BACKGROUND AND AIM:The five cardiovascular diseases (CVDs): Coronary artery disease (CAD), stroke, heart failure (HF), peripheral arterial disease (PAD), and atrial fibrillation(Afib) often co-occur. Most polygenic risk scores (PRSs) are developed for individual CVDs, limiting their clinical utility across multiple CVDs. We investigated associations between five PRSs and (1) their CVDs, (2) cross-disease associations for pleiotropy, and (3) across multimorbidity clusters. METHODS:We calculated restricted PRSs using genome-wide significant variants from published genome-wide association studies and validated them in 484,154 UK Biobank participants. For the time-to-onset and sequence analysis, we included 34,394 individuals with a CAD diagnosis. We used logistic regression, Cox regression, and accelerated failure time analysis; p ≤ 0.05 was considered significant. RESULTS:All PRSs were significantly associated with their respective CVDs (ORs 1.08-1.60 per 1-SD). The odds of CAD increased by 55% per 1-SD increase in PRSCAD (OR 1.55, 95% CI: 1.53-1.57). In cross-disease associations, all 25 PRS-CVD pairs were significant. For some CVDs, PRS from other CVDs showed a stronger association than their own. For instance, HF was more strongly associated with PRSAfib than PRSHF (OR 1.21 vs 1.17). In CAD patients, those in the top 10% of PRSCAD had a 2.66-fold higher PAD risk (HR 2.66, 95% CI: 1.06-6.71) and developed PAD 5.6 years earlier than those in the bottom 10% (time ratio 0.40, 95% CI: 0.17-0.93). CONCLUSION:PRSs reveal shared polygenic risk across CVDs and provide insight into CVD multimorbidity trajectories, such as timing and sequence of events.

Metabolic alterations are increasingly implicated in neurological disorders, including Alzheimer's disease (AD), highlighting the relevance of the peripheral metabolome, shaped by genetic and environmental exposures, for brain health. We examined the relation of 991 blood metabolites with cognition and magnetic resonance imaging (MRI) measures cross-sectionally in 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study and quantified contributions of genetic variation, lifestyle, comorbidities, medication and gut microbiota to metabolite variance. Cognition-associated metabolites were replicated in two independent cohorts of older adults and tested for associations with incident AD longitudinally in one cohort. Twenty-two metabolites were associated with MRI measures. Fourteen metabolites showed replicated associations with cognition, with ergothioneine exhibiting the largest effect. The metabolite signature of cognition mirrored that of incident AD. Lifestyle, clinical variables and medication were the strongest determinants of cognition-associated and MRI-associated metabolites, explaining up to 28.6% of their variance. Antacid use was associated with worse cognition and lower ergothioneine levels, which mediated 31.5% of the negative medication effect, suggesting implications for AD prevention.

Polycystic ovary syndrome (PCOS) and its underlying features remain poorly understood. In this genetic study (n = 544,513), we expand the number of genetic loci from 16 to 29, and additionally identify 31 associated plasma proteins. Many risk-increasing loci were associated with later age at menopause, underscoring the reproductive longevity related to an increased oocyte number and/or availability across the lifespan. Hormonal regulation in the etiology of this condition, through metabolic and reproductive features, was emphasized. The proteomic analysis highlighted metabolic biology known to be related to PCOS. A polygenic risk score (PRS) was associated with adverse cardiometabolic outcomes, with differing relevance of testosterone and body mass index in women and men. Finally, while oligo-anovulation and anovulatory infertility are features of PCOS, we observed no impact of PCOS susceptibility on childlessness. We suggest that PCOS susceptibility confers balanced pleiotropic influences on fertility in women, and life-long adverse metabolic consequences in both sexes.

