Individuals Of European Ancestry Research Articles

Characterization of gene-environment interactions for vitamin D through variance quantitative trait loci: a UK Biobank-based genetic epidemiology study.

Understanding gene-environment interactions associated with vitamin D status may refine nutrition and public health strategies for vitamin D deficiency. Recent methodological advances have enabled the identification of variance quantitative trait loci (vQTLs) where gene-environment interactions are enriched. To identify vQTLs for serum 25-hydroxy vitamin D (25OHD) concentrations and characterize potential gene-environment interactions of vQTLs. We conducted vQTL discovery for 25OHD using a newly developed quantile integral linear model in the UK Biobank individuals of European (N = 313,514), African (N = 7,800), East Asian (N = 2,146), and South Asian (N = 8,771) ancestries, respectively. We tested for interactions between the identified vQTL lead variants and 18 environmental, biological, or lifestyle factors, followed by multiple sensitivity analyses. We identified 19 independent vQTL lead variants (p-value <5x10-8) in the European ancestry population. No vQTLs were identified in the non-European ancestry populations, likely due to limited sample sizes. A total of 32 interactions were detected with a false discovery rate <0.05. While known gene-season of measurement interactions were confirmed, additional interactions were identified involving modifiable risk factors, including time spent outdoors and body mass index. The magnitudes of these interactions were consistent within each locus upon adjusting for season of measurement and other covariates. We also identified a gene-sex interaction at a vQTL that implicates DHCR7. Integrating transcript- and protein-level evidence, we found that the sex-differentiated genetic associations may act through sex-biased expression of DHCR7 isoforms in skin tissues due to alternative splicing. Through the lens of vQTLs, we identified additional gene-environment interactions affecting vitamin D status in addition to season of measurement. These findings may provide new insights into the etiology of vitamin D deficiency and encourage personalized prevention and management of associated diseases for at-risk individuals.

Increased risk of hypertrophic scarring in estrogen receptor-positive breast cancer: a bidirectional mendelian randomization study.

Breast cancer (BC) is a prevalent malignancy in women, often necessitating tumor resection and breast reconstruction surgeries. However, the post-operation scars can be of concern, as hypertrophic scars (HS) can profoundly impact patients' quality of life. Our study used the bidirectional Mendelian randomization (MR) method to explore the potential relationship between BC and HS. We conducted a comprehensive genetic analysis using a large meta-analysis dataset comprising the BCAC, FinnGen, MRC-IEU, and GTEx eQTL datasets. We identified independent genetic loci associated with BC and HS among individuals of European ancestry. These loci served as instrumental variables (SNPs) in MR analysis. We used the inverse-variance weighted and weighted median methods, with the odds ratio (OR) as the effect measure, to investigate the causal relationship between breast disease and HS, and all significance was corrected by FDR and Bonferroni. We assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-PRESSO, and MR-Egger intercept tests. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual SNPs on the MR study. The MR analysis showed a significant positive association between BC and HS (OR = 1.31, 95% CI 1.10-1.56, P = 0.002). However, there was no significant association between benign breast neoplasm and HS (OR = 0.97, P = 0.73). Subgroup analysis of BC revealed that estrogen receptor-positive (ER+) BC increased the risk of HS (OR = 1.32, 95% confidence interval: 1.11-1.57, P = 0.0016), while ER-negative (ER-) BC did not increase the risk of HS (OR = 0.99, P = 0.91). Analysis of the GTEx database demonstrated that increased ER level alone in healthy individuals (n = 3301) did not increase the risk of HS (P = 0.42). This result was well replicated in two independent HS datasets, and the causal effect of BC on HS was unidirectional. BC is positively associated with the incidence of HS, especially in the case of ER+BC. However, there is a lack of substantial evidence regarding the relationship between ER-BC and HS.

GWAS of CRP response to statins further supports the role of APOE in statin response: A GIST consortium study.

