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BackgroundEpidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties.ObjectiveWe used results from genome-wide association studies (GWASs) to study the genetic correlation between PD and different cancers to identify common genetic risk factors.MethodsWe used individual data for participants of European ancestry from the Courage-PD (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease; PD, N = 16,519) and EPITHYR (differentiated thyroid cancer, N = 3527) consortia and summary statistics of GWASs from iPDGC (International Parkinson Disease Genomics Consortium; PD, N = 482,730), Melanoma Meta-Analysis Consortium (MMAC), Breast Cancer Association Consortium (breast cancer), the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (prostate cancer), International Lung Cancer Consortium (lung cancer), and Ovarian Cancer Association Consortium (ovarian cancer) (N comprised between 36,017 and 228,951 for cancer GWASs). We estimated the genetic correlation between PD and cancers using linkage disequilibrium score regression. We studied the association between PD and polymorphisms associated with cancers, and vice versa, using cross-phenotypes polygenic risk score (PRS) analyses.ResultsWe confirmed a previously reported positive genetic correlation of PD with melanoma (Gcorr = 0.16 [0.04; 0.28]) and reported an additional significant positive correlation of PD with prostate cancer (Gcorr = 0.11 [0.03; 0.19]). There was a significant inverse association between the PRS for ovarian cancer and PD (odds ratio [OR] = 0.89 [0.84; 0.94]). Conversely, the PRS of PD was positively associated with breast cancer (OR = 1.08 [1.06; 1.10]) and inversely associated with ovarian cancer (OR = 0.95 [0.91; 0.99]). The association between PD and ovarian cancer was mostly driven by rs183211 located in an intron of the NSF gene (17q21.31).ConclusionsWe show evidence in favor of a contribution of pleiotropic genes to the association between PD and specific cancers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Pleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome-wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10−8) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4-rs34517439-A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13-1.32, P = 4.81 × 10−7). rs34517439-A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA-binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4-rs34517439 as a potentially novel MM risk locus.

Background: Several recent studies have suggested a role for infections in the development of mental disorders; however, the genetic contribution to this association is understudied. Methods: We use the iPSYCH case-cohort genotyped sample (n = 65,534) and Danish health care registry data to study the genetic association between infections and mental disorders. To test the hypothesis that these associations are due to genetic pleiotropy, we estimated the genetic correlation between infection and mental disorders. Polygenic risk scores (PRSs) were used to assess whether genetic pleiotropy of infections and mental disorders was mediated by actual infection diagnoses. Results: We observed that schizophrenia, attention-deficit/hyperactivity disorder, major depressive disorder, bipolar disorder, and posttraumatic stress disorder (rg ranging between 0.18 and 0.83), but not autism spectrum disorder and anorexia nervosa, were significantly genetically correlated with infection diagnoses. PRSs for infections were associated with modest increase in risk of attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia in the iPSYCH case-cohort (hazard ratios = 1.04 to 1.10) but was not associated with risk of anorexia, autism, or bipolar disorder. Using mediation analysis, we show that infection diagnoses account for only a small proportion (6%–14%) of the risk for mental disorders conferred by infection PRSs. Conclusions: Infections and mental disorders share a modest genetic architecture. Infection PRSs can predict risk of certain mental disorders; however, this effect is moderate. Finally, recorded infections partially explain the relationship between infection PRSs and mental disorders.

Background Hearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies. Methods Leveraging the UK Biobank, the Nurses’ Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors. Results We identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes. Conclusions The results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.

BackgroundSubstance use occurs at a high rate in persons with a psychiatric disorder. Genetically informative studies have the potential to elucidate the etiology of these phenomena. Recent developments in genome-wide association studies (GWAS) allow new avenues of investigation.MethodUsing results of GWAS meta-analyses, we performed a factor analysis of the genetic correlation structure, a genome-wide search of shared loci, and causally informative tests for six substance use phenotypes (four smoking, one alcohol, and one cannabis use) and five psychiatric disorders (ADHD, anorexia, depression, bipolar disorder, and schizophrenia).ResultsTwo correlated externalizing and internalizing/psychosis factor were found, although model fit was beneath conventional standards. Of 458 loci reported in previous univariate GWAS of substance use and psychiatric disorders, about 50% (230 loci) were pleiotropic with additional 111 pleiotropic loci not reported from past GWAS. Of the 341 pleiotropic loci, 152 were associated with both substance use and psychiatric disorders, implicating neurodevelopment, cell morphogenesis, biological adhesion pathways, and enrichment in 13 different brain tissues. Seventy-five and 114 pleiotropic loci were specific to either psychiatric disorders or substance use phenotypes, implicating neuronal signaling pathway and clathrin-binding functions/structures, respectively. No consistent evidence for phenotypic causation was found across different Mendelian randomization methods.ConclusionsGenetic etiology of substance use and psychiatric disorders is highly pleiotropic and involves shared neurodevelopmental path, neurotransmission, and intracellular trafficking. In aggregate, the patterns are not consistent with vertical pleiotropy, more likely reflecting horizontal pleiotropy or more complex forms of phenotypic causation.