Copy-number variants (CNVs) are major contributors to human disease. In Alzheimer disease (AD), APP duplications cause autosomal-dominant forms, but the role of CNVs in non-monogenic AD remains poorly characterized. We analyzed rare CNVs (frequency <1%) from 22,319 exomes (4,150 early-onset AD [EOAD, ≤65 years], 8,519 late-onset AD [LOAD], 9,650 unaffected control subjects) using harmonized calling and quality control. After identifying 17 individuals with a pathogenic CNV, we performed exome-wide and gene-set burden analyses. EOAD-affected individuals showed increased burdens of rare CNVs affecting coding genes, particularly deletions in AD-related genes. Integrated loss-of-function (LoF) analysis gathering short truncating variants with deletions showed that ABCA1 (odds ratio [OR] = 5.77 [95% confidence interval 2.25; 17.06], p = 0.0002) and ABCA7 deletions contribute to this deletion burden (OR = 2.29 [1.44; 3.65], p = 0.0006), while CTSB LoF alleles appear as candidates (OR = 5.03 [1.50; 20.71], p = 0.0089). We then performed exome-wide gene-level dosage analysis and highlighted 18 genes across five loci with a false discovery rate of <10%, including the 22q11.21 central region, where deletions were restricted to EOAD (including one de novo event) and duplications were enriched in control individuals, with intermediate frequencies in LOAD. We narrowed this locus to the SCARF2-KLHL22-MED15 region after integrating short truncating variants. Replication in 33,977 affected individuals and 362,322 control subjects confirmed association for 22q11.21 dosage with exome-wide significance (ORSCARF2 = 0.34 [0.21; 0.53]; mega-p value = 5.52 × 10-7). SCARF2 overexpression significantly increased amyloid-β uptake, congruent with duplication-associated decreased AD risk. We conclude that rare coding CNVs in a proportion of AD-associated genes and 22q11.21 deletions, including some found in DiGeorge syndrome, increase AD risk. Conversely, we identify 22q11.21 duplication as a strong AD-risk-decreasing factor.

The genome of Europe project for Pan-European population genomics Genomics improves healthcare and prevention of all major diseases by early personalized insight into health, and Europe needs to invest in creating genomics data to secure national resilience and European leadership. Europe and its Member States face sustained pressure from ageing populations, rising expenditure on chronic disease, global competition in biotechnology, and rapid advances in artificial intelligence (AI)-driven healthcare. Chronic diseases increase with old age and account for the majority of national health spending. Modelling shows that even modest improvements in prevention and earlier intervention could translate into preventing premature deaths and multi-billion-euro long-term savings for national budgets. (1,2)

BACKGROUND:Genetic risk scores may be useful for analyzing risks for coronary artery disease (CAD). However, comparisons between restricted and genome-wide scores have been underexplored, particularly for individuals at increased risk by one score but not the other. Here, we compared restricted polygenic risk scores with 181 high-confidence genetic variants (PRS181) and genome-wide risk scores that encompass 6.6 million single-nucleotide polymorphisms (GRS6.6M). METHODS:Data were from the RS (Rotterdam Study; n=11 001), MESA (Multi-Ethnic Study of Atherosclerosis; n=2685), and the Sanford Health study (n=25 166). We analyzed score associations with CAD (prevalent and incident), age at onset, and lipid medication use. Combined use of both scores was also examined. RESULTS:There were robust associations with CAD per SD of the scores for men (PRS181: hazard ratio [HR], 1.19 [95% CI, 1.13-1.26]; GRS6.6M: HR, 1.32 [95% CI, 1.26-1.39]) and women (PRS181: HR, 1.24 [95% CI, 1.16-1.32]; GRS6.6M: HR, 1.32 [95% CI, 1.25-1.40]). PRS181 was more strongly associated with early-onset CAD in men (β=-0.93 [95% CI, -1.36 to -0.50]) and women (β=-0.76 [95% CI, -1.31 to -0.21]). Both scores correlated with lipid medication use, but the scores were also associated with CAD among nonusers. Individuals at high risk by both scores had the highest risk and the earliest age at onset. CONCLUSIONS:PRS181 and GRS6.6M appear to identify different subsets of individuals. Use of both scores together may provide better association information on CAD risk and age at onset than each score alone.

Supplemental Figure 2 shows the QQ plot of the initial discovery analysis.

Supplemental Table 3 shows the multivariable clinical model, performed in the CCSS Original cohort, to determine treatment factors for stratified GWASs.

