The variant call format provides efficient and robust storage of GWAS summary statistics

Evidence of causal effects of blood pressure on back pain and back pain on type II diabetes provided by a bidirectional Mendelian randomization study

BACKGROUND CONTEXT

Cardiovascular risk factors (hypertension, dyslipidemia, and type II diabetes) have been proposed as risk factors for back pain. However, few longitudinal studies have found significant associations between cardiovascular risk factors and back pain, and these may be explained by confounding or reverse causation.

PURPOSE

To examine potential causal effects of cardiovascular risk factors on back pain, and vice versa.

STUDY DESIGN

Bidirectional Mendelian randomization (MR) study.

PATIENT SAMPLES

Genome-wide association studies (GWAS) with sample sizes between 173,082 and 1,028,947 participants.

OUTCOME MEASURES

Outcomes included (1) back pain associated with health care use (BP-HC) in the forward MR; and (2) seven cardiovascular phenotypes in the reverse MR, including 2 measurements used for the evaluation of hypertension (diastolic blood pressure and systolic blood pressure), 4 phenotypes related to dyslipidemia (LDL cholesterol, HDL cholesterol, total cholesterol, and triglycerides), and type II diabetes.

METHODS

We used summary statistics from large, publicly available GWAS for BP-HC and the 7 cardiovascular phenotypes to obtain genetic instrumental variables. We examined MR evidence for causal associations using inverse-variance weighted (IVW) analysis, Causal Analysis Using Summary Effect (CAUSE), and sensitivity analyses.

RESULTS

In forward MR analyses of seven cardiovascular phenotypes, diastolic blood pressure was associated with BP-HC across all analyses (IVW estimate: OR = 1.10 per 10.5 mm Hg increase [1.04-1.17], p-value = .001), and significant associations of systolic blood pressure with BP-HC were also found (IVW estimate: OR = 1.09 per 19.3 mm Hg increase [1.04-1.15], p-value = .0006). In reverse MR analyses, only type II diabetes was associated with BP-HC across all analyses (IVW estimate: OR = 1.40 [1.13-1.73], p-value = .002).

CONCLUSIONS

These findings from analyses of large, population-based samples indicate that higher blood pressure increases the risk of BP-HC, and BP-HC itself increases the risk of type II diabetes.

Effects of Polyunsaturated Fatty Acid Serum Levels on Vascular Dementia: A Two-Sample Mendelian Randomization Study

INTRODUCTION

Several observational studies have indicated that polyunsaturated fatty acid serum levels (PUFAs) are associated with vascular dementia (VaD), but their causal relationships remain elusive. Therefore, we attempted to evaluate the causal effect of PUFAs on VaD in a two-sample Mendelian randomization (MR) analysis by using summary statistics from aggregated genome-wide association studies.

METHODS

The inverse-variance weighted (IVW) method was performed as the primary analysis. Sensitivity analyses (MR-Egger regression, weighted median, penalized weighted median, and MR pleiotropy residual sum and outlier methods) were also implemented to estimate the effects of potential violations of MR hypotheses.

RESULTS

No causality was found for PUFAs (odds ratio, 1.14; 95% confidence interval, 0.91-1.42; p = 0.25) on VaD in the IVW model. The results were consistent in sensitivity analyses. There was no notable horizontal pleiotropy or heterogeneity.

CONCLUSION

In this two-sample MR analysis, our findings did not support the assumption that PUFAs play causal role in the occurrence or development of VaD.

The STROMICS genome study: deep whole-genome sequencing and analysis of 10K Chinese patients with ischemic stroke reveal complex genetic and phenotypic interplay

Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.

