Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability

Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.

Association of education with cholelithiasis and mediating effects of cardiometabolic factors: A Mendelian randomization study

BACKGROUND

Education, cognition, and intelligence are associated with cholelithiasis occurrence, yet which one has a prominent effect on cholelithiasis and which cardiometabolic risk factors mediate the causal relationship remain unelucidated.

AIM

To explore the causal associations between education, cognition, and intelligence and cholelithiasis, and the cardiometabolic risk factors that mediate the associations.

METHODS

Applying genome-wide association study summary statistics of primarily European individuals, we utilized two-sample multivariable Mendelian randomization to estimate the independent effects of education, intelligence, and cognition on cholelithiasis and cholecystitis (FinnGen study, 37041 and 11632 patients, respectively; n = 486484 participants) and performed two-step Mendelian randomization to evaluate 21 potential mediators and their mediating effects on the relationships between each exposure and cholelithiasis.

RESULTS

Inverse variance weighted Mendelian randomization results from the FinnGen consortium showed that genetically higher education, cognition, or intelligence were not independently associated with cholelithiasis and cholecystitis; when adjusted for cholelithiasis, higher education still presented an inverse effect on cholecystitis [odds ratio: 0.292 (95%CI: 0.171-0.501)], which could not be induced by cognition or intelligence. Five out of 21 cardiometabolic risk factors were perceived as mediators of the association between education and cholelithiasis, including body mass index (20.84%), body fat percentage (40.3%), waist circumference (44.4%), waist-to-hip ratio (32.9%), and time spent watching television (41.6%), while time spent watching television was also a mediator from cognition (20.4%) and intelligence to cholelithiasis (28.4%). All results were robust to sensitivity analyses.

CONCLUSION

Education, cognition, and intelligence all play crucial roles in the development of cholelithiasis, and several cardiometabolic mediators have been identified for prevention of cholelithiasis due to defects in each exposure.

Bi-directional association of body size and composition with heart failure: A Mendelian randomization study

PURPOSE

The effect of obesity on the development of heart failure (HF) has received attention, and this study intends to further explore the bidirectional association between body size or composition and HF by using Mendelian Randomization (MR) approach.

DESIGN

We performed a two-sample bidirectional MR study to investigate the association between body size or composition and the risk of HF using aggregated data from genome-wide association studies. Univariable MR analysis was used to investigate the causal relationship, and multivariable MR analysis was used to explore the mediating role of general and central obesity in the relationship between body size or composition and HF.

RESULTS

This forward MR study found that body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), fat mass (FM) and fat-free mass (FFM) were risk factors for the development of HF with the strength of causal association BMI > FM > WC > FFM > WHR. After adjusting for BMI, the observed associations between the remaining indicators and heart failure attenuated to null. After adjusting for WC, only BMI (OR = 1.59, 95%CI: 1.32-1.92, P = 9.53E-07) and FM (OR = 1.39, 95%CI: 1.20-1.62, P = 1.35E-0.5) kept significantly related to the risk of HF. Reverse MR analysis showed no association of changes in body size or composition with the onset of HF.

CONCLUSION

The two-sample bidirectional MR study found that general obesity, measured by BMI, was an independent indicator of the development of HF, while other related indicators were associated with HF incidence dependent on BMI, besides, no association was observed between HF diagnosis and the body size or composites.

Causal effects of genetic birth weight and gestational age on adult esophageal diseases: Mendelian randomization study

BACKGROUND

Few studies have investigated the association between gestational age, birth weight, and esophageal cancer risk; however, causality remains debated. We aimed to establish causal links between genetic gestational age and birth weight traits and gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and esophageal adenocarcinoma (EA). Additionally, we explored if known risk factors mediate these links.

AIM

To analyze of the relationship between gestational age, birth weight and GERD, BE, and EA.

METHODS

Genetic data on gestational age and birth weight (n = 84689 and 143677) from the Early Growth Genetics Consortium and outcomes for GERD (n = 467253), BE (n = 56429), and EA (n = 21271) from genome-wide association study served as instrumental variables. Mendelian randomization (MR) and mediation analyses were conducted using MR-Egger, weighted median, and inverse variance weighted methods. Robustness was ensured through heterogeneity, pleiotropy tests, and sensitivity analyses.

RESULTS

Birth weight was negatively correlated with GERD and BE risk [odds ratio (OR) = 0.78; 95% confidence interval (CI): 0.69-0.8] and (OR = 0.75; 95%CI: 0.60-0.9), respectively, with no significant association with EA. No causal link was found between gestational age and outcomes. Birth weight was positively correlated with five risk factors: Educational attainment (OR = 1.15; 95%CI: 1.01-1.31), body mass index (OR = 1.06; 95%CI: 1.02-1.1), height (OR = 1.12; 95%CI: 1.06-1.19), weight (OR = 1.13; 95%CI: 1.10-1.1), and alcoholic drinks per week (OR = 1.03; 95%CI: 1.00-1.06). Mediation analysis showed educational attainment and height mediated the birth weight-BE link by 13.99% and 5.46%.

CONCLUSION

Our study supports the protective role of genetically predicted birth weight against GERD, BE, and EA, independent of gestational age and partially mediated by educational attainment and height.

Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

BACKGROUND

Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance.

RESULTS

We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis.

CONCLUSIONS

Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.

Association between obstructive sleep apnea and risk of lung cancer: findings from a collection of cohort studies and Mendelian randomization analysis

Background

Previous cohort studies conducted on large populations have suggested a potential association between obstructive sleep apnea (OSA) and an elevated risk of developing lung cancer. However, limited research has comprehensively investigated the correlation between the two conditions, and the causal effect remains unknown.

Methods

A comprehensive and systematic search was conducted across various databases, including PubMed, Web of Science, Cochrane Library, and Embase, from their inception dates to November 1, 2023. To assess the relationship between OSA and lung cancer, a meta-analysis was performed. Additionally, a two-sample Mendelian randomization (MR) study was conducted using summary data. The datasets included 336,659 individuals from the FinnGen study for OSA and 27,209 individuals from the International Lung Cancer Consortium study, as well as 420,473 individuals from the UK Biobank study for lung cancer. The estimates from each study were aggregated using the inverse variance-weighted method.

Results

Data from six population-based cohort studies, encompassing 6,589,725 individuals, indicated a significant increase in the risk of developing lung cancer among patients with OSA (HR 1.28, 95% CI 1.07-1.54). However, the MR analysis did not support a causal relationship between OSA and lung cancer (OR 1.001, 95% CI 0.929-1.100). This lack of association was consistent across specific subtypes of lung cancer, including non-small-cell lung cancer (OR 1.000, 95% CI 0.999-1.000, p = 0.974), lung adenocarcinoma (OR 0.996, 95% CI 0.906-1.094, p = 0.927), and squamous cell lung carcinoma (OR 1.034, 95% CI 0.937-1.140, p = 0.507).

Conclusions

Our meta-analysis findings suggest an elevated risk of lung cancer among individuals with OSA. However, the MR analysis did not provide evidence supporting a causal relationship between OSA and lung cancer. Further investigation is required to uncover the underlying factors contributing to the observed association between OSA and lung cancer risk.