The widespread application of high-throughput Next Generation Sequencing (NGS) technologies has made microbiome research an emerging field in public health and biomedical sciences. However, there are still many challenges that need to be addressed in this field. Pipelines available to generate microbiome data across cohorts are diverse, and sources of variation to be recorded and evaluated during microbiome profiling have not been standardized. Moreover, meticulous quality control of the microbiome data processing, from collection to computational quantification is still challenging, especially in large population studies. Innovative approaches are required to handle samples and to minimize the potential bias introduced by logistic hurdles in biobanking. In this paper, we describe the methodological steps surrounding the optimization of the 16s rRNA gut microbiome profiling in two large prospective cohorts the Generation R Study (mean age 9.83 years, SD:0.32 years) and the Rotterdam Study (mean age 62.67,SD:5.66 years). This paper also highlights potential solutions to sample mislabeling in large-scale microbiome analysis. To summarize, our study addresses common problems in human microbiome research. It aims to improve the research quality and reliability by integrating more stringent quality control standards into microbiome research. ### Competing Interest Statement The authors have declared no competing interest.

Background Genetic risk scores hold potential for predicting depression in the general population. These scores must be validated for their associations with relevant characteristics of depression-related phenotypes, such as severity. We validated a genome-wide risk score (GRS) and a restricted polygenic risk score (PRS) for depression based on a meta-analysis of three genome-wide association studies and assessed their associations with depression in three subcohorts of middle-aged and older adults from the Dutch population-based Rotterdam Study.Methods Of participants with genotype data, 9,198 had longitudinally measured data (mean follow-up: 11.3 years) on three depression-related phenotypes (depressive symptoms, depressive syndrome, and major depressive disorder). Generalized linear models estimated the associations of standardized GRS and PRS with depression phenotypes per subcohort and were then meta-analyzed. One unit of the GRS/PRS represents 1 standard deviation, following z-transformation per cohort.Results A one unit higher GRS and PRS were associated with any longitudinally measured depression phenotype (odds ratio (OR)GRS = 1.20 [1.15-1.26], ORPRS = 1.10 [1.05-1.16]). Effect sizes were highest for episodes of major depressive disorder: for individuals with the 10% highest GRS and PRS, the ORs were 1.99 [1.53-2.57] and 1.51 [1.13-1.99], respectively, compared to the middle 50% of the distribution.Conclusions The GRS and PRS for depression showed modest associations across multiple depression-related phenotypes in a population-based setting. The strength of associations generally increased with the severity of the phenotype. While effect sizes were generally larger for GRS compared to PRS, the difference was mostly not statistically significant.

We performed a genome-wide association meta-analysis (GWAMA) of 290,134 attention-deficit/hyperactivity disorder (ADHD) symptom measures of 70,953 unique individuals from multiple raters, ages and instruments (ADHDSYMP). Next, we meta-analyzed the results with a study of ADHD diagnosis (ADHDOVERALL). ADHDSYMP returned no genome-wide significant variants. We show that the combined ADHDOVERALL GWAMA identified 39 independent loci, of which 17 were new. Using a recently developed gene-mapping method, Fine-mapped Locus Assessment Model of Effector genes, we identified 22 potential ADHD effector genes implicating several new biological processes and pathways. Moderate negative genetic correlations (rg < -0.40) were observed with multiple cognitive traits. In three cohorts, polygenic scores (PGSs) based on ADHDOVERALL outperformed PGSs based on ADHD symptoms and diagnosis alone. Our findings support the notion that clinical ADHD is at the extreme end of a continuous liability that is indexed by ADHD symptoms. We show that including ADHD symptom counts helps to identify new genes implicated in ADHD.

Facial appearance, one of the most recognizable and heritable human traits, exhibits substantial variation across individuals within and between populations due to its complex genetic underpinning, which remains largely elusive. Here, we report a combined genome-wide association study (C-GWAS) of 946 facial features derived from 44 landmarks obtained from 3D digital facial images of 11,662 individuals of European descent. We identify 253 unlinked single nucleotide polymorphisms (SNPs) across 188 distinct genetic loci significantly associated with facial variation, including 64 SNPs at 62 novel loci and 33 novel SNPs within 29 previously reported face loci that are in very low LD with the previously reported top SNPs. Together, these SNPs account for up to 7.9% of the facial variation per trait, marking an average 2.25-fold increase over previous estimates. Cross-ancestry replication in 9,674 Chinese confirms the effect of 70% of these SNPs. A 382-SNPs prediction model of five nose traits achieves an AUC of 0.67 for individual re-identification from nose images. DNA predicted faces of archaic humans differ more from those of Europeans than from Africans. In genetically modelled Neanderthal faces, 15 of 16 DNA-predicted facial features are in line with skull evidence. Ten DNA-predicted facial features differentiate Neanderthals from Denisovans. Overall, this study substantially enhances our genetic understanding of human facial variation and provides improvements of genetic face prediction in modern and archaic humans.