Statins are first-line treatments in the primary and secondary prevention of cardiovascular disease. Clinical studies show statins act independently of lipid-lowering mechanisms to decrease C-reactive protein (CRP), an inflammation marker. We aim to elucidate genetic loci associated with CRP statin response. CRP statin response is the change in log-CRP between off-treatment and on-treatment measurements. Cohort-level Genome-Wide Association Studies (GWAS) of CRP response were performed using 1000 Genomes imputed data, testing ∼10 million common genetic variants. GWAS meta-analysis combined results from seven cohorts and clinical trials totalling 14,070 statin-treated individuals of European ancestry within the GIST consortium. Secondary analyses included statin-by-placebo interaction analyses, and lookups in African ancestry cohorts. Our GWAS identified two genome-wide significant (P < 5e-8) loci: APOE and HNF1A for CRP statin response corrected for baseline CRP. The missense lead variant rs429358 at APOE, contributing to the APOE-E4 haplotype, is a risk locus for dyslipidaemia, Alzheimer's and coronary artery disease (CAD). The HNF1A locus is associated with diabetes, cholesterol levels, and CAD. Both loci are also associated with baseline CRP levels, and neither locus achieved a significant (P < 0.05) result from the statin v. placebo interaction meta-analysis using randomized clinical trial data. However, the interaction result (P-int=0.09) for APOE was suggestive and possibly underpowered. The APOE-E4 signal may therefore be associated with both CRP and LDL-cholesterol statin response. Combined with suggestions in the literature that APOE also leads to differential statin benefit in Alzheimer's, the APOE locus warrants further investigation for potential genetic effects on healthcare with statin treatment.

Deleterious coding variation associated with autism is consistent across populations, as exemplified by admixed Latin American populations.

Branko Aleksic, Mykyta Artomov, Mafalda Barbosa, Elisa Benetti, Catalina Betancur, Monica Biscaldi-Schafer, Anders D. Børglum, Harrison Brand, Alfredo Brusco, Joseph D. Buxbaum, Gabriele Campos, Simona Cardaropoli, Diana Carli, Angel Carracedo, Marcus C. Y. Chan, Andreas G. Chiocchetti, Brian H. Y. Chung, Brett Collins, Ryan L. Collins, Edwin H. Cook, Hilary Coon, Claudia I. S. Costa, Michael L. Cuccaro, David J. Cutler, Mark J. Daly, Silvia De Rubeis, Bernie Devlin, Ryan N. Doan, Enrico Domenici, Shan Dong, Chiara Fallerini, Magdalena Fernandez, Montserrat Fernández-Prieto, Giovanni Battista Ferrero, Eugenio Ferro, Jennifer Foss Feig, Christine M. Freitag, Jack M. Fu, Liliana Galeano, J. Jay Gargus, Sherif Gerges, Elisa Giorgio, Ana Cristina Girardi, Stephen Guter, Emily Hansen-Kiss, Erina Hara, Danielle Halpern, Gail E. Herman, Luis C. Hernandez, Irva Hertz-Picciotto, David M. Hougaard, Christina M. Hultman, Suma Jacob, Miia Kaartinen, Lambertus Klei, Alexander Kolevzon, Itaru Kushima, Maria C. Lattig, So Lun Lee, Terho Lehtimäki, Lindsay Liang, Carla Lintas, Alicia Ljungdahl, Andrea del Pilar Lopez, Caterina Lo Rizzo, Yunin Ludena, Patricia Maciel, Behrang Mahjani, Nell Maltman, Marianna Manara, Dara S. Manoach, Dalia Marquez, Gal Meiri, Idan Menashe, Judith Miller, Nancy Minshew, Matthew Mosconi, Marina Natividad Avila, Rachel Nguyen, Norio Ozaki, Aarno Palotie, Mara Parellada, Maria Rita Passos-Bueno, Lisa Pavinato, Katherine P. Peña, Minshi Peng, Margaret Pericak-Vance, Antonio M. Persico, Isaac N. Pessah, Thariana Pichardo, Kaija Puura, Abraham Reichenberg, Alessandra Renieri, Kathryn Roeder, Catherine Sancimino, Stephan J. Sanders, Sven Sandin, F. Kyle Satterstrom, Stephen W. Scherer, Sabine Schlitt, Rebecca J. Schmidt, Lauren Schmitt, Katja Schneider-Momm, Paige M. Siper, Laura Sloofman, Moyra Smith, Renee Soufer, Christine R. Stevens, Pål Suren, James S. Sutcliffe, John A. Sweeney, Michael E. Talkowski, Flora Tassone, Karoline Teufel, Elisabetta Trabetti, Slavica Trajkova, Maria del Pilar Trelles, Brie Wamsley, Jaqueline Y. T. Wang, Lauren A. Weiss, Mullin H. C. Yu, Ryan Yuen, Jessica Zweifach.

