongside scores from the Wender-Utah Rating Scale (WURS) and the Adult ADHD Self-Report Scale (ASRS) as predictors of ADHD diagnosis in a set of nested logistic regression models. These models were compared by likelihood ratio (LR) tests, Akaike information criterion corrected for small samples (AICc), and Lee R². These analyses were repeated with family history of ADHD as a covariate in all models. Results: The ADHD PGS increased the variance explained of the ASRS by 0.58% points (pp) (R2ASRS = 61.11%, R2ASRS + PGS=61.69%), the WURS by 0.61pp (R2WURS = 77.33%, R2WURS + PGS= 77.94%), of ASRS and WURS together by 0.57pp (R2ASRS + WURS=80.84%, R2ASRS + WURS+PGS=81.40%), and of self-reported family history by 1.40pp (R2family = 28.06%, R2family + PGS=29.46%). These increases were statistically significant, as measured by LR tests and AICc. Conclusion: We found that the ADHD PGS contributed additional information to common diagnostic aids. However, the increase in variance explained was small, suggesting that the ADHD PGS is currently not a clinically useful diagnostic aid. Future studies should examine the utility of ADHD PGS in ADHD prediction alongside non-genetic risk factors, and the diagnostic utility of the ADHD PGS should be evaluated as more genetic data is accumulated and computational tools are further refined. [ABSTRACT FROM AUTHOR]

Introduction: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort. Research design and methods: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications. Results: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P <.05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions. Conclusion: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D. [ABSTRACT FROM AUTHOR]

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time. We introduce shaPRS, a polygenic risk score (PRS) pre-processing method that improves predictive performance of PRSs by leveraging the genetic overlap between traits or ancestries. Importantly, shaPRS requires only GWAS summary statistics of two partially correlated traits or ancestries and is agnostic with respect to the method used to generate the PRSs. [ABSTRACT FROM AUTHOR]

Background: Hyperactivity and inattention, the symptoms of ADHD, are marked by high levels of heritability and intergenerational transmission. Two distinct pathways of genetic intergenerational transmission are distinguished: direct genetic transmission when parental genetic variants are passed to the child's genome and genetic nurture when the parental genetic background contributes to the child's outcomes through rearing environment. This study assessed genetic contributions to hyperactivity and inattention in childhood through these transmission pathways. Methods: The sample included 415 families from the Quebec Newborn Twin Study. Twins' hyperactivity and inattention were assessed in early childhood by parents and in primary school by teachers. The polygenic scores for ADHD (ADHD‐PGS) and educational attainment (EA‐PGS) were computed from twins' and parents' genotypes. A model of intergenerational transmission was developed to estimate (1) the contributions of parents' and children's PGS to the twins' ADHD symptoms and (2) whether these variances were explained by genetic transmission and/or genetic nurture. Results: ADHD‐PGS explained up to 1.6% of the variance of hyperactivity and inattention in early childhood and primary school. EA‐PGS predicted ADHD symptoms at both ages, explaining up to 1.6% of the variance in early childhood and up to 5.5% in primary school. Genetic transmission was the only significant transmission pathway of both PGS. The genetic nurture channeled through EA‐PGS explained up to 3.2% of the variance of inattention in primary school but this association was non‐significant. Conclusions: Genetic propensities to ADHD and education predicted ADHD symptoms in childhood, especially in primary school. Its intergenerational transmission was driven primarily by genetic variants passed to the child, rather than by environmentally mediated parental genetic effects. The model developed in this study can be leveraged in future research to investigate genetic transmission and genetic nurture while accounting for parental assortative mating. [ABSTRACT FROM AUTHOR]

This paper presents one of the first marketing applications of molecular genetics. We report evidence that salespeople's genetic variants linked to educational attainment predict sales performance. Both genetics and selling effort contribute to sales performance, whereas genetics contribute more than personality traits. We further show that adaptive learning, as captured in salespeople's customer orientation and opportunity recognition skills, may explain the gene-sales relationship. We discuss the implications of these findings for sales management and the value of genetic research for the marketing field. This paper was accepted by Matthew Shum, marketing. Funding: S. Gong acknowledges financial support from the NSFC [Grant 71972040] and Fundamental Research Funds for the Central Universities [2022NTSS43]. Q. Li acknowledges financial support from the NSFC [Grant 72072014] and Young Talent Program of Beijing Foreign Studies University. S. Su acknowledges financial support from the NSFC [Grant 71872016]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4879. [ABSTRACT FROM AUTHOR]

