1. Characterizing susceptibility to phenotypic variations of psoriasis by comparing allelic association signals on chromosome 6 J. Panko ¹, S. Schrodi ², B. Wong ¹, K. Callis ¹, N. Matsunami1 ¹, M. A. Cargill ², G. G. Krueger ¹ ¹ Department of Dermatology, University of Utah, Salt Lake City, UT, USA ² Celera Diagnostics, Alameda, CA, USA Funding by the Department of Dermatology, The National Psoriasis Foundation, LineaGen Research Corporation, and The Richard Movitz Foundation REFERENCES: Christophers, E., 2003 Genotyping Psoriasis. J Invest Dermatol 120: xvii-xvii. Griffiths, C. E., 2004 Psoriasis: future research needs and goals for the twenty-first century. Dermatol Clin 22: 493-499. Orru, S et al. 2005 Mapping of the Major Psoriasis-Susceptibility Locus (PSORS1) in a 70-Kb Interval around the Corneodesmosin Gene (CDSN) OBSERVATIONS / CONCLUSIONS: Though the existence of a genetic component of psoriasis can be easily observed, the genes predisposing patients to psoriasis and its varied morphologies remain largely unknown. Previous studies of psoriasis cases have shown disease linkage to many possible loci. The most highly significant of these has been linkage to the MHC region of chromosome 6p. But, because psoriasis is a complex multi-genic disease with a variable expression pattern, it is unlikely that any one locus is responsible for all forms of psoriasis. We propose that when psoriasis cases are stratified into phenotypic pools, variants of psoriasis can be more clearly differentiated from age and gender matched controls by comparing the allelic association peaks of SNP markers on chromosome 6 between the two groups. For this analysis we collected phenotypic and genetic data using the Utah Psoriasis Initiative (UPI). The UPI was established to classify subtypes of psoriasis based upon observable and patient-reported characteristics. By stratifying psoriasis cases by known phenotypes of disease we can facilitate the identification of loci where cases vary more distinctly from controls and therefore begin to localize genes responsible for phenotypic characteristics of psoriasis. RESULTS: METHODS: Pregnancy Response and Mean Age Onset n=22n=15n=28 N=68 N=65 Poster # P-09 Family History – yes Psoriatic Arthritis – no Thin Disease Age at onset - 27 Family History – no Psoriatic Arthritis – yes Thick Disease Age at onset - 45 Family History yes Family History no Ps Arthritis yes Thin Plaques Ps Arthritis no Thick Plaques Age at Onset 18-32 Age at Onset >32 Age at Onset <18 Individual Phenotype Phenotype Pools The phenotypic and genotypic profiles of 466 unrelated patients with psoriasis were collected through enrollment into the Utah Psoriasis Initiative. Enrollment consisted of three parts: completion of a questionnaire with extensive psoriasis and other medical history questions, examination by a trained physician using a structured exam registry form, and DNA isolation. Phenotypic information was compiled and patients were stratified into pools of well known phenotypes of psoriasis e.g., the presence or absence of psoriatic arthritis in patients who had the diagnosis of psoriasis for over 10 years, the presence or absence of a known family history of psoriasis, and age at onset of 32. (Figure 1.) We also stratified based on a predominance of thick or thin disease, phenotypic characteristics which we have proposed represent two distinct morphologies of the disease. (Figure 8) A 26K functional genome scan for psoriasis loci was completed in all pools of the 466 cases and their age and gender matched controls. Both public and proprietary SNP assays were run using allelic specific PCR. The case and control data from the markers located on chromosome 6 was analyzed. The 1056 markers with allelic association, p <0.05, were then sorted by location on chromosome 6. For each stratification, a 10 SNP moving average of the associated SNP markers was calculated and the logs of the transformed significant allelic association values were plotted against the location of the SNP markers on chromosome 6. Charted plots of opposing phenotypic strata were superimposed and examined for comparison of location of allelic peaks. BACKGROUND: Figure 2 shows the plots of the logs of the moving averages of the significant