scholarly journals Mendelian Randomization Analysis Using Multiple Biomarkers of an Underlying Common Exposure

2021 ◽  
Author(s):  
Jin Jin ◽  
Guanghao Qi ◽  
Zhi Yu ◽  
Nilanjan Chatterjee
Keyword(s):  
Structural Equation ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Error Rates ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Multiple Biomarkers

AbstractMendelian Randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers, or other types of traits, that are co-regulated by the exposure. We propose method MRLE, which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of summary association statistics, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies showed that MRLE has well-controlled type I error rates and increased power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α and MCP-1) provided evidence for potential causal effects of inflammation on increased risk of coronary artery disease, colorectal cancer and rheumatoid arthritis, while standard MR analysis for individual biomarkers often failed to detect consistent evidence for such effects.

scholarly journals Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

2019 ◽  
Author(s):  
Chong Wu
Keyword(s):  
Type I Error ◽  
Association Studies ◽  
Error Rates ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Type I Error Rates ◽  
Trait Association ◽  
Genome Wide ◽  
Inflation Factor

AbstractMany genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits and may yield inflated Type I error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type I error rates were well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

scholarly journals Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

2019 ◽  
Author(s):  
Zhongshang Yuan ◽  
Huanhuan Zhu ◽  
Ping Zeng ◽  
Sheng Yang ◽  
Shiquan Sun ◽  
...  
Keyword(s):  
Complex Traits ◽  
Molecular Mechanisms ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Pleiotropic Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Wide Spread ◽  
Disease Etiology

AbstractIntegrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

2021 ◽  
Author(s):  
Qing Cheng ◽  
Tingting Qiu ◽  
Xiaoran Chai ◽  
Baoluo Sun ◽  
Yingcun Xia ◽  
...  
Keyword(s):  
Complex Traits ◽  
Error Control ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Bivariate Normal Distribution ◽  
Supplementary Information ◽  
Type I ◽  
Genome Wide Association Studies ◽  
The Impact

Abstract Motivation Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. Results To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure–outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. Availability and implementation The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 736
Author(s):  
Xiaotian Dai ◽  
Guifang Fu ◽  
Shaofei Zhao ◽  
Yifei Zeng
Keyword(s):  
Type I Error ◽  
Association Studies ◽  
Case Control ◽  
Error Rates ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Learning Approaches ◽  
Genome Wide ◽  
Control Disease

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

scholarly journals Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

2020 ◽  
Author(s):  
Wenjian Bi ◽  
Wei Zhou ◽  
Rounak Dey ◽  
Bhramar Mukherjee ◽  
Joshua N Sampson ◽  
...  
Keyword(s):  
Mixed Model ◽  
Type I Error ◽  
Association Studies ◽  
Error Rates ◽  
Genome Wide Association ◽  
Alternative Methods ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Type I Error Rates ◽  
Genome Wide

AbstractIn genome-wide association studies (GWAS), ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, due to the lack of analysis tools, methods designed for binary and quantitative traits have often been used inappropriately to analyze categorical phenotypes, which produces inflated type I error rates or is less powerful. To accurately model the dependence of an ordinal categorical phenotype on covariates, we propose an efficient mixed model association test, Proportional Odds Logistic Mixed Model (POLMM). POLMM is demonstrated to be computationally efficient to analyze large datasets with hundreds of thousands of genetic related samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than other alternative methods. We applied POLMM to 258 ordinal categorical phenotypes on array-genotypes and imputed samples from 408,961 individuals in UK Biobank. In total, we identified 5,885 genome-wide significant variants, of which 424 variants (7.2%) are rare variants with MAF < 0.01.

scholarly journals Causal effect of adiposity on the risk of 19 gastrointestinal diseases: a Mendelian randomization study

2021 ◽  
Author(s):  
Min Seo Kim ◽  
Minku Song ◽  
Soyeon Kim ◽  
Beomsu Kim ◽  
Wonseok Kang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Liver Disease ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Alcoholic Fatty Liver ◽  
Increased Risk

Objectives: We applied Mendelian randomization (MR) to investigate the causal associations of body mass index (BMI) and waist circumference (WC) with 19 gastrointestinal (GI) disorders. Design: MR study. Setting: The UK Biobank, Genetic Investigation of Anthropometric Traits (GIANT) Consortium, FinnGen consortium, and genome-wide association studies. Participants: Overall, >400,000 UK Biobank participants, >170,000 participants of Finnish descent, and numerous consortia participants with predominantly European ancestry. Interventions: Single-nucleotide polymorphisms associated with BMI and WC were used as instrumental variables to estimate the causal associations with the GI conditions. Main outcome measures: Risk of developing 19 GI diseases Results: After correction for multiple testing (Bonferroni-corrected threshold of P<0.05/19) and testing for consistencies using several MR methods with varying assumptions (inverse variance weighted, weighted median, MR-Egger, and MR-PRESSO), genetically predicted BMI was associated with increased risks of non-alcoholic fatty liver disease (NAFLD), cholecystitis, cholelithiasis, and primary biliary cholangitis. The odds ratio (OR) per one standard deviation (SD) increased in genetically predicted BMI (4.77 kg/m2) from 1.22 (95% confidence interval [CI] 1.12 to 1.34; P<0.0001) for NAFLD to 1.65 (95% CI 1.31 to 2.06; P<0.0001) for cholecystitis. Genetically predicted WC was associated with increased risks of NAFLD, alcoholic liver disease (ALD), cholecystitis, cholelithiasis, colon cancer, and gastric cancer. ALD was associated with WC even after adjustment for alcohol consumption in multivariable MR analysis. The OR per 1 SD increased in genetically predicted WC (12.52 cm) from 1.41 (95% CI 1.17 to 1.70; P=0.0015) for gastric cancer to 1.74 (95% CI 1.21 to 1.78; P<0.0001) for cholelithiasis. Conclusions: Higher BMI and WC are causally associated with an increased risk of GI abnormalities, particularly of hepatobiliary organs (liver, biliary tract, and gallbladder) that are functionally related to fat metabolism. Abdominal obesity measured by WC might be more influential and relevant with a diverse span of GI diseases than BMI, highlighting a possible pathophysiological role of visceral abdominal fats in the development of GI disorders and cancers.

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

scholarly journals Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

2021 ◽  
Author(s):  
Shuai Yuan ◽  
Maria Bruzelius ◽  
Susanna C. Larsson
Keyword(s):  
Renal Function ◽  
Venous Thromboembolism ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genome Wide ◽  
Increased Risk ◽  
Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

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