Genome-wide Association Statistics Research Articles

Does autoimmune diseases increase the risk of frailty? A Mendelian randomization study.

The causality of autoimmune diseases with frailty has not been firmly established. We conducted this Mendelian randomization (MR) study to unveil the causal associations between autoimmune diseases with frailty. A MR analyses were performed to explore the relationships between autoimmune disease and frailty, using summary genome-wide association statistics. Through a comprehensive and meticulous screening process, we incorporated 46, 7, 12, 20, 5, and 53 single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for hypothyroidism, hyperthyroidism, rheumatoid arthritis (RA), type 1 diabetes (T1D), multiple sclerosis (MS), and overall autoimmune disease, respectively. Our analysis revealed that hypothyroidism (OR = 1.023, 95% CI: 1.008-1.038, p = 0.0015), hyperthyroidism (OR = 1.024, 95% CI: 1.004-1.045, p = 0.0163), RA (OR = 1.031, 95% CI: 1.011-1.052, p = 0.0017), T1D (OR = 1.011, 95% CI: 1.004-1.017, p = 0.0012), and overall autoimmune disease (OR = 1.044, 95% CI: 1.028-1.061, p = 5.32*10^-8) exhibited a positive causal effect on frailty. Conversely, there may be a negative causal association between MS (OR = 0.984, 95% CI: 0.977-0.992, p = 4.87*10^-5) and frailty. Cochran's Q test indicated heterogeneity among IVs derived from hypothyroidism, hyperthyroidism, T1D, and overall autoimmune diseases. The MR-Egger regression analyzes revealed an absence of horizontal pleiotropy in any of the conducted analyses. This study elucidates that hypothyroidism, hyperthyroidism, RA, T1D, and overall autoimmune disease were linked to an elevated risk of frailty. Conversely, MS appears to be associated with a potential decrease in the risk of frailty.

Exploring the causal association between frailty index with the common types of arthritis: a Mendelian randomization analysis

BackgroundPrevious observational studies indicated a complex association between frailty and arthritis.AimsTo investigate the genetic causal relationship between the frailty index and the risk of common arthritis.MethodsWe performed a large-scale Mendelian randomization (MR) analysis to assess frailty index associations with the risk of common arthritis in the UK Biobank (UKB), and the FinnGen Biobank. Summary genome-wide association statistics for frailty, as defined by the frailty index, and common arthritis including rheumatoid arthritis (RA), osteoarthritis (OA), psoriatic arthritis (PSA), and ankylosing spondylitis (AS). The inverse-variance weight (IVW) method served as the primary MR analysis. Heterogeneity testing and sensitivity analysis were also conducted.ResultsOur results denoted a genetic association between the frailty index with an increased risk of OA, the odds ratio (OR)IVW in the UKB was 1.03 (95% confidence interval [CI]: 1.01–1.05; P = 0.007), and ORIVW was 1.55 (95% CI: 1.16–2.07; P = 0.003) in the FinnGen. For RA, the ORIVW from UKB and FinnGen were 1.03 (1.01–1.05, P = 0.006) and 4.57 (1.35–96.49; P = 0.025) respectively. For PSA, the frailty index was associated with PSA (ORIVW = 4.22 (1.21–14.67), P = 0.023) in FinnGen, not in UKB (P > 0.05). However, no association was found between frailty index and AS (P > 0.05). These results remained consistent across sensitivity assessments.ConclusionThis study demonstrated a potential causal relationship that genetic predisposition to frailty index was associated with the risk of arthritis, especially RA, OA, and PSA, not but AS. Our findings enrich the existing body of knowledge on the subject matter.

Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders.