Background:Trabecular bone score (TBS) is a texture-based measurement derived from DXA scans, which describes the distribution of mineral across the vertebral bodies. Identifying its genetic determinants is crucial for enhancing understanding of its biological basis and clarifying its relationship with fracture risk. Methods:We conducted a large-scale genome-wide association study (GWAS) of TBS from 44,767 participants (95.08% European ancestry). Sex heterogeneity was assessed through sex-stratified GWAS. Post-GWAS analyses included functional annotation, gene-set enrichment analysis, and cis -expression quantitative trait locus ( cis- eQTL) colocalization. Two-sample and multivariable Mendelian randomization (MR) were employed to investigate the causal effect of TBS on fractures. Findings:We identified 33 independent variants associated with TBS, which explained 4.06% of TBS phenotypic variance. All the discovered signals map to previously identified BMD-associated loci. Additionally, we identified genetic variants in the RAB11FIP3 locus that exhibited a sex-heterogeneous effect, being associated with TBS exclusively in males. Functional annotation and colocalization identified functional genes related to TBS. The two-sample and multivariable MR analyses indicated that TBS potentially has an independent causal effect on fracture risk. Interpretation:Our study unveiled 33 independent loci associated with TBS, all in BMD-associated loci. One locus was further identified as associated with TBS only in males. MR results suggested that genetically derived TBS may be causally associated with fracture risk at different sites, potentially beyond BMD. This study provides insights into the TBS genetic architecture and uncovers its potential clinical applications in fracture risk prediction. Funding:All funding information can be found in the Acknowledgements section. Research in context:Evidence before this study: Osteoporosis is a common disease prevalent in older adults, mainly diagnosed by low bone mineral density and disruption of bone architecture. While the genetic determinants of bone mass have been well studied, the genetic architecture of bone features beyond BMD remains largely unknown.Added value of this study: This study presents a large genome-wide association meta-analysis of the trabecular bone score (TBS), a measurement of trabecular distribution of bone mineralization across vertebral bodies. Our study identified 33 independent variants associated with TBS and one association signal only in males. Functional annotation highlighted crucial genes related to TBS. The two-sample and multivariable Mendelian randomization (MR) analyses suggested that TBS may be causally linked with fracture risk, potentially independent of BMD.Implications of all the available evidence: Our study represents an expansion of the traditional bone density phenotype used in most previous skeletal genetic studies. Based on the findings that all of the significant loci for TBS have been previously identified in GWAS of BMD, we conclude that TBS-associated variants have pleiotropic effects affecting not only mineral density but also the distribution patterns of minerals in the cancellous bone. Continuing to perform genetic studies of new skeletal phenotypes beyond BMD could ultimately impact the management and prediction of osteoporosis patients.

Supplemental Figure 3 shows an overview of the performed analyses and outcomes.

Supplemental Table 2 shows the demographic and treatment characteristics of the discovery and replication cohorts, stratified per dyslipidemia status.

Supplemental Figure 1 shows the flowcharts of the CCSS Original, SJLIFE, CCSS Expansion, and DCCSS-LATER cohorts, indicating included and excluded subjects.

BACKGROUND:Dyslipidemia can occur as a long-term side effect of childhood cancer treatment. The difference in prevalence among children receiving comparable treatment suggests a role for genetic variation. We conducted the first genome-wide association study on dyslipidemia in a large childhood cancer survivor cohort, using three additional cohorts for replication. METHODS:Discovery analysis was performed in the original Childhood Cancer Survivor Study (CCSS) cohort (N = 4,332). Replication analyses were carried out in the CCSS expansion (N = 2,212), St. Jude Lifetime (N = 2,829), and Dutch Childhood Cancer Survivor Study (DCCSS-LATER) (N = 1,814) cohorts. In the CCSS cohorts, dyslipidemia was defined as Common Terminology Criteria for Adverse Events grade 2 self-reported high cholesterol or high triglycerides, whereas in the St. Jude Lifetime and DCCSS-LATER cohorts, it was assessed by serum lipid measurements. Association analysis was performed in the entire cohort and stratified by cancer treatment. RESULTS:The initial discovery analysis yielded one genome-wide significant (p < 5 × 10-8) and 16 suggestive (p < 5 × 10-6) loci associated with dyslipidemia risk. Of these, one genome-wide significant and eight suggestive loci with biological plausibility were selected for replication analysis, but none replicated. Additionally, treatment-stratified analysis revealed six significant (p < 5 × 10-8) loci, none of which replicated in meta-analysis. CONCLUSIONS:Further research with clinically assessed data and larger sample sizes is needed to explore the genetic contributions to dyslipidemia risk in childhood cancer survivors. IMPACT:The establishment of larger, internationally collaborative consortia of childhood cancer survivors is critical for generating more robust findings, which will help the identification of those survivors at risk for dyslipidemia and subsequently cardiovascular disease.

