Showing papers by "Jie Zheng published in 2018"

The MR-Base platform supports systematic causal inference across the human phenome

Abstract: Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

Abstract: Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

Education, intelligence and Alzheimer's disease: Evidence from a multivariable two-sample Mendelian randomization study

Abstract: Background: Higher levels of educational attainment are associated with lower risk of dementia. However, the mechanisms underlying the association (for example, the role of education-related traits such as intelligence) are unknown. Identifying these mechanisms using observational methods is difficult due to bias from measurement error, confounding and reverse causation. Aims: To estimate the bidirectional causal effects of education on intelligence, and the total and independent effects of both education and intelligence on risk of Alzheimer9s disease (AD). Methods: Using univariable and multivariable two-sample Mendelian randomization (MR) we estimated (i) the overall effect of educational attainment on intelligence and vice versa (ii) the overall effects of both educational attainment and intelligence on AD risk and (iii) the effects of educational attainment and intelligence on AD risk that are independent of the other trait. Results: There was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions after filtering SNPs to check they are instrumenting the correct exposure. Similar overall effects were observed for both educational attainment and intelligence on AD risk in the univariable MR analysis; with each SD increase in years of schooling and intelligence, the odds of AD were, on average, 37% (95% CI: 23% to 49%) and 35% (95% CI: 25% to 43%) lower, respectively . There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account, but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis. Conclusions: There is robust evidence for an independent, causal effect of intelligence in lowering AD risk, potentially supporting a role for cognitive training interventions to improve aspects of intelligence. However, given the causal effect of educational attainment on intelligence observed in this analysis, there may also be support for policies aimed at increasing length of schooling to lower incidence of AD.

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors.

Abstract: Gliomas are a group of primary brain tumors, the most common and aggressive subtype of which is glioblastoma. Glioblastoma has a median survival of just 15 months after diagnosis. Only previous exposure to ionizing radiation and particular inherited genetic syndromes are accepted risk factors for glioma; the vast majority of cases are thought to occur spontaneously. Previous observational studies have described associations between several risk factors and glioma, but studies are often conflicting and whether these associations reflect true casual relationships is unclear because observational studies may be susceptible to confounding, measurement error and reverse causation. Mendelian randomization (MR) is a form of instrumental variable analysis that can be used to provide supporting evidence for causal relationships between exposures (e.g., risk factors) and outcomes (e.g., disease onset). MR utilizes genetic variants, such as single nucleotide polymorphisms (SNPs), that are robustly associated with an exposure to determine whether there is a causal effect of the exposure on the outcome. MR is less susceptible to confounding, reverse causation and measurement errors as it is based on the random inheritance during conception of genetic variants that can be relatively accurately measured. In previous studies, MR has implicated a genetically predicted increase in telomere length with an increased risk of glioma, and found little evidence that obesity related factors, vitamin D or atopy are causal in glioma risk. In this review, we describe MR and its potential use to discover and validate novel risk factors, mechanistic factors, and therapeutic targets in glioma.

Evaluating causal associations between previously reported risk factors and epithelial ovarian cancer: a Mendelian randomization analysis