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

The ecological stress theory of aging incorporates the normally harsh environments of natural populations and hence restricted resources. Especially towards lethal extremes, positive associations are expected among fitness traits underlain by selection for energetic efficiency favoring genotypes for stress resistance. Positive pleiotropy is therefore expected for fitness traits across varying ages under these conditions. Furthermore, hormetic zones are regions of maximum energetic efficiency, also implying positive pleiotropy. Negative pleiotropy may therefore be mainly a phenomenon of benign environments.

Epidemiological studies report beneficial associations of higher educational attainment (EDU) with Alzheimer’s disease (AD). Prior genome-wide association studies (GWAS) also reported variants associated with AD and EDU separately. The analysis of pleiotropic associations with these phenotypes may shed light on EDU-related protection against AD. We performed pleiotropic meta-analyses using Fisher’s method and omnibus test applied to summary statistics for single nucleotide polymorphisms (SNPs) associated with AD and EDU in large-scale univariate GWAS at suggestive-effect (5 × 10−8 < p < 0.1) and genome-wide (p ≤ 5 × 10−8) significance levels. We report 53 SNPs that attained p ≤ 5 × 10−8 at least in one of the pleiotropic meta-analyses and were reported in the univariate GWAS at 5 × 10−8 < p < 0.1. Of them, there were 46 pleiotropic SNPs according to Fisher’s method. Additionally, Fisher’s method identified 25 of 206 SNPs with pleiotropic effects, which attained p ≤ 5 × 10−8 in the univariate GWAS. We showed that a large fraction of the pleiotropic associations was affected by a counterintuitive phenomenon of antagonistic genetic heterogeneity, which explains the increase, rather than decrease, of the significance of the pleiotropic associations in the omnibus test. Functional enrichment analysis showed that apart from cancers, gene set harboring the non-pleiotropic SNPs was characterized by late-onset AD and neurodevelopmental disorders. The pleiotropic gene set was characterized by a broad spectrum of progressive neurological and neuromuscular diseases and immune-mediated conditions, including progressive motor neuropathy, multiple sclerosis, Parkinson’s disease, and severe AD. Our results suggest that disentangling genes harboring variants with and without pleiotropic associations with AD and EDU is promising for dissecting heterogeneity in biological mechanisms of AD. Supplementary Information The online version contains supplementary material available at 10.1007/s11357-021-00484-1.

The availability of genome-wide association studies (GWASs) for human blood metabolome provides an excellent opportunity for studying metabolism in a heritable disease such as migraine. Utilizing GWAS summary statistics, we conduct comprehensive pairwise genetic analyses to estimate polygenic genetic overlap and causality between 316 unique blood metabolite levels and migraine risk. We find significant genome-wide genetic overlap between migraine and 44 metabolites, mostly lipid and organic acid metabolic traits (FDR < 0.05). We also identify 36 metabolites, mostly related to lipoproteins, that have shared genetic influences with migraine at eight independent genomic loci (posterior probability > 0.9) across chromosomes 3, 5, 6, 9, and 16. The observed relationships between genetic factors influencing blood metabolite levels and genetic risk for migraine suggest an alteration of metabolite levels in individuals with migraine. Our analyses suggest higher levels of fatty acids, except docosahexaenoic acid (DHA), a very long-chain omega-3, in individuals with migraine. Consistently, we found a causally protective role for a longer length of fatty acids against migraine. We also identified a causal effect for a higher level of a lysophosphatidylethanolamine, LPE(20:4), on migraine, thus introducing LPE(20:4) as a potential therapeutic target for migraine.

