Using genetic markers to orient the edges in quantitative trait networks: The NEO software

<p>Abstract</p> <p>Background</p> <p>Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers.</p> <p>Results</p> <p>We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue.</p> <p>Conclusion</p> <p>The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: <url>http://www.genetics.ucla.edu/labs/horvath/aten/NEO</url>.</p

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm

The Transformation of Social Institutions in the North American Southeast

Corporate institutions, which transformed in the American Southeast over some 14,000 years, include social heterarchies and hierarchies that arose within the institutional contexts of descent groups, ritual sodalities, and social houses. The strategic and tactical actions of competitive and cooperative agents contributed to differing expressions of organizational changes through a variety of forms, including feasting, feuding/warfare, inalienable goods circulation, indebtedness, monumental constructions, mortuary events, processions/rogations, strategic marriages, and additional ritual and social practices. The nexus of social institutions that evolved along these pathways served as a catalyst for social changes, including the ways through which social institutions became transformed. Such social processes inform archaeologists of the agency, organization, and practice of people who not only invented and manipulated cosmologies, ideologies, institutions, and resources to achieve varying degrees of inequality, power, and wealth, but also those who resisted the efforts of aggrandizers. The author’s arguments focus on aristocratic social actions and actors, and the practices that enabled them to gain power and wealth through exclusive and restrictive corporate institutions