Individuals suffering from chronic pain develop substance use disorders (SUDs) more often than others. Understanding the shared genetic influences underlying the comorbidity between chronic pain and SUDs will lead to a greater understanding of their biology. Genome-wide association statistics were obtained from the UK Biobank for multisite chronic pain (MCP, Neffective = 387,649) and from the Million Veteran Program and the Psychiatric Genomics Consortium meta-analyses for alcohol use disorder (AUD, Neffective = 296,974), cannabis use disorder (CanUD, Neffective = 161,053), opioid use disorder (OUD, Neffective = 57,120), and problematic tobacco use (PTU, Neffective = 270,120). SNP-based heritability was estimated for each of the traits and genetic correlation (rg) analyses were performed to assess MCP-SUD pleiotropy. Bidirectional Mendelian Randomization analyses evaluated possible causal relationships. Finally, to identify and characterize individual loci, we performed a genome-wide pleiotropy analysis and a brain-wide analysis using imaging phenotypes available from the UK Biobank. MCP was positively genetically correlated with AUD (rg = 0.26, p = 7.55 × 10-18), CanUD (rg = 0.37, p = 8.21 × 10-37), OUD (rg = 0.20, p = 1.50 × 10-3), and PTU (rg = 0.29, p = 8.53 × 10-12). Although the MR analyses supported bi-directional relationships, MCP had larger effects on AUD (pain-exposure: beta = 0.18, p = 8.21 × 10-4; pain-outcome: beta = 0.07, p = 0.018), CanUD (pain-exposure: beta = 0.58, p = 2.70 × 10-6; pain-outcome: beta = 0.05, p = 0.014) and PTU (pain-exposure: beta = 0.43, p = 4.16 × 10-8; pain-outcome: beta = 0.09, p = 3.05 × 10-6) than the reverse. The genome-wide analysis identified two SNPs pleiotropic between MCP and all SUD investigated: IHO1 rs7652746 (ppleiotropy = 2.69 × 10-8), and CADM2 rs1248857 (ppleiotropy = 1.98 × 10-5). In the brain-wide analysis, rs7652746 was associated with multiple cerebellum and amygdala imaging phenotypes. When analyzing MCP pleiotropy with each SUD separately, we found 25, 22, and 4 pleiotropic variants for AUD, CanUD, and OUD, respectively. To our knowledge, this is the first large-scale study to provide evidence of potential causal relationships and shared genetic mechanisms underlying MCP-SUD comorbidity.

Assessment of relationships between frailty and chronic pain: a bidirectional two-sample Mendelian randomisation study.

Previous observational studies have indicated a complex association between chronic pain and frailty. This study aimed to examine the bidirectional causal relationship between frailty and chronic pain and to quantify mediating effects of known modifiable risk factors. A bidirectional two-sample Mendelian randomisation (MR) analysis was applied in this study. Summary genome-wide association statistics for frailty, as defined by both frailty index (FI) and Fried Frailty Score (FFS), pain at seven site-specific chronic pain (SSCP) (headache, facial, neck/shoulder, stomach/abdominal, back, hip and knee) and multisite chronic pain (MCP) were extracted from populations of European ancestry. Genetic instrumental variables strongly correlated with each exposure were selected. The inverse-variance-weighted method was the primary method used in the MR, supplemented by a range of sensitivity and validation analyses. Two-step MR analysis was undertaken to evaluate the mediating effects of several proposed confounders. Genetically predicted higher FI and FFS were associated with an increased risk of MCP and specific types of SSCP, including neck/shoulder pain, stomach/abdominal pain, back pain, hip pain and knee pain. In the reverse direction analysis, genetic liability to MCP was found to be associated with increased FI and FFS. These results remained consistent across sensitivity and validation assessments. Two-step MR suggested a mediating role for body mass index, smoking initiation, physical inactivity, educational attainment and depression. Our research provided genetic evidence that the association between frailty and chronic pain was bidirectional where the coexistence of both conditions will exacerbate each other.

Causal Effects of Basal Metabolic Rate on Cardiovascular Disease: A Bidirectional Mendelian Randomization Study.

