1. Leprosy as a human model for the genetic study of common infectious diseases Erwin Schurr Centre for the Study of Host Resistance Departments of Human Genetics and Medicine McGill University

2. Spectrum of genetic predisposition Casanova, Abel EMBO J 2007

3. Complexity: strength of genetic effect Relative Risk: P(affected/DD)/P(affected/dd) Modest EffectMajor Effect…..Mendelian Effect 1 2 5 10 100 polygenic“major genes”PIDs RR Two issues: (i) Functional validation/causality (ii) Population effect Number of genes One big question: What proportion of the host genetics component of disease susceptibility is explained by these different effect classes

4. Tracking of major genetic effects by modelling gene-environment interactions or by understanding genetic heterogeneity Example: NRAMP1 candidate gene in tuberculosis Major effects

5. Tracking of major genetic effects by linkage based genome scanning in tuberculosis and leprosy Major effects

6. Leprosy Chronic human infectious disease Mycobacterium leprae Infection rate unknown Primary tropism  Macrophages  Schwann cells

7. Leprosy incidence and prevalence

8. Clinical spectrum Infection Leprosy per se No disease Single lesion Paucibacillary (Cell mediated) Multibacillary (Antibody mediated) Concordance MonozygoticDizygotic Leprosy per se60%20% Clinical form52%15% Chakravartti and Vogel, 1974

9. Genome scan, 2003 86 Vietnamese multiplex families 388 markers tested for linkage with leprosy per se Chromosome 6 Chromosome 10 Chromosome 13 6p21 LOD = 2.7 6q25 – q27 LOD = 4.3 10p13 LOD = 2.0 (Paucibacillary) 13q13 LOD = 1.4 Mira et al., 2003

10. 81 SNPs 2 Genes Association Plot rs1040079 PARK2_e01(-2599) Association Map 0.84/0.16 0.66/0.34 0.51/0.49 High Resolution Association Mapping Gene Map: SNP Map: Mira et al Nature 2004

11. Marker namePosition Vietnamese sample Brazilian sample Risk alleleqp-valueRisk alleleqp-value rs2803104162972608A0.680.011A0.58ns 10Kb_target_5_2162976030T0.510.013T0.840.008 PARK2_e01 (-697)162982925G0.170.013G0.380.0002 PARK2_e01 (-2599) 162984827T0.670.0006T0.610.0006 PARK2_e01(-3024)162985252C0.520.029C0.83ns PARK2_e01(-3800)162986028G0.660.001G0.610.003 28Kb_target_2_1163032514T0.670.017T0.58ns 28Kb_target_4_1163045217A0.660.002A0.530.0009 rs1514343163046511T0.170.03T0.230.023 rs1333955163046882C0.660.0007C0.520.016 rs1040079163047455C0.170.004C0.290.0002 40Kb_target_8_F60163047538A0.160.034A0.240.015 40Kb_target_8_F706163048194G0.160.017G0.23ns SNP1 SNP2

12. Chromosome region 6p21 Objective: Identify susceptibility variants located in the linked chromosomal region on 6p21 following the same strategy used for positional cloning of PARK2/PACRG variants, i.e. systematic association scanning of markers located in the linkage peak confidence interval POSTER # 64 Andrea Alter et al.

13. Study design I 197 simplex families Approximately 50% PB and 50% MB 385 cases and 399 controls 51% PB and 49% MB 364 cases and 371 controls 30% PB and 70% MB

14. Study design II Family based transmission disequilibrium test Case – control A1/A2A2/A2 A1/A2 a = # of A1 transmissions/not A2 b = # of A2 transmissions/not A1 Test statistic = (a - b) 2 /(a + b) Χ 2 distribution Conditional logistic regression CCCTTT Cases613 Control s 316 Test statistic = Σ all cells (observed - expected) 2 / expected Χ 2 distribution Logistic regression

15. Low resolution association scan 418 SNPs - 55 failed 307 SNPs over 10 Mb - 44 non informatifs: f(a)<0.05 - 12 non Hardy Weinberg 363 SNPs 319 SNPs

16. 33.035.037.039.041.0[Mb]31.0 Chromosome 6 Position -log 10 P IIIII HLA Non-HLA P = 0.01 a b c f d e Low resolution association scan Linkage peak 108 genes 116 genes Alcais et al Nat Genet 2007

17. Linkage disequilibrium fine mapping of 90kb interval overlapping LTA PPIAP9 MCCD1 ATP6V1G2 LTA TNF LTB LST1 BAT1 NFKBIL1 NCR3 100 bp 5’UTR Exon 2 Exon 3 LTA+10LTA+80 LTA+252LTA+368 LTA-293LTA-294 rs1041981 LT A c a -log 10 P 10 genes Alcais et al Nat Genet 2007

18. ns *ns1.60 (1.10-2.33) 0.01 ns ns*1.82 (1.38-2.41) 0.00003 Multivariate OR (95% CI) P  Any SNP in the bin is sufficient to explain the observed association with leprosy § MAF (Minor Allele Frequency) corresponds to the frequency of the risk allele for each of the associated SNPs 0.410.280.420.280.260.220.23 MAF § 0.300.490.300.500.26 0.16 MAF § ns 1.78 (1.29-2.45) 0.0004 1.87 (1.37-2.57) 0.00009 Univariate OR (95% CI) P Bin structure Indian ns  ns ns  ns1.97 (1.30-2.99) 0.0009 ns Multivariate OR (95% CI) P 1.63 (1.09-2.43) 0.02 ns1.74 (1.16-2.60) 0.007 ns1.97 (1.30-2.99) 0.0009 ns Univariate OR (95% CI) P Bin structure Vietnamese LTA +368 rs746868 LTA +252 rs909253 LTA +80 rs2239704 LTA +10 rs1800683 LTA -293 rs2071590 LTA -294 rs2844482 MCCD1-NS rs2259435 Replication in the Indian sample