Integrative proteogenomic analyses provide novel interpretations of type 1 diabetes risk loci through circulating proteins

Circulating proteins may be promising biomarkers or drug targets. Leveraging genome-wide association studies of type 1 diabetes (18,942 cases and 501,638 controls of European ancestry) and circulating protein abundances (10,708 European ancestry individuals), Mendelian randomization analyses were conducted to assess the associations between circulating abundances of 1,560 candidate proteins and the risk of type 1 diabetes, followed by multiple sensitivity and colocalization analyses, horizontal pleiotropy examinations, and replications. Bulk tissue and single-cell gene expression enrichment analyses were performed to explore candidate tissues and cell types for prioritized proteins. After validating Mendelian randomization assumptions and colocalization evidence, we found that genetically predicted circulating abundances of CTSH (OR=1.17 per one standard deviation increase; 95% CI:1.10-1.24), IL27RA (OR=1.13; 95% CI:1.07-1.19), SIRPG (OR=1.37; 95% CI:1.26-1.49), and PGM1 (OR=1.66; 95% CI:1.40-1.96) were associated with the risk of type 1 diabetes. These findings were consistently replicated in other cohorts. CTSH, IL27RA, and SIRPG were strongly enriched in immune system-related tissues, while PGM1 was enriched in muscle and liver tissues. Amongst immune cells, CTSH was enriched in B cells and myeloid cells, while SIRPG was enriched in T cells and natural killer cells. These proteins may be explored as biomarkers or drug targets for type 1 diabetes.

Sensitivity to Environmental Stress and Adversity and Lung Cancer.

Sensitivity to environmental stress and adversity may influence lung cancer risk, highlighting a critical link between psychosocial factors and cancer etiology. To evaluate whether genetically estimated sensitivity to environmental stress and adversity is associated with lung cancer risk. Data were obtained from a genome-wide association study identifying 37 independent genetic variants strongly associated with sensitivity to environmental stress and adversity and a cross-ancestry genome-wide meta-analysis from the International Lung Cancer Consortium. Data were extracted between October 2023 and January 2024 and analyzed between February 2024 and June 2024. Genetically estimated sensitivity to environmental stress and adversity. The main outcome was lung cancer risk, and odds ratios and 95% CIs were used to assess the association between sensitivity to environmental stress and adversity with lung cancer risk. This genetic association study used a 2-sample Mendelian randomization (MR) to estimate the association between genetically estimated sensitivity to environmental stress and adversity and lung cancer risk across different histologic types. Using data from 351 827 individuals from the UK Biobank and a cross-ancestry genome-wide analysis of 61 047 lung cancer cases and 947 237 controls from the International Lung Cancer Consortium, sensitivity to environmental stress and adversity was significantly associated with an increased risk of lung cancer among individuals of European ancestry (OR, 1.49; 95% CI, 1.13-1.98; P = .005) and in the cross-ancestry analysis (OR, 1.45; 95% CI, 1.13-1.85; P = .004). There were heterogeneities in the observed associations across histologic subtypes among different population ancestries. Among individuals of European ancestry, a significant association was noted among squamous cell carcinoma (OR, 1.69; 95% CI, 1.06-2.70; P = .03). Among individuals of East Asian ancestry, an increased risk was observed for adenocarcinoma (OR, 2.04; 95% CI, 1.00-4.18; P = .05). Associations were not observed across any of the 3 histologic subtypes among individuals of African ancestry. In this genetic association study, sensitivity to environmental stress and adversity was associated with lung cancer risk. These results underscore the necessity for further research into the nuanced association between psychosocial stress and cancer risk, with a particular focus on diverse populations.

Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups.