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

BackgroundMyasthenia gravis (MG) is a rare autoimmune disorder affecting the neuromuscular junction (NMJ). Here, we investigate the genetic architecture of MG via a genome-wide association study (GWAS) of the largest MG data set analysed to date.MethodsWe performed GWAS meta-analysis integrating three different data sets (total of 1401 cases and 3508 controls). We carried out human leucocyte antigen (HLA) fine-mapping, gene-based and tissue enrichment analyses and investigated genetic correlation with 13 other autoimmune disorders as well as pleiotropy across MG and correlated disorders.ResultsWe confirmed the previously reported MG association with TNFRSF11A (rs4369774; p=1.09×10−13, OR=1.4). Furthermore, gene-based analysis revealed AGRN as a novel MG susceptibility gene. HLA fine-mapping pointed to two independent MG loci: HLA-DRB1 and HLA-B. MG onset-specific analysis reveals differences in the genetic architecture of early-onset MG (EOMG) versus late-onset MG (LOMG). Furthermore, we find MG to be genetically correlated with type 1 diabetes (T1D), rheumatoid arthritis (RA), late-onset vitiligo and autoimmune thyroid disease (ATD). Cross-disorder meta-analysis reveals multiple risk loci that appear pleiotropic across MG and correlated disorders.DiscussionOur gene-based analysis identifies AGRN as a novel MG susceptibility gene, implicating for the first time a locus encoding a protein (agrin) that is directly relevant to NMJ activation. Mutations in AGRN have been found to underlie congenital myasthenic syndrome. Our results are also consistent with previous studies highlighting the role of HLA and TNFRSF11A in MG aetiology and the different risk genes in EOMG versus LOMG. Finally, we uncover the genetic correlation of MG with T1D, RA, ATD and late-onset vitiligo, pointing to shared underlying genetic mechanisms.

Background The coronavirus disease 2019 (COVID-19) pandemic has struck globally and is exerting a devastating toll on humans. The pandemic has led to calls for widespread vitamin D supplementation in public. However, evidence supporting the role of vitamin D in the COVID-19 pandemic remains controversial. Methods We performed a two-sample Mendelian randomization (MR) analysis to analyze the causal effect of the 25-hydroxyvitamin D [25(OH)D] concentration on COVID-19 susceptibility, severity and hospitalization traits by using summary-level GWAS data. The causal associations were estimated with inverse variance weighted (IVW) with fixed effects (IVW-fixed) and random effects (IVW-random), MR-Egger, weighted edian and MR Robust Adjusted Profile Score (MR.RAPS) methods. We further applied the MR Steiger filtering method, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test and PhenoScanner tool to check and remove single nucleotide polymorphisms (SNPs) that were horizontally pleiotropic. Results We found no evidence to support the causal associations between the serum 25(OH)D concentration and the risk of COVID-19 susceptibility [IVW-fixed: odds ratio (OR) = 0.9049, 95% confidence interval (CI) 0.8197–0.9988, p = 0.0473], severity (IVW-fixed: OR = 1.0298, 95% CI 0.7699–1.3775, p = 0.8432) and hospitalized traits (IVW-fixed: OR = 1.0713, 95% CI 0.8819–1.3013, p = 0.4878) using outlier removed sets at a Bonferroni-corrected p threshold of 0.0167. Sensitivity analyses did not reveal any sign of horizontal pleiotropy. Conclusions Our MR analysis provided precise evidence that genetically lowered serum 25(OH)D concentrations were not causally associated with COVID-19 susceptibility, severity or hospitalized traits. Our study did not provide evidence assessing the role of vitamin D supplementation during the COVID-19 pandemic. High-quality randomized controlled trials are necessary to explore and define the role of vitamin D supplementation in the prevention and treatment of COVID-19.

