Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Molecular Approaches to Understand Nutritional Potential of Coarse Cereals

Coarse grains are important group of crops that constitutes staple food for large population residing primarily in the arid and semi-arid regions of the world. Coarse grains are designated as nutri-cereals as they are rich in essential amino acids, minerals and vitamins. In spite of having several nutritional virtues in coarse grain as mentioned above, there is still scope for improvement in quality parameters such as cooking qualities, modulation of nutritional constituents and reduction or elimination of anti-nutritional factors. Besides its use in traditional cooking, coarse grains have been used mainly in the weaning food preparation and other malted food production. Improvement in quality parameters will certainly increase consumer's preference for coarse grains and increase their demand. The overall genetic gain in quality traits of economic importance in the cultivated varieties will enhance their industrial value and simultaneously increase income of farmers growing these varieties. The urgent step for improvement of quality traits in coarse grains requires a detailed understanding of molecular mechanisms responsible for varied level of different nutritional contents in different genotypes of these crops. In this review we have discussed the progresses made in understanding of coarse grain biology with various omics tool coupled with modern breeding approaches and the current status with regard to our effort towards dissecting traits related to improvement of quality and nutritional constituents of grains.

Physiological and comparative proteomic analysis reveals different drought responses in roots and leaves of drought-tolerant wild wheat (Triticum boeoticum)

To determine the proteomic-level responses of drought tolerant wild wheat (Triticum boeoticum), physiological and comparative proteomic analyses were conducted using the roots and the leaves of control and short term drought-stressed plants. Drought stress was imposed by transferring hydroponically grown seedlings at the 3-leaf stage into 1/2 Hoagland solution containing 20% PEG-6000 for 48 h. Root and leaf samples were separately collected at 0 (control), 24, and 48 h of drought treatment for analysis. Physiological analysis indicated that abscisic acid (ABA) level was greatly increased in the drought-treated plants, but the increase was greater and more rapid in the leaves than in the roots. The net photosynthetic rate of the wild wheat leaves was significantly decreased under short-term drought stress. The deleterious effects of drought on the studied traits mainly targeted photosynthesis. Comparative proteomic analysis identified 98 and 85 differently changed protein spots (DEPs) (corresponding to 87 and 80 unique proteins, respectively) in the leaves and the roots, respectively, with only 6 mutual unique proteins in the both organs. An impressive 86% of the DEPs were implicated in detoxification and defense, carbon metabolism, amino acid and nitrogen metabolism, proteins metabolism, chaperones, transcription and translation, photosynthesis, nucleotide metabolism, and signal transduction. Further analysis revealed some mutual and tissue-specific responses to short-term drought in the leaves and the roots. The differences of drought-response between the roots and the leaves mainly included that signal sensing and transduction-associated proteins were greatly up-regulated in the roots. Photosynthesis and carbon fixation ability were decreased in the leaves. Glycolysis was down-regulated but PPP pathway enhanced in the roots, resulting in occurrence of complex changes in energy metabolism and establishment of a new homeostasis. Protein metabolism was down-regulated in the roots, but enhanced in the leaves. These results will contribute to the existing knowledge on the complexity of root and leaf protein changes that occur in response to drought, and also provide a framework for further functional studies on the identified proteins.

Deciphering genetic diversity and inheritance of tomato fruit weight and composition through a systems biology approach

Integrative systems biology proposes new approaches to decipher the variation of phenotypic traits. In an effort to link the genetic variation and the physiological and molecular bases of fruit composition, the proteome (424 protein spots), metabolome (26 compounds), enzymatic profile (26 enzymes), and phenotypes of eight tomato accessions, covering the genetic diversity of the species, and four of their F1 hybrids, were characterized at two fruit developmental stages (cell expansion and orange-red). The contents of metabolites varied among the genetic backgrounds, while enzyme profiles were less variable, particularly at the cell expansion stage. Frequent genotype by stage interactions suggested that the trends observed for one accession at a physiological level may change in another accession. In agreement with this, the inheritance modes varied between crosses and stages. Although additivity was predominant, 40% of the traits were non-additively inherited. Relationships among traits revealed associations between different levels of expression and provided information on several key proteins. Notably, the role of frucktokinase, invertase, and cysteine synthase in the variation of metabolites was highlighted. Several stress-related proteins also appeared related to fruit weight differences. These key proteins might be targets for improving metabolite contents of the fruit. This systems biology approach provides better understanding of networks controlling the genetic variation of tomato fruit composition. In addition, the wide data sets generated provide an ideal framework to develop innovative integrated hypothesis and will be highly valuable for the research community.