Transcriptome-wide association studies (TWAS) investigate the links between genetically regulated gene expression and complex traits. TWAS involves imputing gene expression using expression quantitative trait loci (eQTL) as predictors and testing the association between the imputed expression and the trait. The effectiveness of TWAS depends on the accuracy of these imputation models, which require genotype and gene expression data from the same samples. However, publicly accessible resources, such as the Genotype Tissue Expression (GTEx) Project, are biased toward individuals of European ancestry, potentially reducing prediction accuracy into other ancestry groups. This study explored eQTL transferability across ancestry groups by comparing two imputation models: PrediXcan (tissue-specific) and UTMOST (cross-tissue). Both models were trained on tissues from the GTEx Project using European ancestry individuals and then tested on data sets of European ancestry and African American individuals. Results showed that both models performed best when the training and testing data sets were from the same ancestry group, with the cross-tissue approach generally outperforming the tissue-specific approach. This study underscores that eQTL detection is influenced by ancestry and tissue context. Developing ancestry-specific reference panels across tissues can improve prediction accuracy, enhancing TWAS analysis and our understanding of the biological processes contributing to complex traits.

Type 1 diabetes genetic risk score variation across ancestries using whole genome sequencing and array-based approaches

A Type 1 Diabetes Genetic Risk Score (T1DGRS) aids diagnosis and prediction of Type 1 Diabetes (T1D). While traditionally derived from imputed array genotypes, Whole Genome Sequencing (WGS) provides a more direct approach and is now increasingly used in clinical and research studies. We investigated the concordance between WGS-based and array-based T1DGRS across genetic ancestries in 149,265 UK Biobank participants using WGS, TOPMed-imputed, and 1000 Genomes-imputed array genotypes. In the overall cohort, WGS-based T1DGRS demonstrated strong correlation with TOPMed-imputed array-based score (r = 0.996, average WGS-based score 0.0028 standard deviations (SD) lower, p < 10− 31), while showing lower correlation with 1000 Genomes-imputed array-based scores (r = 0.981, 0.043 SD lower in WGS, p < 10− 300). Ancestry-stratified analyses between WGS-based and TOPMed-imputed array-based score showed the highest correlation with European ancestry (r = 0.996, 0.044 SD lower in WGS, p < 10− 300) followed by African ancestry (r = 0.989, 0.0193 SD lower in WGS, p < 10− 14) and South Asian ancestry (r = 0.986, 0.0129 SD lower in WGS, p < 10 − 6). These differences were more pronounced when comparing WGS based score with 1000 Genomes-imputed array-based scores (r = 0.982, 0.975, 0.957 for European, South Asian, African respectively). Population-level analysis using WGS-based T1DGRS revealed significant ancestry-based stratification, with European ancestry individuals showing the highest scores, followed by South Asian (average 0.28 SD lower than Europeans, p < 10− 58) and African ancestry individuals (average 0.89 SD lower than Europeans, p < 10− 300). Notably, when applying the European ancestry-derived 90th centile risk threshold, only 0.71% (95% CI 0.41–1.13) of African ancestry individuals and 6.4% (95% CI 5.6–7.2) of South Asian individuals were identified as high-risk, substantially below the expected 10%. In conclusion, while WGS is viable for generating T1DGRS, with TOPMed-imputed genotypes offering a cost-effective alternative, the persistence of ancestry-based variations in T1DGRS distribution even using whole genome sequencing emphasises the need for ancestry-specific or pan-ancestry standards in clinical practice.

Causal relationship between genetically predicted mental disorders and frailty: a bidirectional and multivariable mendelian randomization study

