-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 BXD_TEMP : PCATraits_1119170457/PC01

Fig. 16. Correlation of the first principal component for several hippocampal anatomy and adult neurogenesis phenotypes with Hoxd8 expression in BXD RI strains.

-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 BXD_TEMP : PCATraits_1119170457/PC01

Fig. 16. Correlation of the first principal component for several hippocampal anatomy and adult neurogenesis phenotypes with Hoxd8 expression in BXD RI strains.

VIII. Conclusions: Building a Model of Brain Function from Multipoint, Multi-tissue Gene Expression, and Phenomic Observation

A. An ExpaNdiNG Notion of the NeurochemicAL Pathway

Genomic technology has increased our awareness that the notion of ''one gene-one phenotype'' is an oversimplification. Even our consideration of the simple classical neurotransmitter pathways must now be expanded to include all proteins involved in the synthesis, posttranslational modification, traYcking, anchoring, and coupling of the enzymes, receptors, and vesicle proteins that were former targets of attention. As annotation of the genome improves, we will better understand the various roles of these proteins and their interactions, and the hypothesis-generating functions of genetic correlation analysis can be a useful vehicle in driving improved knowledge of the complete functional proteome associated with these neuronal activities. By treating gene expression as a complex phenotype, we can detect these large networks due to the presence of individual diVerences that occur in many of their molecular players.

B. A Discovery-Based Approach to Dissection of Gene Regulatory Relations

Hypothesis-driven science, focused on single-gene analysis has left us with insights into the associations between genes and phenotypes for a small subset of the 30,000 known genes. Genetic genomics is an approach that takes advantage of naturally occurring variation and genome-wide screening of both the candidate transcripts and their regulators. This screening of both dependent and independent variables across the genome casts a wide net for discovery of gene regulatory relations, unconstrained by prior hypotheses. Discovery-based geno-mic science is gradually increasing our depth of understanding of the broad set of genes and polymorphisms, in a manner that allows rapid assessment of relationships. In time, the precision of these relationships will increase. Tools for integrating the wealth of transcriptome data with our existing gene and pheno-typical knowledge base are rapidly becoming available. Accumulating both depth and breadth of data and devising intelligent methods to mine these relations will be the key to the success. Already, confirmation of major known regulatory relations is evident in transcription QTL analysis, and many highly plausible novel hypotheses about gene—gene and gene-phenotype relations have been developed.

C. A Relational Model for Data Integration

Using a reference panel of inbred strains and deep phenotype data acquisition and analysis, we can study the complexities of genetic, environmental, and trait interactions between and among many tissue types. It is essential to relate data through a common genetic reference panel to assemble and assess this information efficiently. Naturally occurring genetic variation, viable in the laboratory mouse, is likely to be conserved in other species, including humans. Although the sites of polymorphisms will differ, viability of a variation and viability of alleles in one species indicates potential nonlethality of similar alleles in other species. Thus, the examination of the genetic basis of individual differences of these traits in mice will also translate to human gene regulatory pathways. As new strains are developed, precision and accuracy for detection of regulatory loci increases. All that is needed to tap into or expand the resource is collection of phenotypic data, ranging from additional transcriptomic and proteomic assays in multiple brain regions, to the numerous developmental, functional, and behavioral phenotypes.


We would like to thank Dr. Lu Lu, Dr. Kenneth F. Manly, Dr. Jintao Wang, Dr. Michael Langston, Dr. Siming Shou, Dr. Li Zhang, Dr. Michael Miles, Dr. Cheng Li, Dr. Bing Zhang, Dr. Jing Gu, Dr. Yanhua Qu, Dr. David Threadgill, Dr. Hui Chen Hsu, Dr. Gerd Kempermann, Dr. John K. Belknap, Dr. John C. Crabbe, Dr. Thomas Sutter, Dr. Divyan Patel, and Mary-Kathleen Sullivan. Our thanks for financial support from (1) the Informatics Center for Mouse Neurogenetics; P20-MH62009 from NIMH, NIDA, and NSF. (2) INIA grants U01AA13499 and U24AA135B from NIAAA and the William and Dorothy Dunavant Chair of Excellence.


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