Despite the health effects of basal metabolic rate (BMR), the causal effect of BMR on cardiovascular diseases (CVDs) remains undetermined. To elucidate the causal nature, Mendelian randomization (MR) analyses were performed. Summary genome-wide association statistics regarding BMR and 5 CVDs were obtained from European databases. A 2-sample bidirectional MR was performed to assess the causal association between BMR and CVDs. The causal effects were estimated using inverse variance weighting. Simultaneously, multiple sensitivity analyses were performed to validate the robustness and reliability of the results. Our results indicated that genetically predicted BMR was significantly positively associated with the risk of heart failure (odds ratio, 1.53 [95% CI, 1.39-1.67]; P<0.001), atrial fibrillation and flutter (odds ratio, 2.12 [95% CI, 1.87-2.40]; P<0.001), and aortic aneurysm (odds ratio, 1.64 [95% CI, 1.41-1.92]; P<0.001). Genetically predicted BMR may not be causally associated with coronary artery disease and ischemic stroke risk. Furthermore, a significant causal effect of CVDs on BMR was not found in the reverse MR analysis. Multivariable MR was applied to further assess the direct effect of BMR on CVDs. Multivariable MR indicated that a high level of BMR still increased the risk of heart failure and atrial fibrillation and flutter after adjustment independent of possible confounders. However, the P value of aortic aneurysm was not significant. The present study provides robust evidence that genetically predicted BMR is independently causally associated with heart failure and atrial fibrillation and flutter but not vice versa. These findings have implications for the prevention and treatment of CVDs in clinical practice.

Causal relationship between COVID-19 and myocarditis or pericarditis risk: a bidirectional Mendelian randomization study.

Coronavirus disease 2019 (COVID-19) is strongly associated with myocarditis or pericarditis risk in observational studies, however, there are still studies that do not support the above conclusion. Whether the observed association reflects causation needs to be confirmed. We performed a bidirectional Mendelian randomization (MR) study to assess the causal relationship of COVID-19, which was divided into three groups, namely severe COVID-19, hospitalized COVID-19, and COVID-19 infection, measured by myocarditis or pericarditis. We extracted summary genome-wide association statistics for the severe COVID-19 (case: 13,769, control: 1,072,442), hospitalized COVID-19 (case: 32,519, control: 2,062,805), COVID-19 infection (case: 122,616, control: 2,475,240), myocarditis (case 1,521, control 191,924), and pericarditis (case 979, control 286,109) among individuals of European ancestry. Independent genetic variants that exhibited a significant association with each phenotype at the genome-wide level of significance were utilized as instrumental variables. Estimation of the causal effect was mainly performed using the random effects inverse-variance weighted method (IVW). Additionally, other tests such as MR-Egger intercept, MR-PRESSO, Cochran's Q-test, "Leave-one-out", and funnel plots were conducted to assess the extent of pleiotropy and heterogeneity. Non-associations in the IVW and sensitivity analyses were observed for COVID-19 with myocarditis or pericarditis. Severe COVID-19 was not associated with myocarditis [odds ratio (OR), 1.00; 95% confidence interval (CI), 0.89-1.12; P = 0.99], pericarditis (OR = 0.90, 95% CI, 0.78-1.04, P = 0.17). Similar results can be observed in hospitalized COVID-19, and COVID-19 infection. At the same time, null associations were observed for myocarditis or pericarditis with COVID-19 traits in the reverse direction. The main results are kept stable in the sensitivity analysis. There is no evidence that COVID-19 is independently and causally associated with myocarditis or pericarditis.

Proteomic insights into the associations between obesity, lifestyle factors, and coronary artery disease

BackgroundWe aimed to investigate the protein pathways linking obesity and lifestyle factors to coronary artery disease (CAD).MethodsSummary-level genome-wide association statistics of CAD were obtained from the CARDIoGRAMplusC4D consortium (60,801 cases and 123,504 controls) and the FinnGen study (R8, 39,036 cases and 303,463 controls). Proteome-wide Mendelian randomization (MR) analysis was conducted to identify CAD-associated blood proteins, supplemented by colocalization analysis to minimize potential bias caused by linkage disequilibrium. Two-sample MR analyses were performed to assess the associations of genetically predicted four obesity measures and 13 lifestyle factors with CAD risk and CAD-associated proteins’ levels. A two-step network MR analysis was conducted to explore the mediating effects of proteins in the associations between these modifiable factors and CAD.ResultsGenetically predicted levels of 41 circulating proteins were associated with CAD, and 17 of them were supported by medium to high colocalization evidence. PTK7 (protein tyrosine kinase-7), RGMB (repulsive guidance molecule BMP co-receptor B), TAGLN2 (transgelin-2), TIMP3 (tissue inhibitor of metalloproteinases 3), and VIM (vimentin) were identified as promising therapeutic targets. Several proteins were found to mediate the associations between some modifiable factors and CAD, with PCSK9, C1S, AGER (advanced glycosylation end product-specific receptor), and MST1 (mammalian Ste20-like kinase 1) exhibiting highest frequency among the mediating networks.ConclusionsThis study suggests pathways explaining the associations of obesity and lifestyle factors with CAD from alterations in blood protein levels. These insights may be used to prioritize therapeutic intervention for further study.