Skin Colonization with S. aureus Can Lead to Increased NLRP1 Inflammasome Activation in Patients with Atopic Dermatitis

The role of NLRP1 inflammasome activation and subsequent production of IL-1 family cytokines in the development of atopic dermatitis (AD) is not clearly understood. Staphylococcus aureus is known to be associated with increased mRNA levels of IL1 family cytokines in the skin and more severe AD. In this study, the altered expression of IL-1 family cytokines and inflammasome-related genes was confirmed, and a positive relationship between mRNA levels of inflammasome sensor NLRP1 and IL1B or IL18 was determined. Enhanced expression of the NLRP1 and PYCARD proteins and increased caspase-1 activity were detected in the skin of patients with AD. The genetic association of IL18R1 and IL18RAP with AD was confirmed, and the involvement of various immune cell types was predicted using published GWAS and expression quantitative trait loci datasets. In keratinocytes, the inoculation with S. aureus led to the increased secretion of IL-1β and IL-18, whereas small interfering RNA silencing of NLRP1 inhibited the production of these cytokines. Our results suggest that skin colonization with S. aureus may cause the activation of the NLRP1 inflammasome in keratinocytes, which leads to the secretion of IL-1β and IL-18 and thereby may contribute to the pathogenesis of AD, particularly in the presence of genetic variations in the IL-18 pathway.

Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity

Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.

Using Mendelian randomization analysis to determine the causal connection between unpleasant emotions and coronary atherosclerosis

Objective

Observational studies have shown a correlation between unpleasant emotions and coronary atherosclerosis, but the underlying causal linkages are still uncertain. We conducted a Mendelian randomization (MR) investigation on two samples for this purpose.

Methods

In genome-wide association studies in the UK Biobank (total = 459,561), we selected 40 distinct single-nucleotide polymorphisms (SNPs) related to unpleasant emotions as genome-wide statistically significant instrumental variables. FinnGen consortium provided summary-level data on coronary atherosclerosis for 211,203 individuals of Finnish descent. MR-Egger regression, the inverse variance weighted technique (IVW), and the weighted median method were used in the process of conducting data analysis.

Results

There was sufficient evidence to establish a causal connection between unpleasant emotions and coronary atherosclerosis risk. For each unit increase in the log-odds ratio of unpleasant feelings, the odds ratios were 3.61 (95% CI: 1.64-7.95; P = 0.001). The outcomes of sensitivity analyses were comparable. There was no indication of heterogeneity or directional pleiotropy.

Conclusion

Our findings provide causal evidence for the effects of unpleasant emotions on coronary atherosclerosis.

Insights into risk factors for urolithiasis: a mendelian randomization study

BACKGROUND

Risk factors for urolithiasis have not been identified. Here, we aimed to identify potentially causal risk factors driving the risk of urolithiasis.

METHODS

Two sets of instrumental variables were used for analysis, derived from publicly available databases. Summary-level statistical data for urolithiasis were obtained from the MRC-IEU Consortium and UK biobank (Neale Lab). Mendelian randomization (MR) was conducted to identify causal risk of urolithiasis. Finally, the results of the two databases were combined and a meta-analysis was performed.

RESULTS

In the MRC-IEU consortium, the odds of urolithiasis increased per 1-SD increase of body mass index (BMI) (OR = 1.0016, 95% CI:1.0004-1.0029, p = 0.010), triglycerides (OR = 1.0016, 95% CI:1.0003-1.0029, p = 0.017), adiponectin (OR = 1.0027, 95% CI:1.0003-1.0050, p = 0.024), and body fat percentage (OR = 1.008, 95% CI:1.0001-1.0161, p = 0.047). In addition, alcohol intake also increased the incidence of urolithiasis (OR = 1.0030, 95% CI:1.0009-1.0051, p = 0.005). In the UK biobank, the odds of urolithiasis increased per 1-SD increase of waist circumference (OR = 1.0215, 95% CI:1.0061-1.0372, p = 0.008) and body fat percentage (OR = 1.0239, 95% CI:1.0043-1.0440, p = 0.020). Surprisingly, we found that the risk of urolithiasis decreased with increasing hip circumference (OR = 0.9954, 95% CI:0.9915-0.9992, p = 0.017). In a meta-analysis of MR results, higher BMI (OR = 1.0016, 95% CI:1.0004-1.0027, p = 0.009), waist circumference (OR = 1.0073, 95% CI:1.0020-1.0126, p = 0.007), adiponectin (OR = 1.0026, 95% CI:1.0008-1.0043, p = 0.004), triglycerides (OR = 1.0015, 95% CI:1.0004-1.0026, p = 0.008) and body fat percentage (OR = 1.0104, 95% CI:1.0030-1.0178, p = 0.006) increased the risk of urolithiasis. Furthermore, alcohol intake also increased the incidence of urolithiasis (OR = 1.0033, 95% CI:1.0012-1.0053, p = 0.002).

