The Experts below are selected from a list of 19449 Experts worldwide ranked by ideXlab platform
George Davey Smith - One of the best experts on this subject based on the ideXlab platform.
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Exploiting horizontal Pleiotropy to search for causal pathways within a Mendelian randomization framework.
Nature Communications, 2020Co-Authors: Yoonsu Cho, George Davey Smith, Philip C Haycock, Eleanor Sanderson, Tom R. Gaunt, Jie Zheng, Andrew P. Morris, Gibran HemaniAbstract:In Mendelian randomization (MR) analysis, variants that exert horizontal Pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we develop MR-TRYX, a framework that exploits horizontal Pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure–outcome MR analysis, hypothesising they are due to horizontal Pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We develop a multi-trait Pleiotropy model of the heterogeneity in the exposure–outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses. In Mendelian randomization (MR) studies, one typically selects SNPs as instrumental variables that do not directly affect the outcome to avoid violation of MR assumptions. Here, Cho et al. present a framework, MR-TRYX, that leverages knowledge of such outliers of horizontal Pleiotropy to identify putative causal relationships between exposure and outcome.
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evaluating the potential role of Pleiotropy in mendelian randomization studies
Human Molecular Genetics, 2018Co-Authors: Gibran Hemani, Jack Bowden, George Davey SmithAbstract:: Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If Pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another ('vertical Pleiotropy'), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical Pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways ('horizontal Pleiotropy'). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal Pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.
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alcohol intake and cardiovascular risk factors a mendelian randomisation study
Scientific Reports, 2015Co-Authors: Soyoun Shin, George Davey Smith, Caroline L Relton, Minjeong ShinAbstract:Mendelian randomisation studies from Asia suggest detrimental influences of alcohol on cardiovascular risk factors, but such associations are observed mainly in men. The absence of associations of genetic variants (e.g. rs671 in ALDH2) with such risk factors in women – who drank little in these populations – provides evidence that the observations are not due to genetic Pleiotropy. Here, we present a Mendelian randomisation study in a South Korean population (3,365 men and 3,787 women) that 1) provides robust evidence that alcohol consumption adversely affects several cardiovascular disease risk factors, including blood pressure, waist to hip ratio, fasting blood glucose and triglyceride levels. Alcohol also increases HDL cholesterol and lowers LDL cholesterol. Our study also 2) replicates sex differences in associations which suggests Pleiotropy does not underlie the associations, 3) provides further evidence that association is not due to Pleiotropy by showing null effects in male non-drinkers, and 4) illustrates a way to measure population-level association where alcohol intake is stratified by sex. In conclusion, population-level instrumental variable estimation (utilizing interaction of rs671 in ALDH2 and sex as an instrument) strengthens causal inference regarding the largely adverse influence of alcohol intake on cardiovascular health in an Asian population.
Minjeong Shin - One of the best experts on this subject based on the ideXlab platform.
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alcohol intake and cardiovascular risk factors a mendelian randomisation study
Scientific Reports, 2015Co-Authors: Soyoun Shin, George Davey Smith, Caroline L Relton, Minjeong ShinAbstract:Mendelian randomisation studies from Asia suggest detrimental influences of alcohol on cardiovascular risk factors, but such associations are observed mainly in men. The absence of associations of genetic variants (e.g. rs671 in ALDH2) with such risk factors in women – who drank little in these populations – provides evidence that the observations are not due to genetic Pleiotropy. Here, we present a Mendelian randomisation study in a South Korean population (3,365 men and 3,787 women) that 1) provides robust evidence that alcohol consumption adversely affects several cardiovascular disease risk factors, including blood pressure, waist to hip ratio, fasting blood glucose and triglyceride levels. Alcohol also increases HDL cholesterol and lowers LDL cholesterol. Our study also 2) replicates sex differences in associations which suggests Pleiotropy does not underlie the associations, 3) provides further evidence that association is not due to Pleiotropy by showing null effects in male non-drinkers, and 4) illustrates a way to measure population-level association where alcohol intake is stratified by sex. In conclusion, population-level instrumental variable estimation (utilizing interaction of rs671 in ALDH2 and sex as an instrument) strengthens causal inference regarding the largely adverse influence of alcohol intake on cardiovascular health in an Asian population.
Caleb A Lareau - One of the best experts on this subject based on the ideXlab platform.
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polygenic risk assessment reveals Pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry
Genes and Immunity, 2017Co-Authors: Caleb A Lareau, Colby F Deweese, Indra Adrianto, Christopher J Lessard, Patrick M Gaffney, Michael C Iannuzzi, Benjamin A Rybicki, Albert M LevinAbstract:Polygenic risk assessment reveals Pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry
Hongyu Zhao - One of the best experts on this subject based on the ideXlab platform.