allelic association of SNP markers for all phenotypic stratifications analyzed. A map of chromosome 6 is included to illustrate the locations of the allelic peaks (Figure 3). The most significant peaks are in the MHC region (6p21.1-6p21.3). The genes where the significant SNP markers were located are also listed in Table 1. When focusing closer on this area of chromosome 6 and graphing separately the four phenotypic stratifications that were evaluated in this analysis, there were two distinct peaks in allelic association values for family history and no family history (Figure 4), psoriatic arthritis and no psoriatic arthritis (Figure 6), and thick and thin plaques (Figure 5). All of the peaks for these strata were seen between 6p21.2-p22.1 on chromosome 6. By each allelic association peak is a list of the genes in which the 10 SNP markers at that make up that point are contained. Though plaque thickness is not a formally recognised phenotypic stratification of psoriasis, we have previously described in thick and thin plaque psoriasis as distinct morphological variants (Society for Investigational Dermatology Conference 2004) and have included this variant in our genetic analysis. (Figure 5). For age of onset (Figure 7), for which the cohort was divided into three strata, there was a wide allelic association increase for age of onset 32. This analysis localizes general areas on chromosome 6 where there is a group of markers with high allelic association P-values for cases of psoriasis sharing phenotypic characteristics. When the association values for markers in these areas are compared between a cohort of cases with a specific phenotype and a matched control group, there is a greater difference than when cases without that phenotypic characteristic are compared with controls. A moving average of allelic association P-values was used to smooth the data and to generate a visual demonstration of these peaks. Because this technique was used, the peaks represent general areas of increase rather than specific genes responsible for the difference. To further evaluate these loci, additional individual genotyping of patients will need to be done with subsequent analysis of the phenotypic characteristics of those patients to assess whether these data obtained from pooled DNA can be replicated with individual DNA samples. We conclude that by segregating psoriasis cases into phenotypic subclasses and merging DNA from pools of subjects with these phenotypic variants, we can begin to localize possible genetic markers for psoriatic arthritis, family history of disease age of onset, and plaque thickness. Variations in the allelic association of markers in this region may dictate phenotypic differences in patients with psoriasis. Figure 2 Figure 5 Figure 6 Figure 7 Figure 1 Figure 4 HLA C MICA (3) BAT2 (2) Private (4) HLA C (4) MICA NRC3 BAT2 CSNK2B LY6G5B Private OR2W1 (2) OR11A1 OR2H1 UBD Private (5) C6orf18 (6) POU5F1 (2) HLA-C (2) BAT2 (3) HSPA1A SKIV2L DOM3Z STK19 TNXB FKBPL Private CDSN C6orf18 (4) POU5F1 (2) HLA-C (2) Private Example of thin plaque psoriasis Example of thick plaque psoriasis LY6G6E C6orf27 SKIV2L STK19 TNXB FKBPL PPT2 (2) PBX2 (2) PKHD1 (3) LRRC1 TINAG COL21A1 RAB23 (2) EGFL11 Private C6orf18 (5) PSORS1C1 POU5F1 HLA-C (2) Private COL11A2 RPS18 ITPR3 PNPLA1 ETV7 PPIL1 C6orf197 GLO1 DNAH8 Private Figure 3 Genelocation on Chr 6 OR2W129119968-29120951 OR11A129501259-29503488 OR2H129534208-29540076 UBD29631386-29635681 PSORS1C131190586-31215793 CDSN31191339-31196189 C6orf1831218195-31232658 POU5F131240107-31246432 HLAC31344549-31347842 MICA31479364-31491041 CSNK2B31741868-31745785 LY6G5B31746134-31748043 BAT231762714-31779067 LY6G6E31788091-31789568 C6orf2731841494-31852535 HSPA1A31891298-31893696 SKIV2L32034946-32045504 DOM3Z32045571-32047851 STK1932047699-32057201 TNXB32116910-32121883 FKBPL32204462-32206045 PPT232229599-32239430 PBX232262729-32265697 COL11A233238449-33253648 RPS1833347850-33352264 ITPR333697321-33772317 PNPLA136318922-36384350 ETV736441949-36463371 PPIL136930585-36950747 C6orf19737333526-37408723 GLO138751697-38778895 PKHD151588103-52060382 LRRC153767736-53895811 TINAG54281201-54295077 COL21A156029347-56366851 RAB2357161542-57195037 EGFL1166095891-66473839 Table 1 Figure 8