CONCLUSIONS

Our MR study found that higher BMI, triglycerides, waist circumference, adiponectin, body fat percentage, and alcohol intake increased the risk of urolithiasis.

hipFG: High-throughput harmonization and integration pipeline for functional genomics data

Preparing functional genomic (FG) data with diverse assay types and file formats for integration into analysis workflows that interpret genome-wide association and other studies is a significant and time-consuming challenge. Here we introduce hipFG, an automatically customized pipeline for efficient and scalable normalization of heterogenous FG data collections into standardized, indexed, rapidly searchable analysis-ready datasets while accounting for FG datatypes (e.g., chromatin interactions, genomic intervals, quantitative trait loci).

The Association Between Insulin Use and Asthma: An Epidemiological Observational Analysis and Mendelian Randomization Study

BACKGROUND

Asthma is a common respiratory disease caused by genetic and environmental factors, but the contribution of insulin use to the risk of asthma remains unclear. This study aimed to investigate the association between insulin use and asthma in a large population-based cohort, and further explore their causal relationship by Mendelian randomization (MR) analysis.

METHODS

An epidemiological study including 85,887 participants from the National Health and Nutrition Examination Survey (NHANES) 2001-2018 was performed to evaluate the association between insulin use and asthma. Based on the inverse-variance weighted approach, MR analysis were conducted to estimate the causal effect of insulin use on asthma from the UKB and FinnGen datasets, respectively.

RESULTS

In the NHANES cohort, we found that insulin use was associated with an increased risk of asthma [odd ratio (OR) 1.38; 95% CI 1.16-1.64; p < 0.001]. For the MR analysis, we found a causal relationship between insulin use and a higher risk of asthma in both Finn (OR 1.10; p < 0.001) and UK Biobank cohorts (OR 1.18; p < 0.001). Meanwhile, there was no causal association between diabetes and asthma. After multivariable adjustment for diabetes in UKB cohort, the insulin use remained significantly associated with an increased risk of asthma (OR 1.17, p < 0.001).

CONCLUSIONS

An association between insulin use and an increased risk of asthma was found via the real-world data from the NHANES. In addition, the current study identified a causal effect and provided a genetic evidence of insulin use and asthma. More studies are needed to elucidate the mechanisms underlying the association between insulin use and asthma.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

A genome-wide cross-cancer meta-analysis highlights the shared genetic links of five solid cancers

Breast, ovarian, prostate, lung, and head/neck cancers are five solid cancers with complex interrelationships. However, the shared genetic factors of the five cancers were often revealed either by the combination of individual genome-wide association study (GWAS) approach or by the fixed-effect model-based meta-analysis approach with practically impossible assumptions. Here, we presented a random-effect model-based cross-cancer meta-analysis framework for identifying the genetic variants jointly influencing the five solid cancers. A comprehensive genetic correlation analysis (genome-wide, partitioned, and local) approach was performed by using GWAS summary statistics of the five cancers, and we observed three cancer pairs with significant genetic correlation: breast-ovarian cancer (r _g = 0.221, p = 0.0003), breast-lung cancer (r _g = 0.234, p = 7.6 × 10^-6), and lung-head/neck cancer (r _g = 0.652, p = 0.010). Furthermore, a random-effect model-based cross-trait meta-analysis was conducted for each significant cancer pair, and we found 27 shared genetic loci between breast and ovarian cancers, 18 loci between breast and lung cancers, and three loci between lung and head/neck cancers. Functional analysis indicates that the shared genes are enriched in human T-cell leukemia virus 1 infection (HTLV-1) and antigen processing and presentation (APP) pathways. Our study investigates the shared genetic links across five solid cancers and will help to reveal their potential molecular mechanisms.