Identification of a novel polymorphism in X-linked sterol-4-alpha-carboxylate 3-dehydrogenase (Nsdhl) associated with reduced high-density lipoprotein cholesterol levels in I/LnJ mice

Loci controlling plasma lipid concentrations were identified by performing a quantitative trait locus analysis on genotypes from 233 mice from a F2 cross between KK/HlJ and I/LnJ, two strains known to differ in their high-density lipoprotein (HDL) cholesterol levels. When fed a standard diet, HDL cholesterol concentration was affected by two significant loci, the Apoa2 locus on Chromosome (Chr) 1 and a novel locus on Chr X, along with one suggestive locus on Chr 6. Non-HDL concentration also was affected by loci on Chr 1 and X along with a suggestive locus on Chr 3. Additional loci that may be sex-specific were identified for HDL cholesterol on Chr 2, 3, and 4 and for non-HDL cholesterol on Chr 5, 7, and 14. Further investigation into the potential causative gene on Chr X for reduced HDL cholesterol levels revealed a novel, I/LnJ-specific nonsynonymous polymorphism in Nsdhl, which codes for sterol-4-alpha-carboxylate 3-dehydrogenase in the cholesterol synthesis pathway. Although many lipid quantitative trait locus have been reported previously, these data suggest there are additional genes left to be identified that control lipid levels and that can provide new pharmaceutical targets.

Leaf proteome alterations in the context of physiological and morphological responses to drought and heat stress in barley (Hordeum vulgare L.)

The objective of this study was to identify barley leaf proteins differentially regulated in response to drought and heat and the combined stresses in context of the morphological and physiological changes that also occur. The Syrian landrace Arta and the Australian cultivar Keel were subjected to drought, high temperature, or a combination of both treatments starting at heading. Changes in the leaf proteome were identified using differential gel electrophoresis and mass spectrometry. The drought treatment caused strong reductions of biomass and yield, while photosynthetic performance and the proteome were not significantly changed. In contrast, the heat treatment and the combination of heat and drought reduced photosynthetic performance and caused changes of the leaf proteome. The proteomic analysis identified 99 protein spots differentially regulated in response to heat treatment, 14 of which were regulated in a genotype-specific manner. Differentially regulated proteins predominantly had functions in photosynthesis, but also in detoxification, energy metabolism, and protein biosynthesis. The analysis indicated that de novo protein biosynthesis, protein quality control mediated by chaperones and proteases, and the use of alternative energy resources, i.e. glycolysis, play important roles in adaptation to heat stress. In addition, genetic variation identified in the proteome, in plant growth and photosynthetic performance in response to drought and heat represent stress adaption mechanisms to be exploited in future crop breeding efforts.

Genetic analysis of the Trichuris muris-induced model of colitis reveals QTL overlap and a novel gene cluster for establishing colonic inflammation

Background

Genetic susceptibility to colonic inflammation is poorly defined at the gene level. Although Genome Wide Association studies (GWAS) have identified loci in the human genome which confer susceptibility to Inflammatory Bowel Disease (Crohn’s and Ulcerative Colitis), it is not clear if precise loci exist which confer susceptibility to inflammation at specific locations within the gut e.g. small versus large intestine. Susceptibility loci for colitis in particular have been defined in the mouse, although specific candidate genes have not been identified to date. We have previously shown that infection with Trichuris muris (T. muris) induces chronic colitis in susceptible mouse strains with clinical, histological, and immunological homology to human colonic Crohn’s disease. We performed an integrative analysis of colitis susceptibility, using an F2 inter-cross of resistant (BALB/c) and susceptible (AKR) mice following T. muris infection. Quantitative Trait Loci (QTL), polymorphic and expression data were analysed alongside in silico workflow analyses to discover novel candidate genes central to the development and biology of chronic colitis.

Results

7 autosomal QTL regions were associated with the establishment of chronic colitis following infection. 144 QTL genes had parental strain SNPs and significant gene expression changes in chronic colitis (expression fold-change ≥ +/-1.4). The T. muris QTL on chromosome 3 (Tm3) mapped to published QTL in 3 unrelated experimental models of colitis and contained 33 significantly transcribed polymorphic genes. Phenotypic pathway analysis, text mining and time-course qPCR replication highlighted several potential cis-QTL candidate genes in colitis susceptibility, including FcgR1, Ptpn22, RORc, and Vav3.