Exploring the causal relationship between body mass index and keratoconus: a Mendelian randomization study

Background

Despite reports suggesting a link between obesity and keratoconus, the causal relationship is not fully understood.

Methods

We used genome-wide association study (GWAS) data from public databases for a two-sample Mendelian randomization analysis to investigate the causal link between body mass index (BMI) and keratoconus. The primary method was inverse variance weighted (IVW), complemented by different analytical techniques and sensitivity analyses to ensure result robustness. A meta-analysis was also performed to bolster the findings' reliability.

Results

Our study identified a significant causal relationship between BMI and keratoconus. Out of 20 Mendelian randomization (MR) analyses conducted, 9 showed heterogeneity or pleiotropy. Among the 11 analyses that met all three MR assumptions, 4 demonstrated a significant causal difference between BMI and keratoconus, while the remaining 7 showed a positive trend but were not statistically significant. Meta-analysis confirmed a significant causal relationship between BMI and keratoconus.

Conclusion

There is a significant causal relationship between BMI and keratoconus, suggesting that obesity may be a risk factor for keratoconus.

Mendelian randomization analyses clarify the effects of height on cardiovascular diseases

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by cardiovascular, anthropometric, lung function, and lifestyle-related risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

Gastroesophageal Reflux and Chronic Rhinosinusitis: A Mendelian Randomization Study

OBJECTIVE

Chronic rhinosinusitis (CRS) is associated with gastroesophageal reflux (GERD). However, the causal relationship is controversial. We conducted a two-sample Mendelian Randomization (MR) analysis to explore this potential association.

METHODS

Based on genome-wide association studies (GWAS), a univariable MR was performed to explore the causal relationship of GERD with CRS. Instrumental variables (IVs) pertinent to anti-GERD treatment were employed as a means of validation. The primary MR outcome was established using an inverse variance weighted (IVW) method, supplemented by multiple sensitivity analyses. Subsequently, a multivariable MR was conducted to account for potential confounding variables, thereby ascertaining a direct effect of GERD on CRS. Finally, a network MR analysis was carried out to elucidate the mediating role of asthma in the relationship between GERD and CRS.

RESULTS

The univariable MR demonstrated an association between GERD and an elevated risk of CRS (IVW OR = 1.30, 95% CI = 1.18-1.45, p = 4.19 × 10-7). Omeprazole usage was associated with a reduction in CRS risk (IVW OR = 0.64, 95% CI = 0.42-0.98, p = 0.039). The causal relationship between GERD and CRS remained after adjusting for potential confounders, such as smoking characteristics, body mass index, asthma, allergic rhinitis, in the multivariable MR analysis. Besides, the proportion of the causal effect of GERD on CRS mediated by asthma was 19.65% (95% CI = 2.69%-36.62%).

CONCLUSION

GERD was independently associated with an increased risk of CRS. The mediating role of asthma between GERD and CRS also reveals that GERD is one of the mechanisms underlying unified airway disease.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3086-3092, 2024.

Bi-directional two-sample Mendelian randomization identifies causal association of depression with temporomandibular disorders

BACKGROUND

Depression and anxiety have been suggested to be associated with temporomandibular disorders (TMD) in observational studies. However, the causal association and the direction in the relationship between depression/anxiety and TMD remain unknown.

OBJECTIVES

This study investigated the potential causal relationship between depression/anxiety and TMD with two-sample bi-directional Mendelian randomization (MR).

METHODS

Summary statistics of depression (N = 500 199), anxiety disorder (N = 17 310) and TMD (N = 195 930) were sourced from large-scale genome-wide association studies (GWAS). The primary Mendelian randomization (MR) estimation employed the inverse-variance weighted meta-analysis (IVW). Additional MR sensitivity methods and multivariate MR (MVMR) were applied to address pleiotropy.

RESULTS

IVW results indicated a causal effect of genetically predicted depression on TMD (OR = 1.887, 95% CI = 1.504-2.367, p < .001), which was supported by other sensitivity MR approaches. MVMR results suggested that the negative effect of depression on TMD persisted after conditioning on other potential confounders. The association of anxiety disorder with TMD was not supported by our findings. In the reverse direction, we did not find compelling evidence suggesting the causal effect of TMD on depression and anxiety disorder.

CONCLUSIONS

The present study suggests a potential causal association between genetic liability for depression and the risk of TMD. Our MR findings align with prior epidemiological research, underscoring the significance of early detection and prevention of depression in the treatment of TMD.

Shared genetic basis and causality between schizophrenia and inflammatory bowel disease: evidence from a comprehensive genetic analysis

BACKGROUND

The comorbidity between schizophrenia (SCZ) and inflammatory bowel disease (IBD) observed in epidemiological studies is partially attributed to genetic overlap, but the magnitude of shared genetic components and the causality relationship between them remains unclear.

METHODS

By leveraging large-scale genome-wide association study (GWAS) summary statistics for SCZ, IBD, ulcerative colitis (UC), and Crohn's disease (CD), we conducted a comprehensive genetic pleiotropic analysis to uncover shared loci, genes, or biological processes between SCZ and each of IBD, UC, and CD, independently. Univariable and multivariable Mendelian randomization (MR) analyses were applied to assess the causality across these two disorders.

RESULTS

SCZ genetically correlated with IBD (rg = 0.14, p = 3.65 × 10−9), UC (rg = 0.15, p = 4.88 × 10−8), and CD (rg = 0.12, p = 2.27 × 10−6), all surpassed the Bonferroni correction. Cross-trait meta-analysis identified 64, 52, and 66 significantly independent loci associated with SCZ and IBD, UC, and CD, respectively. Follow-up gene-based analysis found 11 novel pleiotropic genes (KAT5, RABEP1, ELP5, CSNK1G1, etc) in all joint phenotypes. Co-expression and pathway enrichment analysis illustrated those novel genes were mainly involved in core immune-related signal transduction and cerebral disorder-related pathways. In univariable MR, genetic predisposition to SCZ was associated with an increased risk of IBD (OR 1.11, 95% CI 1.07–1.15, p = 1.85 × 10−6). Multivariable MR indicated a causal effect of genetic liability to SCZ on IBD risk independent of Actinobacteria (OR 1.11, 95% CI 1.06–1.16, p = 1.34 × 10−6) or BMI (OR 1.11, 95% CI 1.04–1.18, p = 1.84 × 10−3).

CONCLUSIONS

We confirmed a shared genetic basis, pleiotropic loci/genes, and causal relationship between SCZ and IBD, providing novel insights into the biological mechanism and therapeutic targets underlying these two disorders.

Causal relationships between lung cancer and sepsis: a genetic correlation and multivariate mendelian randomization analysis

Background

Former research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear.

Method

This study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness.

Results

LDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083-1.269, p = 8.29 × 10-5, P fdr = 2.49 × 10-4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021-1.181, p = 0.012, P fdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024-1.178, p = 0.009, P fdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047-1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship (p > 0.05 and P fdr > 0.05).

Conclusion

The study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients.