19. LTA+80 MAF:0.414 AdditiveDominantRecessive 0.79 [0.96 (0.762-1.209)0.95[1.01(0.72-1.43)]0.48[0.86(0.57-1.30)] P [OR(95% CI)] Replication in Brazilian sample No replication in the Brazilian sample: WHY? Alcais et al Nat Genet 2007

20. 20 Odds ratio [95%CI] 0 - 1516 - 2526 - 35 Age [years] 1 10 >35All ages 2 5 0 10 30 40 50 Proportion of cases (%) 20 The effect of LTA+80 is strongly age-dependent Alcais et al Nat Genet 2007

21. Classical HLA I genes and HLA-DRB1 123 SNPs, 94 samples, >95% non- single SNP bins HLA I HLA III HLA II HLA-CHLA-B LTA DRB1

22. HLA class II and leprosy in Vietnam HLA allelesFrequencyP-valueOR (CI 95%) DRB1*01<0.01-- DRB1*030.08-- DRB1*040.070.030.48 (0.24-0.96) DRB1*070.07-- DRB1*080.03-- DRB1*090.09-- DRB1*100.080.042.03 (1.20-4.05) DRB1*110.03-- DRB1*120.26-- DRB1*130.03-- DRB1*140.07-- DRB1*150.15-- DRB1*160.04-- Vanderborght et al Genes Immun 2007 April, epub

23. Effect of HLA class II in Vietnamese MarkerP-value aloneP-value with LTA-80 LTA -800.0009 DRB1 – DR4 (*0404 *0411)0.030.0009 DRB1 – DR10 (*1001)0.040.0007 DRB1 – DR2 (*1501 *1502)0.080.0011 Marker 1Marker 2r2r2 LTA -80 1.0 LTA -80DRB1 – DR4 (*0404 *0411)0.02 LTA -80DRB1 – DR10 (*1001)0.19 LTA -80DRB1 – DR2 (*1501 *1502)0.22 India: LTA is not in LD with HLA – DRB1, HLA – DQA1 and HLA – DQB1 Brazil: association of LTA is also independent of HLA – DRB1 Similar results were obtained for 5 other LTA markers

24. R2 was not calculated for f(a)<0.05 Linkage disequilibrium between LTA+80 and class I alleles Class I alleles were determined for 37 Vietnamese individuals Alcais et al Nat Genet 2007

25. Functional role for LTA +80 Knight et al. 2002 Nat Genet ABF – 1  Transcriptional repressor  Lymphoid tissue specific  Binds only if ‘A’ allele at LTA+80 Consistent with ‘A’ allele as risk factor for leprosy TACCGCCCAGCAGTGTCCTG Luciferase reporter ABF – 1 TACCGCCCCGCAGTGTCCTG E2 – box: CAGCTG E47

26. Take home lesson The unit of replication in genetic association studies is the “bin” not the “SNP” (recommended even if functional SNPs are being tested) Multivariate analysis is obligatory Proper consideration of non-genetic covariates can be essential for successful replication

27. Detection of Granuloma-Forming Capacity in Leprosy “Mitsuda’s Test” (1919) Lepromin Antigen: Lepromin Heat-killed M. leprae from human or armadillo (1.6 x 10 8 bacilli/ml) 0.1 ml intradermal injection DTH (24-48 hrs) Fernandez reaction granuloma (3-4 wks) Mitsuda reaction + = CMI, good prognosis _ = ?? >10mm = pos., CMI, good prognosis <3mm = neg., anergy, poor prognosis < Specificity <

28. Mitsuda distribution PatientsControls Ranque et al JID 2007 in press POSTER # 105

29. Mitsuda – Why the fuss? The Mitsuda test is an in-vivo assay for the innate capacity of a person to form an infectious granuloma Efficient formation of infectious granuloma is an important defense against numerous infectious diseases In tuberculosis, inability to maintain granuloma underlies reactivational tuberculosis, the disease type that mainly contributes to transmission of the disease

30. Genome scan in 20 multiplex families Ranque et al JID 2007, in press POSTER # 105

31. Acknowledgments McGill Centre for Study of Host Resistance, Canada A Alter L Simkin L de Léséleuc A Verville M Girard M Mira McGill University and Genome Quebec Innovation Centre, Canada TJ Hudson A Montpetit P Lepage A Bélisle Oswaldo Cruz Institute, Brazil MO Moraes P Vanderborght Université de Paris René Descartes, INSERM U550 France B Ranque G Antoni A Alcaïs L Abel Hospital for Dermato- Venereology, Vietnam N Van Thuc V Hong Thai N Thu Huong N Ngoc Ba All India Institute of Medical Sciences + JALMA, India NK Mehra M Singh K Katoch SUPPORT: CIHR, HHMI, ANR

32. Related Posters # 64 Alter et al. # 73 Gallant et al. # 105 Ranque et al. #188 Di Pietrantonio et al.