Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases
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Valeriia Haberland | Stephen Burgess | Jie Zheng | Michael V Holmes | Tom R Gaunt | Karol Estrada | Denis Baird | Gibran Hemani | John Danesh | Adam S Butterworth | James R Staley | Yi Liu | Benjamin Elsworth | Alex Gutteridge | Tom G Richardson | Charles Laurin | James Yarmolinsky | Philip C Haycock | Pau Erola | Benjamin B Sun | Heiko Runz | Robert A Scott | George Davey Smith | Jimmy Z Liu | Matthew R Nelson | Mark R Hurle | Dawn Waterworth | Venexia Walker | Shan Luo | Jamie Robinson | Joseph C Maranville | Hannah M Martin | Rita Santos | Linda McCarthy
[1] Michael Wainberg,et al. Opportunities and challenges for transcriptome-wide association studies , 2019, Nature Genetics.
[2] D. Lawlor,et al. Cohort Profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort , 2012, International journal of epidemiology.
[3] Cristian Pattaro,et al. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. , 2016, Journal of the American Society of Nephrology : JASN.
[4] S. Ebrahim,et al. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.
[5] J. H. Steiger. Tests for comparing elements of a correlation matrix. , 1980 .
[6] G. Davey Smith,et al. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.
[7] Tom R. Gaunt,et al. Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study , 2017 .
[8] Nicola J. Rinaldi,et al. Genetic effects on gene expression across human tissues , 2017, Nature.
[9] P. Visscher,et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.
[10] J. Cummings,et al. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures , 2014, Alzheimer's Research & Therapy.
[11] Christian Gieger,et al. Genome-wide Association Study Of Plasma Proteins Identifies Putatively Causal Genes, Proteins, And Pathways For Cardiovascular Disease , 2017, bioRxiv.
[12] Xia Yang,et al. Co-regulatory networks of human serum proteins link genetics to disease , 2018, Science.
[13] William W. Greenwald,et al. Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry , 2018, Circulation. Genomic and precision medicine.
[14] George Davey Smith,et al. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology , 2008, Statistics in medicine.
[15] C. Wallace,et al. Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.
[16] Stephen Burgess,et al. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods , 2015, Statistics in medicine.
[17] Tom R. Gaunt,et al. PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics , 2017, bioRxiv.
[18] Neil M Davies,et al. Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities , 2017, bioRxiv.
[19] H. Gerstein,et al. Novel Drug Targets for Ischemic Stroke Identified Through Mendelian Randomization Analysis of the Blood Proteome. , 2019, Circulation.
[20] A. Butterworth,et al. Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data , 2013, Genetic epidemiology.
[21] M. Laakso,et al. Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution. , 2019, Human molecular genetics.
[22] P. Visscher,et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.
[23] N. Sheehan,et al. A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization , 2017, Statistics in medicine.
[24] Sarah E. Medland,et al. Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates , 2013, PLoS genetics.
[25] G. Davey Smith,et al. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies1 , 2016, The American journal of clinical nutrition.
[26] T. Frayling,et al. C-reactive protein levels and body mass index: Elucidating direction of causation through reciprocal Mendelian randomization , 2010, International Journal of Obesity.
[27] G. von Heijne,et al. Tissue-based map of the human proteome , 2015, Science.
[28] D. Lawlor,et al. Cohort Profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children , 2012, International journal of epidemiology.
[29] Stephen Burgess,et al. Genomic atlas of the human plasma proteome , 2018, Nature.
[30] Dylan S. Small,et al. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score , 2018, The Annals of Statistics.
[31] Tudor I. Oprea,et al. A comprehensive map of molecular drug targets , 2016, Nature Reviews Drug Discovery.
[32] D. Nyholt. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. , 2004, American journal of human genetics.
[33] J. Danesh,et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors , 2017, The Lancet.
[34] Olena O Yavorska,et al. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data , 2017, International journal of epidemiology.
[35] G. Davey Smith,et al. Evaluating the potential role of pleiotropy in Mendelian randomization studies , 2018, Human molecular genetics.
[36] A. Morris,et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease , 2017, PLoS genetics.
[37] Mulin Jun Li,et al. Nature Genetics Advance Online Publication a N a Ly S I S the Support of Human Genetic Evidence for Approved Drug Indications , 2022 .
[38] Dermot F. Reilly,et al. Phenome-wide association studies (PheWAS) across large “real-world data” population cohorts support drug target validation , 2017, bioRxiv.
[39] Benjamin B. Sun,et al. Author Correction: Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease , 2018, Nature Communications.