BackgroundIn observational studies, frailty has been strongly associated with mental disorders. However, the mechanisms underlying the association between frailty and mental disorders remain unclear.MethodsWe conducted a two-sample Mendelian randomization (MR) study to assess the causal relationship between frailty, as measured by the frailty index (FI), and ten common mental disorders. The datasets involved European ancestry individuals and included measurements of the FI (N = 175,226), schizophrenia (SCZ; N = 320,404), major depressive disorder (MDD; N = 143,265), bipolar disorder (N = 337,199), insomnia (N = 462,341), obsessive–compulsive disorder (N = 33,925), anxiety disorders (N = 463,010), autism spectrum disorder (N = 46,351), anorexia nervosa (N = 14,477), opioid-related mental and behavioral disorders (N = 215,650), and mental and behavioral disorders due to use of other stimulants including caffeine (N = 215,570).ResultsTwo-sample MR analyses were performed using inverse variance weighting followed by various sensitivity and validation analyses. Genetically predicted SCZ (odds ratio [OR] = 1.019, 95% confidence interval [CI] 1.005–1.033) and MDD (OR = 1.211, 95% CI 1.092–1.343) had significant causal effects on FI. In the reverse MR analysis, we discovered that MDD was significantly and causally affected by FI (OR = 1.290, 95% CI 1.133–1.469). No causal links were identified between the FI and the other eight common mental disorders. In the Multivariable MR, the estimated MDD effect on FI is comparable to the univariate IVW estimate (OR = 1.298; 95% CI, 1.175 to 1.435), while the estimated SCZ effect on FI fails to be significant compared to the univariate estimate. The results of the sensitivity and validation analyses confirmed stabilization.ConclusionsOur study found evidence of a causal relationship between SCZ, MDD, and frailty and explored the underlying mechanisms.

Genome-Wide European Ancestry Study Identifies Coronary Artery Disease-Associated Loci Through Gene-Sex Hormone Interaction.

Although sex differences in coronary artery disease (CAD) risk have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD-risk loci and extend our knowledge of its genetic architecture. A total of 365 662 individuals of European ancestry enrolled in the UK Biobank were considered. Genetic interaction of total testosterone, bioavailable testosterone, and SHBG (sex hormone-binding globulin) were evaluated. Gene-environment interactions in millions of samples software was used to conduct sex-stratified genome-wide interaction analysis with prevalent CAD as the outcome. Participant age at enrollment and principal components 1 to 10 were adjusted as covariates. We identified 45 loci in men and 8 loci in women that reached genome-wide significance (P < 5 × 10-8) for CAD. Ten of the loci identified (5 loci in both men and women) were through joint effects and would not have been picked up using a traditional genome-wide association study. Two of the joint effect loci in women were independently identified with significant single nucleotide polymorphism-total testosterone interactions. This genome-wide gene-sex hormone interaction study identified genomic-risk loci that may contribute to the differential CAD risk between men and women, which otherwise would not have been discovered in a traditional genome-wide association study solely including marginal genetic effects.

Human genetics implicate thromboembolism in the pathogenesis of long COVID in individuals of European ancestry.

SARS-CoV-2 infection can result in long COVID, characterized by post-acute symptoms from multiple organs. Current hypotheses on mechanisms underlying long COVID include persistent inflammation and thromboembolism; however, compelling evidence from humans is limited and causal associations remain unclear. Here, we tested the association of thromboembolism-related genetic variants with long COVID in the Long COVID Host Genetics Initiative ( n cases =3,018; n controls =994,582). Primary analyses revealed that each unit increase in the log-odds of genetically predicted venous thromboembolism risk was associated with 1.21-fold odds of long COVID (95%CI: 1.08-1.35; P =1.2 × 10 -3 ). This association was independent of acute COVID-19 severity, robust across genetic instruments and methods, and replicated in external datasets for both venous thromboembolism and long COVID. Downstream analyses using gene-specific instruments, along with protein and gene expression data, suggested the protease-activated receptor 1 (PAR-1) as a potential molecular contributor to long COVID. These findings provide human genetic evidence implicating thromboembolism in long COVID pathogenesis. .

Does COVID-19 impact the QT interval prolongation? Answers from genetic causal inference.

