Systems biology strategy to study lipotoxicity and the metabolic syndrome

Systems biology views and studies the biological systems in the context of complex interactions between their building blocks and processes. Given its multi-level complexity, metabolic syndrome (MetS) makes a strong case for adopting the systems biology approach. Despite many MetS traits being highly heritable, it is becoming evident that the genetic contribution to these traits is mediated via gene–gene and gene–environment interactions across several spatial and temporal scales, and that some of these traits such as lipotoxicity may even be a product of long-term dynamic changes of the underlying genetic and molecular networks. This presents several conceptual as well as methodological challenges and may demand a paradigm shift in how we study the undeniably strong genetic component of complex diseases such as MetS. The argument is made here that for adopting systems biology approaches to MetS an integrative framework is needed which glues the biological processes of MetS with specific physiological mechanisms and principles and that lipotoxicity is one such framework. The metabolic phenotypes, molecular and genetic networks can be modeled within the context of such integrative framework and the underlying physiology.     

Comparative Analyses of QTLs Influencing Obesity and Metabolic Phenotypes in Pigs and Humans

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.     

Discovery of novel genetic networks associated with 19 economically important traits in beef cattle

Quantitative or complex traits are determined by the combined effects of many loci, and are affected by genetic networks or molecular pathways. In the present study, we genotyped a total of 138 mutations, mainly single nucleotide polymorphisms derived from 71 functional genes on a Wagyu x Limousin reference population. Two hundred forty six F₂ animals were measured for 5 carcass, 6 eating quality and 8 fatty acid composition traits. A total of 2,280 single marker-trait association runs with 120 tagged mutations selected based on the HAPLOVIEW analysis revealed 144 significant associations (P < 0.05), but 50 of them were removed from the analysis due to the small number of animals (≤ 9) in one genotype group or absence of one genotype among three genotypes. The remaining 94 single-trait associations were then placed into three groups of quantitative trait modes (QTMs) with additive, dominant and overdominant effects. All significant markers and their QTMs associated with each of these 19 traits were involved in a linear regression model analysis, which confirmed single-gene associations for 4 traits, but revealed two-gene networks for 8 traits and three-gene networks for 5 traits. Such genetic networks involving both genotypes and QTMs resulted in high correlations between predicted and actual values of performance, thus providing evidence that the classical Mendelian principles of inheritance can be applied in understanding genetic complexity of complex phenotypes. Our present study also indicated that carcass, eating quality and fatty acid composition traits rarely share genetic networks. Therefore, marker-assisted selection for improvement of one category of these traits would not interfere with improvement of another.     

Sex Differences in Genetic Architecture of Complex Phenotypes?

We examined sex differences in familial resemblance for a broad range of behavioral, psychiatric and health related phenotypes (122 complex traits) in children and adults. There is a renewed interest in the importance of genotype by sex interaction in, for example, genome-wide association (GWA) studies of complex phenotypes. If different genes play a role across sex, GWA studies should consider the effect of genetic variants separately in men and women, which affects statistical power. Twin and family studies offer an opportunity to compare resemblance between opposite-sex family members to the resemblance between same-sex relatives, thereby presenting a test of quantitative and qualitative sex differences in the genetic architecture of complex traits. We analyzed data on lifestyle, personality, psychiatric disorder, health, growth, development and metabolic traits in dizygotic (DZ) same-sex and opposite-sex twins, as these siblings are perfectly matched for age and prenatal exposures. Sample size varied from slightly over 300 subjects for measures of brain function such as EEG power to over 30,000 subjects for childhood psychopathology and birth weight. For most phenotypes, sample sizes were large, with an average sample size of 9027 individuals. By testing whether the resemblance in DZ opposite-sex pairs is the same as in DZ same-sex pairs, we obtain evidence for genetic qualitative sex-differences in the genetic architecture of complex traits for 4% of phenotypes. We conclude that for most traits that were examined, the current evidence is that same the genes are operating in men and women.     

