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The emerging picture of host genetic control of susceptibility and outcome in meningococcal disease - evidence from multiple GWAS
 

The emerging picture of host genetic control of susceptibility and outcome in meningococcal disease - evidence from multiple GWAS

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Professor Michael Levin's presentation at Meningitis Research Foundation's 2013 conference Meningitis & Septicaemia in Children & Adults www.meningitis.org/conference2013

Professor Michael Levin's presentation at Meningitis Research Foundation's 2013 conference Meningitis & Septicaemia in Children & Adults www.meningitis.org/conference2013

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    The emerging picture of host genetic control of susceptibility and outcome in meningococcal disease - evidence from multiple GWAS The emerging picture of host genetic control of susceptibility and outcome in meningococcal disease - evidence from multiple GWAS Presentation Transcript

    • The emerging picture of host genetic control of meningococcal disease: Insights from Genome wide association studies (GWAS) Michael Levin Imperial college London MRF Conference November 2013 © Imperial College London
    • WHY Do some children develop Meningococcal Infection or other forms of meningitis and not others ? Is it an accident or in the genes?
    • Epidemiology and Infection June 2003 Number of affected case/sibling Sibling familial risk ratio pairs Duration between case/sibling MD onsets 35 30 25 20 12 10 11 AIM 8 Quantify the host genetic component of 6 6 meningococcal disease susceptibility 15 4 10 2 1 4 0 Method 1 7 1 5 0 All data< 1 > 1 w eek 1 > 1 month > 3 >3 mths 6 months > 9 months > 12 months > >1 mth- months > 6 > > 12 Calculate the co-efficient of>familial 9 week week-< <3 < clustering: 6 R mths < mths < mths Duration between MD onsets in case/affected sibling pairs 1 mth mths mths 9 mths 12 mths © Imperial College London
    • Why undertake genetic studies of Infectious diseases? • Identify fundamental pathways causing susceptibility • Identify those susceptible • Identify key components of pathophysiology • Explain individual differences in clinical presentation or outcome • Identify targets for therapy © Imperial College London
    • © Imperial College London
    • Sites for genetic influence Nasopharyngeal colonisation Invasion of bloodstream Survival in blood Asymptomatic Bacteraemia Meningitis Septic Shock Purpura Fulminans © Imperial College London
    • Susceptibility versus severity genes Susceptibility Severity Control colonisation invasion Survival in blood Determine inflammatory response Extent of coagulopathy Organ failure Susceptibility genes present with greater frequency in cases vs controls Severity genes may have same frequency in cases and controls preferentially in severe/fatal cases © Imperial College London
    • How to identify these genes? • Candidate gene approach – Biology-based, mice & men – Limited to what you know • Genome-wide association – Can detect ‘novel’ genes – Use SNP arrays • Linkage analysis – Small family studies – Not population based • Next-gen sequencing – Whole exome – Whole genome?
    • The St Mary's / Imperial College approach 1995+ Detailed clinical phenotype Immunology, biochemistry, microbiology, haematology Candidate genes based on pathophysiology Case control association study Confirm in second cohort Family study Other cohorts Protein expression biological studies Identify gene defects © Imperial College London
    • © Imperial College London Lots of Candidate gene Studies
    • Problems with candidate gene genetic association studies •Innumerable candidates? •Only tests what is known •Many published studies methodologically flawed • Few replication studies •Inadequate correction for multiple hypothesis testing, genetic admixture © Imperial College London
    • The feasibility of Genome-Wide Association Studies • Now possible to type thousands to millions of SNPS across entire genome in each individual • Cost of genotyping decreasing Requires Large patient and control cohorts High throughput technology and Bioinformatics expertise © Imperial College London
    • HapMap A program to chart genetic variation within the human genome • Single neucleotide polymorphisms (SNPs)scattered across genome 10 million sequence variants in each individual
    • Exponential increase in Validated GWAS findings
    • Genome wide study of meningococcal disease • An ESPID collaboration • MRF support for establishment of St Mary’s/ UK Cohort 19952009 and on-going analysis
    • Meningococcal GWAS Sonia Davila Martin Hibberd Chui Chin Lim DNA QC: Chang Hua Wong Dennis Tan Jie Wen Tay Computing: Jieming Chen Mike Levin Victoria Wright Jan Hazelzet David Inwald Taco Kuijpers Marieke Emonts Simon Nadel Willemijn Breunis Helen Betts Lachlan Coin Enitan Carrol Ronald de Groot Peter Hermans Werner Zenz Alexander Binder University of Santiago de Compostela (Spain) Federico Martinon-Torres Antonio Salas © Imperial College London
    • Study design of the MRF/ ESPID GWAS UK Caucasian MD cases UK Caucasian controls Genome-Wide Association Study using Illumina 610 Hap-quad chip Discovery study Bioinformatic Analysis Top ‘hits’ & selection of candidate genes Validation in European cohorts (Austria, Holland, Spain ) using Illumina SQNM Replication study Bioinformatic Analysis Fine-mapping using Illumina ISelect Functional Studies Gene Expression/Proteomics Identification of Key Pathways © Imperial College London