Mendelian randomization is the use of genetic variants as instruments to assess the existence of a causal relationship between a risk factor and an outcome. A Mendelian randomization analysis requires a set of genetic variants that are strongly associated with the risk factor and only associated with the outcome through their effect on the risk factor. We describe a novel variable selection algorithm for Mendelian randomization that can identify sets of genetic variants which are suitable in both these respects. Our algorithm is applicable in the context of two-sample summary-data Mendelian randomization and employs a recently proposed theoretical extension of the traditional Bayesian statistics framework, including a loss function to penalize genetic variants that exhibit pleiotropic effects. One of our algorithm’s main advantages is more robust inference through the use of model averaging, as we illustrate by running it on a wide range of simulation scenarios and comparing it against established pleiotropy-robust Mendelian randomization methods. In a real data application, we study the effect of systolic and diastolic blood pressure on the risk of suffering from coronary heart disease. Although this application has been studied in the past, we add to the literature by using for the first time a recent large-scale GWAS on blood pressure, allowing us to select 395 genetic variants for systolic and 391 variants for diastolic blood pressure. Both traits are shown to have significant risk-increasing effects on coronary heart disease risk.

Background and Aims Inflammatory bowel disease [IBD] phenotypes are very heterogeneous between patients, and current clinical and molecular classifications do not accurately predict the course that IBD will take over time. Genetic determinants of disease phenotypes remain largely unknown but could aid drug development and allow for personalised management. We used genetic risk scores [GRS] to disentangle the genetic contributions to IBD phenotypes. Methods Clinical characteristics and imputed genome-wide genetic array data of patients with IBD were obtained from two independent cohorts [cohort A, n = 1097; cohort B, n = 2156]. Genetic risk scoring [GRS] was used to assess genetic aetiology shared across traits and IBD phenotypes. Significant GRS–phenotype (false-discovery rate [FDR] corrected p Results Crohn’s disease [CD] GRS were associated with fibrostenotic CD [R2 = 7.4%, FDR = 0.02] and ileocaecal resection [R2 = 4.1%, FDR = 1.6E-03], and this remained significant after correcting for previously identified clinical and genetic risk factors. Ulcerative colitis [UC] GRS [R2 = 7.1%, FDR = 0.02] and primary sclerosing cholangitis [PSC] GRS [R2 = 3.6%, FDR = 0.03] were associated with colonic CD, and these two associations were largely driven by genetic variation in MHC. We also observed pleiotropy between PSC genetic risk and smoking behaviour [R2 = 1.7%, FDR = 0.04]. Conclusions Patients with a higher genetic burden of CD are more likely to develop fibrostenotic disease and undergo ileocaecal resection, whereas colonic CD shares genetic aetiology with PSC and UC that is largely driven by variation in MHC. These results further our understanding of specific IBD phenotypes.

Genetic architecture predisposes regions of the human genome to copy-number variants, which confer substantial disease risk, most prominently towards neurodevelopmental disorders. These variants typically contain multiple genes and are often associated with extensive pleiotropy and variable phenotypic expressivity. Despite the expansion of the fidelity of CNV detection, and the study of such lesions at the population level, understanding causal mechanisms for CNV phenotypes will require biological testing of constituent genes and their interactions. In this regard, model systems amenable to high-throughput phenotypic analysis of dosage-sensitive genes (and combinations thereof) are beginning to offer improved granularity of CNV-driven pathology. Here, we review the utility of Drosophila and zebrafish models for pathogenic CNV regions, highlight the advances made in discovery of single gene drivers and genetic interactions that determine specific CNV phenotypes, and argue for their validity in dissecting conserved developmental mechanisms associated with CNVs.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify novel lipid-associated genes and study the relationships between lipids, genotypes, gene expression and hundreds of complex human diseases from electronic Medical Records and Genomics (347 traits) and UK Biobank (549 traits) cohorts. Aside from 67 novel lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (rg = 0.06, p = 1.37 × 10−5) and between headache and T2D (rg = 0.07, p = 3.0 × 10−4). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D (Pbinomial-test = 2.83 × 10−46) and headache and T2D (Pbinomial-test = 4.08 × 10−29). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, ORIVW = 0.90, Pivw = 7 × 10−3) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.