From genetic correlations of Alzheimer’s disease to classification with artificial neural network models

Sporadic Alzheimer’s disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer’s gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.

Associations between modifiable risk factors and frailty: a Mendelian randomisation study

BackgroundEarly identification of modifiable risk factors is essential for the prevention of frailty. This study aimed to explore the causal relationships between a spectrum of genetically predicted risk factors and...

Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

ABSTRACT Endometriosis is a chronic gynecologic disease that causes pelvic pain and is often seen in association with depression and anxiety. The association with these mental health conditions was previously linked to chronic pain, although some evidence suggests this is not the sole factor. A genetic correlation has shown a potential causal effect of depression on endometriosis; however, further analyses are needed to determine whether pleiotropy observed is due to cause-effect relationships or to shared biological pathways. This genetic association study (GWAS) aimed to conduct phenotypic and genetic association analyses on the comorbidity of endometriosis with depression, anxiety, and eating disorders. Individual-level data were obtained from the UK Biobank (UKB) and combined with genome-wide association statistics from data sets including the Psychiatric Genomics Consortium, the Million Veteran Program, the FinnGen study (Finland), and the CHARGE consortium. Individuals with endometriosis were identified by the International Classification of Diseases Tenth Revision codes as well as self-reported diagnoses from UKB surveys, and individuals with depression, anxiety, and eating disorders were identified using the International Classification of Diseases Tenth Revision codes. Logistic regression models were used to evaluate the association, and bidirectional 1-sample Mendelian randomization was used to test causal associations between endometriosis and anxiety, depression, and eating disorders. The GWAS studies were conducted for each of the mental health disorders being studied, and the Scalable Genetic Correlation Estimator (SCORE) method was used to compute single-nucleotide variant (SNV)–based heritability and genetic correlation from individual genotype and phenotype data. The linkage disequilibrium score regression method was used to calculate SNV-based heritability and genetic correlation using GWAS data. A total of 8276 participants with endometriosis (mean age, 53.1 years) and 194,000 controls (mean age, 56.7 years) were included in this analysis. Regression models accounting for demographic and obstetric covariates, as well as psychiatric comorbidities, found endometriosis to be associated with increased odds of depression (odds ratio [OR], 3.61; 95% confidence interval [CI], 3.32–3.92), eating disorders (OR, 2.94; 95% CI, 1.96–4.41), and anxiety (OR, 2.61; 95% CI, 2.30–2.97). The GWAS studies showed endometriosis to be correlated with depression (genetic correlation [rg] = 0.36, P = 1.5 × 10−9), anxiety (rg = 0.33, P = 1.17 × 10−5), and eating disorders (r = 0.61, P = 0.03). The SCORE analysis of UKB phenotypic and genetic data calculated female-specific SNV-based heritability (SNV-h2) for endometriosis (mean [SE] SNV-h2, 0.086 [0.015]), depression (SNV-h2, 0.019 [0.003]), anxiety (SNV-h2, 0.012 [0.002]), and eating disorders (SNV-h2, 0.004 [0.002]). When applying linkage disequilibrium score regression to UKB genome-wide association statistics, endometriosis showed a consistent genetic correlation with anxiety (rg = 0.36, P = 3 × 10−4) and depression (rg = 0.34, P = 1 × 10−5). Mendelian randomization found significant associations for depression (OR, 1.09; 95% CI, 1.08–1.11) and anxiety (OR, 1.39; 95% CI, 1.13–1.65) with endometriosis, but no association with eating disorders (OR, 0.73; 95% CI, 0.04–1.43) was observed. Only the pleiotropic variant DGKB rs12666606 between endometriosis and depression survived genome-wide multiple testing correction. The results of this GWAS show that eating disorders, depression, and anxiety are all associated with endometriosis and that genetic correlations were in line with phenotypic associations.

