Templeton, A. R. | Search bioRxiv

bioRxiv2020

Synchronized genetic activities in Alzheimer's brains revealed by heterogeneity-capturing network analysis

It is becoming increasingly evident that the efficacy of single-gene computational analyses for complex traits is nearly exhausted and future advances hinge on unraveling the intricate combinatorial interactions among multiple genes. However, the discovery of modules of genes working in concert to manifest a complex trait has been crippled by combinatorial complexity, genetic heterogeneity, and validation biases. We introduce Maestro, a novel network approach that employs a multifaceted correlation measure, which captures heterogeneity, and a rigorous validation method. Maestros utilization for Alzheimers disease (AD) reveals an expression pattern that has virtually zero probability of simultaneous expression by an individual, assuming independence. Yet this pattern is exhibited by 19.0% of AD cases and 7.3% of controls, establishing an unprecedented pattern of synchronized genetic activities in the human brain. This pattern is significantly associated with AD, with an odds ratio of 3.0. This study substantiates Maestros power for discovery of orchestrated genetic activities underlying complex traits. More generally, Maestro can be applied in diverse domains in which heterogeneity exists. HighlightsO_LISynchronized genetic activities associated with Alzheimers disease C_LIO_LINovel vector-based correlation measure that captures genetic heterogeneity C_LIO_LIEnhanced network model for revealing combinatorial genetic interactions C_LIO_LIPro-survival genetic activities associated with Alzheimers disease C_LIO_LIGeneral approach for revealing patterns in data subject to heterogeneity C_LI

bioinformatics↗

bioRxiv2019

Network-based hierarchical population structure analysis for large genomic datasets

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic datasets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these datasets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We demonstrate the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrates how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic datasets.

genetics↗