Sanjak, J. | bioRxiv Science

bioRxiv2023

Clustering rare diseases within an ontology-enriched knowledge graph

Structured AbstractO_ST_ABSObjectiveC_ST_ABSIdentifying sets of rare diseases with shared aspects of etiology and pathophysiology may enable drug repurposing and/or platform based therapeutic development. Toward that aim, we utilized an integrative knowledge graph-based approach to constructing clusters of rare diseases. Materials and MethodsData on 3,242 rare diseases were extracted from the National Center for Advancing Translational Science (NCATS) Genetic and Rare Diseases Information center (GARD) internal data resources. The rare disease data was enriched with additional biomedical data, including gene and phenotype ontologies, biological pathway data and small molecule-target activity data, to create a knowledge graph (KG). Node embeddings were used to convert nodes into vectors upon which k-means clustering was applied. We validated the disease clusters through semantic similarity and feature enrichment analysis. ResultsA node embedding model was trained on the ontology enriched rare disease KG and k-means clustering was applied to the embedding vectors resulting in 37 disease clusters with a mean size of 87 diseases. We validate the disease clusters quantitatively by looking at semantic similarity of clustered diseases, using the Orphanet Rare Disease Ontology. In addition, the clusters were analyzed for enrichment of associated genes, revealing that the enriched genes within clusters were shown to be highly related. DiscussionWe demonstrate that node embeddings are an effective method for clustering diseases within a heterogenous KG. Semantically similar diseases and relevant enriched genes have been uncovered within the clusters. Connections between disease clusters and approved or investigational drugs are enumerated for follow-up efforts. ConclusionOur study lays out a method for clustering rare diseases using the graph node embeddings. We develop an easy to maintain pipeline that can be updated when new data on rare diseases emerges. The embeddings themselves can be paired with other representation learning methods for other data types, such as drugs, to address other predictive modeling problems. Detailed subnetwork analysis and in-depth review of individual clusters may lead to translatable findings. Future work will focus on incorporation of additional data sources, with a particular focus on common disease data.

bioinformatics↗

bioRxiv2017

The Evolutionary Genomics of Grape (Vitis vinifera ssp. vinifera) Domestication

We gathered genomic data from grapes (Vitis vinifera ssp. vinifera), a clonally propagated perennial crop, to address three ongoing mysteries about plant domestication. The first is the duration of domestication; archaeological evidence suggests that domestication occurs over millennia, but genetic evidence indicates it can occur rapidly. We estimated that our wild and cultivated grape samples diverged ~22,000 years ago and that the cultivated lineage experienced a steady decline in population size (Ne) thereafter. The long decline may reflect low intensity management by humans prior to domestication. The second mystery is the identification of genes that contribute to domestication phenotypes. In cultivated grapes, we identified candidate-selected genes that function in sugar metabolism, flower development and stress responses. In contrast, candidate selected genes in the wild sample were limited to abiotic and biotic stress responses. A genomic region of high divergence corresponded to the sex determination region and included a candidate male sterility factor and additional genes with sex-specific expression. The third mystery concerns the cost of domestication. Annual crops accumulate putatively deleterious variants, in part due to strong domestication bottlenecks. The domestication of perennial crops differs from annuals in several ways, including the intensity of bottlenecks, and it is not yet clear if they accumulate deleterious variants. We found that grape accessions contained 5.2% more deleterious variants than wild individuals, and these were more often in a heterozygous state. Using forward simulations, we confirm that clonal propagation leads to the accumulation of recessive deleterious mutations but without decreasing fitness.\n\nSignificance StatementWe generated genomic data to estimate the population history of grapes, the most economically important horticultural crop in the world. Domesticated grapes experienced a protracted, 22,000 year population decline prior to domestication; we hypothesize that this decline reflects low intensity cultivation by humans prior to domestication. Domestication altered the mating system of grapes. The sex determination region is detectable as a region of heightened genetic divergence between wild and cultivated accessions. Based on gene expression analyses, we propose new candidate genes that alter sex determination. Finally, grapes contain more deleterious mutations in heterozygous states than their wild ancestors. The accumulation of deleterious mutations is due in part to clonal propagation, which shelters deleterious, recessive mutations.

evolutionary biology↗