Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis -only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets. Mendelian randomization (MR) and colocalization analyses are used to estimate causal effects of 1,002 plasma proteins on 225 phenotypes. Evidence from drug developmental programs shows that target-indication pairs with MR and colocalization support were more likely to be approved, highlighting the value of this approach for prioritizing therapeutic targets.

[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.