Conclusion

Genetic susceptibility to induced colonic mucosal inflammation in the mouse is conserved at Tm3 and overlays Cdcs1.1. Genes central to the maintenance of intestinal homeostasis reside within this locus, implicating several candidates in susceptibility to colonic inflammation. Combined methodology incorporating genetic, transcriptional and pathway data allowed identification of biologically relevant candidate genes, with Vav3 newly implicated as a colitis susceptibility gene of functional relevance.

Apolipoprotein A-IV as a novel gene associated with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine-metabolic disorder, affecting 6-10% of women of reproductive age. The etiology remains poorly understood. To investigate the differentially expressed proteins from PCOS patients versus healthy women, the protein expression in follicular fluid was analyzed using two-dimensional electrophoresis (2-DE). Since follicular fluid contains a number of secretory proteins required for oocyte fertilization and follicle maturation, it is possible that follicular fluid can be used as a provisional source for identifying pivotal proteins associated with PCOS. In this study, six overexpressed proteins [kininogen 1, cytokeratin 9, antithrombin, fibrinogen γ-chain, apolipoprotein A-IV (apoA-IV) precursor and α-1-B-glycoprotein (A1BG)] in follicular fluids from PCOS patients were identified with matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS) and nano LC-MS/MS. Western blot analysis confirmed that the protein expression levels of apoA-IV precursor and A1BG were increased in follicular fluid from PCOS patients compared with those from normal controls. The analysis of protein expression for other proteins revealed individual variation. These results facilitate the understanding of the molecular mechanisms of PCOS and provide candidate biomarkers for the development of diagnostic and therapeutic tools.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

Gene expression variations: potentialities of master regulator polymorphisms in colorectal cancer risk

Colorectal cancer (CRC) is one of the most common cancers worldwide with a peak of incidence in industrialised countries. It is a complex disease related to environmental and genetic risk factors. Low-penetrance genetic variations contribute significantly to sporadic and familial form of CRC. Genome-wide association studies (GWAS) have uncovered numerous robust associations between common variants and CRC risk; only a few of those were protein altering non-synonymous polymorphisms. One of the hypotheses is that non-coding and intergenic variants may change the expression levels of one or several target genes and, thus, account for a fraction of phenotypic differences, including susceptibility to CRC. Such genetic variations have been detected as expression quantitative loci (eQTLs) that show linkage/association to a large number of genes and have been defined as "master regulators of transcription". In the present work, we overview the potentialities to use results from GWAS and eQTL studies in the identification as well as investigation of master regulators in CRC susceptibility.

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.

Genetic basis of atherosclerosis: insights from mice and humans

Atherosclerosis is a complex and heritable disease involving multiple cell types and the interactions of many different molecular pathways. The genetic and molecular mechanisms of atherosclerosis have, in part, been elucidated by mouse models; at least 100 different genes have been shown to influence atherosclerosis in mice. Importantly, unbiased genome-wide association studies have recently identified a number of novel loci robustly associated with atherosclerotic coronary artery disease. Here, we review the genetic data elucidated from mouse models of atherosclerosis, as well as significant associations for human coronary artery disease. Furthermore, we discuss in greater detail some of these novel human coronary artery disease loci. The combination of mouse and human genetics has the potential to identify and validate novel genes that influence atherosclerosis, some of which may be candidates for new therapeutic approaches.

Advances in genetical genomics of plants

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research.

Untangling HDL quantitative trait loci on mouse chromosome 5 and identifying Scarb1 and Acads as the underlying genes

Two high-density lipoprotein cholesterol quantitative trait loci (QTL), Hdlq1 at 125 Mb and Hdlq8 at 113 Mb, were previously identified on mouse distal chromosome 5. Our objective was to identify the underlying genes. We first used bioinformatics to narrow the Hdlq1 locus to 56 genes. The most likely candidate, Scarb1 (scavenger receptor B1), was supported by gene expression data consistent with knockout and transgenic mouse models. Then we confirmed Hdlq8 as an independent QTL by detecting it in an intercross between NZB and NZW (LOD = 12.7), two mouse strains that have identical genotypes for Scarb1. Haplotyping narrowed this QTL to 9 genes; the most likely candidate was Acads (acyl-coenzymeA dehydrogenase, short chain). Sequencing showed that Acads had an amino acid polymorphism, Gly94Asp, in a conserved region; Western blotting showed that protein levels were significantly different between parental strains. A previously known spontaneous deletion causes loss of ACADS activity in BALB/cBy mice. We showed that HDL levels were significantly elevated in BALB/cBy compared with BALB/c mice and that this HDL difference cosegregated with the Acads mutation. We confirmed that Hdlq1 and Hdlq8 are independent QTL on mouse chromosome 5 and demonstrated that Scarb1 and Acads are the underlying genes.