This study aims to expand our understanding of the genetic architecture of crown morphology in the human diphyodont dentition. Here, we present bivariate genetic correlation estimates for deciduous and permanent molar traits and evaluate the patterns of pleiotropy within (e.g., m1–m2) and between (e.g., m2–M1) dentitions. Morphology was observed and scored from dental models representing participants of an Australian twin and family study (deciduous n = 290, permanent n = 339). Data collection followed Arizona State University Dental Anthropology System standards. Genetic correlation estimates were generated using maximum likelihood variance components analysis in SOLAR v.8.1.1. Approximately 23% of deciduous variance components models and 30% of permanent variance components models yielded significant genetic correlation estimates. By comparison, over half (56%) of deciduous–permanent homologues (e.g., m2 hypocone–M1 hypocone) were significantly genetically correlated. It is generally assumed that the deciduous and permanent molars represent members of a meristic molar field emerging from the primary dental lamina. However, stronger genetic integration among m2–M1/M2 homologues than among paired deciduous traits suggests the m2 represents the anterior-most member of a “true” molar field. The results indicate genetic factors act at distinct points throughout development to generate homologous molar form, starting with the m2, which is later replaced by a permanent premolariform crown.

Biobanks with exomes linked to electronic health records (EHRs) enable the study of genetic pleiotropy between rare variants and seemingly disparate diseases. We performed robust clinical phenotyping of rare, putatively deleterious variants (loss-of-function [LoF] and deleterious missense variants) in ERCC6, a gene implicated in inherited retinal disease. We analyzed 213,084 exomes, along with a targeted set of retinal, cardiac, and immune phenotypes from two large-scale EHR-linked biobanks. In the primary analysis, a burden of deleterious variants in ERCC6 was strongly associated with 1) retinal disorders; 2) cardiac and electrocardiogram perturbations; and 3) immunodeficiency and decreased immunoglobulin levels. Meta-analysis of results from the BioMe Biobank and UK Biobank showed significant association of deleterious ERCC6 burden with retinal dystrophy (OR=2.6, 95% CI 1.5-4.6; P=8.7 x 10-4 ), atypical atrial flutter (OR=3.5, 95% CI 1.9-6.5; P=6.2 x 10-5 ), arrhythmia (OR=1.5, 95% CI 1.2-2.0; P=2.7 x 10-3 ), and lymphocyte immunodeficiency (OR=3.8, 95% CI 2.1-6.8; P=5.0 x 10-6 ). Carriers of ERCC6 LoF variants who lacked a diagnosis of these conditions exhibited increased symptoms, indicating underdiagnosis. These results reveal a unique genetic link among retinal, cardiac, and immune disorders and underscore the value of EHR-linked biobanks in assessing the full clinical profile of carriers of rare variants. This article is protected by copyright. All rights reserved.

Context Although the role of n-6 polyunsaturated fatty acids (PUFAs) in age-related macular degeneration (AMD) has been studied in previous observational studies, the precise manner in which 1 or more n-6 PUFAs account for this relationship remains unclear. Objective Using genetic instruments for n-6 PUFAs traits implemented through mendelian randomization (MR), we aimed to study possible causal associations between n-6 PUFAs and AMD. Methods The 2-sample MR method was used to obtain unconfounded causal estimates. We selected genetic variants strongly associated (P Results Our MR analysis suggested that circulating LA was a causal protective factor for AMD, with an odds ratio (OR) estimate of 0.967 (95% CI 0.945 to 0.990; P = .005) per percentage in total fatty acid increase in LA. In contrast, higher genetically predicted circulating AA causally increased the AMD risk (OR = 1.034; 95% CI 1.012 to 1.056; P = .002). Sensitivity analysis provided no indication of unknown pleiotropy. The findings from different single-nucleotide polymorphism selections and analytic methods were consistent, suggesting the robustness of the causal associations. Conclusion Our study provided genetic evidence that circulating LA accounted for protective effects of n-6 PUFAs against the risk of AMD, whereas AA was responsible for deleterious effects on higher AMD risk.

Human blood cells (HBCs) play essential roles in multiple biological processes but their roles in development of uterine polyps are unknown. Here we implemented a Mendelian randomization (MR) analysis to investigate the effects of 36 HBC traits on endometrial polyps (EPs) and cervical polyps (CPs). The random-effect inverse-variance weighted method was adopted as standard MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Genetic instruments of HBC traits was extracted from a large genome-wide association study of 173,480 individuals, while data for EPs and CPs were obtained from the UK Biobank. All samples were Europeans. Using genetic variants as instrumental variables, our study found that both eosinophil count (OR 0.85, 95% CI 0.79–0.93, P = 1.06 × 10−4) and eosinophil percentage of white cells (OR 0.84, 95% CI 0.77–0.91, P = 2.43 × 10−5) were associated with decreased risk of EPs. The results were robust in sensitivity analyses and no evidences of horizontal pleiotropy were observed. While we found no significant associations between HBC traits and CPs. Our findings suggested eosinophils might play important roles in the pathogenesis of EPs. Besides, out study provided novel insight into detecting uterine polyps biomarkers using genetic epidemiology approaches.