Prescription glucocorticoid medication and iridocyclitis are associated with an increased risk of senile cataract occurrence: a Mendelian randomization study

Iridocyclitis and the use of glucocorticoid medication have been widely studied as susceptibility factors for cataracts. However, the causal relationship between them remains unclear. This study aimed to investigate the causal relationship between the development of iridocyclitis and the genetic liability of glucocorticoid medication use on the risk of senile cataracts occurrence by performing Two-sample Mendelian randomization (MR) analyses. Instrumental variables (IVs) significantly associated with exposure factors (P < 5 × 10-8) were identified using published genome-wide association data from the FinnGen database and UK Biobank. Reliability analyses were conducted using five approaches, including inverse-variance weighted (IVW), MR-Egger regression, simple median, weighted median, and weighted mode. A sensitivity analysis using the leave-one-out method was also performed. Genetic susceptibility to glucocorticoid use was associated with an increased risk of developing senile cataracts (OR, 1.10; 95% CI, 1.02-1.17; P < 0.05). Moreover, iridocyclitis was significantly associated with a higher risk of developing senile cataracts (OR, 1.03; 95% CI, 1.01-1.05; P < 0.05). Nonetheless, some heterogeneity in the IVs was observed, but the MR results remained consistent after penalizing for outliers. The estimates were consistent in multivariate analyses by adjusting for body mass index (BMI) and diabetes mellitus type 2 (T2DM). This study provides new insights into the prevention and management of senile cataracts by highlighting the increased risk associated with iridocyclitis and the use of glucocorticoids.

Olanzapine Modulate Lipid Metabolism and Adipose Tissue Accumulation via Hepatic Muscarinic M3 Receptor-Mediated Alk-Related Signaling

Olanzapine is an atypical antipsychotic drug and a potent muscarinic M3 receptor (M3R) antagonist. Olanzapine has been reported to cause metabolic disorders, including dyslipidemia. Anaplastic lymphoma kinase (Alk), a tyrosine kinase receptor well known in the pathogenesis of cancer, has been recently identified as a key gene in the regulation of thinness via the regulation of adipose tissue lipolysis. This project aimed to investigate whether Olanzapine could modulate the hepatic Alk pathway and lipid metabolism via M3R. Female rats were treated with Olanzapine and/or Cevimeline (an M3R agonist) for 9 weeks. Lipid metabolism and hepatic Alk signaling were analyzed. Nine weeks' treatment of Olanzapine caused metabolic disturbance including increased body mass index (BMI), fat mass accumulation, and abnormal lipid metabolism. Olanzapine treatment also led to an upregulation of Chrm3, Alk, and its regulator Ptprz1, and a downregulation of Lmo4, a transcriptional repressor of Alk in the liver. Moreover, there were positive correlations between Alk and Chrm3, Alk and Ptprz1, and a negative correlation between Alk and Lmo4. However, cotreatment with Cevimeline significantly reversed the lipid metabolic disturbance and adipose tissue accumulation, as well as the upregulation of the hepatic Alk signaling caused by Olanzapine. This study demonstrates evidence that Olanzapine may cause metabolic disturbance by modulating hepatic Alk signaling via M3R, which provides novel insight for modulating the hepatic Alk signaling and potential interventions for targeting metabolic disorders.

Discovering non-additive heritability using additive GWAS summary statistics

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease

BACKGROUND

Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages.

METHODS

We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo.

RESULTS

We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus.

CONCLUSIONS

Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.

Comparison of caffeine consumption behavior with plasma caffeine levels as exposure measures in drug-target Mendelian randomization

Mendelian randomization is an epidemiological technique that can explore the potential effect of perturbing a pharmacological target. Plasma caffeine levels can be used as a biomarker to measure the pharmacological effects of caffeine. Alternatively, this can be assessed using a behavioral proxy, such as average number of caffeinated drinks consumed per day. Either variable can be used as the exposure in a Mendelian randomization investigation, and to select which genetic variants to use as instrumental variables. Another possibility is to choose variants in gene regions with known biological relevance to caffeine level regulation. These choices affect the causal question that is being addressed by the analysis, and the validity of the analysis assumptions. Further, even when using the same genetic variants, the sign of Mendelian randomization estimates (positive or negative) can change depending on the choice of exposure. Some genetic variants that decrease caffeine metabolism associate with higher levels of plasma caffeine, but lower levels of caffeine consumption, as individuals with these variants require less caffeine consumption for the same physiological effect. We explore Mendelian randomization estimates for the effect of caffeine on body mass index, and discuss implications for variant and exposure choice in drug target Mendelian randomization investigations.

Associations between polygenic risk scores for cardiometabolic phenotypes and adolescent depression and body dissatisfaction

BACKGROUND

Adolescents with elevated body mass index (BMI) are at an increased risk for depression and body dissatisfaction. Type 2 diabetes (T2D) is an established risk factor for depression. However, shared genetic risk between cardiometabolic conditions and mental health outcomes remains understudied in youth.

METHODS

The current study examined associations between polygenic risk scores (PRS) for BMI and T2D, and symptoms of depression and body dissatisfaction, in a sample of 827 community adolescents (Mage = 13.63, SDage = 1.01; 76% girls). BMI, depressive symptoms, and body dissatisfaction were assessed using validated self-report questionnaires.

RESULTS

BMI-PRS was associated with phenotypic BMI (β = 0.24, p < 0.001) and body dissatisfaction (β = 0.17, p < 0.001), but not with depressive symptoms. The association between BMI-PRS and body dissatisfaction was significantly mediated by BMI (indirect effect = 0.10, CI [0.07-0.13]). T2D-PRS was not associated with depression or body dissatisfaction.

CONCLUSIONS

The results suggest phenotypic BMI may largely explain the association between genetic risk for elevated BMI and body dissatisfaction in adolescents. Further research on age-specific genetic effects is needed, as summary statistics from adult discovery samples may have limited utility in youth.

IMPACT

The association between genetic risk for elevated BMI and body dissatisfaction in adolescents may be largely explained by phenotypic BMI, indicating a potential pathway through which genetic predisposition influences body image perception. Furthermore, age-specific genetic research is needed to understand the unique influences on health outcomes during adolescence. By identifying BMI as a potential mediator in the association between genetic risk for elevated BMI and body dissatisfaction, the current findings offer insights that could inform interventions targeting body image concerns and mental health in this population.

Causal relationships between body mass index, low-density lipoprotein and bone mineral density: Univariable and multivariable Mendelian randomization

SUMMARY

Utilizing the Mendelian randomization technique, this research clarifies the putative causal relationship between body mass index (BMI) andbone mineral density (BMD), and the mediating role of low-density lipoprotein (LDL). The implications of these findings present promising opportunities for enhancing our understanding of complex bone-related characteristics and disorders, offering potential directions for treatment and intervention.

OBJECTIVE

The objective of this study is to examine the correlation between BMI and BMD, while exploring the intermediary role of LDL in mediating the causal impact of BMI on BMD outcomes via Mendelian randomization.

METHODS

In this study, we employed genome-wide association study (GWAS) data on BMI, LDL, and BMD to conduct a comparative analysis using both univariate and multivariate Mendelian randomization.