Quantitative lymphatic vessel trait analysis suggests Vcam1 as candidate modifier gene of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic debilitating disease resulting from a complex interaction of multiple genetic factors with the environment. To identify modifier genes of IBD, we used an F2 intercross of IBD-resistant C57BL/6J-Il10(-/-) mice and IBD-susceptible C3H/HeJBir-Il10(-/-) (C3Bir-Il10(-/-)) mice. We found a prominent involvement of lymphatic vessels in IBD and applied a scoring system to quantify lymphatic vascular changes. Quantitative trait locus (QTL) analyses revealed a large-effect QTL on chromosome 3, mapping to an interval of 43.6 Mbp. This candidate interval was narrowed by fine mapping to 22 Mbp, and candidate genes were analyzed by a systems genetics approach that included quantitative gene expression profiling, search for functional polymorphisms, and haplotype block analysis. We identified vascular adhesion molecule 1 (Vcam1) as a candidate modifier gene in the interleukin 10-deficient mouse model of IBD. Importantly, VCAM1 protein levels were increased in susceptible C3H/HeJ mice, compared with C57BL/6J mice; systemic blockade of VCAM1 in C3Bir-Il10(-/-) mice reduced their inflammatory lymphatic vessel changes. These results indicate that genetically determined expression differences of VCAM1 are associated with susceptibility to colon inflammation, which is accompanied by extensive lymphatic vessel changes. VCAM1 is, therefore, a promising therapeutic target for IBD.

eQTL analysis in mice and rats

Since the introduction of genetical genomics in 2001, many studies have been published on various organisms, including mouse and rat. Genetical genomics makes use of the latest microarray profiling technologies and combines vast amounts of genotype and gene expression information, a strategy that has proven very successful in inbred line crosses. The data are analyzed using standard tools for linkage analysis to map the genetic determinants of gene expression variation. Typically, studies have singled out hundreds of genomic loci regulating the expression of nearby and distant genes (called local and distant expression quantitative trait loci, respectively; eQTLs). In this chapter, we provide a step-by-step guide to performing genome-wide linkage analysis in an eQTL mapping experiment by using the R statistical software framework.

Digital candidate gene approach (DigiCGA) for identification of cancer genes

The candidate gene approach is one of the most commonly used methods for identifying genes underlying disease traits. Advances in genomics have greatly contributed to the development of this approach in the past decade. More recently, with the explosion of genomic resources accessible via the public Web, digital candidate gene approach (DigiCGA) has emerged as a new development in this field. DigiCGA, an approach still in its infancy, has already achieved some primary success in cancer gene discovery. However, a detailed discussion concerning the applications of DigiCGA in cancer gene identification has not been addressed. This chapter will focus on discussing DigiCGA in a generalized sense and its applications to the identification of cancer genes, including the cancer gene resources, application status, platform and tools, challenges, and prospects.

Metabolomics, a New Promising Technology for Toxicological Research

Metabolomics which deals with the biological metabolite profile produced in the body and its relation to disease state is a relatively recent research area for drug discovery and biological sciences including toxicology and pharmacology. Metabolomics, based on analytical method and multivariate analysis, has been considered a promising technology because of its advantage over other toxicogenomic and toxicoproteomic approaches. The application of metabolomics includes the development of biomarkers associated with the pathogenesis of various diseases, alternative toxicity tests, high-throughput screening (HTS), and risk assessment, allowing the simultaneous acquisition of multiple biochemical parameters in biological samples. The metabolic profile of urine, in particular, often shows changes in response to exposure to xenobiotics or disease-induced stress, because of the biological system's attempt to maintain homeostasis. In this review, we focus on the most recent advances and applications of metabolomics in toxicological research.