The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study.

Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology. Methods: We identified nine genome-wide association statistics of suicidal behaviour (sample sizes, n, ranging from 62,648 to 125,844), ten psychiatric traits [n up to 386,533] and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits [n ranging from 58,610 to 941,280]. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation. Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm (rg range, 0.71-1.09) and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder (rg = 0.86, p = 1.62 × 10-36). Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained 38.7% of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt [OR = 0.55 (0.44-0.70), p = 1.29 × 10-5] and while further investigation is needed, a nominal significant effect of smoking on suicide attempt [OR = 1.24 (1.04-1.44), p = 0.026]. Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour.

Smoking, alcohol consumption, and frailty: A Mendelian randomization study.

Background: Tobacco smoking and alcohol consumption have been associated with frailty in observational studies. We sought to examine whether these associations reflect causality using the two-sample Mendelian randomization (MR) design. Methods: We used summary genome-wide association statistics for smoking initiation (N = 2,669,029), alcohol consumption (N = 2,428,851), and the frailty index (FI, N = 175,226) in participants of European ancestry. Both univariable and multivariable MR were performed to comprehensively evaluate the independent effects of smoking and alcohol consumption on the FI, accompanied by multiple sensitivity analyses. Results were verified using lifetime smoking and alcohol use disorder. Reverse direction MR was undertaken to assess the potential for reverse causation. Results: Genetic predisposition to smoking initiation was significantly associated with increased FI (univariable MR: β = 0.345; 95% confidence interval [CI] = 0.316 to 0.374; p = 1.36E-113; multivariable MR: β = 0.219; 95% CI = 0.197 to 0.241; p = 2.44E-83). Genetically predicted alcohol consumption showed a suggestive association with the FI (univariable MR: β = -0.090; 95% CI = -0.151 to -0.029; p = 0.003; multivariable MR β = -0.153; 95% CI = -0.212 to -0.094; p = 2.03E-07), with inconsistent results in sensitivity analyses. In complementary analysis, genetic predicted lifetime smoking, but not alcohol use disorder was associated with the FI. There is no convincing evidence for reverse causation. Conclusion: The present MR study supported smoking as a causal risk factor of frailty. Further research is warranted to investigate whether alcohol consumption has a causal role in frailty.

Epidemiologic and Genetic Associations of Endometriosis With Depression, Anxiety, and Eating Disorders

Endometriosis is a common chronic gynecologic pathology with a large negative impact on women's health. Beyond severe physical symptoms, endometriosis is also associated with several psychiatric comorbidities, including depression and anxiety. To investigate whether pleiotropy contributes to the association of endometriosis with depression, anxiety, and eating disorders. This genetic association study was performed between September 13, 2021, and June 24, 2022, in 202 276 unrelated female participants. Genotypic and phenotypic information from the UK Biobank was combined with genome-wide association statistics available from the Psychiatric Genomics Consortium (11 countries), the Million Veteran Program (US), the FinnGen study (Finland), and the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium (5 countries). The main outcomes were the phenotypic and genetic associations of endometriosis with anxiety, depression, and eating disorders. A total of 8276 women with endometriosis (mean [SD] age, 53.1 [7.9] years) and 194 000 female controls (mean [SD] age, 56.7 [7.9] years) were included in the study. In a multivariate regression analysis accounting for age, body mass index, socioeconomic status, chronic pain-related phenotypes, irritable bowel syndrome, and psychiatric comorbidities, endometriosis was associated with increased odds of depression (odds ratio [OR], 3.61; 95% CI, 3.32-3.92), eating disorders (OR, 2.94; 95% CI, 1.96-4.41), and anxiety (OR, 2.61; 95% CI, 2.30-2.97). These associations were supported by consistent genetic correlations (rg) (depression rg, 0.36, P = 1.5 × 10-9; anxiety rg, 0.33, P = 1.17 × 10-5; and eating disorders rg, 0.61, P = .02). With the application of a 1-sample mendelian randomization, the genetic liabilities to depression and anxiety were associated with increased odds of endometriosis (depression: OR, 1.09; 95% CI, 1.08-1.11; anxiety: OR, 1.39; 95% CI, 1.13-1.65). A genome-wide analysis of pleiotropic associations shared between endometriosis and psychiatric disorders identified 1 locus, DGKB rs12666606, with evidence of pleiotropy between endometriosis and depression after multiple testing correction (z = -9.46 for endometriosis, z = 8.10 for depression, P = 5.56 × 10-8; false discovery rate q = 4.95 × 10-4). These findings highlight that endometriosis is associated with women's mental health through pleiotropic mechanisms. To our knowledge, this is the first large-scale study to provide genetic and phenotypic evidence of the processes underlying the psychiatric comorbidities of endometriosis.

