Systems-level genetic patterns underlying disseminated coccidioidomycosis in humans

项目总结 播散性球孢子菌病(DCM)是一种罕见且可能危及生命的感染后果。 一种原产于美国西南部的荒漠土壤真菌(Coccidioidesspp.)原因 为什么一部分(5%)原本健康的人在感染后会出现这种不良后果，而大多数其他人 在很大程度上，人们并不为人所知。然而，证据指向遗传学，主要涉及免疫系统的变异。 系统。为了发现与DCM相关的系统遗传模式和途径，我们将研究 在全基因组范围内差异分布具有生物学意义的基因集中的变异以寻找模式 这是疾病易感性的基础。关注聚合的系统级集合使我们能够在 跨患者差异的存在，并通过减少 直接测试的功能数量。这项研究将是其范围和类型的第一次，使用最大的 曾经为这种疾病聚集的队列(从147例易感DCM病例和388例耐药DCM病例中收集DNA 表现为自限性肺球菌病的对照组)。在此项下收集的数据和结果 这一建议为DCM发病机制的研究奠定了重要的基础。这个 DNA已经进行了(I)全基因组常见变异的基因分型，以及(Ii)外显子组测序以寻找 罕见的改变蛋白质的变异。在我们的第一个目标中，我们将以三种不同的方式研究基因数据。第一, 我们将寻找扩张型心肌病与人类白细胞抗原区(HLA)变异之间的联系 在许多传染病中起着重要的作用。第二，我们将查看患者的分布情况 如果DCM和PUL在感染相关的reQTL变异集上建立模型，则有差异相关 与它们的表型有关。这些reQTL变异集是一组DNA位置，不同的等位基因可以导致 感染或刺激后基因表达反应的增强或减弱，以及DCM和 在这些位置上的PUL可能意味着它们对球虫的反应能力可能不同。第三，我们将进行 一项使用假设驱动和无偏见选择的路径(基因组)来进行的路径关联研究 看看这些基因组集是否丰富了与DCM相关的变异。对于我们的第二个目标，我们将分析 在患者外显子组中发现稀有变异以比较DCM参与者是否有过量的稀有和 免疫或其他候选途径中的蛋白质破坏性突变。我们将使用两步版本的 小样本优化序列核关联检验，比较这些突变的分布 DCM和PUL参与者之间的关系。使用路径或基因集方法使我们能够以不同的方式 而不是要求每个参与者携带相同的单基因突变。评选结果 在这两个目标下进行的研究将有助于更好地理解与 DCM，将帮助我们了解这种新出现的传染病(NIAID C类)，识别较低或 更高的风险，并最终改善临床护理和患者体验。

PROJECT SUMMARY Disseminated coccidioidomycosis (DCM) is a rare and potentially life-threatening consequence of infection with a desert soil dwelling fungal pathogen native to the Southwestern USA (Coccidioides spp.). The reason why a subset (<5%) of otherwise healthy people develop this adverse outcome after infection while most others do not is largely unknown. However, evidence points to genetics, primarily involving variation in the immune system. To discover the systems genetic patterns and pathways associated with DCM, we will examine the differential distribution of variants in biologically meaningful gene sets at genome-wide scale to find patterns that underlie disease susceptibility. Focusing on aggregated systems-level sets allows us to find patterns in the presence of cross-patient differences, and substantially increases our statistical discovery power by reducing the number of features being directly tested. This study will be the first of its scope and kind, using the largest cohort ever assembled for this disease (DNA collected from 147 susceptible DCM cases and 388 resistant controls presenting as self-limited pulmonary coccidioidomycosis). The data and results gathered under this proposal thus present a unique resource to lay important foundations for the study of DCM pathogenesis. The DNA has been both (i) genome-wide genotyped for common variation, and (ii) exome sequenced to look for rare, protein-altering variation. In our first Aim, we will study the genotype data in three different ways. First, we will look for association between DCM and variation in the human leukocyte antigen region (HLA) which plays an important role in many infectious diseases. Second, we will look at the distribution of patient genotypes at infection-relevant reQTL variant sets to model if DCM versus PUL have differences correlated with their phenotype. These reQTL variant sets are groups of DNA positions where different alleles can cause stronger or weaker gene expression responses after infection or stimulation, and differences between DCM and PUL at those positions could imply that their Coccidioides-response capacity may differ. Third, we will conduct a pathway-association study using both hypothesis-driven and unbiasedly selected pathways (sets of genes) to see if these genesets are enriched for variants associated with DCM. For our second Aim, we will analyze the rare variants found in patient exomes to compare whether DCM participants have an excess of rare and protein-damaging mutations in immune or other candidate pathways. We will use a two-step version of the small sample size optimized Sequence Kernel Association test, comparing the distribution of these mutations between DCM and PUL participants. Using a pathway, or gene set approach allows us to look at differentially impacted systems, rather than requiring each participant to carry the same single-gene mutation. Results of the studies under these two aims will lead to a better understanding the human genetic variation associated with DCM, will help us understand this emerging infectious disease (NIAID Category C), identify people at lower or higher risk, and ultimately build towards improvements in clinical care and patient experience.