SUMMER: a Mendelian randomization interactive server to systematically evaluate the causal effects of risk factors and circulating biomarkers on pan-cancer survival

Genome-wide association studies (GWASs) underlying case-control design have uncovered hundreds of genetic loci involved in tumorigenesis and provided rich resources for identifying risk factors and biomarkers associated with cancer susceptibility. However, the application of GWAS in determining the genetic architecture of cancer survival remains unestablished. Here, we systematically evaluated genetic effects at the genome-wide level on cancer survival that included overall survival (OS) and cancer-specific survival (CSS), leveraging data deposited in the UK Biobank cohort of a total of 19 628 incident patients across 17 cancer types. Furthermore, we assessed the causal effects of risk factors and circulating biomarkers on cancer prognosis via a Mendelian randomization (MR) analytic framework, which integrated cancer survival GWAS dataset, along with phenome-wide association study (PheWAS) and blood genome-wide gene expression/DNA methylation quantitative trait loci (eQTL/meQTL) datasets. On average, more than 10 traits, 700 genes, and 4,500 CpG sites were prone to cancer prognosis. Finally, we developed a user-friendly online database, SUrvival related cancer Multi-omics database via MEndelian Randomization (SUMMER; http://njmu-edu.cn:3838/SUMMER/), to help users query, browse, and download cancer survival results. In conclusion, SUMMER provides an important resource to assist the research community in understanding the genetic mechanisms of cancer survival.

GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data

SUMMARY

With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures.

AVAILABILITY AND IMPLEMENTATION

The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Associations of Lipids and Lipid-Lowering Drugs with Risk of Vascular Dementia: A Mendelian Randomization Study

Accumulating observational studies suggested that hypercholesterolemia is associated with vascular dementia (VaD); however, the causality between them remains unclear. Hence, the aim of this study is to infer causal associations of circulating lipid-related traits [including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), apolipoprotein A-I (apoA-I), and apolipoprotein B (apoB)] with VaD jointly using univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR methods. Then, the summary-data-based MR (SMR) and two-sample MR analysis were conducted to investigate the association of lipid-lowering drugs target genes expression (including HMGCR, PCSK9, NPC1L1, and APOB) and LDL-C level mediated by these target genes with VaD. The results of forward MR analyses found that genetically predicted HDL-C, LDL-C, TG, apoA-I, and apoB concentrations were not significantly associated with the risk of VaD (all p > 0.05). Notably, there was suggestive evidence for a causal effect of genetically predicted VaD on HDL-C via reverse MR analysis [odds ratio (OR), 0.997; 95% confidence interval (CI), 0.994−0.999; p = 0.022]. On the contrary, the MR results showed no significant relationship between VaD with LDL-C, TG, apoA-I, and apoB. The results for the SMR method found that there was no evidence of association for expression of HMGCR, PCSK9, NPC1L1, and APOB gene with risk of VaD. Furthermore, the result of MR analysis provided evidence for the decreased LDL-C level mediated by gene HMGCR reduced the risk of VaD (OR, 18.381; 95% CI, 2.092−161.474; p = 0.009). Oppositely, none of the IVW methods indicated any causal effects for the other three genes. Using genetic data, this study provides evidence that the VaD risk may cause a reduction of HDL-C level. Additionally, the finding supports the hypothesis that lowering LDL-C levels using statins may be an effective prevention strategy for VaD risk, which requires clinical trials to confirm this result in the future.