RESULTS

Our study employed a two-sample Mendelian randomization design. Considering BMI as the exposure and BMD as the outcome, our results suggest that BMI may function as a potential protective factor for BMD (β = 0.05, 95% CI 1.01 to 1.09, P = 0.01). However, when treating LDL as the exposure and BMD as the outcome, our findings indicate LDL as a risk factor for BMD (β = -0.04, 95% CI 0.92 to 0.99, P = 0.04). In our multivariate Mendelian randomization (MVMR) model, the combined influence of BMI and LDL was used as the exposure for BMD outcomes. The analysis pointed towards a substantial protective effect of LDL on BMD (β = 0.08, 95% CI 0.85 to 0.97, P = 0.006). In the analysis of mediation effects, LDL was found to mediate the relationship between BMI and BMD, and the effect was calculated at (β = 0.05, 95% CI 1.052 to 1.048, P = 0.04).

CONCLUSION

Our findings suggest that BMI may be considered a protective factor for BMD, while LDL may act as a risk factor. Moreover, LDL appears to play a mediatory role in the causal influence of BMI on BMD.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• HHSN268201500001I NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• HHSN268201500001C NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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Ribosomal DNA copy number is associated with body mass in humans and other mammals

Body mass results from a complex interplay between genetics and environment. Previous studies of the genetic contribution to body mass have excluded repetitive regions due to the technical limitations of platforms used for population scale studies. Here we apply genome-wide approaches, identifying an association between adult body mass and the copy number (CN) of 47S-ribosomal DNA (rDNA). rDNA codes for the 18 S, 5.8 S and 28 S ribosomal RNA (rRNA) components of the ribosome. In mammals, there are hundreds of copies of these genes. Inter-individual variation in the rDNA CN has not previously been associated with a mammalian phenotype. Here, we show that rDNA CN variation associates with post-pubertal growth rate in rats and body mass index in adult humans. rDNA CN is not associated with rRNA transcription rates in adult tissues, suggesting the mechanistic link occurs earlier in development. This aligns with the observation that the association emerges by early adulthood.

Genome-wide association study of the common retinal disorder epiretinal membrane: Significant risk loci in each of three American populations

Epiretinal membrane (ERM) is a common retinal condition characterized by the presence of fibrocellular tissue on the retinal surface, often with visual distortion and loss of visual acuity. We studied European American (EUR), African American (AFR), and Latino (admixed American, AMR) ERM participants in the Million Veteran Program (MVP) for genome-wide association analysis-a total of 38,232 case individuals and 557,988 control individuals. We completed a genome-wide association study (GWAS) in each population separately, and then results were meta-analyzed. Genome-wide significant (GWS) associations were observed in all three populations studied: 31 risk loci in EUR subjects, 3 in AFR, and 2 in AMR, with 48 in trans-ancestry meta-analysis. Many results replicated in the FinnGen sample. Several GWS variants associate to alterations in gene expression in the macula. ERM showed significant genetic correlation to multiple traits. Pathway enrichment analyses implicated collagen and collagen-adjacent mechanisms, among others. This well-powered ERM GWAS identified novel genetic associations that point to biological mechanisms for ERM.

Depression and risk of infectious diseases: A mendelian randomization study

Previous observational inquiries have revealed a correlation between depression and infectious maladies. This study seeks to elucidate the causal linkages between depression, specifically Major Depressive Disorder (MDD), and infectious diseases. Nevertheless, the causative nature of the association between MDD and infectious diseases remains elusive. Two-sample Mendelian Randomization (MR) analyses was executed utilizing single nucleotide polymorphisms (SNPs) significantly connected with MDD and infectious diseases as instrumental variables (IVs). A series of sensitivity analyses were subsequently conducted. Genetic variants linked to MDD were employed as instrumental variables sourced from a genome-wide meta-analyses comprising 500,199 individuals. Summary-level data on five infectious diseases, including candidiasis, pneumonia, skin and soft tissue infections (SSTI), upper respiratory tract infections (URTI), and urinary tract infections (UTI), were acquired from the UK Biobank and FinnGen study. Our findings evinced that genetically predicted MDD exhibited a heightened risk of candidiasis (OR = 1.52, 95% CI 1.06-2.17; P = 2.38E-02), pneumonia (OR = 1.14, 95% CI 1.01-1.29; P = 3.16E-02), URTI (OR = 1.23, 95% CI 1.12-1.36; P = 3.71E-05), and UTI (OR = 1.26, 95% CI 1.12-1.42; P = 8.90E-05). Additionally, we identified bidirectional causal relationships between UTI and MDD. The associations between MDD and the risk of URTI and UTI remained consistent in multivariable MR analyses, accounting for genetically predicted smoking and body mass index. In conclusion, this investigation ascertained a causal connection between MDD and the susceptibility to infectious diseases, particularly URTI and UTI.

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

Longitudinal Associations Between MicroRNAs and Weight in the Diabetes Prevention Program

OBJECTIVE

Circulating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial.

RESEARCH DESIGN AND METHODS

A subset of participants (n=150) from the Diabetes Prevention Program were included. MicroRNAs were measured from banked plasma using a Fireplex Assay. We used generalized linear mixed models to evaluate relationships between microRNAs and changes in weight at baseline, year-1, and year-2. Logistic regression was used to evaluate whether microRNAs at baseline were associated with weight change after 2 years.

RESULTS

In fully adjusted models that included relevant covariates, seven miRs (i.e., miR-126, miR-15a, miR-192, miR-23a, and miR-27a) were statistically associated with weight over 2 years. MiR-197 and miR-320a remained significant after adjustment for multiple comparisons. Baseline levels of let-7f, miR-17, and miR-320c were significantly associated with 3% weight loss after 2 years in fully adjusted models.

DISCUSSION

This study provided evidence for longitudinal relationships between circulating microRNAs and weight. Because microRNAs characterize the combined effects of genetic determinants and responses to behavioral determinants, they may provide insights about the etiology of overweight and obesity in the context or risk for common, complex diseases. Additional studies are needed to validate the potential genes and biological pathways that might be targeted by these microRNA biomarkers and have mechanistic implications for weight loss and disease prevention.

An integrative framework to prioritize genes in more than 500 loci associated with body mass index

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

Assessing the causal relationship of birth weight and childhood obesity on osteoarthritis: a Mendelian randomization study

Obesity is associated with osteoarthritis (OA), but few studies have used fetal origin to explore the association. Our study aims to disentangle the causality between birth weight, childhood obesity, and adult OA using Mendelian randomization (MR). We identified single nucleotide polymorphisms (SNPs) related to birth weight (n = 298,142) and childhood obesity (n = 24,160) from two genome-wide association studies contributed by the Early Growth Genetics Consortium. Summary statistics of OA and its phenotypes (knee, hip, spine, hand, thumb, and finger OA) from the Genetics of Osteoarthritis Consortium (n = 826,690) were used to estimate the effects of SNPs on OA. Multivariable MR (MVMR) was conducted to investigate the independent effects of exposures. It turned out that genetically predicted standard deviation increase in birth weight was not associated with OA. In contrast, there was a marginally positive effect of childhood obesity on total [odds ratio (OR) = 1.07, 95% confidence interval (CI) = 1.00, 1.15 using IVW], knee (OR = 1.13, 95% CI = 1.05, 1.22 using weighted median), hip (OR = 1.13, 95% CI = 1.04, 1.24 using IVW), and spine OA (OR = 1.12, 95% CI = 1.03, 1.22 using IVW), but not hand, thumb, or finger OA. MVMR indicated a potential adulthood body mass index-dependent causal pathway between childhood obesity and OA. In conclusion, no association of birth weight with OA was suggested. Childhood obesity, however, showed a causality with OA in weight-bearing joints, which seems to be a general association of obesity with OA.

