Analysis of behavior using genetical genomics in mice as a model: from alcohol preferences to gene expression differences.

Most familial behavioral phenotypes result from the complex interaction of multiple genes. Studies of such phenotypes involving human subjects are often inconclusive owing to complexity of causation and experimental limitations. Studies of animal models argue for the use of established genetic strains as a powerful tool for genetic dissection of behavioral disorders and have led to the identification of rare genes and genetic mechanisms implicated in such phenotypes. We have used microarrays to study global gene expression in adult brains of four genetic strains of mice (C57BL/6J, DBA/2J, A/J, and BALB/c). Our results demonstrate that different strains show expression differences for a number of genes in the brain, and that closely related strains have similar patterns of gene expression as compared with distantly related strains. In addition, among the 24 000 genes and ESTs on the microarray, 77 showed at least a 1.5-fold increase in the brains of C57BL/6J mice as compared with those of DBA/2J mice. These genes fall into such functional categories as gene regulation, metabolism, cell signaling, neurotransmitter transport, and DNA/RNA binding. The importance of these findings as a novel genetic resource and their use and application in the genetic analysis of complex behavioral phenotypes, susceptibilities, and responses to drugs and chemicals are discussed.     

Network‐based global inference of human disease genes

Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome‐wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome‐wide prioritization of candidate genes for over 5000 human phenotypes, including those with under‐characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.     

Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity

The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE^−/−) background. This BXH.ApoE^−/− population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits.     

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF,OMIM and PubMed records

Background

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization.

Results

We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance.

Conclusion

We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-315) contains supplementary material, which is available to authorized users.     

Integrating genetic and network analysis to characterize genes related to mouse weight

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.     

Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells

To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.     

Defining the phenotype in human genetic studies: forward genetics and reverse phenotyping

The definition of phenotypes for genetic study is a challenging endeavor. Just as we apply strict quality standards to genotype data, we should expect that phenotypes meet consistently high standards of reproducibility and validity. The methods for achieving accurate phenotype assignment in the research setting--the 'research diagnosis'--are different from the methods used in clinical diagnosis in the patient care setting. We evaluate some of the main challenges of phenotype definition in human genetics, and begin to outline a set of standards to which phenotypes used in genetics studies may aspire with the goal of increasing the quality and reproducibility of linkage and association studies. Revisiting the traditional phenotype definitions through a focus on familial components and heritable endophenotypes is a time-honored approach. Reverse phenotyping, where phenotypes are refined based on genetic marker data, may be a promising new approach. The stakes are high, since the success of gene mapping in genetically complex disorders hinges on the ability to delineate the target phenotype with accuracy and precision.     

Genome-wide Introgression Lines and their Use in Genetic and Molecular Dissection of Complex Phenotypes in Rice (Oryza sativa L.)

Tremendous efforts have been taken worldwide to develop genome-wide genetic stocks for rice functional genomic (FG) research since the rice genome was completely sequenced. To facilitate FG research of complex polygenic phenotypes in rice, we report the development of over 20 000 introgression lines (ILs) in three elite rice genetic backgrounds for a wide range of complex traits, including resistances/tolerances to many biotic and abiotic stresses, morpho-agronomic traits, physiological traits, etc., by selective introgression. ILs within each genetic background are phenotypically similar to their recurrent parent but each carries one or a few traits introgressed from a known donor. Together, these ILs contain a significant portion of loci affecting the selected complex phenotypes at which allelic diversity exists in the primary gene pool of rice. A forward genetics strategy was proposed and demonstrated with examples on how to use these ILs for large-scale FG research. Complementary to the genome-wide insertional mutants, these ILs opens a new way for highly efficient discovery, candidate gene identification and cloning of important QTLs for specific phenotypes based on convergent evidence from QTL position, expression profiling, functional and molecular diversity analyses of candidate genes, highlights the importance of genetic networks underlying complex phenotypes in rice that may ultimately lead to more complete understanding of the genetic and molecular bases of quantitative trait variation in rice. Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1007/s11103-005-8519-3     

Inferring gene-phenotype associations via global protein complex network propagation

Background

Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.

Methodology/Principal Findings

We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.

Conclusions/Significance

Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.     

Structural similarity of genetically interacting proteins

Background

The functions of a eukaryotic cell are largely performed by multi-subunit protein complexes that act as molecular machines or information processing modules in cellular networks. An important problem in systems biology is to understand how, in general, these molecular machines respond to perturbations.

Results

In yeast, genes that inhibit growth when their expression is reduced are strongly enriched amongst the subunits of multi-subunit protein complexes. This applies to both the core and peripheral subunits of protein complexes, and the subunits of each complex normally have the same loss-of-function phenotypes. In contrast, genes that inhibit growth when their expression is increased are not enriched amongst the core or peripheral subunits of protein complexes, and the behaviour of one subunit of a complex is not predictive for the other subunits with respect to over-expression phenotypes.

