Systems-level genetic patterns underlying disseminated coccidioidomycosis in humans

项目摘要 播散性球孢子菌病（DCM）是一种罕见的和潜在的威胁生命的感染后果 与原产于美国西南部的沙漠土壤真菌病原体（球孢子菌属）。的原因 为什么一个子集（<5%）的其他健康的人在感染后出现这种不良后果，而大多数其他人 这在很大程度上是未知的。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，在生物学上有意义的基因组中变异的差异分布，以发现模式 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 被直接测试的特征的数量。这项研究将是其范围和种类的第一次，使用最大的 收集了147例敏感DCM病例和388例耐药DCM病例的DNA 对照表现为自限性肺球孢子菌病）。本报告收集的数据和结果 因此，该提案提供了一个独特的资源，为DCM发病机制的研究奠定了重要基础。的 DNA已经（i）全基因组基因型的共同变异，和（ii）外显子测序，以寻找 罕见的蛋白质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型数据。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作用。第二，我们要看病人的分布情况。 如果DCM与PUL具有相关的差异， 与他们的表型。这些reQTL变体集是DNA位置的组，其中不同的等位基因可以引起 感染或刺激后更强或更弱的基因表达反应，以及DCM和 PUL在这些位置可能意味着他们的球孢子菌反应能力可能不同。第三，我们将开展 一项使用假设驱动和无偏选择的途径（基因组）的途径关联研究， 看看这些基因组是否富含与扩张型心肌病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的罕见变异，以比较DCM参与者是否具有过量的罕见和 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 在DCM和PUL参与者之间。使用途径或基因组方法可以让我们看到差异 受影响的系统，而不是要求每个参与者携带相同的单基因突变。结果 这两个目标下的研究将有助于更好地了解与以下疾病相关的人类遗传变异： DCM，将帮助我们了解这种新兴的传染病（NIAID C类），识别低或 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.