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Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics.

ABSTRACT: Mendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.

SUBMITTER: Darrous L

PROVIDER: S-EPMC8671515 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics.

Darrous Liza L Mounier Ninon N Kutalik Zoltán Z

Nature communications 20211214 1

Mendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to ...[more]

PMID: 34907193

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Project description:Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs. Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer's Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson's disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways. Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as "urea cycle" (P = 0.036), "arginine biosynthesis" (P = 0.004) on AD and "phenylalanine, tyrosine and tryptophan biosynthesis" (P = 0.046) on ALS. Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

Project description:Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using only genome-wide association study (GWAS) summary results. Here, we present an integrated R toolkit, PhenoSpD, to use LD score regression to estimate phenotypic correlations using GWAS summary statistics and to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated phenotypic correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic correlations rather than phenotypic correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to an estimated 5,618 pair-wise phenotypic correlations among 107 metabolites using GWAS summary statistics from Kettunen's publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites. PhenoSpD extends the use of summary-level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.

Project description:BackgroundAssociations between type 2 diabetes (T2D) and amyotrophic lateral sclerosis (ALS) were discovered in observational studies in both European and East Asian populations. However, whether such associations are causal remains largely unknown.MethodsWe employed a two-sample Mendelian randomization approach to evaluate the causal relationship of T2D with the risk of ALS in both European and East Asian populations. Our analysis was implemented using summary statistics obtained from large-scale genome-wide association studies with ~660,000 individuals for T2D and ~81,000 individuals for ALS in the European population, and ~191,000 individuals for T2D and ~4100 individuals for ALS in the East Asian population. The causal relationship between T2D and ALS in both populations was estimated using the inverse-variance-weighted methods and was further validated through extensive complementary and sensitivity analyses.ResultsUsing multiple instruments that were strongly associated with T2D, a negative association between T2D and ALS was identified in the European population with the odds ratio (OR) estimated to be 0.93 (95% CI 0.88-0.99, p = 0.023), while a positive association between T2D and ALS was observed in the East Asian population with OR = 1.28 (95% CI 0.99-1.62, p = 0.058). These results were robust against instrument selection, various modeling misspecifications, and estimation biases, with the Egger regression and MR-PRESSO ruling out the possibility of horizontal pleiotropic effects of instruments. However, no causal association was found between T2D-related exposures (including glycemic traits) and ALS in the European population.ConclusionOur results provide new evidence supporting the causal neuroprotective role of T2D on ALS in the European population and provide empirically suggestive evidence of increasing risk of T2D on ALS in the East Asian population. Our results have an important implication on ALS pathology, paving ways for developing therapeutic strategies across multiple populations.

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Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics.

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Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics.

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