Background: Polygenic scores (PGS) have been developed for cancer risk-estimation and show potential as tools to prompt earlier referral for high-risk individuals and aid risk-stratification within cancer screening programmes. This review explores the potential for using PGS to identify individuals at risk of the most common cancers seen in primary care. Methods: Two electronic databases were searched up until November 2023 to identify quantitative, qualitative, and mixed methods studies that reported on the acceptability and clinical impact of using PGS to identify individuals at highest risk of breast, prostate, colorectal and lung cancer in primary care. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of included studies and a narrative synthesis was used to analyse data. Results: A total of 190 papers were identified, 18 of which were eligible for inclusion. A cancer risk-assessment tool incorporating PGS was acceptable to the general practice population and their healthcare providers but major challenges to implementation were identified, including lack of evidence for PGS in non-European ancestry and a need for healthcare provider education in genomic medicine. A PGS cancer risk-assessment had relatively limited impact on psychosocial outcomes and health behaviours. However, for prostate cancer, potential applications for its use in primary care were shown. Conclusions: Cancer risk assessment incorporating PGS in primary care is acceptable to patients and healthcare providers but there is a paucity of research exploring clinical impact. Few studies were identified, and more research is required before clinical implementation of PGS can be recommended. [ABSTRACT FROM AUTHOR]

BackgroundTelomere length (TL) has been linked to cognitive function, decline and dementia. This study aimed to explore whether both measured TL and genetic disposition for TL predict dimensions of cognitive performance in a longitudinal sample of older UK adults.MethodsWe analysed data from PROTECT study participants aged ≥50 years without a dementia diagnosis, who had completed longitudinal cognitive testing. We calculated polygenic scores for telomere length (PGS-TL) for 7,877 participants and measured relative telomere length (RTL) in a subgroup of 846 participants using DNA extracted from saliva samples collected within 6 months either side of their baseline cognitive testing. Latent growth models were used to examine whether RTL and PGS-TL predict both baseline and longitudinal changes in cognitive performance (4 time-points, annually).ResultsIn the whole sample, we did not observe significant associations between either measure of telomere length and initial or longitudinal changes in cognitive performance. Stratifying by median age, in older adults (≥ ∼62 years), longer baseline RTL showed a nominal association with poorer baseline verbal reasoning performance (n = 423, Mintercept = 47.58, B = −1.05, p = .011) and PGS-TL was associated with performance over time (n = 3,939; slope factor, Mslope = 3.23, B = −0.45, p = .001; slope2 factor, Mslope2 = 0.21, B = 0.13, p = .002).ConclusionOur findings suggest either the absence of a significant relationship between telomere length (RTL and PGS-TL) and cognitive performance (baseline and change over time), or possibly a weak age-dependent and domain-specific relationship, in older adults of European ancestry. More research is needed in representative and ancestrally diverse samples over a longer assessment period. Alternative biological ageing indicators may still provide utility in the early detection of individuals at risk for cognitive decline (e.g., pace-of ageing epigenetic clocks).

BACKGROUND: Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligogenic copy number variants (CNVs), multigenic CNVs, and polygenic risk scores (PRSs) as well as idiopathic psychiatric conditions and traits. METHODS: Resting-state functional magnetic resonance imaging data were processed using the same pipeline across 9 datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRSs, 4 idiopathic psychiatric conditions (1022 individuals with autism, schizophrenia, bipolar conditions, or attention-deficit/hyperactivity disorder), and 2 traits (31,424 unaffected control subjects). RESULTS: Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2-0.65 z score), followed by psychiatric conditions (0.15-0.42), neuroticism and fluid intelligence (0.02-0.03), and PRSs (0.01-0.02). Effect sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r = 0.9, p = 5.93 × 10-6). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r = -0.88, p = 8.78 × 10-6). PRSs had disproportionately low effect sizes on connectivity compared with CNVs conferring similar risk for disease. CONCLUSIONS: Heterogeneity and polygenicity affect our ability to detect brain connectivity alterations underlying psychiatric manifestations.

