scholarly journals Widespread pleiotropy confounds causal relationships between complex traits and diseases inferred from Mendelian randomization

2017 ◽  
Author(s):  
Marie Verbanck ◽  
Chia-Yen Chen ◽  
Benjamin Neale ◽  
Ron Do
Keyword(s):  
Complex Traits ◽  
Instrumental Variable ◽  
Complex Disease ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Relationships ◽  
Genome Wide ◽  
Association Data ◽  
The Impact ◽  
Gwa Studies

AbstractA fundamental assumption in inferring causality of an exposure on complex disease using Mendelian randomization (MR) is that the genetic variant used as the instrumental variable cannot have pleiotropic effects. Violation of this ‘no pleiotropy’ assumption can cause severe bias. Emerging evidence have supported a role for pleiotropy amongst disease-associated loci identified from GWA studies. However, the impact and extent of pleiotropy on MR is poorly understood. Here, we introduce a method called the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect and correct for pleiotropy in multi-instrument summary-level MR testing. We show using simulations that existing approaches are less sensitive to the detection of pleiotropy when it occurs in a subset of instrumental variables, as compared to MR-PRESSO. Next, we show that pleiotropy is widespread in MR, occurring in 41% amongst significant causal relationships (out of 4,250 MR tests total) from pairwise comparisons of 82 complex traits and diseases from summary level genome-wide association data. We demonstrate that pleiotropy causes distortion between-168% and 189% of the causal estimate in MR. Furthermore, pleiotropy induces false positive causal relationships-defined as those causal estimates that were no longer statistically significant in the pleiotropy corrected MR test but were previously significant in the naive MR test-in up to 10% of the MR tests using a P < 0.05 cutoff that is commonly used in MR studies. Finally, we show that MR-PRESSO can correct for distortion in the causal estimate in most cases. Our results demonstrate that pleiotropy is widespread and pervasive, and must be properly corrected for in order to maintain the validity of MR.

scholarly journals MR-APSS: a unified approach to Mendelian Randomization accounting for pleiotropy and sample structure using genome-wide summary statistics

2021 ◽  
Author(s):  
Xianghong Hu ◽  
Jia Zhao ◽  
Zhixiang Lin ◽  
Yang Wang ◽  
Heng Peng ◽  
...  
Keyword(s):  
Complex Traits ◽  
Mendelian Randomization ◽  
High Reliability ◽  
Type I ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Genome Wide ◽  
Wide Range ◽  
Sample Structure ◽  
Genetic Instruments

AbstractMendelian Randomization (MR) has proved to be a powerful tool for inferring causal relationships among a wide range of traits using GWAS summary statistics. Great efforts have been made to relax MR assumptions to account for confounding due to pleiotropy. Here we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, to account for pleiotropy and sample structure simultaneously by leveraging genome-wide information. By further correcting bias in selecting genetic instruments, MR-APSS allows to include more genetic instruments with moderate effects to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls, and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability, in particular for highly polygenic traits.

scholarly journals Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics

2019 ◽  
Author(s):  
Jean Morrison ◽  
Nicholas Knoblauch ◽  
Joseph Marcus ◽  
Matthew Stephens ◽  
Xin He
Keyword(s):  
Genetic Variant ◽  
Mendelian Randomization ◽  
Association Studies ◽  
False Positives ◽  
Pleiotropic Effects ◽  
Alternative Methods ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Genome Wide ◽  
Summary Effect

AbstractMendelian randomization (MR) is a valuable tool for detecting evidence of causal relationships using genetic variant associations. Opportunities to apply MR are growing rapidly with the number of genome-wide association studies (GWAS) with publicly available results. However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Many methods have been proposed loosening these assumptions. However, it has remained challenging to account for correlated pleiotropy, which arises when variants affect both traits through a heritable shared factor. We propose a new MR method, Causal Analysis Using Summary Effect Estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate in simulations that CAUSE is more robust to correlated pleiotropy than other methods. Applied to traits studied in recent GWAS, we find that CAUSE detects causal relationships with strong literature support and avoids identifying most unlikely relationships. Our results suggest that many pairs of traits identified as causal using alternative methods may be false positives due to horizontal pleiotropy.

scholarly journals Smoking, DNA methylation and lung function: a Mendelian randomization analysis to investigate causal relationships

2019 ◽  
Author(s):  
Emily Jamieson ◽  
Roxanna Korologou-Linden ◽  
Robyn E. Wootton ◽  
Anna L. Guyatt ◽  
Thomas Battram ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Lung Function ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Forced Expiratory Volume ◽  
Multiple Trait ◽  
Genome Wide ◽  
Causal Variants ◽  
Association Data ◽  
Randomization Analysis

AbstractWhether smoking-associated DNA methylation has a causal effect on lung function has not been thoroughly evaluated. We investigated the causal effects of 474 smoking-associated CpGs on forced expiratory volume in one second (FEV1) in two-sample Mendelian randomization (MR) using methylation quantitative trait loci and genome-wide association data for FEV1. We found evidence of a possible causal effect for DNA methylation on FEV1 at 18 CpGs (p<1.2×10−4). Replication analysis supported a causal effect at three CpGs (cg21201401 (ZGPAT), cg19758448 (PGAP3) and cg12616487 (AHNAK) (p<0.0028). DNA methylation did not clearly mediate the effect of smoking on FEV1, although DNA methylation at some sites may influence lung function via effects on smoking. Using multiple-trait colocalization, we found evidence of shared causal variants between lung function, gene expression and DNA methylation. Findings highlight potential therapeutic targets for improving lung function and possibly smoking cessation, although large, tissue-specific datasets are required to confirm these results.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals A novel Mendelian randomization method identifies causal relationships between gene expression and low-density lipoprotein cholesterol levels

