Eoli A, Ibing S, Schurmann C, Nadkarni G N, Heyne H O, Böttinger E
Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.
Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
medRxiv. 2023 Oct 12:2023.10.12.23296926. doi: 10.1101/2023.10.12.23296926.
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
慢性肾脏病（CKD）是一种复杂的疾病，会导致肾功能逐渐丧失，影响着全球约9.1%的人口。在此，我们使用一种软聚类算法来解构其遗传异质性。首先，我们从已发表的全基因组关联研究（GWAS）中选择了322个与CKD相关的独立遗传变异，并添加了GWAS目录中229个性状的关联结果。然后，我们应用非负矩阵分解（NMF）来发现相关性状和变异的重叠聚类。我们计算了聚类特异性多基因评分，并在BioMe生物样本库（n = 31,701）上通过全表型组关联研究（PheWAS）对每个聚类进行了验证。NMF识别出九个反映CKD不同方面的聚类，权重最高的性状表明了诸如肾功能、2型糖尿病（T2D）和体重等领域。对于大多数聚类，权重最高的性状在PheWAS分析中得到了证实。尽管也发现了显著的特定血统关联，但在跨血统分析中结果更为显著。虽然所有等位基因都与肾功能下降有关，但仅在较小的变异子集中发现了与CKD相关疾病（如T2D）的关联，并且在不同的遗传血统组中有所不同。我们的研究结果利用遗传学来深入了解CKD的潜在生物学机制，并调查特定人群的关联。