Enhancers are often studied as noncoding regulatory elements that modulate the precise spatiotemporal expression of genes in a highly tissue-specific manner. This paradigm has been challenged by recent evidence of individual enhancers acting in multiple tissues or developmental contexts. However, the frequency of these enhancers with high degrees of “pleiotropy” out of all putative enhancers is not well understood. Consequently, it is unclear how the variation of enhancer pleiotropy corresponds to the variation in expression breadth of target genes. Here, we use multi-tissue chromatin maps from diverse human tissues to investigate the enhancer–gene interaction architecture while accounting for 1) the distribution of enhancer pleiotropy, 2) the variations of regulatory links from enhancers to target genes, and 3) the expression breadth of target genes. We show that most enhancers are tissue-specific and that highly pleiotropy enhancers account for

BackgroundTwo-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.MethodsWe performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.ResultsIn the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.ConclusionsOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.Key messagesSummary genetic associations from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses.Many GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest.In an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses.The bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome.Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided.

A transcriptome-wide association study (TWAS) integrates data from genome-wide association studies and gene expression mapping studies for investigating the gene regulatory mechanisms underlying diseases. Existing TWAS methods are primarily univariate in nature, focusing on analyzing one outcome trait at a time. However, many complex traits are correlated with each other and share a common genetic basis. Consequently, analyzing multiple traits jointly through multivariate analysis can potentially improve the power of TWASs. Here, we develop a method, moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), for analyzing multiple outcome traits in TWAS applications. moPMR-Egger examines one gene at a time, relies on its cis-SNPs that are in potential linkage disequilibrium with each other to serve as instrumental variables, and tests its causal effects on multiple traits jointly. A key feature of moPMR-Egger is its ability to test and control for potential horizontal pleiotropic effects from instruments, thus maximizing power while minimizing false associations for TWASs. In simulations, moPMR-Egger provides calibrated type I error control for both causal effects testing and horizontal pleiotropic effects testing and is more powerful than existing univariate TWAS approaches in detecting causal associations. We apply moPMR-Egger to analyze 11 traits from 5 trait categories in the UK Biobank. In the analysis, moPMR-Egger identified 13.15% more gene associations than univariate approaches across trait categories and revealed distinct regulatory mechanisms underlying systolic and diastolic blood pressures.

Genome-wide association studies of complex diseases, including nonalcoholic fatty liver disease (NAFLD), have demonstrated that a large number of variants are implicated in the susceptibility of multiple traits — a phenomenon known as pleiotropy that is increasingly being explored through phenome-wide association studies. We focused on the analysis of pleiotropy within variants associated with hematologic traits and NAFLD. We used information retrieved from large public National Health and Nutrition Examination Surveys, Genome-wide association studies, and phenome-wide association studies based on the general population and explored whether variants associated with NAFLD also present associations with blood cell-related traits. Next, we applied systems biology approaches to assess the potential biological connection/s between genes that predispose affected individuals to NAFLD and nonalcoholic steatohepatitis, and genes that modulate hematological-related traits—specifically platelet count. We reasoned that this analysis would allow the identification of potential molecular mediators that link NAFLD with platelets. Genes associated with platelet count are most highly expressed in the liver, followed by the pancreas, heart, and muscle. Conversely, genes associated with NAFLD presented high expression levels in the brain, lung, spleen, and colon. Functional mapping, gene prioritization, and functional analysis of the most significant loci (P < 1 × 10-8) revealed that loci involved in the genetic modulation of platelet count presented significant enrichment in metabolic and energy balance pathways. In conclusion, variants in genes influencing NAFLD exhibit pleiotropic associations with hematologic traits, particularly platelet count. Likewise, significant enrichment of related genes with variants influencing platelet traits was noted in metabolic-related pathways. Hence, this approach yields novel mechanistic insights into NAFLD pathogenesis.