BackgroundChronic kidney disease has been linked to worse cognition. However, this association may be dependent on the marker of kidney function used, and studies assessing modification by genetics are lacking. This study examined associations between multiple measures of kidney function and assessed effect modification by a polygenic score for general cognitive function.MethodsIn this cross-sectional study of up to 341,208 European ancestry participants from the UK Biobank study, we examined associations between albuminuria and estimated glomerular filtration rate based on creatinine (eGFRcre) or cystatin C (eGFRcys) with cognitive performance on tests of verbal-numeric reasoning, reaction time and visual memory. Adjustment for confounding factors was performed using multivariate regression and propensity-score matching. Interaction between kidney function markers and a polygenic risk score for general cognitive function was also assessed.ResultsAlbuminuria was associated with worse performance on tasks of verbal-numeric reasoning (β(points) = -0.09, p

Educational systems that separate students into curriculum tracks later may place less emphasis on socioeconomic family background and allow individuals' personal skills and interests more time to manifest. We tested whether postponing tracking from age 11 to 16 results in stronger genetic prediction of education across a population, exploiting the natural experiment of the Finnish comprehensive school reform between 1972 and 1977. The association between polygenic score of education and achieved education strengthened after the reform by one-third among men and those from low-educated families. We observed no evidence for reform effect among women or those from high-educated families. The first cohort experiencing the new system had the strongest increases. From the perspective of genetic prediction, the school reform promoted equality of opportunity and optimal allocation of human capital. The results also suggest that turbulent circumstances, including puberty or ongoing restructuring of institutional practices, may strengthen genetic associations in education.

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the interindividual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. Methods: Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. Results: Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined metaanalysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. Conclusion: This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes. [ABSTRACT FROM AUTHOR]

This study explored the interactions among prenatal stress, child sex, and polygenic risk scores (PGS) for attention‐deficit/hyperactivity disorder (ADHD) on structural developmental changes of brain regions implicated in ADHD. We used data from two population‐based birth cohorts: Growing Up in Singapore Towards healthy Outcomes (GUSTO) from Singapore (n = 113) and Generation R from Rotterdam, the Netherlands (n = 433). Prenatal stress was assessed using questionnaires. We obtained latent constructs of prenatal adversity and prenatal mood problems using confirmatory factor analyses. The participants were genotyped using genome‐wide single nucleotide polymorphism arrays, and ADHD PGSs were computed. Magnetic resonance imaging scans were acquired at 4.5 and 6 years (GUSTO), and at 10 and 14 years (Generation R). We estimated the age‐related rate of change for brain outcomes related to ADHD and performed (1) prenatal stress by sex interaction models, (2) prenatal stress by ADHD PGS interaction models, and (3) 3‐way interaction models, including prenatal stress, sex, and ADHD PGS. We observed an interaction between prenatal stress and ADHD PGS on mean cortical thickness annual rate of change in Generation R (i.e., in individuals with higher ADHD PGS, higher prenatal stress was associated with a lower rate of cortical thinning, whereas in individuals with lower ADHD PGS, higher prenatal stress was associated with a higher rate of cortical thinning). None of the other tested interactions were statistically significant. Higher prenatal stress may promote a slower brain developmental rate during adolescence in individuals with higher ADHD genetic vulnerability, whereas it may promote a faster brain developmental rate in individuals with lower ADHD genetic vulnerability. [ABSTRACT FROM AUTHOR]

