Vivek M. Philip

The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics.

Complex traits and disease comorbidity in humans and in model organisms are the result of naturally occurring polymorphisms that interact with each other and with the environment. To ensure the availability of resources needed to investigate biomolecular networks and systems-level phenotypes underlying complex traits, we have initiated breeding of a new genetic reference population of mice, the Collaborative Cross. This population has been designed to optimally support systems genetics analysis. Its novel and important features include a high level of genetic diversity, a large population size to ensure sufficient power in high-dimensional studies, and high mapping precision through accumulation of independent recombination events. Implementation of the Collaborative Cross has been ongoing at the Oak Ridge National Laboratory (ORNL) since May 2005. Production has been systematically managed using a software-assisted breeding program with fully traceable lineages, performed in a controlled environment. Currently, there are 650 lines in production, and close to 200 lines are now beyond their seventh generation of inbreeding. Retired breeders enter a high-throughput phenotyping protocol and DNA samples are banked for analyses of recombination history, allele drift and loss, and population structure. Herein we present a progress report of the Collaborative Cross breeding program at ORNL and a description of the kinds of investigations that this resource will support.

High-throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains

Genetic reference populations, particularly the BXD recombinant inbred (BXD RI) strains derived from C57BL/6J and DBA/2J mice, are a valuable resource for the discovery of the bio‐molecular substrates and genetic drivers responsible for trait variation and covariation. This approach can be profitably applied in the analysis of susceptibility and mechanisms of drug and alcohol use disorders for which many predisposing behaviors may predict the occurrence and manifestation of increased preference for these substances. Many of these traits are modeled by common mouse behavioral assays, facilitating the detection of patterns and sources of genetic coregulation of predisposing phenotypes and substance consumption. Members of the Tennessee Mouse Genome Consortium (TMGC) have obtained phenotype data from over 250 measures related to multiple behavioral assays across several batteries: response to, and withdrawal from cocaine, 3,4‐methylenedioxymethamphetamine; “ecstasy” (MDMA), morphine and alcohol; novelty seeking; behavioral despair and related neurological phenomena; pain sensitivity; stress sensitivity; anxiety; hyperactivity and sleep/wake cycles. All traits have been measured in both sexes in approximately 70 strains of the recently expanded panel of BXD RI strains. Sex differences and heritability estimates were obtained for each trait, and a comparison of early (N = 32) and recent (N = 37) BXD RI lines was performed. Primary data are publicly available for heritability, sex difference and genetic analyses using the MouseTrack database, and are also available in GeneNetwork.org for quantitative trait locus (QTL) detection and genetic analysis of gene expression. Together with the results of related studies, these data form a public resource for integrative systems genetic analysis of neurobehavioral traits.

Genetic analysis in the Collaborative Cross breeding population

Genetic reference populations in model organisms are critical resources for systems genetic analysis of disease related phenotypes. The breeding history of these inbred panels may influence detectable allelic and phenotypic diversity. The existing panel of common inbred strains reflects historical selection biases, and existing recombinant inbred panels have low allelic diversity. All such populations may be subject to consequences of inbreeding depression. The Collaborative Cross (CC) is a mouse reference population with high allelic diversity that is being constructed using a randomized breeding design that systematically outcrosses eight founder strains, followed by inbreeding to obtain new recombinant inbred strains. Five of the eight founders are common laboratory strains, and three are wild-derived. Since its inception, the partially inbred CC has been characterized for physiological, morphological, and behavioral traits. The construction of this population provided a unique opportunity to observe phenotypic variation as new allelic combinations arose through intercrossing and inbreeding to create new stable genetic combinations. Processes including inbreeding depression and its impact on allelic and phenotypic diversity were assessed. Phenotypic variation in the CC breeding population exceeds that of existing mouse genetic reference populations due to both high founder genetic diversity and novel epistatic combinations. However, some focal evidence of allele purging was detected including a suggestive QTL for litter size in a location of changing allele frequency. Despite these inescapable pressures, high diversity and precision for genetic mapping remain. These results demonstrate the potential of the CC population once completed and highlight implications for development of related populations.

A survey of aspartate-phenylalanine and glutamate-phenylalanine interactions in the protein data bank: searching for anion-π pairs.

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion-π pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion-quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242-8249]. To study the role of anion-π interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification of Phe-Asp or -Glu pairs separated by less than 7 Å in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura-Morokuma energy calculations were performed on roughly 19000 benzene-formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (-2 to -7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion-π pairs are found throughout protein structures, in helices as well as β strands. Numerous pairs also had nearby cation-π interactions as well as potential π-π stacking. While more than 1000 structures did not contain an anion-π pair, the 3134 remaining structures contained approximately 2.6 anion-π pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.

Ontological Discovery Environment: A system for integrating gene-phenotype associations

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODEs gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

Supplementing high-density SNP microarrays for additional coverage of disease-related genes: addiction as a paradigm.

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.

CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis

Contemporary genetic studies are revealing the genetic complexity of many traits in humans and model organisms. Two hallmarks of this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene affects multiple phenotypes. Understanding the genetic architecture of complex traits requires addressing these phenomena, but interpreting the biological significance of epistasis and pleiotropy is often difficult. While epistasis reveals dependencies between genetic variants, it is often unclear how the activity of one variant is specifically modifying the other. Epistasis found in one phenotypic context may disappear in another context, rendering the genetic interaction ambiguous. Pleiotropy can suggest either redundant phenotype measures or gene variants that affect multiple biological processes. Here we present an R package, R/cape, which addresses these interpretation ambiguities by implementing a novel method to generate predictive and interpretable genetic networks that influence quantitative phenotypes. R/cape integrates information from multiple related phenotypes to constrain models of epistasis, thereby enhancing the detection of interactions that simultaneously describe all phenotypes. The networks inferred by R/cape are readily interpretable in terms of directed influences that indicate suppressive and enhancing effects of individual genetic variants on other variants, which in turn account for the variance in quantitative traits. We demonstrate the utility of R/cape by analyzing a mouse backcross, thereby discovering novel epistatic interactions influencing phenotypes related to obesity and diabetes. R/cape is an easy-to-use, platform-independent R package and can be applied to data from both genetic screens and a variety of segregating populations including backcrosses, intercrosses, and natural populations. The package is freely available under the GPL-3 license at http://cran.r-project.org/web/packages/cape.

Genetic variation in hippocampal microRNA expression differences in C57BL/6 J X DBA/2 J (BXD) recombinant inbred mouse strains

BackgroundmiRNAs are short single-stranded non-coding RNAs involved in post-transcriptional gene regulation that play a major role in normal biological functions and diseases. Little is currently known about how expression of miRNAs is regulated. We surveyed variation in miRNA abundance in the hippocampus of mouse inbred strains, allowing us to take a genetic approach to the study of miRNA regulation, which is novel for miRNAs. The BXD recombinant inbred panel is a very well characterized genetic reference panel which allows quantitative trait locus (QTL) analysis of miRNA abundance and detection of correlates in a large store of brain and behavioural phenotypes.ResultsWe found five suggestive trans QTLs for the regulation of miRNAs investigated. Further analysis of these QTLs revealed two genes, Tnik and Phf17, under the miR-212 regulatory QTLs, whose expression levels were significantly correlated with miR-212 expression. We found that miR-212 expression is correlated with cocaine-related behaviour, consistent with a reported role for this miRNA in the control of cocaine consumption. miR-31 is correlated with anxiety and alcohol related behaviours. KEGG pathway analysis of each miRNA’s expression correlates revealed enrichment of pathways including MAP kinase, cancer, long-term potentiation, axonal guidance and WNT signalling.ConclusionsThe BXD reference panel allowed us to establish genetic regulation and characterize biological function of specific miRNAs. QTL analysis enabled detection of genetic loci that regulate the expression of these miRNAs. eQTLs that regulate miRNA abundance are a new mechanism by which genetic variation influences brain and behaviour. Analysis of one of these QTLs revealed a gene, Tnik, which may regulate the expression of a miRNA, a molecular pathway and a behavioural phenotype. Evidence of genetic covariation of miR-212 abundance and cocaine related behaviours is strongly supported by previous functional studies, demonstrating the value of this approach for discovery of new functional roles and downstream processes regulated by miRNA.

Genetic analysis of albuminuria in collaborative cross and multiple mouse intercross populations

Albuminuria is an important marker of nephropathy that increases the risk of progressive renal and chronic cardiovascular diseases. The genetic basis of kidney disease is well-established in humans and rodent models, but the causal genes remain to be identified. We applied several genetic strategies to map and refine genetic loci affecting albuminuria in mice and translated the findings to human kidney disease. First, we measured albuminuria in mice from 33 inbred strains, used the data for haplotype association mapping (HAM), and detected 10 genomic regions associated with albuminuria. Second, we performed eight F(2) intercrosses between genetically diverse strains to identify six loci underlying albuminuria, each of which was concordant to kidney disease loci in humans. Third, we used the Oak Ridge National Laboratory incipient Collaborative Cross subpopulation to detect an additional novel quantitative trait loci (QTL) underlying albuminuria. We also performed a ninth intercross, between genetically similar strains, that substantially narrowed an albuminuria QTL on Chromosome 17 to a region containing four known genes. Finally, we measured renal gene expression in inbred mice to detect pathways highly correlated with albuminuria. Expression analysis also identified Glcci1, a gene known to affect podocyte structure and function in zebrafish, as a strong candidate gene for the albuminuria QTL on Chromosome 6. Overall, these findings greatly enhance our understanding of the genetic basis of albuminuria in mice and may guide future studies into the genetic basis of kidney disease in humans.

Molecular identification of collagen 17a1 as a major genetic modifier of laminin gamma 2 mutation-induced junctional epidermolysis bullosa in mice.

Epidermolysis Bullosa (EB) encompasses a spectrum of mechanobullous disorders caused by rare mutations that result in structural weakening of the skin and mucous membranes. While gene mutated and types of mutations present are broadly predictive of the range of disease to be expected, a remarkable amount of phenotypic variability remains unaccounted for in all but the most deleterious cases. This unexplained variance raises the possibility of genetic modifier effects. We tested this hypothesis using a mouse model that recapitulates a non-Herlitz form of junctional EB (JEB) owing to the hypomorphic jeb allele of laminin gamma 2 (Lamc2). By varying normally asymptomatic background genetics, we document the potent impact of genetic modifiers on the strength of dermal-epidermal adhesion and on the clinical severity of JEB in the context of the Lamc2jeb mutation. Through an unbiased genetic approach involving a combination of QTL mapping and positional cloning, we demonstrate that Col17a1 is a strong genetic modifier of the non-Herlitz JEB that develops in Lamc2jeb mice. This modifier is defined by variations in 1–3 neighboring amino acids in the non-collagenous 4 domain of the collagen XVII protein. These allelic variants alter the strength of dermal-epidermal adhesion in the context of the Lamc2jeb mutation and, consequentially, broadly impact the clinical severity of JEB. Overall the results provide an explanation for how normally innocuous allelic variants can act epistatically with a disease causing mutation to impact the severity of a rare, heritable mechanobullous disorder.