Causal Associations between Vitamin D Levels and Psoriasis, Atopic Dermatitis, and Vitiligo: A Bidirectional Two-Sample Mendelian Randomization Analysis.

Vitamin D level has been reported to be associated with psoriasis, atopic dermatitis, and vitiligo. However, its causal relationship with the risk of these three diseases remains unclear. We obtained genome-wide association statistics for three measures of circulating vitamin D levels (25(OH)D in 120,618 individuals, and 25(OH)D3 and epimeric form C3-epi-25(OH)D3 in 40,562 individuals) and for the diseases psoriasis (3871 cases and 333,288 controls), atopic dermatitis (21,399 cases and 95,464 controls), and vitiligo (4680 cases and 39,586 controls). We performed Mendelian randomization using inverse-variance weighted, weighted median, MR-Egger, and MR-pleiotropy residual sum and outlier methods. We carried out sensitivity analyses to evaluate the robustness of the results. We showed that elevated vitamin D levels protected individuals from developing psoriasis (OR = 0.995, p = 8.84 × 10-4 for 25(OH)D; OR = 0.997, p = 1.81 × 10-3 for 25(OH)D3; and OR = 0.998, p = 0.044 for C3-epi-25(OH)D3). Genetically predicted risk of atopic dermatitis increased the levels of 25(OH)D (OR = 1.040, p = 7.14 × 10-4) and 25(OH)D3 (OR = 1.208, p = 0.048). A sensitivity analysis suggested the robustness of these causal associations. This study reported causal relationships between circulating vitamin D levels and the risk of psoriasis, atopic dermatitis, and vitiligo. These findings provide potential disease intervention and monitoring targets.

Denisovan and Neanderthal archaic introgression differentially impacted the genetics of complex traits in modern populations

BackgroundIntrogression from extinct Neanderthal and Denisovan human species has been shown to contribute to the genetic pool of modern human populations and their phenotypic spectrum. Evidence of how Neanderthal introgression shaped the genetics of human traits and diseases has been extensively studied in populations of European descent, with signatures of admixture reported for instance in genes associated with pigmentation, immunity, and metabolic traits. However, limited information is currently available about the impact of archaic introgression on other ancestry groups. Additionally, to date, no study has been conducted with respect to the impact of Denisovan introgression on the health and disease of modern populations. Here, we compare the way evolutionary pressures shaped the genetics of complex traits in East Asian and European populations, and provide evidence of the impact of Denisovan introgression on the health of East Asian and Central/South Asian populations.ResultsLeveraging genome-wide association statistics from the Biobank Japan and UK Biobank, we assessed whether Denisovan and Neanderthal introgression together with other evolutionary genomic signatures were enriched for the heritability of physiological and pathological conditions in populations of East Asian and European descent. In EAS, Denisovan-introgressed loci were enriched for coronary artery disease heritability (1.69-fold enrichment, p=0.003). No enrichment for archaic introgression was observed in EUR. We also performed a phenome-wide association study of Denisovan and Neanderthal alleles in six ancestry groups available in the UK Biobank. In EAS, the Denisovan-introgressed SNP rs62391664 in the major histocompatibility complex region was associated with albumin/globulin ratio (beta=−0.17, p=3.57×10−7). Neanderthal-introgressed alleles were associated with psychiatric and cognitive traits in EAS (e.g., “No Bipolar or Depression”-rs79043717 beta=−1.5, p=1.1×10−7), and with blood biomarkers (e.g., alkaline phosphatase-rs11244089 beta=0.1, p=3.69×10−116) and red hair color (rs60733936 beta=−0.86, p=4.49×10−165) in EUR. In the other ancestry groups, Neanderthal alleles were associated with several traits, also including the use of certain medications (e.g., Central/South East Asia: indapamide – rs732632 beta=−2.38, p=5.22×10−7).ConclusionsOur study provides novel evidence regarding the impact of archaic introgression on the genetics of complex traits in worldwide populations, highlighting the specific contribution of Denisovan introgression in EAS populations.

