Gene-set distance analysis (GSDA): a powerful tool for gene-set association analysis

Background Identifying sets of related genes (gene sets) that are empirically associated with a treatment or phenotype often yields valuable biological insights. Several methods effectively identify gene sets in which individual genes have simple monotonic relationships with categorical, quantitative, or censored event-time variables. Some distance-based methods, such as distance correlations, may detect complex non-monotone associations of a gene-set with a quantitative variable that elude other methods. However, the distance correlations have yet to be generalized to associate gene-sets with categorical and censored event-time endpoints. Also, there is a need to determine which genes empirically drive the significance of an association of a gene set with an endpoint. Results We develop gene-set distance analysis (GSDA) by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables. We also develop a backward elimination procedure to identify a subset of genes that empirically drive significant associations. In simulation studies, GSDA more effectively identified complex non-monotone gene-set associations than did six other published methods. In the analysis of a pediatric acute myeloid leukemia (AML) data set, GSDA was the only method to discover that event-free survival (EFS) was associated with the 56-gene AML pathway gene-set, narrow that result down to 5 genes, and confirm the association of those 5 genes with EFS in a separate validation cohort. These results indicate that GSDA effectively identifies and characterizes complex non-monotonic gene-set associations that are missed by other methods. Conclusion GSDA is a powerful and flexible method to detect gene-set association with categorical, quantitative, or censored event-time variables, especially to detect complex non-monotonic gene-set associations. Available at https://CRAN.R-project.org/package=GSDA.

Use of a Managed Solitary Bee to Pollinate Almonds: Population Sustainability and Increased Fruit Set

Osmia spp. are excellent orchard pollinators but evidence that their populations can be sustained in orchard environments and their use results in increased fruit production is scarce. We released an Osmia cornuta population in an almond orchard and measured its population dynamics, as well as visitation rates and fruit set at increasing distances from the nesting stations. Honeybees were 10 times more abundant than O. cornuta. However, the best models relating fruit set and bee visitation included only O. cornuta visitation, which explained 41% and 40% of the initial and final fruit set. Distance from the nesting stations explained 27.7% and 22.1% of the variability in initial and final fruit set. Of the 198 females released, 99 (54.4%) established and produced an average of 9.15 cells. Female population growth was 1.28. By comparing our results with those of previous O. cornuta studies we identify two important populational bottlenecks (female establishment and male-biased progeny sex ratios). Our study demonstrates that even a small population of a highly effective pollinator may have a significant impact on fruit set. Our results are encouraging for the use of Osmia managed populations and for the implementation of measures to promote wild pollinators in agricultural environments.

Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome

Prognostic gene signatures are critical in cancer prognosis assessments and their pinpoint treatments. However, their network properties remain unclear. Here, we obtained nine prognostic gene sets including 1439 prognostic genes of different cancers from related publications. Four network centralities were used to examine the network properties of prognostic genes (PG) compared with other gene sets based on the Human Protein Reference Database (HPRD) and String networks. We also proposed three novel network measures for further investigating the network properties of prognostic gene sets (PGS) besides clustering coefficient. The results showed that PG did not occupy key positions in the human protein interaction network and were more similar to essential genes rather than cancer genes. However, PGS had significantly smaller intra-set distance (IAD) and inter-set distance (IED) in comparison with random sets (p-value < 0.001). Moreover, we also found that PGS tended to be distributed within network modules rather than between modules (p-value < 0.01), and the functional intersection of the modules enriched with PGS was closely related to cancer development and progression. Our research reveals the common network properties of cancer prognostic gene signatures in the human protein interactome. We argue that these are biologically meaningful and useful for understanding their molecular mechanism.