Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
Bioinformatics. 2024 Mar 4;40(3). doi: 10.1093/bioinformatics/btae126.
Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.
Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.
The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.
许多疾病，尤其是心血管代谢疾病，彼此之间存在复杂的共病现象。一种直观的建模方法是构建疾病-疾病网络（DDN），其中节点代表疾病，边代表疾病之间的关联，如共享的单核苷酸多态性（SNP）。为了进一步从遗传角度了解疾病关联的分子贡献者，我们提出了一种新型的共享 SNP 疾病网络（ssDDN），称为 ssDDN+，它包括与中间表型的遗传相关性衍生的疾病之间的连接。我们假设，ssDDN+可以为 ssDDN 中的疾病连接提供补充信息，从而深入了解临床实验室测量在疾病相互作用中的作用。
使用来自英国生物银行的 PheWAS 汇总统计信息，我们构建了一个 ssDDN+，揭示了数百种疾病与定量特征之间的遗传相关性。我们的扩展网络揭示了不同疾病类别之间的遗传关联，连接了相关的心血管代谢疾病，并突出了与跨表型关联相关的特定生物标志物。在所考虑的 31 种临床测量中，HDL-C 连接的疾病数量最多，与 2 型糖尿病和心力衰竭均有强烈关联。另一种血液脂质甘油三酯也与非孟德尔疾病的已知遗传原因有关，它也为 ssDDN 添加了大量的边。这项工作表明，与临床生物标志物的关联可以更好地解释心血管代谢疾病之间的共同遗传。我们的研究可以促进未来基于网络的跨表型关联研究，包括多效性和遗传异质性，有可能揭示多病症中遗传缺失的来源。
生成的 ssDDN+可在 https://hdpm.biomedinfolab.com/ddn/biomarkerDDN 上进行探索。