Abstract: Background Various modifiable risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization uses genetic variants as proxies for modifiable risk factors to strengthen causal inference in observational studies. We used Mendelian randomization to evaluate the causal role of 13 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) in overall and histotype-specific epithelial ovarian cancer in up to 25,509 case subjects and 40,941 controls in the Ovarian Cancer Association Consortium. Methods and Findings Genetic instruments to proxy 13 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) robustly (P In Mendelian randomization analyses, there was strong or suggestive evidence that 9 of 13 risk factors had a causal effect on overall or histotype-specific epithelial ovarian cancer. There was strong evidence that genetic liability to endometriosis increased risk of epithelial ovarian cancer (OR per log odds higher liability:1.27, 95% CI: 1.16-1.40; P=6.94×10−7) and suggestive evidence that lifetime smoking exposure increased risk of epithelial ovarian cancer (OR per unit increase in smoking score:1.36, 95% CI: 1.04-1.78; P=0.02). In histotype-stratified analyses, the strongest associations found were between: height and clear cell carcinoma (OR per SD increase:1.36, 95% CI: 1.15-1.61; P=0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset:1.09, 95% CI: 1.02-1.16; P=0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per log odds higher liability:0.74, 95% CI:0.62-0.90; P=0.002). There was little evidence for an effect of genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone-binding globulin on ovarian cancer or its subtypes. The primary limitations of this analysis include: modest statistical power for analyses of risk factors in relation to some less common ovarian cancer histotypes (low grade serous, mucinous, and clear cell carcinomas), the inability to directly examine the causal effects of some ovarian cancer risk factors that did not have robust genetic variants available to serve as proxies (e.g., oral contraceptives, hormone replacement therapy), and the assumption of linear relationships between risk factors and ovarian cancer risk. Conclusions Our comprehensive examination of possible etiological drivers of ovarian carcinogenesis using germline genetic variants to proxy risk factors supports a causal role for few of these factors in epithelial ovarian cancer and suggests distinct etiologies across histotypes. The identification of novel modifiable risk factors remains an important priority for the control of epithelial ovarian cancer.

MR-TRYX: Exploiting horizontal pleiotropy to infer novel causal pathways

Abstract: Background: In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy, influencing the outcome through a pathway excluding the hypothesised exposure, are typically treated as a nuisance. However, they could provide valuable information for identifying novel pathways to the traits under investigation. Methods: Following the advice of William Bateson to "TReasure Your eXceptions", we developed the MR-TRYX framework. Here, we begin by detecting outliers in a single exposure-outcome MR analysis. Outliers are hypothesised to arise due to horizontal pleiotropy, so we search through the MR-Base database of GWAS summary statistics to systematically identify other ("candidate") traits that associate with the outliers. We developed a LASSO-based multivariable MR approach to model the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Results: Through simulations we showed that commonly used outlier removal methods can increase type 1 error rates, but adjustment for detected pleiotropic pathways can improve power without the increase in type 1 error rates. We illustrate the use of MR-TRYX through investigation of several causal relationships: i) systolic blood pressure on coronary heart disease (CHD); ii) urate on CHD; iii) sleep duration on schizophrenia; and iv) education level on body mass index. Many pleiotropic pathways were uncovered with already established causal effects, validating the approach. Novel putative causal pathways, such as pain related phenotypes influencing CHD, were also identified. Adjustment for these pleiotropic pathways substantially reduced the heterogeneity across the analyses. Conclusion: Incorporating GWAS on thousands of traits in MR-Base to model horizontal pleiotropy in MR analysis can improve power through reducing heterogeneity, whilst enabling the identification of novel causal relationships.

The effect of plasma lipids and lipid lowering interventions on bone mineral density: a Mendelian randomization study

Abstract: Statin treatment increases bone mineral density (BMD) and reduces fracture risk, but the underlying mechanism is unclear. We used Mendelian randomization (MR) to assess whether this relation is explained by a specific effect in response to statin use, or by a general effect of lipid-lowering. We utilized 400 single nucleotide polymorphisms (SNPs) robustly associated with plasma lipid levels and results from a heel BMD GWAS (derived from quantitative ultrasound) in 426,824 individuals from the UK Biobank. We performed univariate and multivariable MR analyses of low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and triglyceride levels on BMD. To test whether the effect of statins on BMD was mediated by lowering lipid levels, MR was repeated with and without SNPs in the HMGCR region, the gene targeted by statins. Univariate MR analyses provided evidence for a causal effect of LDL-C on BMD (β = -0.060; -0.084 to -0.036; P = 4x10-6; standard deviation change in BMD per standard deviation change in LDL-C, with 95% CI), but not HDL or triglycerides. Multivariable MR analysis suggested that the effect of LDL-C on BMD was independent of HDL-C and triglycerides, and sensitivity analyses involving MR Egger and weighted median MR approaches suggested that the LDL-C results were robust to pleiotropy. MR analyses of LDL-C restricted to SNPs in the HMGCR region showed similar effects on BMD (β = -0.083; -0.132 to -0.034; P = 0.001) to those excluding these SNPs (β= -0.063; -0.090 to -0.036; P = 8x10-6). Bidirectional MR analyses provided some evidence for a causal effect of BMD on plasma LDL-C levels. Our results suggest that effects of statins on BMD are at least partly due to their LDL-C lowering effect. Further studies are required to examine the potential role of modifying plasma lipid levels in treating osteoporosis.