Mendelian randomization and genetic colocalization infer the effects of the multi-tissue proteome on 211 complex disease-related phenotypes

BACKGROUND

Human proteins are widely used as drug targets. Integration of large-scale protein-level genome-wide association studies (GWAS) and disease-related GWAS has thus connected genetic variation to disease mechanisms via protein. Previous proteome-by-phenome-wide Mendelian randomization (MR) studies have been mainly focused on plasma proteomes. Previous MR studies using the brain proteome only reported protein effects on a set of pre-selected tissue-specific diseases. No studies, however, have used high-throughput proteomics from multiple tissues to perform MR on hundreds of phenotypes.

METHODS

Here, we performed MR and colocalization analysis using multi-tissue (cerebrospinal fluid (CSF), plasma, and brain from pre- and post-meta-analysis of several disease-focus cohorts including Alzheimer disease (AD)) protein quantitative trait loci (pQTLs) as instrumental variables to infer protein effects on 211 phenotypes, covering seven broad categories: biological traits, blood traits, cancer types, neurological diseases, other diseases, personality traits, and other risk factors. We first implemented these analyses with cis pQTLs, as cis pQTLs are known for being less prone to horizontal pleiotropy. Next, we included both cis and trans conditionally independent pQTLs that passed the genome-wide significance threshold keeping only variants associated with fewer than five proteins to minimize pleiotropic effects. We compared the tissue-specific protein effects on phenotypes across different categories. Finally, we integrated the MR-prioritized proteins with the druggable genome to identify new potential targets.

RESULTS

In the MR and colocalization analysis including study-wide significant cis pQTLs as instrumental variables, we identified 33 CSF, 13 plasma, and five brain proteins to be putative causal for 37, 18, and eight phenotypes, respectively. After expanding the instrumental variables by including genome-wide significant cis and trans pQTLs, we identified a total of 58 CSF, 32 plasma, and nine brain proteins associated with 58, 44, and 16 phenotypes, respectively. For those protein-phenotype associations that were found in more than one tissue, the directions of the associations for 13 (87%) pairs were consistent across tissues. As we were unable to use methods correcting for horizontal pleiotropy given most of the proteins were only associated with one valid instrumental variable after clumping, we found that the observations of protein-phenotype associations were consistent with a causal role or horizontal pleiotropy. Between 66.7 and 86.3% of the disease-causing proteins overlapped with the druggable genome. Finally, between one and three proteins, depending on the tissue, were connected with at least one drug compound for one phenotype from both DrugBank and ChEMBL databases.

CONCLUSIONS

Integrating multi-tissue pQTLs with MR and the druggable genome may open doors to pinpoint novel interventions for complex traits with no effective treatments, such as ovarian and lung cancers.

No Evidence to Support a Causal Relationship between Circulating Adiponectin Levels and Ankylosing Spondylitis: A Bidirectional Two-Sample Mendelian Randomization Study

Based on previous observational studies, the causal association between circulating adiponectin (CA) levels and ankylosing spondylitis (AS) risk remains unclear. Therefore, this study aims to investigate whether CA levels are related to the risk of AS. We carried out a bidirectional two-sample Mendelian randomization (MR) analysis to examine the causal correlation between CA levels and AS via published genome-wide association study (GWAS) datasets. Single-nucleotide polymorphisms (SNPs) related to CA levels were derived from a large GWAS that included 39,883 individuals of European descent. SNPs related to AS were obtained from the FinnGen consortium (2252 cases and 227,338 controls). The random-effects inverse variance weighted (IVW) method was the primary method utilized in our research. We also used four complementary approaches to improve the dependability of this study (MR-Egger regression, Weighted median, Weighted mode, and Simple mode). Random-effects IVW (odds ratio [OR], 1.00; 95% confidence interval [CI], 0.79-1.27, p = 0.984) and four complementary methods all indicated that genetically predicted CA levels were not causally related to the risk of AS. In reverse MR analysis, there is little evidence to support the genetic causality between the risk of AS and CA levels.

SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration

Background

Generating polygenic risk scores for diseases and complex traits requires high quality GWAS summary statistic files. Often, these files can be difficult to acquire either as a result of unshared or incomplete data. To date, bioinformatics tools which focus on restoring missing columns containing identification and association data are limited, which has the potential to increase the number of usable GWAS summary statistics files.