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Adaptive and Innate Immunity Are Key Drivers of Age at Onset of Multiple Sclerosis

Background and Objectives

Multiple sclerosis (MS) age at onset (AAO) is a clinical predictor of long-term disease outcomes, independent of disease duration. Little is known about the genetic and biological mechanisms underlying age of first symptoms. We conducted a genome-wide association study (GWAS) to investigate associations between individual genetic variation and the MS AAO phenotype.

Methods

The study population was comprised participants with MS in 6 clinical trials: ADVANCE (N = 655; relapsing-remitting [RR] MS), ASCEND (N = 555; secondary-progressive [SP] MS), DECIDE (N = 1,017; RRMS), OPERA1 (N = 581; RRMS), OPERA2 (N = 577; RRMS), and ORATORIO (N = 529; primary-progressive [PP] MS). Altogether, 3,905 persons with MS of European ancestry were analyzed. GWAS were conducted for MS AAO in each trial using linear additive models controlling for sex and 10 principal components. Resultant summary statistics across the 6 trials were then meta-analyzed, for a total of 8.3 × 10-6 single nucleotide polymorphisms (SNPs) across all trials after quality control and filtering for heterogeneity. Gene-based tests of associations, pathway enrichment analyses, and Mendelian randomization analyses for select exposures were also performed.

Results

Four lead SNPs within 2 loci were identified (p < 5 × 10-8), including a) 3 SNPs in the major histocompatibility complex and their effects were independent of HLA-DRB1*15:01 and b) a LOC105375167 variant on chromosome 7. At the gene level, the top association was HLA-C (p = 1.2 × 10-7), which plays an important role in antiviral immunity. Functional annotation revealed the enrichment of pathways related to T-cell receptor signaling, autoimmunity, and the complement cascade. Mendelian randomization analyses suggested a link between both earlier age at puberty and shorter telomere length and earlier AAO, while there was no evidence for a role for either body mass index or vitamin D levels.

Discussion

Two genetic loci associated with MS AAO were identified, and functional annotation demonstrated an enrichment of genes involved in adaptive and complement immunity. There was also evidence supporting a link with age at puberty and telomere length. The findings suggest that AAO in MS is multifactorial, and the factors driving onset of symptoms overlap with those influencing MS risk.

Unveiling challenges in Mendelian randomization for gene-environment interaction

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

The healthcare costs of increased body mass index-evidence from The Trøndelag Health Study

BACKGROUND

Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses.

METHODS

In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations.

RESULTS

There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations.

CONCLUSION

The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.

Differential age-specific associations of LDL cholesterol and body mass index with coronary heart disease

BACKGROUND AND AIMS

Low-density lipoprotein cholesterol (LDLc) and body mass index (BMI) are not always correlated and their relationship is probably dependent on age, indicating differential age-specific associations of these factors with health outcomes. We aim to discriminate the roles of LDLc and BMI in coronary heart disease (CHD) across different age groups.

METHODS

This is a prospective cohort study of 368,274 participants aged 38-73 years and free of CHD at baseline. LDLc and BMI were measured at baseline, and incident CHD was the main outcome. Cox proportional hazards model and restricted cubic spline (RCS) regression were used to estimate hazard ratio (HR) and 95% confidence interval (CI) of exposure on CHD.

RESULTS

After a mean of 12 years of follow-up, similar relationships of LDLc and BMI with CHD risk were observed in the overall population but in differential age-specific patterns. Across the age groups of <50, 50-54, 55-59, 60-64 and ≥ 65 years, the LDLc-CHD association diminished with the adjusted HRs decreasing from 1.35, 1.26, 1.19, 1.11 to 1.08; while no declining trend was found in BMI-CHD relationship with the adjusted HRs of 1.15, 1.11, 1.12, 1.13 and 1.15, respectively. The interaction and mediation between LDLc and BMI on CHD risk were more pronounced at young-age groups. LDLc-CHD but not BMI-CHD association was dependent on sex, metabolic syndrome and lipid-lowering drugs use.

CONCLUSIONS

There were differential age-specific associations of LDLc and BMI with the risk of developing CHD, calling for future efforts to discriminate the age-different benefits from lipids management or weight control on the primary prevention for CHD.

Interindividual differences of dietary fat-inducible Mest in white adipose tissue of C57BL/6J mice are not heritable

OBJECTIVE

Differences in white adipose tissue (WAT) expression of mesoderm-specific transcript (Mest) in C57BL6/J mice fed a high-fat diet (HFD) are concomitant with and predictive for the development of obesity. However, the basis for differences in WAT Mest among mice is unknown. This study investigated whether HFD-inducible WAT Mest, as well as susceptibility to obesity, is transmissible from parents to offspring.

METHODS

WAT biopsies of mice fed an HFD for 2 weeks identified parents with low and high WAT Mest for breeding. Obesity phenotypes, WAT Mest, hepatic gene expression, and serum metabolites were determined in offspring fed an HFD for 2 weeks.

RESULTS

Offspring showed no heritability of obesity or WAT Mest phenotypes from parents but did show hepatic and serum metabolite changes consistent with their WAT Mest. Importantly, retired male breeders showed WAT Mest expression congruent with initial WAT biopsies even though HFD exposure occurred early in life.

CONCLUSIONS

Disparity of HFD-induced Mest in mice is not heritable but, rather, is reestablished during each generation and remains fixed from an early age to adulthood. Short-term HFD feeding reveals variation of WAT Mest expression within isogenic mice that is positively associated with the development of obesity.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Associations of Lifestyle and Genetic Risks with Obesity and Related Chronic Diseases in the UK Biobank: A Prospective Cohort Study

BACKGROUND

Interplay between lifestyle risk scores (LRSs) and genetic risk scores (GRSs) on obesity and related chronic diseases are underinvestigated and necessary for understanding obesity causes and developing prevention strategies.

OBJECTIVES

This study aimed to investigate independent and joint associations and interactions of LRS and GRS with obesity prevalence and risks of diabetes, cardiovascular disease (CVD), and obesity-related cancer.

METHODS

In this cohort study of 444,957 UK Biobank participants [age: 56.5 ± 8.1 y; BMI (in kg/m2): 27.4 ± 4.7], LRS included physical activity, dietary score, sedentary behavior, sleep duration, and smoking (range: 0-20, each factor had 5 levels). GRS was calculated based on 941 genetic variants related to BMI. Both scores were categorized into quintiles. Obesity (n = 106,301) was defined as baseline BMI ≥30. Incident diabetes (n = 16,311), CVD (n = 18,076), and obesity-related cancer (n = 17,325) were ascertained through linkage to registries over a median of 12-y follow-up.

