An Integrative Systems Genetics Approach to Nephrotic Syndrome

描述（由申请人提供）：肾病综合征（NS）是由肾小球通透性异常引起的大量蛋白尿的临床综合征。这种蛋白尿伴随着感染、静脉血栓栓塞和肾功能进行性丧失的风险增加。用于治疗该病的非特异性免疫抑制药物的副作用也会导致发病。影响儿童的原发性NS的主要组织学分类是微小病变病（MCD）和局灶节段性肾小球硬化（FSGS）。为风险增加的人提供有针对性的个性化护理是具有挑战性的， 或目前受NS影响。应对这一挑战的一个策略是更全面地了解NS的基因组基础。该研究小组正在研究450例所有年龄段的NS患者，在肾活检时招募到两个前瞻性观察队列。研究人员将首先确定已知引起类固醇耐药NS（SRNS）单基因形式的20个基因的等位基因频谱中变异的患病率，并且先前已观察到基因型-表型相关性。将确定这些基因中具有罕见变异的患者（次要等位基因频率<0.5%）与临床结局（如发病年龄、蛋白尿缓解和肾功能下降）的相关性。还将研究隐性SRNS基因中的杂合罕见变异或意义未知的罕见变异对SRNS结局的影响，以及不太罕见的变异（0.5-1%）的可能贡献。除了表征已知SRNS基因中的变体的临床影响之外，还将使用表达数量性状基因座（eQTL）研究来寻找与NS相关的新基因或非编码调控基因组元件的发现。与传统的全基因组关联研究相反，该eQTL研究的结果将是来自NS受试者的肾活检的基因表达。使用基因表达作为中间内表型提高了检测显著相关性的能力，并且还将直接阐明否则无法检测到的生物学途径。最后，研究人员将继续与他的合作者在计算遗传学， 开发新的，全基因组的方法，以确定强负选择下的基因，并预测在NS的测序或整合基因组学研究中发现的变异的功能影响。这将通过整合来自不同高质量比较基因组学、群体遗传学和疾病特异性数据集的基因组和功能特征来实现。在整个项目中，申请人将通过这些分析和生物信息学编程，系统生物学和高级统计遗传学的正式课程获得进行综合基因组学研究的专业知识。总而言之，整合基因组变异与前瞻性收集的基因表达，组织学，生化和临床数据将最大限度地提高能力，以获得有关NS发病机制的机制见解，识别风险，治疗反应或疾病进展的新生物标志物，并指导有针对性的治疗开发策略。

DESCRIPTION (provided by applicant): Nephrotic syndrome (NS) is a clinical syndrome of massive proteinuria caused by abnormal glomerular permeability. This proteinuria is accompanied by increased risk of infection, venous thromboembolism, and progressive loss of renal function. Morbidity also results from side effects of nonspecific immunosuppression medications used to treat this disease. The major histologic classifications of primary NS affecting children are minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS). It is challenging to provide targeted, individualized care for people at increased risk of, or currently affected by, NS. One strategy to meet this challenge is to more fully understand the genomic underpinnings of NS. This research team is studying a subset of 450 patients of all ages with incident NS recruited into two prospective observational cohorts at the time of renal biopsy. The investigators will first determine the prevalence of variants across the allele frequency spectrum for 20 genes known to cause monogenic forms of steroid-resistant NS (SRNS) and for which genotype-phenotype correlations have been observed previously. The association with clinical outcomes such as age of onset, remission of proteinuria, and decline of renal function will be determined for patients with rare variants in these genes (minor allele frequency <0.5%). The impact on SRNS outcomes from heterozygous rare variants in recessive SRNS genes or rare variants of unknown significance will also be studied, as well as possible contributions from less rare variants (0.5-1%). In addition to characterizing the clinical impact o variants in known SRNS genes, the discovery of novel genes or non-coding, regulatory genomic elements associated with NS will also be sought using an expression quantitative trait loci (eQTL) study. As opposed to a traditional genome-wide association study, the outcome for this eQTL study will be gene expression from the kidney biopsies of NS subjects. Using gene expression as an intermediate endophenotype improves power to detect significant associations and will also directly illuminate biologic pathways that would not have been detected otherwise. Finally, the investigator will continue to work with his collaborators in computational genetics to develop novel, genome-wide methods to identify genes under strong negative selection and to predict the functional impact of variants discovered in sequencing or integrative genomics studies of NS. This will be achieved by integrating genomic and functional characteristics from diverse high quality comparative genomics, population genetics, and disease specific datasets. Throughout this project, the applicant will acquire expertise in conducting integrative genomics research through these analyses and via formal coursework in bioinformatics programming, systems biology, and advanced statistical genetics. Altogether, integrating genomic variants with prospectively collected gene expression, histologic, biochemical, and clinical data will maximize ability to derive mechanistic insight about NS pathogenesis, identify novel biomarkers of risk, therapeutic response, or disease progression, and guide targeted therapeutic development strategies.