Results

SumStatsRehab was able to restore rsID, effect/other alleles, chromosome, base pair position, effect allele frequencies, beta, standard error, and p-values to a better extent than any other currently available tool, with minimal loss.

Conclusions

SumStatsRehab offers a unique tool utilizing both functional programming and pipeline-like architecture, allowing users to generate accurate data restorations for incomplete summary statistics files. This in turn, increases the number of usable GWAS summary statistics files, which may be invaluable for less researched health traits.

Shared Genetic Regulatory Networks Contribute to Neuropathic and Inflammatory Pain: Multi-Omics Systems Analysis

The mechanisms of chronic pain are complex, and genetic factors play an essential role in the development of chronic pain. Neuropathic pain (NP) and inflammatory pain (IP) are two primary components of chronic pain. Previous studies have uncovered some common biological processes in NP and IP. However, the shared genetic mechanisms remained poorly studied. We utilized multi-omics systematic analyses to investigate the shared genetic mechanisms of NP and IP. First, by integrating several genome-wide association studies (GWASs) with multi-omics data, we revealed the significant overlap of the gene co-expression modules in NP and IP. Further, we uncovered the shared biological pathways, including the previously reported mitochondrial electron transport and ATP metabolism, and stressed the role of genetic factors in chronic pain with neurodegenerative diseases. Second, we identified 24 conservative key drivers (KDs) contributing to NP and IP, containing two well-established pain genes, IL1B and OPRM1, and some novel potential pain genes, such as C5AR1 and SERPINE1. The subnetwork of those KDs highlighted the processes involving the immune system. Finally, gene expression analysis of the KDs in mouse models underlined two of the KDs, SLC6A15 and KCNQ5, with unidirectional regulatory functions in NP and IP. Our study provides strong evidence to support the current understanding of the shared genetic regulatory networks underlying NP and IP and potentially benefit the future common therapeutic avenues for chronic pain.

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Genetic correlation between female infertility and mental health and lifestyle factors: A linkage disequilibrium score regression study

Background and Aims

Female fertility is a heterogeneous condition of complete psychosocial and physical well‐being. Observational studies have revealed that women with infertility have varying degrees of poor mental status and lifestyle choices in varying degrees. However, the genetic contribution to female infertility remains elusive. Our study aimed to explore the genetic correlations between female infertility and mental health and lifestyle factors.

Methods

The genome‐wide association study (GWAS) data sets of characteristics related to mental health and lifestyle were obtained from the IEU OpenGWAS database. The GWAS data sets of female infertility were derived from the Finggen database. Linkage disequilibrium score regression was performed to systematically estimate the pairwise genetic correlations between female infertility and a set of mental health‐ and lifestyle‐related traits.

Results

The genetic correlation analysis revealed a moderate and positive genetic correlation between depressive symptoms, major depressive disorder, and female infertility. Similarly, worry and the personality trait of neuroticism displayed a moderate positive genetic correlation with female infertility. Adversely, a negative and moderate genetic correlation was observed between strenuous sports or exercises and female infertility.

Conclusion

The study demonstrated genetic correlations between female infertility and mental health status, including depression, worry, and neuroticism. Additionally, we observed that females with better physical activity may have reduced risks of female infertility. These findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of mental health and lifestyle factors on female infertility.

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

Assessment of the Causal Effects of IgG N-Glycosylation Level on Risk of Dementia: A 2-Sample Mendelian Randomization Study

Background: Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer’s disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation. Objective: The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR). Methods: Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers. Results: There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all p >  0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change. Conclusion: Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.