RESULTS

The LRS and GRS were independently positively associated with all outcomes. Additive interactions of LRS and GRS were observed for all outcomes (P < 0.021). Comparing the top with bottom LRS quintile, prevalence differences (95% CIs) for obesity were 17.8% (15.9%, 19.7%) in the top GRS quintile and 10.7% (8.3%, 13.1%) in the bottom GRS quintile; for diabetes, CVD, and obesity-related cancer, incidence rate differences associated with per SD increase in LRS were greater in the top than that in the bottom GRS quintile. Participants from top quintiles of both LRS and GRS had 6.16-fold, 3.81-fold, 1.56-fold, and 1.44-fold higher odds/risks of obesity, diabetes, CVD, and obesity-related cancer, respectively, than those from bottom quintiles of both scores.

CONCLUSIONS

Higher LRS was associated with higher obesity prevalence and risks of related chronic diseases regardless of GRS, highlighting the broad benefits of healthy lifestyles. Additive gene-lifestyle interactions emphasize the public health importance of lifestyle interventions among people with high genetic risks.

Impact of insomnia on ovarian cancer risk and survival: a Mendelian randomization study

BACKGROUND

Insomnia is the most common sleep disorder in patients with epithelial ovarian cancer (EOC). We investigated the causal association between genetically predicted insomnia and EOC risk and survival through a two-sample Mendelian randomization (MR) study.

METHODS

Insomnia was proxied using genetic variants identified in a genome-wide association study (GWAS) meta-analysis of UK Biobank and 23andMe. Using genetic associations with EOC risk and overall survival from the Ovarian Cancer Association Consortium (OCAC) GWAS in 66,450 women (over 11,000 cases with clinical follow-up), we performed Iterative Mendelian Randomization and Pleiotropy (IMRP) analysis followed by a set of sensitivity analyses. Genetic associations with survival and response to treatment in ovarian cancer study of The Cancer Genome Atlas (TCGA) were estimated controlling for chemotherapy and clinical factors.

FINDINGS

Insomnia was associated with higher risk of endometrioid EOC (OR = 1.60, 95% CI 1.05-2.45) and lower risk of high-grade serous EOC (HGSOC) and clear cell EOC (OR = 0.79 and 0.48, 95% CI 0.63-1.00 and 0.27-0.86, respectively). In survival analysis, insomnia was associated with shorter survival of invasive EOC (OR = 1.45, 95% CI 1.13-1.87) and HGSOC (OR = 1.4, 95% CI 1.04-1.89), which was attenuated after adjustment for body mass index and reproductive age. Insomnia was associated with reduced survival in TCGA HGSOC cases who received standard chemotherapy (OR = 2.48, 95% CI 1.13-5.42), but was attenuated after adjustment for clinical factors.

INTERPRETATION

This study supports the impact of insomnia on EOC risk and survival, suggesting treatments targeting insomnia could be pivotal for prevention and improving patient survival.

FUNDING

National Institutes of Health, National Cancer Institute. Full funding details are provided in acknowledgments.

Management and Nursing Approaches to Low Back Pain: Investigating the Causal Association with Lifestyle-Related Risk Factors

BACKGROUND

Notwithstanding a plethora of observational studies, the causal implications of obesity, encompassing both body mass index (BMI) and waist circumference (WC), as well as type 2 diabetes (T2D), and lifestyle factors, in relation to the vulnerability to low back pain (LBP), remain enigmatic.

AIMS

This study was designed to investigate the related causal associations DESIGN: A two-sample Mendelian randomization (MR) analysis.

SETTINGS

By utilizing genetic variants associated with pertinent factors gleaned from genome-wide association studies (GWASs), We extracted independent genetic variants about exposures such as BMI, WC, T2D, smoking, alcohol consumption, and coffee intake from published GWASs, ensuring their genome-wide significance.

PARTICIPANTS/SUBJECTS

The GWASs were selected from the most up-to-date and largest publicly accessible databases.

METHODS

The summary data concerning LBP emanated from a GWAS of European cases and controls, which was based on the esteemed MRC-IEU (Medical Research Council Integrative Epidemiology Unit) consortium.

RESULTS

Heightened body mass index and waist circumference exhibited odds ratios of 1.003 (95% confidence interval [CI] = 1.002-1.004, p < 0.001) and 1.003 (95% CI = 1.002-1.004, p < 0.001) for LBP, respectively, per each standard deviation (SD) increase. As for smoking initiation and every SD increase in the frequency of alcohol intake, the odds ratios were 1.002 (95% CI = 1.001-1.003, p = 0.003) and 1.002 (95% CI = 1.000-1.003, p = 0.011), respectively, for LBP. Conversely, an increased log odds ratio for T2D, and prevalence of coffee intake, divulged no discernible causal effects on the risk of LBP.

CONCLUSION

This study provides suggestive evidence to support the causal involvement of obesity, smoking, and the frequency of alcohol intake in the development of LBP, which suggests that implementing measures to mitigate these risk factors may aid in preventing LBP.

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation

OBJECTIVE

Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques.

DESIGN

We applied two-sample MR to investigate the causal relationships between genetic liability for select risk factors and spinal stenosis. Next, we examined the genetic relationship between OA and spinal stenosis with linkage disequilibrium score regression and Causal Analysis Using Summary Effect estimates MR method. Finally, we used multivariable MR (MVMR) to explore whether OA and body mass index (BMI) mediate the causal pathways identified.

RESULTS

Our analysis revealed strong evidence for the effect of higher BMI (odds ratio [OR] = 1.54, 95%CI: 1.41-1.69, p-value = 2.7 × 10-21), waist (OR = 1.43, 95%CI: 1.15-1.79, p-value = 1.5 × 10-3) and hip (OR = 1.50, 95%CI: 1.27-1.78, p-value = 3.3 × 10-6) circumference on spinal stenosis. Strong evidence of causality was also observed for higher bone mineral density (BMD): total body (OR = 1.21, 95%CI: 1.12-1.29, p-value = 1.6 × 10-7), femoral neck (OR = 1.35, 95%CI: 1.09-1.37, p-value = 7.5×10-7), and lumbar spine (OR = 1.38, 95%CI: 1.25-1.52, p-value = 4.4 × 10-11). We detected high genetic correlations between spinal stenosis and OA (rg range: 0.47-0.66), with Causal Analysis Using Summary Effect estimates results supporting a causal effect of OA on spinal stenosis (ORallOA = 1.6, 95%CI: 1.41-1.79). Direct effects of BMI, BMD on spinal stenosis remained after adjusting for OA in the MVMR.

CONCLUSIONS

Genetic susceptibility to anthropometric risk factors, particularly higher BMI and BMD can increase the risk of spinal stenosis, independent of OA status. These results may inform preventative strategies and treatments.

Causal effects of sleep traits on metabolic syndrome and its components: a Mendelian randomization study

PURPOSE

Previous observational studies have suggested an association between sleep disturbance and metabolic syndrome (MetS). However, it remains unclear whether this association is causal. This study aims to investigate the causal effects of sleep-related traits on MetS using Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms strongly associated with daytime napping, insomnia, chronotype, short sleep, and long sleep were selected as genetic instruments from the corresponding genome-wide association studies (GWAS). Summary-level data for MetS were obtained from two independent GWAS datasets. Univariable and multivariable MR analyses were conducted to investigate and verify the causal effects of sleep traits on MetS.