Background: Individuals with serious mental illness have a markedly shorter life expectancy. A major contributor to premature death is cardiovascular disease (CVD). We investigated associations of (genetic liability for) depressive disorder, bipolar disorder and schizophrenia with a range of CVD traits and examined to what degree these were driven by important confounders. Methods: We included participants of the Dutch Lifelines cohort (N = 147 337) with information on self-reported lifetime diagnosis of depressive disorder, bipolar disorder, or schizophrenia and CVD traits. Employing linear mixed-effects models, we examined associations between mental illness diagnoses and CVD, correcting for psychotropic medication, demographic and lifestyle factors. In a subsample (N = 73 965), we repeated these analyses using polygenic scores (PGSs) for the three mental illnesses. Results: There was strong evidence that depressive disorder diagnosis is associated with increased arrhythmia and atherosclerosis risk and lower heart rate variability, even after confounder adjustment. Positive associations were also found for the depression PGSs with arrhythmia and atherosclerosis. Bipolar disorder was associated with a higher risk of nearly all CVD traits, though most diminished after adjustment. The bipolar disorder PGSs did not show any associations. While the schizophrenia PGSs was associated with increased arrhythmia risk and lower heart rate variability, schizophrenia diagnosis was not. All mental illness diagnoses were associated with lower blood pressure and a lower risk of hypertension. Conclusions: Our study shows widespread associations of (genetic liability to) mental illness (primarily depressive disorder) with CVD, even after confounder adjustment. Future research should focus on clarifying potential causal pathways between mental illness and CVD. [ABSTRACT FROM AUTHOR]

Background: Familial hypercholesterolemia (FH) comprises high LDL-cholesterol (LDL-c) levels and high cardiovascular disease risk. In the absence of pathogenic variants in causative genes, a polygenic basis was hypothesized. Methods: In a population of 418 patients (excluding homozygotes) with clinical suspicion of FH, the FH-causative genes and the regions of single nucleotide polymorphisms (SNPs) included in 12-SNP and 6-SNP scores were sequenced by next-generation sequencing, allowing for the detection of pathogenic variants (V+) in 220 patients. To make a comparison, only patients without uncertain significance variants (V−/USV−) were considered (n = 162). Results: Higher values of both scores were observed in V+ than in V−. Considering a cut-off leading to 80% of V−/USV− as score-positive, a lower prevalence of patients positive for both 12-SNP and 6-SNP scores was observed in V+ (p = 0.010 and 0.033, respectively). Mainly for the 12-SNP score, among V+ patients, higher LDL-c levels were observed in score-positive (223 mg/dL -IQR 187-279) than in negative patients (212 mg/dL -IQR 162–240; p = 0.006). Multivariate analysis confirmed the association of scores and LDL-c levels independently of age, sex, and presence of pathogenic variants and revealed a greater association in children. Conclusions: The 12-SNP and 6-SNP polygenic scores could explain hypercholesterolemia in patients without pathogenic variants as well as the variability of LDL-c levels among patients with FH-causative variants. [ABSTRACT FROM AUTHOR]

Identification of replicable neuroimaging correlates of attention-deficit hyperactivity disorder (ADHD) has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given evidence that ADHD is associated with alterations in widely distributed brain networks and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. The use of large, multisite samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro risk score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N= 5,543, 51.5% female) study, and was further tested in the independent Oregon-ADHD-1000 case-control cohort (N= 553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both cohorts after accounting for relevant covariates (p<0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen's d =-0.318, robust p=5.5×10-4) than those with persistent ADHD (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders. [ABSTRACT FROM AUTHOR]

Background: Stress is a universal phenomenon and one of the most common precipitants of insomnia. However, not everyone develops insomnia after experiencing a stressful life event. This study aims to test aspects of Spielman's '3P model of insomnia' (during adolescence) by exploring the extent to which: (a) insomnia symptoms are predicted by polygenic scores (PGS); (b) life events predict insomnia symptoms; (c) the interaction between PGS and life events contribute to the prediction of insomnia symptoms; (d) gene–environment interaction effects remain after controlling for sex. Methods: The sample comprised 4,629 twins aged 16 from the Twin Early Development Study who reported on their insomnia symptoms and life events. PGS for insomnia were calculated. In order to test the main hypothesis of this study (a significant interaction between PGS and negative life events), we fitted a series of mixed effect regressions. Results: The best fit was provided by the model including sex, PGS for insomnia, negative life events, and their interactions (AIC = 26,158.7). Our results show that the association between insomnia symptoms and negative life events is stronger for those with a higher genetic risk for insomnia. Conclusions: This work sheds light on the complex relationship between genetic and environmental factors implicated for insomnia. This study has tested for the first time the interaction between genetic predisposition (PGS) for insomnia and environmental stressors (negative life events) in adolescents. This work represents a direct test of components of Spielman's 3P model for insomnia which is supported by our results. [ABSTRACT FROM AUTHOR]