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register hereBackground: Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts.

背景：了解免疫介导的炎症性疾病的遗传基础对于改善治疗至关重要。单细胞RNA测序（scRNA-seq）可识别疾病中扩展的细胞状态，但由于成本和较小的基因分型队列，通常会忽略遗传因果关系。

Conversely, large genome-wide association studies (GWAS) are commonly accessible. Methods: We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability.

相反，通常可以进行大型全基因组关联研究（GWAS）。方法：我们提出了整合GWAS和scRNA-seq的三步稳健基准分析，以鉴定炎症性疾病中的遗传相关细胞状态和基因。首先，我们根据准确性/敏感性和可解释性，基于途径（scGWAS），单细胞疾病评分（scDRS）或两者（scPagwas），应用并比较了三种最新算法的结果。

While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,1,73 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas.

虽然以前的研究集中在粗细胞类型上，但我们使用疾病特异性，细粒度的单细胞图谱（183742和228211个细胞）和GWAS数据（Ns为97,1,73和45975）治疗类风湿性关节炎（RA）和溃疡性结肠炎（UC）。其次，鉴于许多GWAS疾病缺乏scRNA-seq，我们通过区分只有一个细粒度scRNA-seq图谱的类似疾病，进一步测试了工具的分辨率限制。

Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. Results: We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS.

最后，我们通过测试提供了对非编码SNP掺入方法的新颖评估，该方法可以实现已知细胞状态调用的最高灵敏度/准确性。结果：我们首先发现基于单细胞的工具scDRS和sCPAG被称为被scGWAS忽视的支持细胞状态的优越数量。

While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statist.

虽然scGWAS和scPagwas有利于基因探索，但scDR有效地解释了批次效应，并在没有单细胞基因分型的情况下捕获了疾病相关性的细胞异质性。对于非编码SNP集成，我们发现了statist之间的关键权衡。