Bioinformatic Approach to the Genetics of Preeclampsia

Abstract

Objective To identify candidate genes and genetic variants for preeclampsia using a bioinformatic approach to extract and organize genes and variants from the published literature. Methods Semantic data mining and natural language processing were used to identify articles from the published literature meeting criteria for potential association with preeclampsia. Articles were manually reviewed by trained curators. Cluster analysis was used to aggregate the extracted genes into gene sets associated with preeclampsia or severe preeclampsia, early or late preeclampsia, maternal or fetal tissue sources, and concurrent conditions (i.e., fetal growth restriction (FGR), gestational hypertension, or hemolysis, elevated liver enzymes, and low platelet count). Gene ontology was used to organize this large group of genes into ontology groups. Results From more than 22 million records in PubMed, with 28,000 articles on preeclampsia, our data mining tool identified 2,300 articles with potential genetic associations with preeclampsia-related phenotypes. After curation, 729 articles were “accepted” that contained ‘statistically significant’ associations with 535 genes. We saw distinct segregation of these genes by severity and timing of preeclampsia, by maternal or fetal source, and with associated conditions (e.g., gestational hypertension, fetal growth restriction, or hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome). Conclusion The gene sets and ontology groups identified through our systematic literature curation indicate that preeclampsia represents several distinct phenotypes, with distinct and overlapping maternal and fetal genetic contributions.

Natural language processing to facilitate breast cancer research and management

“…“Mastermind” is an NLP‐based genomic search engine that can provide a list of prioritized articles for a specific variant and help researchers in variant interpretations, verifying variant‐disease associations, and genomic analysis . Other teams have trained NLP models to mine the PubMed data base for candidate genes for preeclampsia, G6PD deficiency, and endometriosis …”

Influence of NKG2C gene deletion and CCR5Δ32 in Pre‐eclampsia—Approaching the effect of innate immune gene variants in pregnancy

“…Pre‐eclampsia (PE) is a hypertensive disorder that affects 2%–8% of all pregnant women (Duley, ). PE is a polygenic disorder resulting from both foetal/placental and maternal genetic contributions and manifests as a complex phenotype (Michita, Kaminski, & Chies, ; Triche et al, ). In pathophysiological terms, PE is characterized by de novo hypertension after 20 weeks of gestation combined with proteinuria (Duley, ; Mol et al, ).…”

Current controversies in prenatal diagnosis 2: prediction and prevention of adverse pregnancy outcomes requires a genomic rather than proteomic solution

“…Therefore, there is an increasing need to improve our understanding of normal placental development and the etiopathophysiology of placental dysfunction disorders. During the last few years, a variety of research approaches have been used to address these topics, but the current knowledge is still far from the basic clinical needs for the development of useful tools in early detection, prevention, and therapy of APO.…”