Observational and Genetic Associations of Modifiable Risk Factors with Aortic Valve Stenosis: A Prospective Cohort Study of 0.5 Million Participants

Background: Observational studies have shown that modifiable risk factors are associated with aortic valve stenosis (AVS). However, the causality behind these associations remains largely unknown. Objectives: To explore the associations of modifiable risk factors, including metabolic factors, biochemical measures, education, and lifestyles with AVS and their potential causal associations. Methods: We enrolled 361,930 British white people with genetic data in the UK biobank. Cox proportional risk regression models were used to estimate the hazard ratios between 28 modifiable risk factors and AVS. We used genetic instruments for modifiable risk factors to determine the potential causal relationships using a one-sample Mendelian randomization (MR) approach. Results: A total of 1602 participants developed AVS during an 8.4-year follow-up. Observational analyses showed higher adiposity, blood pressure, heart rate, low-density lipoprotein, urate, C-reactive protein, creatinine, albumin, and glycated hemoglobin, but lower serum vitamin D, and education, unhealthy lifestyle, and poor sleep quality were related to a higher risk of AVS after adjusting for the Bonferroni correction (p < 0.0013). Genetically predicted 1-SD higher levels of body mass index [HR: 1.09, 95% CI: 1.03 to 1.16], body fat percentage (1.17, 1.03 to 1.33), triglyceride (TG) [1.08, 1.00 to 1.16], low-density lipoprotein (LDL) (1.15, 1.08 to 1.21) and serum total cholesterol (TC) (1.13, 1.02 to 1.25) were associated with a higher risk of AVS, respectively. Genetically determined per category higher insomnia (1.32, 1.13 to 1.55) was also associated with AVS. The abovementioned genetic associations with the incident AVS showed an increasing relationship pattern. Conclusions: This study provides strong evidence for the potential causal roles of cardiometabolic factors in developing AVS, highlighting that an idea of metabolic status through a healthy lifestyle may help prevent AVS.

Robust inference of bi-directional causal relationships in presence of correlated pleiotropy with GWAS summary data

To infer a causal relationship between two traits, several correlation-based causal direction (CD) methods have been proposed with the use of SNPs as instrumental variables (IVs) based on GWAS summary data for the two traits; however, none of the existing CD methods can deal with SNPs with correlated pleiotropy. Alternatively, reciprocal Mendelian randomization (MR) can be applied, which however may perform poorly in the presence of (unknown) invalid IVs, especially for bi-directional causal relationships. In this paper, first, we propose a CD method that performs better than existing CD methods regardless of the presence of correlated pleiotropy. Second, along with a simple but yet effective IV screening rule, we propose applying a closely related and state-of-the-art MR method in reciprocal MR, showing its almost identical performance to that of the new CD method when their model assumptions hold; however, if the modeling assumptions are violated, the new CD method is expected to better control type I errors. Notably bi-directional causal relationships impose some unique challenges beyond those for uni-directional ones, and thus requiring special treatments. For example, we point out for the first time several scenarios where a bi-directional relationship, but not a uni-directional one, can unexpectedly cause the violation of some weak modeling assumptions commonly required by many robust MR methods. We also offer some numerical support and a modeling justification for the application of our new methods (and more generally MR) to binary traits. Finally we applied the proposed methods to 12 risk factors and 4 common diseases, confirming mostly well-known uni-directional causal relationships, while identifying some novel and plausible bi-directional ones such as between body mass index and type 2 diabetes (T2D), and between diastolic blood pressure and stroke.

Inferring causal relationships between two traits based on observational data is one of the most important as well as challenging problems in scientific research. The potential impact of such an application on and beyond genetics/genomics is significant, such as in prioritizing molecular, clinical and behavioral targets for therapeutic and behavioral interventions. However, this problem, especially on a possibly bi-directional causal relationship between a pair of traits (in which the causal direction may be from either trait to the other, and both causal directions may be present at the same time), had been largely neglected until very recently. The increasing availability of large-scale GWAS summary data of various traits has popularized the development and application of Mendelian randomization (MR) methods for such a purpose. We point out some severe limitations with the current methods, mainly due to some new and unique challenges facing inference of bi-directional relationships as compared to that of uni-directional relationships that has been more commonly and exclusively considered in MR. By combining two basic ideas of bidirectional (or reciprocal) MR and Steiger’s correlation-based screening methods, we develop two new approaches based on constrained maximum likelihood (cML) and GWAS summary data to infer causal effects (as in typical MR) and SNP-trait correlations (as in Steiger’s method), called MR-cML and CD-cML respectively, demonstrating their similar effectiveness and more importantly their advantages over existing methods through extensive simulations, real data examples and statistical theory. In particular, our proposed two methods are robust to violations of all three valid IV assumptions, including presence of correlated pleiotropy.