RESULTS

The univariable MR analysis demonstrated that genetically predicted daytime napping and insomnia were associated with increased risk of MetS in both discovery dataset (OR daytime napping = 1.630, 95% CI 1.273, 2.086; OR insomnia = 1.155, 95% CI 1.108, 1.204) and replication dataset (OR daytime napping = 1.325, 95% CI 1.131, 1.551; OR insomnia = 1.072, 95% CI 1.046, 1.099). For components, daytime napping was positively associated with triglycerides (beta = 0.383, 95% CI 0.160, 0.607) and waist circumference (beta = 0.383, 95% CI 0.184, 0.583). Insomnia was positively associated with hypertension (OR = 1.101, 95% CI 1.042, 1.162) and waist circumference (beta = 0.067, 95% CI 0.031, 0.104). The multivariable MR analysis indicated that the adverse effect of daytime napping and insomnia on MetS persisted after adjusting for BMI, smoking, drinking, and another sleep trait.

CONCLUSION

Our study supported daytime napping and insomnia were potential causal factors for MetS characterized by central obesity, hypertension, or elevated triglycerides.

Genetic association of serum lipids and lipid-modifying targets with endometriosis: Trans-ethnic Mendelian-randomization and mediation analysis

BACKGROUND

Prior observational research identified dyslipidemia as a risk factor for endometriosis (EMS) but the causal relationship remains unestablished due to inherent study limitations.

METHODS

Genome-wide association study data for high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and total cholesterol (TC) from European (EUR) and East Asian (EAS) ancestries were sourced from the Global Lipids Genetics Consortium. Multi-ancestry EMS data came from various datasets. Univariable Mendelian randomization (MR) examined causal links between serum lipids and EMS. Multivariable and mediation MR explored the influence of seven confounding factors and mediators. Drug-target MR investigates the association between lipid-lowering target genes identified in positive results and EMS. The primary method was inverse-variance weighted (IVW), with replication datasets and meta-analyses reinforcing causal associations. Sensitivity analyses included false discovery rate (FDR) correction, causal analysis using summary effect estimates (CAUSE), and colocalization analysis.

RESULTS

IVW analysis in EUR ancestry showed a significant causal association between TG and increased EMS risk (OR = 1.112, 95% CI 1.033-1.198, P = 5.03×10-3, PFDR = 0.03), supported by replication and meta-analyses. CAUSE analysis confirmed unbiased results (P < 0.05). Multivariable and mediation MR revealed that systolic blood pressure (Mediation effect: 7.52%, P = 0.02) and total testosterone (Mediation effect: 10.79%, P = 0.01) partly mediated this relationship. No causal links were found between other lipid traits and EMS (P > 0.05 & PFDR > 0.05). In EAS ancestry, no causal relationships with EMS were detected (P > 0.05 & PFDR > 0.05). Drug-target MR indicated suggestive evidence for the influence of ANGPTL3 on EMS mediated through TG (OR = 0.798, 95% CI 0.670-0.951, P = 0.01, PFDR = 0.04, PP.H4 = 0.85%).

CONCLUSIONS

This MR study in EUR ancestry indicated an increased EMS risk with higher serum TG levels.

Variability of polygenic prediction for body mass index in Africa

BACKGROUND

Polygenic prediction studies in continental Africans are scarce. Africa's genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required.

METHODS

Using the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions.

RESULTS

Polygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa's East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status.

CONCLUSIONS

Our findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.

Rapid response of fly populations to gene dosage across development and generations

Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges remains limited. Here, we have examined the phenotypic consequences of disturbing the bicoid regulatory network in early Drosophila embryos. We generated flies with two extra copies of bicoid, which causes a posterior shift of the network's regulatory outputs and a decrease in fitness. We subjected these flies to EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level.

Assessment of causal relationships between omega-3 and omega-6 polyunsaturated fatty acids in autoimmune rheumatic diseases: a brief research report from a Mendelian randomization study

Background

Currently, the association between the consumption of polyunsaturated fatty acids (PUFAs) and the susceptibility to autoimmune rheumatic diseases (ARDs) remains conflict and lacks substantial evidence in various clinical studies. To address this issue, we employed Mendelian randomization (MR) to establish causal links between six types of PUFAs and their connection to the risk of ARDs.

Methods

We retrieved summary-level data on six types of PUFAs, and five different types of ARDs from publicly accessible GWAS statistics. Causal relationships were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the reliability of our research findings, we used four complementary approaches and conducted multivariable MR analysis (MVMR). Additionally, we investigated reverse causality through a reverse MR analysis.

Results

Our results indicate that a heightened genetic predisposition for elevated levels of EPA (ORIVW: 0.924, 95% CI: 0.666-1.283, P IVW = 0.025) was linked to a decreased susceptibility to psoriatic arthritis (PsA). Importantly, the genetically predicted higher levels of EPA remain significantly associated with an reduced risk of PsA, even after adjusting for multiple testing using the FDR method (P IVW-FDR-corrected = 0.033) and multivariable MR analysis (P MV-IVW < 0.05), indicating that EPA may be considered as the risk-protecting PUFAs for PsA. Additionally, high levels of LA showed a positive causal relationship with a higher risk of PsA (ORIVW: 1.248, 95% CI: 1.013-1.538, P IVW = 0.037). It is interesting to note, however, that the effects of these associations were weakened in our MVMR analyses, which incorporated adjustment for lipid profiles (P MV-IVW > 0.05) and multiple testing using the FDR method (P IVW-FDR-corrected = 0.062). Moreover, effects of total omega-3 PUFAs, DHA, EPA, and LA on PsA, were massively driven by SNP effects in the FADS gene region. Furthermore, no causal association was identified between the concentrations of other circulating PUFAs and the risk of other ARDs. Further analysis revealed no significant horizontal pleiotropy and heterogeneity or reverse causality.

Conclusion

Our comprehensive MR analysis indicated that EPA is a key omega-3 PUFA that may protect against PsA but not other ARDs. The FADS2 gene appears to play a central role in mediating the effects of omega-3 PUFAs on PsA risk. These findings suggest that EPA supplementation may be a promising strategy for preventing PsA onset. Further well-powered epidemiological studies and clinical trials are warranted to explore the potential mechanisms underlying the protective effects of EPA in PsA.

Genetic landscape of interval and screen detected breast cancer

Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.

Metabolome-wide Mendelian randomization for age at menarche and age at natural menopause

BACKGROUND

The role of metabolism in the variation of age at menarche (AAM) and age at natural menopause (ANM) in the female population is not entirely known. We aimed to investigate the causal role of circulating metabolites in AAM and ANM using Mendelian randomization (MR).

METHODS

We combined MR with genetic colocalization to investigate potential causal associations between 658 metabolites and AAM and between 684 metabolites and ANM. We extracted genetic instruments for our exposures from four genome-wide association studies (GWAS) on circulating metabolites and queried the effects of these variants on the outcomes in two large GWAS from the ReproGen consortium. Additionally, we assessed the mediating role of the body mass index (BMI) in these associations, identified metabolic pathways implicated in AAM and ANM, and sought validation for selected metabolites in the Avon Longitudinal Study of Parents and Children (ALSPAC).