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Pervasive Pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS
Human Genetics, 2015Co-Authors: Qian Wang, Can Yang, Joel Gelernter, Hongyu ZhaoAbstract:Although some existing epidemiological observations and molecular experiments suggested that brain disorders in the realm of psychiatry may be influenced by immune dysregulation, the degree of genetic overlap between psychiatric disorders and immune disorders has not been well established. We investigated this issue by integrative analysis of genome-wide association studies of 18 complex human traits/diseases (five psychiatric disorders, seven immune disorders, and others) and multiple genome-wide annotation resources (central nervous system genes, immune-related expression-quantitative trait loci (eQTL) and DNase I hypertensive sites from 98 cell lines). We detected Pleiotropy in 24 of the 35 psychiatric-immune disorder pairs. The strongest Pleiotropy was observed for schizophrenia-rheumatoid arthritis with MHC region included in the analysis ( $$p=3.9\times 10^{-285}$$ p = 3.9 × 10 - 285 ), and schizophrenia-Crohn’s disease with MHC region excluded ( $$p=1.1\times 10^{-36}$$ p = 1.1 × 10 - 36 ). Significant enrichment (>1.4 fold) of immune-related eQTL was observed in four psychiatric disorders. Genomic regions responsible for Pleiotropy between psychiatric disorders and immune disorders were detected. The MHC region on chromosome 6 appears to be the most important with other regions, such as cytoband 1p13.2, also playing significant roles in Pleiotropy. We also found that most alleles shared between schizophrenia and Crohn’s disease have the same effect direction, with similar trend found for other disorder pairs, such as bipolar-Crohn’s disease. Our results offer a novel bird’s-eye view of the genetic relationship and demonstrate strong evidence for pervasive Pleiotropy between psychiatric disorders and immune disorders. Our findings might open new routes for prevention and treatment strategies for these disorders based on a new appreciation of the importance of immunological mechanisms in mediating risk of many psychiatric diseases.
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Implications of Pleiotropy: Challenges and opportunities for mining Big Data in biomedicine
Frontiers in Genetics, 2015Co-Authors: Can Yang, Dongjun Chung, Cong Li, Qian Wang, Hongyu ZhaoAbstract:Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of Pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate Pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of Pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.
John R. Thompson - One of the best experts on this subject based on the ideXlab platform.
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Mendelian randomization incorporating uncertainty about Pleiotropy.
Statistics in Medicine, 2017Co-Authors: John R. Thompson, Jack Bowden, Cosetta Minelli, Fabiola M. Del Greco, Dipender Gill, Elinor M. Jones, Chin Yang Shapland, Nuala A. SheehanAbstract:Mendelian randomization (MR) requires strong assumptions about the genetic instruments, of which the most difficult to justify relate to Pleiotropy. In a two-sample MR, different methods of analysis are available if we are able to assume, M1 : no Pleiotropy (fixed effects meta-analysis), M2 : that there may be Pleiotropy but that the average pleiotropic effect is zero (random effects meta-analysis), and M3 : that the average pleiotropic effect is nonzero (MR-Egger). In the latter 2 cases, we also require that the size of the Pleiotropy is independent of the size of the effect on the exposure. Selecting one of these models without good reason would run the risk of misrepresenting the evidence for causality. The most conservative strategy would be to use M3 in all analyses as this makes the weakest assumptions, but such an analysis gives much less precise estimates and so should be avoided whenever stronger assumptions are credible. We consider the situation of a two-sample design when we are unsure which of these 3 Pleiotropy models is appropriate. The analysis is placed within a Bayesian framework and Bayesian model averaging is used. We demonstrate that even large samples of the scale used in genome-wide meta-analysis may be insufficient to distinguish the Pleiotropy models based on the data alone. Our simulations show that Bayesian model averaging provides a reasonable trade-off between bias and precision. Bayesian model averaging is recommended whenever there is uncertainty about the nature of the Pleiotropy.
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detecting Pleiotropy in mendelian randomisation studies with summary data and a continuous outcome
Statistics in Medicine, 2015Co-Authors: Fabiola Del Greco M, Nuala A. Sheehan, Cosetta Minelli, John R. ThompsonAbstract:Mendelian randomisation (MR) estimates causal effects of modifiable phenotypes on an outcome by using genetic variants as instrumental variables, but its validity relies on the assumption of no Pleiotropy, that is, genes influence the outcome only through the given phenotype. Excluding Pleiotropy is difficult, but the use of multiple instruments can indirectly address the issue: if all genes represent valid instruments, their MR estimates should vary only by chance. The Sargan test detects Pleiotropy when individual phenotype, outcome and genotype data are measured in the same subjects. We propose an alternative approach to be used when only summary genetic data are available or data on gene-phenotype and gene-outcome come from different subjects. The presence of Pleiotropy is investigated using the between-instrument heterogeneity Q test (together with the I2 index) in a meta-analysis of MR Wald estimates, derived separately from each instrument. For a continuous outcome, we evaluate the approach through simulations and illustrate it using published data. For the scenario where all data come from the same subjects, we compare it with the Sargan test. The Q test tends to be conservative in small samples. Its power increases with the degree of Pleiotropy and the sample size, as does the precision of the I2 index, in which case results are similar to those of the Sargan test. In MR studies with large sample sizes based on summary data, the between-instrument Q test represents a useful tool to explore the presence of heterogeneity due to Pleiotropy or other causes. Copyright © 2015 John Wiley & Sons, Ltd.