PAGEANT: personal access to genome and analysis of natural traits

GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (i) quality assurance of the genetic data; (ii) qualitative assessment of genetic characteristics; (iii) quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (iv) query of third-party variant databases (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2504 genomes made publicly available through the 1000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported.

Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes

Developing functional insight into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here, we systematically apply Mendelian randomization (MR), genetic colocalization, immune-cell-type enrichment, and phenome-wide association methods to investigate the effects of genetically predicted gene expression on ten immune-associated diseases and four cancer outcomes. Using whole blood-derived estimates for regulatory variants from the eQTLGen consortium (n = 31,684), we constructed genetic risk scores for 10,104 genes. Applying the inverse-variance-weighted MR method transcriptome wide while accounting for linkage disequilibrium structure identified 664 unique genes with evidence of a genetically predicted effect on at least one disease outcome (p < 4.81 × 10-5). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci by using gene expression data derived from 18 types of immune cells. This highlighted many cell-type-dependent effects, such as PRKCQ expression and asthma risk (posterior probability = 0.998), which was T cell specific. Phenome-wide analyses on 311 complex traits and endpoints allowed us to explore shared genetic architecture and prioritize key drivers of disease risk, such as CASP10, which provided evidence of an effect on seven cancer-related outcomes. Our atlas of results can be used to characterize known and novel loci in immune-associated disease and cancer susceptibility, both in terms of elucidating cell-type-dependent effects as well as dissecting shared disease pathways and pervasive pleiotropy. As an exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted via our interactive web platform.

Workshop proceedings: GWAS summary statistics standards and sharing

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.

MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

Motivation

Genome‐wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies.

Results

To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object.

Availability and implementation

MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats.

Supplementary information

Supplementary data are available at Bioinformatics online.

A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

Beyond association: successes and challenges in linking non-coding genetic variation to functional consequences that modulate Alzheimer's disease risk

Alzheimer's disease (AD) is the most common type of dementia, affecting millions of people worldwide; however, no disease-modifying treatments are currently available. Genome-wide association studies (GWASs) have identified more than 40 loci associated with AD risk. However, most of the disease-associated variants reside in non-coding regions of the genome, making it difficult to elucidate how they affect disease susceptibility. Nonetheless, identification of the regulatory elements, genes, pathways and cell type/tissue(s) impacted by these variants to modulate AD risk is critical to our understanding of disease pathogenesis and ability to develop effective therapeutics. In this review, we provide an overview of the methods and approaches used in the field to identify the functional effects of AD risk variants in the causal path to disease risk modification as well as describe the most recent findings. We first discuss efforts in cell type/tissue prioritization followed by recent progress in candidate causal variant and gene nomination. We discuss statistical methods for fine-mapping as well as approaches that integrate multiple levels of evidence, such as epigenomic and transcriptomic data, to identify causal variants and risk mechanisms of AD-associated loci. Additionally, we discuss experimental approaches and data resources that will be needed to validate and further elucidate the effects of these variants and genes on biological pathways, cellular phenotypes and disease risk. Finally, we discuss future steps that need to be taken to ensure that AD GWAS functional mapping efforts lead to novel findings and bring us closer to finding effective treatments for this devastating disease.

Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome

OBJECTIVE

The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the "AD phenome": AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42 ), tau, and ptau181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI).

METHODS

Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome.

RESULTS

PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness.

INTERPRETATION

Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54-65.