Background Vitamin D has been linked to a variety of diseases in observational studies. However, the causal role of vitamin D in human complex traits and diseases remains unclear and is under much debate. In this study, we conducted Mendelian randomization (MR) analyses using genetic variants associated with circulating 25-hydroxyvitamin D [25(OH)D] as instrumental variables to examine the causal role of vitamin D in human complex traits and diseases. Methods We performed MR analyses using genome-wide significant 25(OH)D-associated SNPs (N=58) from a vitamin D GWAS conducted in 329,247 unrelated UK Biobank (UKBB) participants of European ancestry, as well as 78 large-scale meta-GWASs of various outcomes to determine the causal role of 25(OH)D. In addition to inverse-variance weighted meta-analysis, we applied MR-Egger regression, MR-PRESSO and the weighted median approach to estimate the causal effect of 25(OH)D while examining and controlling for potential biases from horizontal pleiotropy. We conducted a sensitivity analysis by including additional genetic instruments (at a relaxed association threshold of P

Childhood obesity is a global public health problem. Understanding the molecular mechanisms that underlie early origins of childhood obesity can facilitate interventions. Consistent phenotypic and genetic correlations have been found between childhood obesity traits and birth weight (a proxy for in-utero growth), suggesting shared genetic influences (pleiotropy). We aimed to (1) investigate whether there is significant shared genetic influence between birth weight and childhood obesity traits, and (2) to identify genetic loci with shared effects. Using a statistical approach that integrates summary statistics and functional annotations for paired traits, we found strong evidence of pleiotropy (P –127) and enrichment of functional annotations (P –39) between birth weight and childhood body mass index (BMI)/obesity. The pleiotropic loci were enriched for regulatory features in skeletal muscle, adipose and brain tissues and in cell lines derived from blood lymphocytes. At 5% false discovery rate, 6 loci were associated with birth weight and childhood BMI and 13 loci were associated with birth weight and childhood obesity. Out of these 19 loci, one locus (EBF1) was novel to childhood obesity and one locus (LMBR1L) was novel to both birth weight and childhood BMI/obesity. These findings give evidence of substantial shared genetic effects in the regulation of both fetal growth and childhood obesity.

Pleiotropy is a widespread phenomenon in complex human diseases. Jointly analyzing multiple phenotypes can improve power performance of detecting genetic variants and uncover the underlying genetic mechanism. This study aims to detect the association between genetic variants in a genomic region and multiple phenotypes. We develop the aggregated Cauchy association test to detect the association between rare variants in a genomic region and multiple phenotypes (abbreviated as “Multi-ACAT”). Multi-ACAT first detects the association between each rare variant and multiple phenotypes based on reverse regression and obtains variant-level p-values, then takes linear combination of transformed p-values as the test statistic which approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies show that when the proportion of causal variants in a genomic region is extremely small, Multi-ACAT is more powerful than the other several methods and is robust to bi-directional effects of causal variants. Finally, we illustrate our proposed method by analyzing two phenotypes [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] from Genetic Analysis Workshop 19 (GAW19). The Multi-ACAT computes extremely fast, does not consider complex distributions of multiple correlated phenotypes, and can be applied to the case with noise phenotypes.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Vascular malformations (VMs) are caused by localized defects of vascular development. Most VMs are due to sporadic, postzygotic mutations, while some are the result of autosomal dominant germline mutations. Genotype-phenotype correlation is influenced by many factors. Individual genes can induce different phenotypes (pleiotropy), and similar phenotypes can be due to different genes/mutations (redundancy). The phenotypic spectrum of somatic mutations is wide, and depends on variant allele frequency, timing during embryogenesis, cell type(s) involved and type of mutation. The phenotype of germline mutations is determined by penetrance and expressivity, and is influenced by epigenetic factors (DNA methylation, histone modification) or 'second-hit' somatic mutations. Except for disorders with pathognomonic phenotypes such as Proteus syndrome or a characteristic constellation of symptoms such as CLOVES [congenital lipomatous (fatty) overgrowth, vascular malformations, epidermal naevi and scoliosis/skeletal/spinal anomalies] or PIK3CA-related overgrowth spectrum syndrome, differential diagnosis of VM is therefore difficult. It will be greatly facilitated with increasing analytic sensitivity of sequencing techniques such as next-generation sequencing. High-sensitivity molecular techniques are a prerequisite for targeted pharmacotherapy, i.e. selective therapeutic inhibition of activating mutations underlying VM, which has shown promising results in preliminary studies.