Conclusion

We propose the principle that the overall activity of a protein complex is in general robust to an increase, but not to a decrease in the expression of its subunits. This means that whereas phenotypes resulting from a decrease in gene expression can be predicted because they cluster on networks of protein complexes, over-expression phenotypes cannot be predicted in this way. We discuss the implications of these findings for understanding how cells are regulated, how they evolve, and how genetic perturbations connect to disease in humans.     

UDP-N-acetylglucosamine:alpha-3-D-mannoside beta-1,2-N-acetylglucosaminyltransferase I and UDP-N-acetylglucosamine:alpha-6-D-mannoside beta-1,2-N-acetylglucosaminyltransferase II in Caenorhabditis elegans

UDP-N-acetylglucosamine:alpha-3-D-mannoside beta-1,2-N-acetylglucosaminyltransferase I (GnT I) and UDP-N-acetylglucosamine:alpha-6-D-mannoside beta-1,2-N-acetylglucosaminyltransferase II (GnT II) are key enzymes in the synthesis of Asn-linked hybrid and complex glycans. We have cloned cDNAs from Caenorhabditis elegans for three genes homologous to mammalian GnT I (designated gly-12, gly-13 and gly-14) and one gene homologous to mammalian GnT II. All four cDNAs encode proteins which have the domain structure typical of previously cloned Golgi-type glycosyltransferases and show enzymatic activity (GnT I and GnT II, respectively) on expression in transgenic worms. We have isolated worm mutants lacking the three GnT I genes by the method of ultraviolet irradiation in the presence of trimethylpsoralen (TMP); null mutants for GnT II have not yet been obtained. The gly-12 and gly-14 mutants as well as the gly-14;gly-12 double mutant displayed wild-type phenotypes indicating that neither gly-12 nor gly-14 is necessary for worm development under standard laboratory conditions. This finding and other data indicate that the GLY-13 protein is the major functional GnT I in C. elegans. The mutation lacking the gly-13 gene is partially lethal and the few survivors display severe morphological and behavioral defects. We have shown that the observed phenotype co-segregates with the gly-13 deletion in genetic mapping experiments although a second mutation near the gly-13 gene cannot as yet be ruled out. Our data indicate that complex and hybrid N-glycans may play critical roles in the morphogenesis of C. elegans, as they have been shown to do in mice and men.     

Mutations in NDUFAF3 (C3ORF60), Encoding an NDUFAF4 (C6ORF66)-Interacting Complex I Assembly Protein, Cause Fatal Neonatal Mitochondrial Disease

Mitochondrial complex I deficiency is the most prevalent and least understood disorder of the oxidative phosphorylation system. The genetic cause of many cases of isolated complex I deficiency is unknown because of insufficient understanding of the complex I assembly process and the factors involved. We performed homozygosity mapping and gene sequencing to identify the genetic defect in five complex I-deficient patients from three different families. All patients harbored mutations in the NDUFAF3 (C3ORF60) gene, of which the pathogenic nature was assessed by NDUFAF3-GFP baculovirus complementation in fibroblasts. We found that NDUFAF3 is a genuine mitochondrial complex I assembly protein that interacts with complex I subunits. Furthermore, we show that NDUFAF3 tightly interacts with NDUFAF4 (C6ORF66), a protein previously implicated in complex I deficiency. Additional gene conservation analysis links NDUFAF3 to bacterial-membrane-insertion gene cluster SecF/SecD/YajC and to C8ORF38, also implicated in complex I deficiency. These data not only show that NDUFAF3 mutations cause complex I deficiency but also relate different complex I disease genes by the close cooperation of their encoded proteins during the assembly process.     

The role and mode of action of apolipoproteins CIII and AV: synergistic actors in triglyceride metabolism?

PURPOSE OF REVIEW: Apolipoprotein (apo)CIII and apoAV play an important role in triglyceride metabolism as evidenced by the unambiguous and opposing phenotypes of transgenic and knockout mouse models. In this review we discuss studies on the genetics, protein structure, and regulation of apoCIII and apoAV and compare their potential molecular mechanisms of action in triglyceride metabolism. We examine the hypothesis that apoCIII and apoAV synergistically affect triglyceride metabolism. RECENT FINDINGS: It has now been firmly established that variation in plasma triglyceride levels in a wide range of human populations is strongly associated with genetic variation at the chromosomal locus encoding both the APOC3 and APOA5 genes, the APOA1/C3/A4/A5 gene cluster. The close physical linkage of these genes and the frequent concurrence of genetic variants, however, complicate the assignment of specific metabolic defects to specific polymorphisms. Recent insight into the regulation of APOC3 and APOA5 gene expression and structural modeling studies on the apoAV protein have provided novel clues for the potential molecular mechanisms responsible for the effects of apoCIII and apoAV on triglyceride metabolism. SUMMARY: Hypertriglyceridemia is a major independent risk factor in the development of cardiovascular disease. Moreover, triglyceride-derived fatty acids are thought to play a key role in the development and progression of the metabolic syndrome. As modulators of triglyceride metabolism, apoCIII and apoAV are key players and potential therapeutic targets. However, little is known of their molecular mechanism and potential cooperativity. Rational therapeutic application will require the filling of this hiatus in our knowledge.