During the COVID-19 pandemic, there has been heightened interest in the QT interval, a crucial indicator of ventricular electrical activity. Mendelian randomization (MR) is used here to investigate the genetic causation between QT interval alterations and COVID-19. Genetic proxies representing three COVID-19 phenotypes-severe, hospitalized, and COVID-19-were identified in over 1,000,000 individuals of European ancestry. Univariate two-sample Mendelian randomization (TSMR) and multi-exposure-adjusted multivariate Mendelian randomization (MVMR) were used to assess genetic causal associations between COVID-19 and QT intervals in 84,630 UK Biobank participants. The MR-robust adjusted profile score (MR-RAPS) method and radial MR frame were utilized for effect robustness and outlier variant detection, with sensitivity analyses conducted to identify horizontal pleiotropy. For every COVID-19 phenotype, univariate TSMR analysis revealed non-significant causal estimates between COVID-19 and the QT interval [COVID-19: βIVW (95% CI): -0.44 (-1.72, 0.84), P = 0.50; hospitalization: βIVW: 0.12 (-0.57, 0.80), P = 0.74; severe case: βIVW: 0.11 (-0.29, 0.51), P = 0.58]. MR-RAPS and outlier-corrected radial MR analyses further supported this null causal estimation. In confounder-adjusted MVMR analysis, this nonsignificant causality was independent of BMI, smoking, and alcohol consumption [βBMI+Alcohol+Smoking (95% CI): -0.77 (-2.44, 0.91), P = 0.37]. Sensitivity analyses did not detect any evidence of bias from horizontal pleiotropy, abnormal data distribution, or weak instruments. These findings suggest that COVID-19 does not directly causally prolong the QT interval. Inconsistent findings in observational research may be attributed to residual confounding.

Sex-stratified Genomic Structural Equation Models of Posttraumatic Stress Inform PTSD Etiology: L'utilisation de la modélisation génomique par équations structurelles stratifiée par sexe du stress post-traumatique pour expliquer l'étiologie du TSPT.

Posttraumatic stress disorder (PTSD) affects 3.9%-5.6% of the worldwide population, with well-documented sex-related differences. While psychosocial and hormonal factors affecting sex differences in PTSD and posttraumatic stress (PTS) symptom etiology have been explored, there has been limited focus on the genetic bases of these differences. Many symptom combinations may confer a PTSD diagnosis. We hypothesized that these symptom combinations have sex-specific patterns, the examination of which could inform etiological differences in PTSD genetics between males and females. To investigate this, we performed a sex-stratified multivariate genome-wide association study (GWAS) in unrelated UK Biobank (UKB) individuals of European ancestry. Using GWAS summary association data, genomic structural equation modelling was performed to generate sex-specific factor models using 6 indicator variables: trouble concentrating, feeling distant from others, irritability, disturbing thoughts, upset feelings, and avoidance of places/activities which remind the individual of a traumatic event. Models of male and female PTSD symptoms differed substantially (local standardized root mean square difference = 3.12) and significantly (χ2(5) = 28.03, P = 3.6 × 10-5). Independent 2-factor models best fit the data in both males and females; these factors were subjected to GWAS in each sex, revealing 3 genome-wide significant loci in females, mapping to SCAND3, WDPCP, and FAM120A. No genome-wide significant loci were identified in males. All 4 PTS factors (2 in males and 2 in females) were heritable. By assessing the relationship between sex and PTSD symptoms, this study informs correlative and putatively causal etiological differences between males and females which support further investigation of sex differences in PTSD genetics.

Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.

Genome-wide association studies (GWAS) have increased our understanding of Parkinson's disease (PD) genetics by identifying common disease-associated variants. However, much of the heritability remains unaccounted for and we hypothesized that this could be partly explained by epistasis, the statistical interaction between two or more genetic variants. Here, we developed a genome-wide non-exhaustive epistasis screening pipeline called Variant-variant interaction through variable thresholds (VARI3) and applied it to diverse PD GWAS cohorts. We used 14 cohorts of European ancestry (14,671 cases and 17,667 controls) as a discovery stage, identifying 14 significant candidate variant-variant interactions. We then used four independent cohorts (13,377 cases and 413,789 controls) as replication stage, successfully replicating three epistasis signals located nearby SNCA and within MAPT and WNT3. Admixture analysis showed that the epistatic effect on PD of those variants at these loci was observed in both European ancestry and Native American ancestry individuals. We assessed the functional impact of the epistasis signals across a range of functional/-omics datasets identifying significant single-variant eQTLs across brain tissues, epistasis eQTL signals in whole-blood, PD-relevant pathways and ontologies, and chromatin interactions between the regions of the interacting SNPs. In conclusion, we identified and replicated novel epistatic signals associated with PD risk across multiple diverse genetic ancestry cohorts, highlighting their enrichment in pathways relevant to Parkinson's disease.

