Genetic and Genomic Dissection of Psoriatic Arthritis

银屑病性关节炎（PsA）在炎性/自身免疫性关节疾病中的独特之处在于其发病 通常在皮肤银屑病（PsC）之前。这为我们提供了一个无与伦比的机会， 鉴定预测性生物标志物，以确定约25%的寻常型银屑病（PsV） 患者会出现PsA。在过去的十年中，我们已经扩大了对PsV的遗传研究，将重点放在PsA上， 结果在密歇根州收集了1,279名PsA患者，其中743人已经接受了 GWAS。国际银屑病关节炎研究小组（IPART）于2007年发起， 加拿大PsA患者，其中1,370人已经接受了GWAS。在2015年，我们完成了一个Meta- PsC和PsA的GWAS涉及1，430例PsA病例和1，417例对照的发现队列，其中9，293 额外的PsV复制样品（3，061 PsA，3，110 PsC）和13，670对照。我们发现了10种关联 PsA和11 PsC，以及一个新的协会PsV。利用创新的核心外显子组阵列， 基因型额外的病例和对照，我们进行了迄今为止最大的PsV荟萃GWAS（~ 40，000 受试者），并发现了16个易感区域，突出了干扰素信号传导和NF κ B B的作用 级联，并证明银屑病遗传信号在T细胞调节元件中的强烈富集。 使用机器学习对我们的PsA与PsC GWAS中的~200个遗传变异进行建模，我们实现了82%的面积 在区分PsA与PsC的受试者工作曲线下，在前10%的受试者中准确率为98%。 遗传负荷最高的病人我们还对来自65对大肠杆菌的mRNA和miRNA进行了RNA-seq。 转化前和转化后的样本来自发展为PsA的PsC患者。暗示着从皮肤- 我们专注于系统性自身免疫，发现中央记忆CD 4+在转换后显着富集 上调基因中的T细胞（CD 4-Tcm）转录本。我们还观察到， 54种差异表达的miRNA及其mRNA靶点的转换行为，以及多种血清 在PsA与PsC中显著差异表达的miRNA。对50例配对患者的代谢组学研究 转换者血清中有293种生化指标发生显著变化，其中275种升高。最后我们 鉴定了与选择性PsA关联区域显著相关的IL 23 R的非编码eQTL。 基于这些结果，我们假设PsA和PsC的致病机制是T细胞， 成骨细胞和破骨细胞驱动，并在向PsA转变期间从皮肤进展到全身。我们建议 这种模式可以用来开发一种有用的测试来预测PsC患者的PsA，同时增加我们的基本 对PSA的理解为了验证这一假设，我们提出了四个目标：（1）保持和发展我们的纵向 临床资源;（2）鉴定PsC患者中PsA发展的生物标志物;（3）整合 目标2中鉴定的生物标志物转化为使用机器学习进行PsA鉴定的临床有用工具;以及（4） 探讨IL 23 R基因参与银屑病发病的机制。

Psoriatic arthritis (PsA) is distinctive amongst the inflammatory/autoimmune joint diseases in that its onset is commonly preceded by cutaneous psoriasis (PsC). This provides an unparalleled opportunity for the identification of predictive biomarkers to determine which of the approximately 25% of psoriasis vulgaris (PsV) patients will develop PsA. Over the past decade, we have expanded our genetic study of PsV to focus on PsA, resulting in the collection of 1,279 PsA patients at Michigan, 743 of whom have already been subjected to GWAS. Initiated in 2007, the International Psoriatic Arthritis Research Team (IPART) has accumulated 1,919 Canadian PsA patients, of whom 1,370 have already been subjected to GWAS. In 2015, we completed a meta- GWAS of PsC and PsA involving a discovery cohort of 1,430 PsA cases and 1,417 controls, with 9,293 additional PsV replication samples (3,061 PsA, 3,110 PsC) and 13,670 controls. We detected 10 associations for PsA and 11 for PsC, as well as a new association for PsV. Utilizing an innovative core exome array to genotype additional cases and controls, we carried out the largest meta-GWAS of PsV to date (~40,000 subjects) and found 16 more susceptibility regions, highlighting the roles of interferon signaling and the NFB cascade, and demonstrating strong enrichment for psoriasis genetic signals in T-cell regulatory elements. Using machine learning to model ~200 genetic variants in our PsA vs. PsC GWAS, we achieved 82% area under receiver operator curve for distinguishing PsA vs PsC, with 98% accuracy among the top 10% of patients with the highest genetic load. We also carried out RNA-seq on mRNA and miRNA from 65 pairs of pre- and post-conversion samples from PsC patients who developed PsA. Suggestive of a shift from skin- focused to systemic autoimmunity, we found significant post-conversion enrichment for central memory CD4+ T-cell (CD4-Tcm) transcripts among the up-regulated genes. We also observed highly correlated pre- and post- conversion behaviors of 54 differentially-expressed miRNAs and their mRNA targets, as well as multiple serum miRNAs that are significantly differentially expressed in PsA vs. PsC. A metabolomic study of 50 paired converter sera revealed 293 biochemicals with significant alterations, 275 of which were increased. Finally, we identified noncoding eQTLs for IL23R that correlate strikingly with a region of selective PsA association. Based on these results, we hypothesize that PsA and PsC have pathogenetic mechanisms that are T-cell, osteoblast and osteoclast-driven and progress from skin to systemic during transition to PsA. We propose that this paradigm can be used to develop a useful test to predict PsA in PsC patients, while increasing our basic understanding of PsA. To test this hypothesis, we propose four aims: (1) to maintain and grow our longitudinal clinical resource; (2) To identify biomarkers for the development of PsA in PsC patients; (3) To integrate the biomarkers identified in Aim 2 into a clinically-useful tool for PsA identification using machine learning ; and (4) to explore the mechanisms by which the IL23R gene contributes to PsA pathogenesis.