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mendelian randomization

Y Kim, P Kraft, M M Asgari
No abstract text is available yet for this article.
June 2018: British Journal of Dermatology
Tom G Richardson, Philip C Haycock, Jie Zheng, Nicholas J Timpson, Tom R Gaunt, George Davey Smith, Caroline L Relton, Gibran Hemani
We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1,148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39x10-08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits...
June 8, 2018: Human Molecular Genetics
Alexander Teumer
Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated...
2018: Frontiers in Cardiovascular Medicine
Wan-Qiang Lv, Xue Zhang, Kun Fan, Xin Xia, Qiang Zhang, Hui-Min Liu, Bu-Ying Jiang, Wei-Dong Zhang, Hong-Wen Deng
Adiposity has been associated with the risk of coronary artery disease (CAD) in observational studies, but their association may differ according to specific characteristics of studies. In Mendelian randomization (MR) analyses, genetic variants are used as instrumental variables (IVs) of exposures to examine causal effects to overcome confounding factors and reverse causation. We performed MR analyses for adiposity (n = 322,154) on risk of CAD (60,801 cases and 123,504 controls) based on the currently largest genome-wide association studies...
June 11, 2018: European Journal of Human Genetics: EJHG
Cristiano A Köhler, Evangelos Evangelou, Brendon Stubbs, Marco Solmi, Nicola Veronese, Lazaros Belbasis, Beatrice Bortolato, Matias C A Melo, Camila A Coelho, Brisa S Fernandes, Mark Olfson, John P A Ioannidis, André F Carvalho
The development of depression may involve a complex interplay of environmental and genetic risk factors. PubMed and PsycInfo databases were searched from inception through August 3, 2017, to identify meta-analyses and Mendelian randomization (MR) studies of environmental risk factors associated with depression. For each eligible meta-analysis, we estimated the summary effect size and its 95% confidence interval (CI) by random-effects modeling, the 95% prediction interval, heterogeneity with I2 , and evidence of small-study effects and excess significance bias...
May 25, 2018: Journal of Psychiatric Research
Camelia C Minică, Conor V Dolan, Dorret I Boomsma, Eco de Geus, Michael C Neale
Although experimental studies are regarded as the method of choice for determining causal influences, these are not always practical or ethical to answer vital questions in health and social research (e.g., one cannot assign individuals to a "childhood trauma condition" in studying the causal effects of childhood trauma on depression). Key to solving such questions are observational studies. Mendelian Randomization (MR) is an influential method to establish causality in observational studies. MR uses genetic variants to test causal relationships between exposures/risk factors and outcomes such as physical or mental health...
June 7, 2018: Behavior Genetics
J T Paiva, M D V de Resende, R T Resende, H R de Oliveira, H T Silva, G C Caetano, P S Lopes, F F Silva
We aimed to estimate transgenerational epigenetic variance for body weight using genealogical and phenotypic information in meat quails. Animals were individually weighted from 1 week after hatching, with weight records at 7, 14, 21, 28, 35 and 42 days of age (BW7, BW14, BW21, BW28, BW35 and BW42, respectively). Single-trait genetic analyses were performed using mixed models with random epigenetic effects. Variance components were estimated by the restricted maximum likelihood method. A grid search for values of autorecursive parameter (λ) ranging from 0 to 0...
June 2018: Journal of Animal Breeding and Genetics, Zeitschrift Für Tierzüchtung und Züchtungsbiologie
Jonathan Y Bernard, Hong Pan, Izzuddin M Aris, Margarita Moreno-Betancur, Shu-E Soh, Fabian Yap, Kok Hian Tan, Lynette P Shek, Yap-Seng Chong, Peter D Gluckman, Philip C Calder, Keith M Godfrey, Mary Foong-Fong Chong, Michael S Kramer, Neerja Karnani, Yung Seng Lee
Background: In randomized trials, supplementation of n-3 (ω-3) long-chain polyunsaturated fatty acids (LC-PUFAs) during pregnancy has resulted in increased size at birth, which is attributable to longer gestation. Objective: We examined this finding by using a Mendelian randomization approach utilizing fatty acid desaturase (FADS) gene variants affecting LC-PUFA metabolism. Design: As part of a tri-ethnic mother-offspring cohort in Singapore, 35 genetic variants in FADS1, FADS2, and FADS3 were genotyped in 898 mothers and 1103 offspring...
June 6, 2018: American Journal of Clinical Nutrition
Niki L Dimou, Konstantinos K Tsilidis
Mendelian randomization (MR) is becoming a popular approach to estimate the causal effect of an exposure on an outcome overcoming limitations of observational epidemiology. The advent of genome-wide association studies and the increasing accumulation of summarized data from large genetic consortia make MR a powerful technique. In this review, we give a primer in MR methodology, describe efficient MR designs and analytical strategies, and focus on methods and practical guidance for conducting an MR study using summary association data...
2018: Methods in Molecular Biology
Niki L Dimou, Katerina G Pantavou, Georgia G Braliou, Pantelis G Bagos
Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies...
2018: Methods in Molecular Biology
Benjamin B Sun, Joseph C Maranville, James E Peters, David Stacey, James R Staley, James Blackshaw, Stephen Burgess, Tao Jiang, Ellie Paige, Praveen Surendran, Clare Oliver-Williams, Mihir A Kamat, Bram P Prins, Sheri K Wilcox, Erik S Zimmerman, An Chi, Narinder Bansal, Sarah L Spain, Angela M Wood, Nicholas W Morrell, John R Bradley, Nebojsa Janjic, David J Roberts, Willem H Ouwehand, John A Todd, Nicole Soranzo, Karsten Suhre, Dirk S Paul, Caroline S Fox, Robert M Plenge, John Danesh, Heiko Runz, Adam S Butterworth
Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases...