    • 2010 Sep;42(9):772-6.
    • UK Meningococcal GWAS -log (observed P-values) How do we detect significance ? -log (expected P-values)
    • Manhattan plot of significant SNPs in meningococcal disease patients Martinón-Torres, ESPID 2012
    • Factor H & Factor H related protein region on Chr.1
    • Replication: Austrian/Dutch & Spanish cohorts Davila Nature genetics 2010
    • Factor H/ FH related protein region • Multiple SNPs spanning FH- FHR protein region • Highly significant in all 3 cohorts • Definitive identification of FH and FHR as influencing susceptibility
    • Complement Factor H binds to Meningococcal protein (FhbP) Fh on endothelial cells bind meningococci Inhibits C3b Reduces killing
    • What is the role of FH related proteins? • Sequence homology to FH • Individual variation in ratio of FH/FHR proteins 1-4 ? • Competition between FH and FHR for binding to Meningococcal FHBP ? • Further work to define mechanisms at a functional and protein level
    • Further exploration of meningococcal genentics EU Childhood Life-threatening Infectious Disease Study
    • Staged programme to identify genetic basis of Meningococcal disease Vaccine GWAS UK GWAS Spanish GWAS Central Europe GWAS Prospective recruitment of MD & other bacterial infection Identify top Hits Replicate in 2nd vaccine cohort Cross-validation between European cohorts Meta-analysis of combined UK, Spanish, CE European GWAS Fine mapping & sequencing of candidate regions Genotype/RNA/Protein studies [Functional studies] Evaluation of variants in animal models Validation in prospective MD & other bacterial infection cohorts [EU & Africa] Genomic analysis of Extreme Phenotype cohorts Predictive biomarkers of susceptibility & severity & clinical translation Severity analysis / Pathway analysis
    • Meningococcal disease and age-related macular degeneration share genetic susceptibility loci ESIGEM network and EUCLIDS consortium ABCA4 top hit after CFH region • ABCA4 encodes a protein expressed in retinal photoreceptors. • Mutations in ABCA4 are associated with degenerative macular diseases. Martinón-Torres, ESPID 2012
    • AMD MD Leading cause of blindness in western societies Leading bacterial threat In children ABCA4 CFH Complement mediated pathogenesis ???? Martinón-Torres, ESPID 2012
    • EUCLIDS • Large sample size enables meta analysis of the European cohort to identify genes controlling severity and outcome • Pathway based analysis
    • Multiple sites for genetic regulation of Inflammatory response to a pathogen © Imperial College London
    • Pathway Based analysis of Genome wide studies • Can the combined effect of mutiple variants in a biological pathway be used for analysis of GWAS and gene expression data ?
    • Test for pathway association 1. Select pathway • • Create a gene list Map all SNPs to genes within 10K 2. Max single-SNP trend test statistic of the 4 genetic models (for every SNP in the pathway) Contigency table of case-control genotype SNP data aa aA AA Total Cases r0 r1 r2 R Controls s0 s1 s2 S Total n0 n1 n2 N H 0 : pi aa aA AA Additive 0 1 2 Dominant 0 1 0 0 1 Heterozygote 0 1 0 0,1, 2 * T U 1 U N 1/ 2 [varH 0 (U )]  var H 0 (U ) N 2 RS 0 3 2 x i ( Sri 2 2 2 i [ R si ) i 0 x ni 2 ( xi ni ) ] 1 Recessive qi , i Cochran-Armitage test statistic2 Z 4 genetic models1 1. 2. Null hypothesis Sasieni, Biometrics, 1997 Freidlin et al, Hum Hered, 2002 N i 0 i 0 Max statistic C A M A X = m ax (C A | x=(0,1,2) , C A | x=(0,1,1) , C A | x=(0,0,1) , C A | x=(0,1,0) )
    • Variable selection and cross validation identifies Genes with consistent association
    • Visualization of genomic risk Individual genomic fingerprint Type 1 diabetes 1 0.5 0 Predicted probability of disease SNPs in T1D logistic model of disease Real disease status a b c Individuals
    • SEVERITY AND PATHWAY ANALYSIS OF EUCLIDS MENINGOCOCCAL COHORTS • UK GWAS 475 cases/ 4703 controls • SPANISH GWAS 417 cases/882 controls • AUSTRIAN/DUTCH GWAS 344 cases/2557 controls End Points : Death; amputations :skin grafting; mechanical ventilation;severity scores
    • Intermediate phenotypes
    • Death associated with second messenger signaling
    • Second messenger signaling
    • Death and severity controlled by second messenger signalling The second messenger signalling systems are used by all cells to switch on and off multiple processes Environment Processes controlled by SMS Genetic variation in the second messanger on/off switch may control intensity of inflammation in MD
    • Amputations and skin loss
    • Severity and pathway analysis of EUCLIDS GWAS is revealing complex control of phenotypes by genes within each individual • Further validation of initial findings planned in new EUCLIDS cohort • New Pathways as therapeutic targets
    • Acknowledgements MD patients and families Control families Sonia Davila Martin Hibberd Chui Chin Lim DNA QC: Chang Hua Wong Dennis Tan Jie Wen Tay Taco Kuijpers Willemijn Breunis Enitan Carrol Genotyping: Wee Yang Meah Khai Koon Heng Sigeeta Ronald de Groot Peter Hermans Rajaram Computing: Kar Seng Sim Jieming Chen Jan Hazelzet Marieke Emonts Victoria Wright David Inwald Simon Nadel Helen Betts Lachlan Coin Harieta Eleftherohorinou University of Santiago de Compostela (Spain) Werner Zenz Alexander Binder Federico Martinon-Torres Antonio Salas © Imperial College London
    • Thank You!