RESULTS

Our analysis identified 10 candidate metabolites for AAM, but none of them colocalized with AAM. For ANM, 76 metabolites were prioritized (FDR-adjusted MR P-value ≤ 0.05), with 17 colocalizing, primarily in the glycerophosphocholines class, including the omega-3 fatty acid and phosphatidylcholine (PC) categories. Pathway analyses and validation in ALSPAC mothers also highlighted the role of omega and polyunsaturated fatty acids levels in delaying age at menopause.

CONCLUSIONS

Our study suggests that metabolites from the glycerophosphocholine and fatty acid families play a causal role in the timing of both menarche and menopause. This underscores the significance of specific metabolic pathways in the biology of female reproductive longevity.

Shared familial risk for type 2 diabetes mellitus and psychiatric disorders: a nationwide multigenerational genetics study

BACKGROUND

Psychiatric disorders and type 2 diabetes mellitus (T2DM) are heritable, polygenic, and often comorbid conditions, yet knowledge about their potential shared familial risk is lacking. We used family designs and T2DM polygenic risk score (T2DM-PRS) to investigate the genetic associations between psychiatric disorders and T2DM.

METHODS

We linked 659 906 individuals born in Denmark 1990-2000 to their parents, grandparents, and aunts/uncles using population-based registers. We compared rates of T2DM in relatives of children with and without a diagnosis of any or one of 11 specific psychiatric disorders, including neuropsychiatric and neurodevelopmental disorders, using Cox regression. In a genotyped sample (iPSYCH2015) of individuals born 1981-2008 (n = 134 403), we used logistic regression to estimate associations between a T2DM-PRS and these psychiatric disorders.

RESULTS

Among 5 235 300 relative pairs, relatives of individuals with a psychiatric disorder had an increased risk for T2DM with stronger associations for closer relatives (parents:hazard ratio = 1.38, 95% confidence interval 1.35-1.42; grandparents: 1.14, 1.13-1.15; and aunts/uncles: 1.19, 1.16-1.22). In the genetic sample, one standard deviation increase in T2DM-PRS was associated with an increased risk for any psychiatric disorder (odds ratio = 1.11, 1.08-1.14). Both familial T2DM and T2DM-PRS were significantly associated with seven of 11 psychiatric disorders, most strongly with attention-deficit/hyperactivity disorder and conduct disorder, and inversely with anorexia nervosa.

CONCLUSIONS

Our findings of familial co-aggregation and higher T2DM polygenic liability associated with psychiatric disorders point toward shared familial risk. This suggests that part of the comorbidity is explained by shared familial risks. The underlying mechanisms still remain largely unknown and the contributions of genetics and environment need further investigation.

Genome-wide cross-disease analyses highlight causality and shared biological pathways of type 2 diabetes with gastrointestinal disorders

Studies suggest links between diabetes and gastrointestinal (GI) traits; however, their underlying biological mechanisms remain unclear. Here, we comprehensively assess the genetic relationship between type 2 diabetes (T2D) and GI disorders. Our study demonstrates a significant positive global genetic correlation of T2D with peptic ulcer disease (PUD), irritable bowel syndrome (IBS), gastritis-duodenitis, gastroesophageal reflux disease (GERD), and diverticular disease, but not inflammatory bowel disease (IBD). We identify several positive local genetic correlations (negative for T2D - IBD) contributing to T2D's relationship with GI disorders. Univariable and multivariable Mendelian randomisation analyses suggest causal effects of T2D on PUD and gastritis-duodenitis and bidirectionally with GERD. Gene-based analyses reveal a gene-level genetic overlap between T2D and GI disorders and identify several shared genes reaching genome-wide significance. Pathway-based study implicates leptin (T2D - IBD), thyroid, interferon, and notch signalling (T2D - IBS), abnormal circulating calcium (T2D - PUD), cardiovascular, viral, proinflammatory and (auto)immune-mediated mechanisms in T2D and GI disorders. These findings support a risk-increasing genetic overlap between T2D and GI disorders (except IBD), implicate shared biological pathways with putative causality for certain T2D - GI pairs, and identify targets for further investigation.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Phenome-Wide Investigation of the Causal Associations Between Pre-Pregnancy Obesity Traits and Gestational Diabetes: A Two-Sample Mendelian Randomization Analyses

Pre-pregnancy obesity was associated with gestational diabetes in observational studies, but whether this relationship is causal remains to be determined. To evaluate whether pre-pregnancy obesity traits causally affect gestational diabetes risk, a two-sample Mendelian randomization (MR) analysis was performed utilizing summary-level statistics from published genome-wide association studies (GWAS). Obesity-related traits included body mass index (BMI), overweight, obesity, obesity class 1, obesity class 2, obesity class 3, childhood obesity, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), percent liver fat, visceral adipose tissue volume, abdominal subcutaneous adipose tissue volume. Effect estimates were evaluated using the inverse-variance weighting method. Weighted median, MR-Egger, simple mode, and weighted mode were performed as sensitivity analyses. Genetically predicted pre-pregnancy BMI [odds ratio (OR) = 1.68; 95% confidence interval (CI): 1.45-1.95; P = 9.13 × 10-12], overweight (OR = 1.49; 95% CI: 1.21-1.85; P = 2.06 × 10-4), obesity (OR = 1.25; 95% CI: 1.18-1.33; P = 8.01 × 10-13), obesity class 1 (OR = 1.31; 95% CI: 1.17-1.46; P = 1.49 × 10-6), obesity class 2 (OR = 1.26; 95% CI: 1.16-1.37; P = 5.23 × 10-8), childhood obesity (OR = 1.33; 95% CI: 1.23-1.44; P = 4.06 × 10-12), and WHR (OR = 2.35; 95% CI: 1.44-3.83; P = 5.89 × 10-4) were associated with increased risk of gestational diabetes. No significant association was observed with obesity class 3, WC, HC, percent liver fat, visceral adipose tissue volume, or abdominal subcutaneous adipose tissue volume. Similar results were observed in sensitivity analyses. Therefore, genetically predicted pre-pregnancy obesity traits may increase the risk of gestational diabetes. Weight control before pregnancy may be beneficial to prevent gestational diabetes.

Unraveling the Variability of Human Satiation: Implications for Precision Obesity Management

Satiation is the physiologic process that regulates meal size and termination, and it is quantified by the calories consumed to reach satiation. Given its role in energy intake, changes in satiation contribute to obesity's pathogenesis. Our study employed a protocolized approach to study the components of food intake regulation including a standardized breakfast, a gastric emptying study, appetite sensation testing, and a satiation measurement by an ad libitummeal test. These studies revealed that satiation is highly variable among individuals, and while baseline characteristics, anthropometrics, body composition and hormones, contribute to this variability, these factors do not fully account for it. To address this gap, we explored the role of a germline polygenic risk score, which demonstrated a robust association with satiation. Furthermore, we developed a machine-learning-assisted gene risk score to predict satiation and leveraged this prediction to anticipate responses to anti-obesity medications. Our findings underscore the significance of satiation, its inherent variability, and the potential of a genetic risk score to forecast it, ultimately allowing us to predict responses to different anti-obesity interventions.