June 2018: Nature
Daniela Zanetti, Emmi Tikkanen, Stefan Gustafsson, James R Priest, Stephen Burgess, Erik Ingelsson
BACKGROUND: Low birthweight has been associated with a higher risk of hypertension, type 2 diabetes mellitus (T2D), and cardiovascular disease. The Barker hypothesis posits that intrauterine growth restriction resulting in lower birthweight is causal for these diseases, but causality is difficult to infer from observational studies. METHODS: We performed regression analyses to assess associations of birthweight with cardiovascular disease and T2D in 237 631 individuals from the UK Biobank...
June 2018: Circulation. Genomic and precision medicine
Stella Aslibekyan, Patrick R Lawler
No abstract text is available yet for this article.
January 2018: Circulation. Genomic and precision medicine
Jie V Zhao, C Mary Schooling
BACKGROUND: Coagulation plays a role in ischemic heart disease (IHD). However, which coagulation factors are targets of intervention is unclear. We assessed how genetically predicted vWF (von Willebrand factor), ETP (endogenous thrombin potential), FVIII (factor VIII), d-dimer, tPA (tissue-type plasminogen activator), and PAI (plasminogen activator inhibitor)-1 affected IHD. We similarly estimated effects on lipids to determine whether any associations were independent of lipids. METHODS AND RESULTS: Separate sample instrumental variable analysis with genetic instruments, that is, Mendelian randomization, was used to obtain unconfounded estimates of effects on IHD using extensively genotyped studies of coronary artery disease/myocardial infarction, CARDIoGRAMplusC4D Metabochip (64 374 cases, 130 681 controls) and CARDIoGRAMplusC4D 1000 Genomes (60 801 cases, 123 504 controls), and on lipids using the Global Lipids Genetics Consortium Results (n=196 475)...
January 2018: Circulation. Genomic and precision medicine
Elżbieta Kuźma, Eilis Hannon, Ang Zhou, Ilianna Lourida, Alison Bethel, Deborah A Levine, Katie Lunnon, Jo Thompson-Coon, Elina Hyppönen, David J Llewellyn
BACKGROUND: Numerous risk factors for dementia are well established, though the causal nature of these associations remains unclear. OBJECTIVE: To systematically review Mendelian randomization (MR) studies investigating causal relationships between risk factors and global cognitive function or dementia. METHODS: We searched five databases from inception to February 2017 and conducted citation searches including MR studies investigating the association between any risk factor and global cognitive function, all-cause dementia or dementia subtypes...
May 26, 2018: Journal of Alzheimer's Disease: JAD
Vinicius Tragante, Folkert W Asselbergs
No abstract text is available yet for this article.
May 17, 2018: International Journal of Cardiology
Angela Prillo, Alberico Luigi Catapano, Norata Giuseppe Danilo
Epidemiological studies have suggested an inverse correlation between high density lipoprotein (HDL) cholesterol levels and the risk of cardiovascular disease. HDLs promote reverse cholesterol transport (RCT) and possess several putative atheroprotective functions, associated to the anti-inflammatory, anti-thrombotic and anti-oxidant properties as well as to the ability to support endothelial physiology. The assumption that increasing HDL-C levels would be beneficial on cardiovascular disease (CVD), however, has been questioned as, in most clinical trials, HDL-C-raising therapies did not result in improved cardiovascular outcomes...
May 29, 2018: Current Medicinal Chemistry
Stephen Burgess, Verena Zuber, Apostolos Gkatzionis, Christopher N Foley
Background: A robust method for Mendelian randomization does not require all genetic variants to be valid instruments to give consistent estimates of a causal parameter. Several such methods have been developed, including a mode-based estimation method giving consistent estimates if a plurality of genetic variants are valid instruments; i.e. there is no larger subset of invalid instruments estimating the same causal parameter than the subset of valid instruments. Methods: We here develop a model-averaging method that gives consistent estimates under the same 'plurality of valid instruments' assumption...
May 26, 2018: International Journal of Epidemiology
Gibran Hemani, Jie Zheng, Benjamin Elsworth, Kaitlin H Wade, Valeriia Haberland, Denis Baird, Charles Laurin, Stephen Burgess, Jack Bowden, Ryan Langdon, Vanessa Y Tan, James Yarmolinsky, Hashem A Shihab, Nicholas J Timpson, David M Evans, Caroline Relton, Richard M Martin, George Davey Smith, Tom R Gaunt, Philip C Haycock
Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (<ext-link ext-link-type="uri" xlink:href="">http://www...
May 30, 2018: ELife
Richard Barfield, Helian Feng, Alexander Gusev, Lang Wu, Wei Zheng, Bogdan Pasaniuc, Peter Kraft
Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data into transcriptome-wide association studies (TWAS) based on predicted expression can boost power to detect novel disease loci or pinpoint the susceptibility gene at a known disease locus. However, it is often the case that multiple eQTL genes colocalize at disease loci, making the identification of the true susceptibility gene challenging, due to confounding through linkage disequilibrium (LD). To distinguish between true susceptibility genes (where the genetic effect on phenotype is mediated through expression) and colocalization due to LD, we examine an extension of the Mendelian randomization (MR) egger regression method that allows for LD while only requiring summary association data for both GWAS and eQTL...
May 29, 2018: Genetic Epidemiology
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