Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous 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.

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bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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Joint Genome-Wide Association Study Identifies Twenty-One Novel Loci for Age at Menarche and Highlights Its Causal Association with Other Complex Diseases

10.21203/rs.3.rs-955340/v1 ◽

2021 ◽

Author(s):

Gui-Juan Feng ◽

Qian Xu ◽

Jing-Jing Ni ◽

Shan-Shan Yang ◽

Bai-Xue Han ◽

...

Keyword(s):

Association Study ◽

Complex Traits ◽

Causal Effect ◽

Association Studies ◽

Genetic Correlations ◽

Genome Wide Association ◽

Age At Menarche ◽

Luteinizing Hormone Level ◽

Genome Wide Association Studies ◽

Genome Wide

Abstract Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 participants from 3 genome-wide association studies of European and East Asian ancestries. Twenty-one novel genomic loci were identified at the genome-wide significance level. Besides, we observed significant genetic correlations between AAM and 67 complex traits, and the highest genetic correlation was observed between AAM and body mass index (rg=-0.19, P=6.11×10−31). Latent causal variable analyses demonstrate that there is a genetically causal effect of AAM on high blood pressure (GCP=0.47, P=0.02), forced vital capacity (GCP=0.63, P=0.02), age at first live birth (GCP=0.51, P=0.03), impedance of right arm (GCP=0.41, P<1×10-7) and right leg fat percentage (GCP=-0.10, P=0.02), etc. Enrichment analysis identified 5 enriched tissues and 51 enriched gene sets. Four of the five enriched tissues were related to the nervous system, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems and hypothalamus. The fifth tissue was the retina in the sensory organ. The most significant gene set was the ‘decreased circulating luteinizing hormone level’ (P=2.45×10-6). Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.

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An atlas of genetic associations in UK Biobank

10.1101/176834 ◽

2017 ◽

Cited By ~ 18

Author(s):

Oriol Canela-Xandri ◽

Konrad Rawlik ◽

Albert Tenesa

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Genome Wide ◽

Related Individuals ◽

Sufficient Statistical Power

ABSTRACTGenome-wide association studies have revealed many loci contributing to the variation of complex traits, yet the majority of loci that contribute to the heritability of complex traits remain elusive. Large study populations with sufficient statistical power are required to detect the small effect sizes of the yet unidentified genetic variants. However, the analysis of huge cohorts, like UK Biobank, is complicated by incidental structure present when collecting such large cohorts. For instance, UK Biobank comprises 107,162 third degree or closer related participants. Traditionally, GWAS have removed related individuals because they comprised an insignificant proportion of the overall sample size, however, removing related individuals in UK Biobank would entail a substantial loss of power. Furthermore, modelling such structure using linear mixed models is computationally expensive, which requires a computational infrastructure that may not be accessible to all researchers. Here we present an atlas of genetic associations for 118 non-binary and 599 binary traits of 408,455 related and unrelated UK Biobank participants of White-British descent. Results are compiled in a publicly accessible database that allows querying genome-wide association summary results for 623,944 genotyped and HapMap2 imputed SNPs, as well downloading whole GWAS summary statistics for over 30 million imputed SNPs from the Haplotype Reference Consortium panel. Our atlas of associations (GeneATLAS,http://geneatlas.roslin.ed.ac.uk) will help researchers to query UK Biobank results in an easy way without the need to incur in high computational costs.

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Bayesian weighted Mendelian randomization for causal inference based on summary statistics

Bioinformatics ◽

10.1093/bioinformatics/btz749 ◽

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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Genome-wide association studies and contribution to cardiovascular physiology

Physiological Genomics ◽

10.1152/physiolgenomics.00004.2015 ◽

2015 ◽

Vol 47 (9) ◽

pp. 365-375 ◽

Cited By ~ 7

Author(s):

Patricia B. Munroe ◽

Andrew Tinker

Keyword(s):

Complex Traits ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cardiovascular Disorders ◽

Genetic Associations ◽

Physiological Processes ◽

Genome Wide ◽

Personal Interests

The study of family pedigrees with rare monogenic cardiovascular disorders has revealed new molecular players in physiological processes. Genome-wide association studies of complex traits with a heritable component may afford a similar and potentially intellectually richer opportunity. In this review we focus on the interpretation of genetic associations and the issue of causality in relation to known and potentially new physiology. We mainly discuss cardiometabolic traits as it reflects our personal interests, but the issues pertain broadly in many other disciplines. We also describe some of the resources that are now available that may expedite follow up of genetic association signals into observations on causal mechanisms and pathophysiology.

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Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments

PLoS Genetics ◽

10.1371/journal.pgen.1009575 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1009575

Author(s):

Jingshu Wang ◽

Qingyuan Zhao ◽

Jack Bowden ◽

Gibran Hemani ◽

George Davey Smith ◽

...

Keyword(s):

Risk Factors ◽

Complex Traits ◽

Causal Effect ◽

Association Studies ◽

Complex Trait ◽

Genome Wide Association Studies ◽

Causal Direction ◽

New Information ◽

Comprehensive Framework ◽

Genetic Instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR 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 to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. 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.

Download Full-text