INTRODUCTION:Polygenic risk scores (PRSs) show promise for improving knee osteoarthritis (KOA) risk prediction. However, many KOA-associated variants are also linked to body mass index (BMI)-a major KOA risk factor-complicating the combined use of PRS and BMI in predictive models. This study aimed to disentangle BMI-mediated from BMI-independent genetic contributions to KOA by partitioning the KOA-PRS. METHOD:Using data from 345,080 white European participants in the UK Biobank, we refined a KOA-PRS based on 146 genome-wide significant variants identified by the GO consortium. KOA variants were partitioned into BMI-associated (KOA-BMI PRS), and BMI-independent (KOA-nonBMI PRS) groups based on linkage disequilibrium with BMI-associated variants from the GIANT consortium. We assessed associations of each PRS with prevalent and incident KOA (with and without BMI adjustment), with BMI in cases and controls, and with KOA across BMI strata. RESULTS:Of the 146 KOA-associated variants, 73 were classified as BMI-associated. The KOA-BMI PRS was associated with incident KOA (OR per SD:1.23;95%CI:1.20-1.26), attenuated after BMI adjustment (1.18;1.15-1.21). In contrast, the KOA-nonBMI PRS remained robust to BMI adjustment (1.24 before vs.1.23 after-adjustment). KOA-nonBMI PRS was negatively associated with BMI in KOA cases (β=-0.09;95%CI:-0.17to-0.01), but not in controls. Stratified analyses showed its effect on KOA diminished in higher BMI strata, while KOA-BMI PRS effects remained stable. CONCLUSION:Half of KOA-associated variants overlap with BMI loci. Partitioning the KOA-PRS improves interpretation by distinguishing BMI-driven from independent genetic effects-enhancing prediction and informing tailored prevention. Validation in diverse populations is warranted.

ObjectivesThe panoramic mandibular index (PMI) and mental index (MI) assessed on dental panoramic radiographs (DPRs) have been postulated as useful for the assessment of adult bone health. However, their utility in children remains to be determined. Our objective was to establish genetic determinants of the PMI/MI and to evaluate the relationship between these indices and total body less-head bone mineral density (TBLH-BMD).MethodsThis study was embedded in the Generation R Study at a mean age of 13 years. BMD was obtained from dual-energy X-ray (DXA) scans, while radiomorphometric measurements of the mandibular bone were obtained from DPRs. Genome-wide association studies (GWAS) on PMI/MI were performed. The association between PMI/MI and BMD was assessed following a combined observational and genetic analysis using a polygenic risk score (PGS).ResultsThe PMI and MI GWAS identified an association signal (p = 2.53 x 10- 9) mapping to the ODF3/BET1L/RIC8A/SIRT3 locus, previously associated with BMD. Significant differences in PMI and MI were observed across the extremes of the TBLH-BMD PGS distribution. One standard deviation (SD) increase in measured TBLH-BMD was associated with 0.244 SD increase in PMI and 0.426 SD increase in MI.ConclusionsOur results suggest that PMI/MI and BMD share common biological pathways, and the former may be considered as relevant markers if screening for children with impaired bone health using DPRs.Clinical relevanceThis study demonstrates that mandibular indices measured from panoramic radiographs can help clinicians identify pediatric patients who may have reduced bone mineral density. Both traits share a shared genetic and the association is not due to confounding factors. These findings highlight the potential of panoramic radiography as a valuable tool in assessing pediatric skeletal health.