Frailty and cardiometabolic diseases: a bidirectional Mendelian randomisation study.

Frailty is strongly associated with cardiometabolic diseases in observational studies. However, whether the observed association reflects causality requires clarification. We performed a bidirectional Mendelian randomisation (MR) study to assess the causal relationship of frailty, measured by the frailty index (FI), with coronary artery disease (CAD), stroke and type 2 diabetes (T2D). We extracted summary genome-wide association statistics for the FI (N = 175,226), CAD (Ncase = 60,801, Ncontrol = 123,504), stroke (Ncase = 40,585, Ncontrol = 406,111) and T2D (Ncase = 55,005, Ncontrol = 400,308) among individuals of European ancestry. Independent genetic variants associated with each phenotype at the genome-wide significance level were taken as instruments. Two-sample MR analyses were primarily conducted using the inverse-variance-weighted method, followed by various sensitivity and validation analyses. Genetically predicted higher FI was significantly associated with increased risk of CAD (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.17-1.96) and T2D (OR 1.80, 95% CI 1.31-2.47) and suggestively associated with higher risk of stroke (OR 1.36, 95% CI 1.01-1.84). In the reverse direction analysis, genetic liability to CAD (beta 0.037, 95% CI 0.019-0.055), stroke (beta 0.096, 95% CI 0.051-0.141) and T2D (beta 0.047, 95% CI 0.036-0.059) showed significant associations with increased FI. Results were stable across sensitivity and validation analyses. Our study strengthened the evidence for a bidirectional causal association between frailty and cardiometabolic diseases. Further understanding of this association will be critical for the optimisation of care in older adults.

Multisite chronic pain as a causal risk factor for coronary artery disease: findings from Mendelian randomization.

The potential consequences of the number of chronic pain sites (referred to multisite chronic pain) on the risk of cardiovascular diseases (CVDs) remain unclear. We attempted to investigate the causality of multisite chronic pain with CVDs and its possible causal mediators. Using summary genome-wide association statistics, two-sample Mendelian randomization (MR) analyses were performed to assess whether multisite chronic pain has a causal effect on the 3 CVDs including coronary artery disease, atrial fibrillation, and stroke. We then conducted MR mediation analyses to establish whether body mass index (BMI), smoking, and depression causally mediate any association. Genetic liability to multisite chronic pain was associated with increased risk of coronary artery disease (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.19-1.95 per one increase in the number of pain locations) but not with atrial fibrillation or stroke. We also found positive causal effects of multisite chronic pain on BMI, smoking, and depression and causal effects of BMI, smoking, and depression on coronary artery disease. In multivariable MR analyses, the excess risk of coronary artery disease was attenuated after adjusting for BMI (OR 1.43, 95% CI 1.05-1.93), smoking (OR 1.49, 95% CI 1.11-2.00), depression (OR 1.44, 95% CI 1.03-2.01), and 3 risk factors combined (OR 1.34, 95% CI 0.88-2.05). Our findings demonstrated that multisite chronic pain led to higher risk of coronary artery disease, which is partly mediated through BMI, smoking, and depression.

Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer

The high-grade serous ovarian cancer (HGSOC) risk locus at chromosome 1p34.3 resides within a frequently amplified genomic region signifying the presence of an oncogene. Here, we integrate in silico variant-to-function analysis with functional studies to characterize the oncogenic potential of candidate genes in the 1p34.3 locus. Fine mapping of genome-wide association statistics identified candidate causal SNPs local to H3K27ac-demarcated enhancer regions that exhibit allele-specific binding for CTCF in HGSOC and normal fallopian tube secretory epithelium cells (FTSECs). SNP risk associations colocalized with eQTL for six genes (DNALI1, GNL2, RSPO1, SNIP1, MEAF6, and LINC01137) that are more highly expressed in carriers of the risk allele, and three (DNALI1, GNL2, and RSPO1) were upregulated in HGSOC compared to normal ovarian surface epithelium cells and/or FTSECs. Increased expression of GNL2 and MEAF6 was associated with shorter survival in HGSOC with 1p34.3 amplifications. Despite its activation of β-catenin signaling, RSPO1 overexpression exerted no effects on proliferation or colony formation in our study of ovarian cancer and FTSECs. Instead, GNL2, MEAF6, and SNIP1 silencing impaired in vitro ovarian cancer cell growth. Additionally, GNL2 silencing diminished xenograft tumor formation, whereas overexpression stimulated proliferation and colony formation in FTSECs. GNL2 influences 60S ribosomal subunit maturation and global protein synthesis in ovarian cancer and FTSECs, providing a potential mechanism of how GNL2 upregulation might promote ovarian cancer development and mediate genetic susceptibility of HGSOC.

Disentangling sex differences in the shared genetic architecture of posttraumatic stress disorder, traumatic experiences, and social support with body size and composition

There is a well-known association of traumatic experiences and posttraumatic stress disorder (PTSD) with body size and composition, including consistent differences between sexes. However, the biology underlying these associations is unclear. To understand the genetic underpinnings of this complex relationship, we investigated genome-wide datasets informative of African and European ancestries from the Psychiatric Genomic Consortium, the UK Biobank, the GIANT Consortium, and the Million Veteran Program. We used genome-wide association statistics to estimate sex-specific genetic correlations (rg) of traumatic experiences, social support, and PTSD with multiple anthropometric traits. After multiple testing corrections (false discovery rate, FDR q < 0.05), we observed 58 significant rg relationships in females (e.g., childhood physical abuse and body mass index, BMI rg = 0.245, p = 3.88 × 10−10) and 21 significant rg relationships in males (e.g., been involved in combat or exposed to warzone and leg fat percentage; rg = 0.405, p = 4.42 × 10−10). We performed causal inference analyses of these genetic overlaps using Mendelian randomization and latent causal variable approaches. Multiple female-specific putative causal relationships were observed linking body composition/size with PTSD (e.g., leg fat percentage→PTSD; beta = 0.319, p = 3.13 × 10−9), traumatic experiences (e.g., childhood physical abuse→waist circumference; beta = 0.055, p = 5.07 × 10−4), and childhood neglect (e.g., “someone to take you to doctor when needed as a child”→BMI; beta = −0.594, p = 1.09 × 10−5). In males, we observed putative causal effects linking anthropometric-trait genetic liabilities to traumatic experiences (e.g., BMI→childhood physical abuse; beta = 0.028, p = 8.19 × 10−3). Some of these findings were replicated in individuals of African descent although the limited sample size available did not permit us to conduct a sex-stratified analysis in this ancestry group. In conclusion, our findings provide insights regarding sex-specific causal networks linking anthropometric traits to PTSD, traumatic experiences, and social support.

MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics.

BackgroundMultivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate tests, an important remaining challenge is to interpret which traits are driving the multivariate association and which traits are just passengers with minor contributions to the genotype-phenotypes association statistic.ResultsWe introduce MetaPhat, a novel bioinformatics tool to conduct GWAS of multiple correlated traits using univariate GWAS results and to decompose multivariate associations into sets of central traits based on intuitive trace plots that visualize Bayesian Information Criterion (BIC) and P-value statistics of multivariate association models. We validate MetaPhat with Global Lipids Genetics Consortium GWAS results, and we apply MetaPhat to univariate GWAS results for 21 heritable and correlated polyunsaturated lipid species from 2,045 Finnish samples, detecting seven independent loci associated with a cluster of lipid species. In most cases, we are able to decompose these multivariate associations to only three to five central traits out of all 21 traits included in the analyses. We release MetaPhat as an open source tool written in Python with built-in support for multi-processing, quality control, clumping and intuitive visualizations using the R software.ConclusionMetaPhat efficiently decomposes associations between multivariate phenotypes and genetic variants into smaller sets of central traits and improves the interpretation and specificity of genome-phenome associations. MetaPhat is freely available under the MIT license at: https://sourceforge.net/projects/meta-pheno-association-tracer.