[40] R. Brook,et al. Effect of naturally random allocation to lower low-density lipoprotein cholesterol on the risk of coronary heart disease mediated by polymorphisms in NPC1L1, HMGCR, or both: a 2 × 2 factorial Mendelian randomization study. , 2015, Journal of the American College of Cardiology.
[41] Jon White,et al. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies , 2016, International journal of epidemiology.
[42] F. Cunningham,et al. The Ensembl Variant Effect Predictor , 2016, Genome Biology.
[43] L. Cardon,et al. Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.
[44] N. Sheehan,et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic , 2016, International journal of epidemiology.
[45] Jennifer G. Robinson,et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis , 2012, The Lancet.
[46] John P. Overington,et al. The druggable genome and support for target identification and validation in drug development , 2016, Science Translational Medicine.
[47] N. Timpson. Commentary: One size fits all: are there standard rules for the use of genetic instruments in Mendelian randomization? , 2016, International journal of epidemiology.
[48] Tom R. Gaunt,et al. Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease , 2018, Human molecular genetics.
[49] P. Libby,et al. Modulation of the interleukin-6 signalling pathway and incidence rates of atherosclerotic events and all-cause mortality: analyses from the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS) , 2018, European heart journal.
[50] Dermot F. Reilly,et al. Association of CETP Gene Variants With Risk for Vascular and Nonvascular Diseases Among Chinese Adults , 2017, JAMA cardiology.
[51] G. Davey Smith,et al. Orienting the causal relationship between imprecisely measured traits using GWAS summary data , 2017, PLoS genetics.
[52] Joseph K. Pickrell,et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations , 2017, Nature Communications.
[53] Tom R. Gaunt,et al. Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome , 2017, bioRxiv.
[54] C. Esmon,et al. Endothelial cell protein C receptor plays an important role in protein C activation in vivo. , 2001, Blood.
[55] Matti Pirinen,et al. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis , 2015, bioRxiv.
[56] David C. Wilson,et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease , 2016, Nature Genetics.
[57] Kathryn Demanelis,et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms , 2018, Nature Communications.
[58] Christian Gieger,et al. Connecting genetic risk to disease end points through the human blood plasma proteome , 2016, Nature Communications.
[59] Alun D. Hughes,et al. Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase , 2016, Journal of the American College of Cardiology.
[60] J. Danesh,et al. Elucidating mechanisms of genetic cross-disease associations: an integrative approach implicates protein C as a causal pathway in arterial and venous diseases , 2020, medRxiv.
[61] J. Arrowsmith,et al. Trial Watch: Phase II and Phase III attrition rates 2011–2012 , 2013, Nature Reviews Drug Discovery.
[62] David Mellis,et al. Human osteoclast-poor osteopetrosis with hypogammaglobulinemia due to TNFRSF11A (RANK) mutations. , 2008, American journal of human genetics.
[63] M. Fay. Confidence intervals that match Fisher's exact or Blaker's exact tests. , 2010, Biostatistics.
[64] Seng-Lai Tan,et al. Tocilizumab, A Humanized Anti-IL-6R Antibody, as an Emerging Therapeutic Option for Rheumatoid Arthritis: Molecular and Cellular Mechanistic Insights , 2015, International reviews of immunology.
[65] Stephen Burgess,et al. Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables , 2017, Genetic epidemiology.
[66] R. Harrison,et al. Phase II and phase III failures: 2013–2015 , 2016, Nature Reviews Drug Discovery.
[67] Valeriia Haberland,et al. The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.
[68] D. Altshuler,et al. Validating therapeutic targets through human genetics , 2013, Nature Reviews Drug Discovery.
[69] N. Cox,et al. Obesity-associated variants within FTO form long-range functional connections with IRX3 , 2014, Nature.
[70] P. Libby,et al. Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease , 2017, The New England journal of medicine.
[71] P. Imming,et al. Drugs, their targets and the nature and number of drug targets , 2006, Nature Reviews Drug Discovery.
[72] George Davey Smith,et al. Recent Developments in Mendelian Randomization Studies , 2017, Current Epidemiology Reports.
[73] David M. Evans,et al. Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. , 2015, Annual review of genomics and human genetics.
[74] G. Smith,et al. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality , 2017, Nature Reviews Cardiology.
[75] Daniel F. Freitag,et al. Functional IL6R 358Ala Allele Impairs Classical IL-6 Receptor Signaling and Influences Risk of Diverse Inflammatory Diseases , 2013, PLoS genetics.