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank

Abstract: Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

Genome-wide mapping identifies beta-1,4-N-acetyl-galactosaminyl-transferase as a novel determinant of sclerostin levels and bone mineral density

Abstract: In bone, sclerostin is mainly osteocyte-derived and plays an important local role in adaptive responses to mechanical loading. Sclerostin is also present at detectable concentrations within the circulation. Our genome wide association study (GWAS) meta-analysis of 10,584 European-descent individuals identified two novel serum sclerostin loci, B4GALNT3 (standard deviation (SD) change in sclerostin per A allele β=0.20, P=4.6x10-49), and GALNT1 (β=0.11 per G allele, P=4.4x10-11), of which the former is a known locus for BMD estimated by heel ultrasound (eBMD). Common variants across the genome explained 16% of the phenotypic variation of serum sclerostin. Mendelian randomization revealed an inverse causal relationship between serum sclerostin and femoral neck BMD and eBMD, and a positive relationship with fracture risk. Colocalization analysis demonstrated common genetic signals within the B4GALNT3 locus for higher sclerostin, lower BMD, and greater B4GALNT3 expression in arterial tissue (Probability>99%). Renal and cortical bone tissue, and osteoblast cultures, were found to express high levels of B4GALNT3, an N-acetylgalactosaminyltransferase which adds a terminal LacdiNAc disaccharide to target glycocoproteins. Together, these findings raise the possibility that sclerostin is a substrate for B4GALNT3, such that its modification leads to higher levels, possibly through greater stability. GALNT1, an enzyme causing mucin-type O-linked glycosylation, may act in a similar capacity. We conclude that genetic variation in glycosylation enzymes represents a novel determinant of BMD and fracture risk, acting via alterations in levels of circulating sclerostin.

Hypothesis-free analysis of deep vein thrombosis aetiology: a Mendelian randomization study

Abstract: Background: Deep vein thrombosis (DVT) is the formation of a thrombus/clot in the deep veins, when part of this clot breaks off it can travel to the lungs, resulting in pulmonary embolism. These two conditions together are known as venous thromboembolism (VTE), a leading cause of death and disability worldwide. Despite the prevalence of VTE, we do not fully understand what causes it and it is often overlooked as a major public health problem. Confirming and identifying risk factors associated with DVT is likely to lead to a reduction in the incidence, morbidity and mortality of VTE especially where these risk factors are modifiable. We can do this, by exploiting the availability of summary genetic data from genome-wide association studies (GWAS) of numerous phenotypes, including DVT, which permits hypothesis-free causal inference. Objectives: To identify novel risk factors for DVT and to assess the causality of factors previously shown to be associated with DVT. Methods: Two-sample Mendelian randomization (MR) was performed using summarised genetic data. Inverse variance weighted (IVW) estimates were calculated and validated by additional methods more robust to horizontal pleiotropy (MR Egger, simple mode, weighted mode, and weighted median). Bidirectional and heterogeneity sensitivity analyses were performed to further evaluate our findings. Results: Forty-seven exposures passed an exposure-exposure correlation-adjusted Bonferroni P-value threshold (5.43E-05). These included previously hypothesised risk factors for DVT (e.g. body mass index, varicose veins, height, hyperthyroidism) and novel associations (e.g. prospective memory, basal metabolic rate). Conclusion: Our analyses confirmed causal associations of risk factors previously associated with DVT and highlighted several novel risk factors for the disease. Our study demonstrates the utility of using a hypothesis free Mendelian randomization approach for the identification of novel disease risk factors.