2019 ◽  
Author(s):  
Adriaan van der Graaf ◽  
Annique Claringbould ◽  
Antoine Rimbert ◽  
Harm-Jan Westra ◽  
Yang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Causal Relationships ◽  
Low Density Lipoprotein Cholesterol ◽  
Individual Level

AbstractRobust inference of causal relationships between gene expression and complex traits using Mendelian Randomization (MR) approaches is confounded by pleiotropy and linkage disequilibrium (LD) between gene expression quantitative loci (eQTLs). Here we propose a new MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data. In simulations, MR-link shows false positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other MR methods we tested, even when only one eQTL variant is present. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals and eQTLs summary statistics from whole blood and liver identified 19 genes causally linked to LDL-C. These include the previously functionally validatedSORT1gene, and thePVRL2gene, located in theAPOElocus, for which a causal role in liver was yet unknown. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

scholarly journals Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

scholarly journals A chromosome level genome of Astyanax mexicanus surface fish for comparing population-specific genetic differences contributing to trait evolution.

2020 ◽  
Author(s):  
Wesley Warren ◽  
Tyler Boggs ◽  
Richard Borowsky ◽  
Brian Carlson ◽  
Estephany Ferrufino ◽  
...  
Keyword(s):  
Complex Traits ◽  
Fish Genome ◽  
Trait Evolution ◽  
Protein Coding ◽  
Astyanax Mexicanus ◽  
Eye Size ◽  
Genome Wide ◽  
Trait Locus ◽  
The Impact ◽  
Chromosome Level

Abstract Identifying the genetic factors that underlie complex traits is central to understanding the mechanistic underpinnings of evolution. In nature, adaptation to severe environmental change, such as encountered following colonization of caves, has dramatically altered genomes of species over varied time spans. Genomic sequencing approaches have identified mutations associated with troglomorphic trait evolution, but the functional impacts of these mutations remain poorly understood. The Mexican Tetra, Astyanax mexicanus, is abundant in the surface waters of northeastern Mexico, and also inhabits at least 30 different caves in the region. Cave-dwelling A. mexicanus morphs are well adapted to subterranean life and many populations appear to have evolved troglomorphic traits independently, while the surface-dwelling populations can be used as a proxy for the ancestral form. Here we present a high-resolution, chromosome-level surface fish genome, enabling the first genome-wide comparison between surface fish and cavefish populations. Using this resource, we performed quantitative trait locus (QTL) mapping analyses for pigmentation and eye size and found new candidate genes for eye loss such as dusp26. We used CRISPR gene editing in A. mexicanus to confirm the essential role of a gene within an eye size QTL, rx3, in eye formation. We also generated the first genome-wide evaluation of deletion variability that includes an analysis of the impact on protein-coding genes across cavefish populations to gain insight into this potential source of cave adaptation. The new surface fish genome reference now provides a more complete resource for comparative, functional, developmental and genetic studies of drastic trait differences within a species.

scholarly journals Causal relationships between genetically determined metabolites and human intelligence: A Mendelian randomization study

2020 ◽  
Author(s):  
Jian Yang ◽  
Binbin Zhao ◽  
Li Qian ◽  
Fengjie Gao ◽  
Yanjuan Fan ◽  
...  
Keyword(s):  
Complex Traits ◽  
Mendelian Randomization ◽  
Sensitivity Analyses ◽  
Human Intelligence ◽  
Causal Relationships ◽  
Biological Mechanisms ◽  
Inverse Variance ◽  
Weighted Median ◽  
Genetic And Environmental Factors ◽  
Genetically Determined

Abstract Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in human complex traits has been recently proposed. However, this strategy has not been applied to human intelligence. Here we implemented a two-sample Mendelian randomization (MR) analysis using GDMs to assess the causal relationships between genetically determined metabolites and human intelligence. The standard inverse-variance weighted (IVW) method was used for the primary MR analysis and three additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses. Using 25 genetic variants as instrumental variables (IVs), our study found that 5-oxoproline was associated with better performance in human intelligence tests (P IVW = 9 · 25×10 -5 ). The causal relationship was robust when sensitivity analyses were applied (P MR-Egger = 0 · 0001, P Weighted median = 6 · 29×10 -6 , P MR-PRESSO = 0 · 0007), and no evidence of horizontal pleiotropy was observed. Similarly, also dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide showed robust association with intelligence. Our study provides novel insight by integrating genomics and metabolomics to estimate causal effects of genetically determined metabolites on human intelligence, which help to understanding of the biological mechanisms related to human intelligence.

scholarly journals Bayesian weighted Mendelian randomization for causal inference based on summary statistics

2019 ◽  
Author(s):  
Jia Zhao ◽  
Jingsi Ming ◽  
Xianghong Hu ◽  
Gang Chen ◽  
Jin Liu ◽  
...  
Keyword(s):  
Causal Inference ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Genome Wide ◽  
Complex Human Traits

Abstract Motivation The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. Results We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. Availability and implementation The BWMR software is available at https://github.com/jiazhao97/BWMR. Supplementary information Supplementary data are available at Bioinformatics online.

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