Evaluation of multiple breast cancer polygenic risk score panels in women of Latin American heritage.

A substantial portion of the genetic predisposition for breast cancer is explained by multiple common genetic variants of relatively small effect. A subset of these variants, which have been identified mostly in individuals of European and Asian ancestry, have been combined to construct a polygenic risk score (PRS) to predict breast cancer risk, but the prediction accuracy of existing PRSs in Hispanic/Latinx individuals (H/L) remain relatively low. We assessed the performance of several existing PRS panels with and without addition of H/L specific variants among self-reported H/L women. PRS performance was evaluated using multivariable logistic regression and the area under the receiver operating characteristic curve (AUC). Both European and Asian PRSs performed worse in H/L samples compared to original reports. The best European PRS performed better than the best Asian PRS in pooled H/L samples. European PRSs had decreased performance with increasing Indigenous American (IA) ancestry while Asian PRSs had increased performance with increasing IA ancestry. The addition of 2 H/L SNPs increased performance for all PRSs, most notably in the samples with high IA ancestry and did not impact the performance of PRSs in individuals with lower IA ancestry. A single PRS that incorporates risk variants relevant to the multiple ancestral components of individuals from Latin America, instead of a set of ancestry specific panels, could be used in clinical practice. Results highlight the importance of population-specific discovery and suggest a straightforward approach to integrate ancestry specific variants into PRS for clinical application.

Causal relationship between inflammatory proteins, immune cells, and gout: a Mendelian randomization study

Prior research has documented the association between certain circulating inflammatory proteins/immune cells and gout. However, the reliability of these associations remains contentious due to the constraints of conventional observational methodologies. This investigation seeks to reassess the causative link between circulating inflammatory proteins/immune cells and gout through the application of Mendelian randomization (MR). The study included 3576 individuals of European ancestry with gout, immune cell data from the GWAS summary of 3757 Sardinians, and circulating inflammatory protein data from 14,824 European ancestry participants for MR analysis. The principal approach employed was inverse variance weighted analysis to investigate the causal relationship between exposure and outcomes. The results indicate that CD28 on CD39+ CD4+ T cells may be associated with a reduced risk of gout. Additionally, CD45RA+ CD28- CD8bright T cells may also be associated with a reduced risk of gout. In contrast, DN (CD4-CD8-) T cells and IL-12β may increase the risk of gout. Some inflammatory proteins and immune cells show potential causal associations with gout. Nevertheless, additional experimental verification is warranted to assess the underlying mechanisms and confirm the causative role of these immune factors in gout pathogenesis.

Mapping genes for human face shape: Exploration of univariate phenotyping strategies.

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Assessing the utility of epigenetic clocks for health prediction in South Korean.

Epigenetic clocks have been developed to track both chronological age and biological age, which is defined by physiological biomarkers and the risk of adverse health outcomes. Epigenetic age acceleration (EAA) has been found to predict various diseases, aging-related factors, and mortality. However, epigenetic clocks have predominantly been developed with individuals of European or Hispanic ancestry, and their association with health outcomes and environmental factors has not been sufficiently assessed in East Asian populations. Here, we investigated nine epigenetic clocks: five trained on chronological age (first-generation) and four on biological age (second-generation), using DNA methylation data from blood samples of South Koreans. EAAs of second-generation epigenetic clocks reflected the risk of chronic diseases (type 2 diabetes and hypertension), levels of health-related blood markers (alanine aminotransferase, aspartate aminotransferase, high density lipoprotein, triglyceride, and high sensitivity C-reactive protein), and lung functions (percentage of predicted FEV1 and percentage of predicted FVC), while EAAs of first generation clocks did not. Using follow-up data, we also found that EAAs of second-generation clocks were associated with the time to onset risks of chronic diseases. Health behavior factors (drinking, smoking, exercise, body mass index, and waist-hip ratio), socioeconomic status (income level and educational attainment), and psychosocial status were associated with EAAs of second-generation clocks, while only smoking status was associated with EAAs of first-generation clocks. We conducted validation analyses in an independent South Korean cohort and replicated the association of EAAs with health outcomes and environmental factors. Age acceleration of epigenetic clocks is influenced by various environmental factors and can serve as an effective predictor of health in South Korea.

