Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

AbstractGenome-wide association studies (GWAS) identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs (eQTLs), indicating their potential involvement in the regulation of gene expression.Here, we propose an advanced transcriptome-wide summary statistics-based Mendelian Randomization approach (called TWMR) that uses multiple SNPs jointly as instruments and multiple gene expression traits as exposures, simultaneously.When applied to 43 human phenotypes it uncovered 2,369 genes whose blood expression is putatively associated with at least one phenotype resulting in 3,913 gene-trait associations; of note, 36% of them had no genome-wide significant SNP nearby in previous GWAS analysis. Using independent association summary statistics (UKBiobank), we confirmed that the majority of these loci were missed by conventional GWAS due to power issues. Noteworthy among these novel links is educational attainment-associated BSCL2, known to carry mutations leading to a mendelian form of encephalopathy. We similarly unraveled novel pleiotropic causal effects suggestive of mechanistic connections, e.g. the shared genetic effects of GSDMB in rheumatoid arthritis, ulcerative colitis and Crohn’s disease.Our advanced Mendelian Randomization unlocks hidden value from published GWAS through higher power in detecting associations. It better accounts for pleiotropy and unravels new biological mechanisms underlying complex and clinical traits.

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

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

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/.

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Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

10.1101/284976 ◽

2018 ◽

Cited By ~ 6

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Confounding Bias ◽

Conserved Regions ◽

Genome Wide ◽

Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

10.1101/2020.05.06.077982 ◽

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.

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Trans effects on gene expression can drive omnigenic inheritance

10.1101/425108 ◽

2018 ◽

Cited By ~ 7

Author(s):

Xuanyao Liu ◽

Yang I Li ◽

Jonathan K Pritchard

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Formal Model ◽

Association Studies ◽

Direct Consequence ◽

Large Contribution ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genetic Contributions ◽

Core Genes

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conﬂict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated – as seems likely – then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

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Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

10.1101/033787 ◽

2015 ◽

Author(s):

Guo-Bo Chen ◽

Sang Hong Lee ◽

Matthew R Robinson ◽

Maciej Trzaskowski ◽

Zhi-Xiang Zhu ◽

...

Keyword(s):

Complex Traits ◽

Statistical Power ◽

Association Studies ◽

False Negative ◽

Genome Wide Association ◽

Effect Sizes ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Unknown Sample ◽

Genome Wide

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

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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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An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics

Bioinformatics ◽

10.1093/bioinformatics/btaa985 ◽

2020 ◽

Author(s):

Xiaofeng Zhu ◽

Xiaoyin Li ◽

Rong Xu ◽

Tao Wang

Keyword(s):

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Real Data ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Causal Relationships ◽

Multiple Traits

Abstract Motivation The overall association evidence of a genetic variant with multiple traits can be evaluated by cross-phenotype association analysis using summary statistics from genome-wide association studies. Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits. Results Here, we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits. Availability and implementation The software IMRP is available at https://github.com/XiaofengZhuCase/IMRP. The simulation codes can be downloaded at http://hal.case.edu/∼xxz10/zhu-web/ under the link: MR Simulations software. Supplementary information Supplementary data are available at Bioinformatics online.

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Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2023184118 ◽

2021 ◽

Vol 118 (25) ◽

pp. e2023184118

Author(s):

Yuchang Wu ◽

Xiaoyuan Zhong ◽

Yunong Lin ◽

Zijie Zhao ◽

Jiawen Chen ◽

...

Keyword(s):

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Genetic Effects ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Phenotypic Data ◽

Individual Level ◽

Indirect Genetic Effects ◽

Genome Wide

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

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Transcriptome-wide association supplements genome-wide association in Zea mays

10.1101/363242 ◽

2018 ◽

Cited By ~ 4

Author(s):

Karl A. G. Kremling ◽

Christine H. Diepenbrock ◽

Michael A. Gore ◽

Edward S. Buckler ◽

Nonoy B. Bandillo

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

New Technologies ◽

Seed Quality ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Biological Organization ◽

Genome Wide

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.

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On the Use of Z-Scores for Fine-Mapping with Related Individuals

10.1101/2021.10.10.463846 ◽

2021 ◽

Author(s):

Jicai Jiang

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Statistical Framework ◽

Genome Wide ◽

Z Scores ◽

Related Individuals ◽

Mapping Complex Traits

Using summary statistics from genome-wide association studies (GWAS) has been widely used for fine-mapping complex traits in humans. The statistical framework was largely developed for unrelated samples. Though it is possible to apply the framework to fine-mapping with related individuals, extensive modifications are needed. Unfortunately, this has often been ignored in summary-statistics-based fine-mapping with related individuals. In this paper, we show in theory and simulation what modifications are necessary to extend the use of summary statistics to related individuals. The analysis also demonstrates that though existing summary-statistics-based fine-mapping methods can be adapted for related individuals, they appear to have no computational advantage over individual-data-based methods.

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