Basal metabolic rate of aged mice is affected by random genetic drift but not by selective breeding for high early-age locomotor activity or chronic wheel access

The study of correlated evolution can lead to new insights about the inheritance patterns of complex traits. In order to better understand the evolution of metabolic rate, we tested whether voluntary activity levels and basal metabolic rate are genetically correlated in 90-wk-old mice (Mus domesticus) from replicated lines of the sixteenth generation of an artificial selection experiment for high early-age wheel-running activity. We measured basal rates of oxygen consumption and carbon dioxide production and also computed the respiratory exchange ratio. Half of the individuals from both selected and control lines had been allowed free access to running wheels since 4 wk of age, while the other half were in standard cages. This design allowed testing of hypotheses about (1) genetic correlations between voluntary activity and metabolic rate and (2) lifetime training effects on metabolic traits. Selection group did not have a significant effect on metabolic traits; therefore, this study does not support some of the implicit assumptions of the aerobic capacity model for the evolution of vertebrate energetics. Activity group also did not affect metabolic rate, indicating that lifetime training does not alter basal metabolism in these mice. However, strong replicate line-within-selection-group differences were detected, indicating the occurrence of random genetic drift. In females, this divergence in metabolic traits attributable to drift was independent of body mass, but in males it was probably caused by a correlated response to selection involving body mass. This study is the first to show such effects of random genetic drift on metabolic traits.     

Expression QTL modules as functional components underlying higher-order phenotypes

Systems genetics studies often involve the mapping of numerous regulatory relations between genetic loci and expression traits. These regulatory relations form a bipartite network consisting of genetic loci and expression phenotypes. Modular network organizations may arise from the pleiotropic and polygenic regulation of gene expression. Here we analyzed the expression QTL (eQTL) networks derived from expression genetic data of yeast and mouse liver and found 65 and 98 modules respectively. Computer simulation result showed that such modules rarely occurred in randomized networks with the same number of nodes and edges and same degree distribution. We also found significant within-module functional coherence. The analysis of genetic overlaps and the evidences from biomedical literature have linked some eQTL modules to physiological phenotypes. Functional coherence within the eQTL modules and genetic overlaps between the modules and physiological phenotypes suggests that eQTL modules may act as functional units underlying the higher-order phenotypes.     

The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes

Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.

Specific metabolic pathways (especially those from amino acid and carbohydrate metabolism) underlie heterosis of six agronomic traits in rice.     

Large-scale in silico mapping of complex quantitative traits in inbred mice

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with approximately 40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.     

Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.     

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     

Characterization of blood pressure and morphological traits in cardiovascular-related organs in 13 different inbred mouse strains.

To better understand the contributions of various genetic backgrounds to complex quantitative phenotypes, we have measured several quantitative traits of cardiovascular interest [i.e., systolic blood pressure, weight (corrected by body weight) of several cardiac compartments and adrenals and kidneys, and histological correlates for kidneys and adrenals] in male and female mice from 13 different inbred strains. We selected strains so that each major genealogical group would be represented and to conform to priorities set by the Mouse Phenome Database project. Interstrain comparisons of phenotypes made it possible to identify strains that displayed values that belonged to either the low or the high end of the interstrain variance for quantitative traits, such as systolic blood pressure, body weight, left ventricular weight, and/or adrenocortical structure. For instance, both male and female C3H/HeJ and A/J mice displayed either low systolic blood pressure or low cardiac ventricular mass, respectively, and male C57BL6/J displayed low adrenal weight. Likewise, intersex comparisons made it possible to identify phenotypic values that were sexually dimorphic for some of the same traits. For instance, female AKR/J mice had relatively higher body weight and systolic blood pressure values than their male counterparts, perhaps constituting an animal model of the metabolic X syndrome. These strain- and sex-specific features will be of value both for future genetic and/or developmental studies and for the development of new animal models that will help in the generation of mechanistic hypotheses. All data have been deposited to the Mouse Phenome Database for future integration with the Mouse Genome Database and can be further analyzed and compared with tools available on the site.     

Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis

Modern systems biology permits the study of complex networks, such as circadian clocks, and the use of complex methodologies, such as quantitative genetics. However, it is difficult to combine these approaches due to factorial expansion in experiments when networks are examined using complex methods. We developed a genomic quantitative genetic approach to overcome this problem, allowing us to examine the function(s) of the plant circadian clock in different populations derived from natural accessions. Using existing microarray data, we defined 24 circadian time phase groups (i.e., groups of genes with peak phases of expression at particular times of day). These groups were used to examine natural variation in circadian clock function using existing single time point microarray experiments from a recombinant inbred line population. We identified naturally variable loci that altered circadian clock outputs and linked these circadian quantitative trait loci to preexisting metabolomics quantitative trait loci, thereby identifying possible links between clock function and metabolism. Using single-gene isogenic lines, we found that circadian clock output was altered by natural variation in Arabidopsis thaliana secondary metabolism. Specifically, genetic manipulation of a secondary metabolic enzyme led to altered free-running rhythms. This represents a unique and valuable approach to the study of complex networks using quantitative genetics.     

Estimating the power of a proposed linkage study for a complex genetic trait.

Many genetic traits have complex modes of inheritance; they may exhibit incomplete or age-dependent penetrance or fail to show any clear Mendelian inheritance pattern. As primary linkage maps for the human genome near completion, it is becoming increasingly possible to map these traits. Prior to undertaking a linkage study, it is important to consider whether the pedigrees available for the proposed study are likely to provide sufficient information to demonstrate linkage, assuming a linked marker is tested. In the current paper, we describe a computer simulation method to estimate the power of a proposed study to detect linkage for a complex genetic trait, given a hypothesized genetic model for the trait. Our method simulates trait locus genotypes consistent with observed trait phenotypes, in such a way that the probability to detect linkage can be estimated by sample statistics of the maximum lod score distribution. The method uses terms available when calculating the likelihood of the trait phenotypes for the pedigree and is applicable to any trait determined by one or a few genetic loci; individual-specific environmental effects can also be dealt with. Our method provides an objective answer to the question, Will these pedigrees provide sufficient information to map this complex genetic trait?     

Meta-analysis for microarray studies of the genetics of complex traits

In comparison to other complex disease traits, alcoholism and alcohol abuse are influenced by the combined effects of many genes that alter susceptibility, phenotypic expression and associated morbidity, respectively. Many genetic studies, in both animal models and humans, have identified genetic intervals containing genes that influence alcoholism or behavioral responses to ethanol. Concurrently, a growing number of microarray studies have identified gene expression differences related to ethanol drinking or other ethanol behaviors. However, concerns about the statistical power of these experiments, combined with the complexity of the underlying phenotypes, have greatly hampered the identification of candidate genes underlying ethanol behaviors. Meta-analysis approaches using recent compilations of large datasets of microarray, behavioral and genetic data promise improved statistical power for detecting the genes or gene networks affecting ethanol behaviors and other complex traits.     

Advances in cereal genomics and applications in crop breeding

Recent advances in cereal genomics have made it possible to analyse the architecture of cereal genomes and their expressed components, leading to an increase in our knowledge of the genes that are linked to key agronomically important traits. These studies have used molecular genetic mapping of quantitative trait loci (QTL) of several complex traits that are important in breeding. The identification and molecular cloning of genes underlying QTLs offers the possibility to examine the naturally occurring allelic variation for respective complex traits. Novel alleles, identified by functional genomics or haplotype analysis, can enrich the genetic basis of cultivated crops to improve productivity. Advances made in cereal genomics research in recent years thus offer the opportunities to enhance the prediction of phenotypes from genotypes for cereal breeding.