Prediction of atrial and ventricular arrhythmias using multiple cardiovascular risk-factor polygenic risk scores.

Atrial fibrillation (AF) prediction improves by combining clinical scores with a polygenic risk score (PRS) for AF (AF-PRS), but there are limited studies of PRS for ventricular arrhythmia (VA) prediction. We assessed the value of including multiple PRS for cardiovascular risk factors (CV-PRS) for incident AF and VA prediction. We used 158,733 individuals of European ancestry from UK Biobank to build 3 models for AF: CHARGE-AF (AF1), AF1 + AF-PRS (AF2), AF2 + CV-PRS (AF3). Models for VA included sex and age (VA1), VA1 + coronary artery disease (CAD) PRS (CAD-PRS, VA2), and VA2 + CV-PRS (VA3), conducting separate analyses in subjects with and without ischemic heart disease (IHD). Performance was evaluated in individuals of European (N = 158,733), African (N = 7200), South Asian (N = 9241) and East Asian (N = 2076) ancestry from UK Biobank. AF2 had a higher C-index than AF1 (0.762 vs 0.746, P < .001), marginally improving to 0.765 for AF3 (P < .001, including PRS for heart failure, electrocardiogram and cardiac magnetic resonance measures). In South Asians, AF2 C-index was higher than AF1 (P < .001). For VA, the C-index for VA2 was greater than VA1 (0.692 vs 0.681, P < .001) in Europeans, which was also observed in South Asians (P < .001). VA3 improved prediction of VA in individuals with IHD. CV-PRS improved AF prediction compared to CHARGE-AF and AF-PRS. A CAD-PRS improved VA prediction, while CV-PRS contributed in IHD. AF- and CAD-PRS were transferable to individuals of South Asian ancestry. Our results inform of the use of CV-PRS for personalized screening.

Investigating the Gene Relation Between Cervical Spondylosis and Depression: Bidirectional Mendelian Randomization Study.

Previous observational studies have suggested a potential link between depression and cervical spondylosis (CS). While it is known that depression and CS can coexist, the specific relationship between them is not fully understood. We hypothesize that there may be connections between the two conditions, but the independent causal relationship of depression as a risk factor for CS, remains uncertain. This particular study has important implications for the future clinical treatment of depression and cervical spondylosis because Mendelian randomization has not been widely used in this field. We obtained valuable results through big data analysis and have guiding significance for future research. We conducted a two-sample Mendelian randomization (MR) study using data from genome-wide association studies to investigate the causal relationship between depression and CS in individuals of European ancestry. Additionally, we examined the impact of CS on susceptibility to depression using large population-level genetic data (number of depression SNPs: 9,761,853; number of CS SNPs: 9,851,867). The primary approach for data analysis was the inverse-variance weighted (IVW) method to estimate potential causal effects. Furthermore, we performed sensitivity analyses utilizing methods such as Manhattan plot (CMplot), linkage disequilibrium (LD), F-filtering, removal of phenoscanner, MR-Egger, weighted median, MR-PRESSO simple mode weighted mode MR pleiotropy test MR heterogeneity assessment leave-one-out analysis to ensure result robustness. Our findings indicated that an elevated likelihood of CS was linked to depression [IVW odds ratio (OR): 1.322, 95% confidence interval (CI): 1.205-1.441, P=0.01243]. There was reciprocal evidence of causation, with the genetic predisposition to depression significantly heightening susceptibility to CS [IVW odds ratio (OR): 1.426, 95% confidence interval (CI): 1.236-1.651, P=0.01775]. This investigation provides genetic support for a bidirectional causal association between depression and CS. Specifically, individuals with depression are at greater risk of developing CS. Addressing depression may serve as an effective approach in mitigating or preventing the burden of CS and vice versa.