Friday February 28, 2014
“Integrative Genomics Strategies for the Cross-species Analysis and Classification of Psychiatric Disorders”
Elissa Chesler, PhD
The Jackson Laboratory
Systems genetics and integrative genomics provide new opportunities to evaluate the relations among behaviors and behavioral disorders. A major challenge has been to describe, define, and discriminate among abstract behavioral processes, in large part by distinguishing among the biological mechanisms of unique but not entirely discrete, entities of behavior. Understanding the complexity of behavior requires integration of data across diverse biological systems, types of data, and levels of scale. Functional genomics enable holistic analysis of relations among genes and behaviors. One prominent strategy for associating genes to behavioral constructs is through the use of trait correlation and co-expression across laboratory mouse strains. However, historical origins of classical strains led to a relatively limited range of genetic and phenotypic variation, particularly in behavior. Recent efforts have resulted in improved diversity and precision of mouse genetic resources for behavioral research, including the Collaborative Cross and Diversity Outcross population. These two populations, derived from an eight way cross of common and wild-derived strains, have high precision and allelic diversity. Behavioral variation in the population is expanded, both qualitatively and quantitatively. Variation that had once been canalized among the various inbred lines has been made amenable to genetic dissection, making a systems genetic analyses of behavior more readily tractable. A second major strategy for integration is through the use of integrative functional genomic strategies. High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores. This leaves bench biologists with the complex and unscalable task of integrating data by identifying and gathering relevant studies, reanalyzing primary data, unifying gene identifiers and applying ad hoc computational analysis to the integrated set. GeneWeaver (https://www.GeneWeaver.org) is a curated repository of genomic experimental results with an accompanying tool set for dynamic integration of data, enabling users to interactively address questions about sets of biobehavioral functions and their relations to sets of genes. Thus, large numbers of independently published genomic results can be organized into new conceptual frameworks driven by the underlying, inferred biological relationships rather than a pre-existing semantic framework. With the perspective and application of these bioinformatic approaches, we can uncover the relationships among these systems and take steps forward in realizing the common and distinct bases of psychiatric disease.