Characterization of Nob3, a major quantitative trait locus for obesity and hyperglycemia on mouse chromosome 1

New Zealand obese (NZO) mice present a metabolic syndrome of obesity, insulin resistance, and diabetes. To identify chromosomal segments associated with these traits, we intercrossed NZO mice with the lean and diabetes-resistant C57BL/6J (B6) strain. Obesity and hyperglycemia in the (NZO x B6)F2 intercross population were predominantly due to a broad quantitative trait locus (QTL) on chromosome 1 (Nob3; logarithm of the odds score 16.1, 16.0, 4.0 for body weight, body fat, and blood glucose, respectively), producing a difference between genotypes of 12.7 or 5.2 g of body weight and 12.0 or 4.0 g of body fat in females or males, respectively. In addition, significant QTL on chromosomes 3 and 13 and suggestive QTL on chromosomes 4, 6, 9, 12, 14, and 19 contributed to the obese phenotype. Distal chromosome 5 was significantly linked with plasma cholesterol (LOD score 10.7). Introgression of two segments of Nob3 into B6 confirmed the adipogenic effect of the QTL and suggested the presence of at least one causal gene. Haplotype mapping reduced the critical region of the distal part of the QTL to 31 Mbp containing the potential candidates Nr1i3, Apoa2, Atp1a2, Prox1, and Hsd11b1. We conclude that obesity and hyperglycemia of NZO is to a large part caused by variant genes located in Nob3 on chromosome 1. Since these exert robust effects on a B6 background, the QTL Nob3 is a prime target for identification of a novel diabesity gene.

Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene

To identify genes controlling plasma HDL and triglyceride levels, quantitative trait locus (QTL) analysis was performed in one backcross, (NZO/H1Lt x NON/LtJ) x NON/LtJ, and three intercrosses, C57BL/6J x DBA/2J, C57BL/6J x C3H/HeJ, and NZB/B1NJ x NZW/LacJ. HDL concentrations were affected by 25 QTL distributed on most chromosomes (Chrs); those on Chrs 1, 8, 12, and 16 were newly identified, and the remainder were replications of previously identified QTL. Triglyceride concentrations were controlled by nine loci; those on Chrs 1, 2, 3, 7, 16, and 18 were newly identified QTL, and the remainder were replications. Combining mouse crosses with haplotype analysis for the HDL QTL on Chr 18 reduced the list of candidates to six genes. Further expression analysis, sequencing, and quantitative complementation testing of these six genes identified Lipg as the HDL QTL gene on distal Chr 18. The data from these crosses further increase the ability to perform haplotype analyses that can lead to the identification of causal lipid genes.

Defining gene and QTL networks

Current technologies for high-throughput molecular profiling of large numbers of genetically different individuals offer great potential for elucidating the genotype-to-phenotype relationship. Variation in molecular and phenotypic traits can be correlated to DNA sequence variation using the methods of quantitative trait locus (QTL) mapping. In addition, the correlation structure in the molecular and phenotypic traits can be informative for inferring the underlying molecular networks. For this, new methods are emerging to distinguish among causality, reactivity, or independence of traits based upon logic involving underlying QTL. These methods are becoming increasingly popular in plant genetic studies as well as in studies on many other organisms.

Integrating post-genomic approaches as a strategy to advance our understanding of health and disease

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.

Understanding quantitative genetics in the systems biology era

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.

Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs

Scientific inquiries in fields ranging from ecology to plant breeding assess phenotypic variation within a plant species either to explain its presence or utilize its consequences. Frequently this natural genetic variation is studied via mapping quantitative trait loci (QTLs); however, elucidation of the underlying molecular mechanisms is a continuing bottleneck. The genomic analysis of transcripts as individual phenotypes has led to the emerging field of expression QTL analysis. This field has begun both to delve into the ecological/evolutionary significance of this transcript variation as well as to use specific eQTLs to speed up our analysis of the molecular basis of quantitative traits. This review introduces eQTL analysis and begins to illustrate how these data can be applied to multiple research fields.

Mouse forward genetics in the study of the peripheral nervous system and human peripheral neuropathy

Forward genetics, the phenotype-driven approach to investigating gene identity and function, has a long history in mouse genetics. Random mutations in the mouse transcend bias about gene function and provide avenues towards unique discoveries. The study of the peripheral nervous system is no exception; from historical strains such as the trembler mouse, which led to the identification of PMP22 as a human disease gene causing multiple forms of peripheral neuropathy, to the more recent identification of the claw paw and sprawling mutations, forward genetics has long been a tool for probing the physiology, pathogenesis, and genetics of the PNS. Even as spontaneous and mutagenized mice continue to enable the identification of novel genes, provide allelic series for detailed functional studies, and generate models useful for clinical research, new methods, such as the piggyBac transposon, are being developed to further harness the power of forward genetics.

Reverse engineering the genotype-phenotype map with natural genetic variation

The genetic variation that occurs naturally in a population is a powerful resource for studying how genotype affects phenotype. Each allele is a perturbation of the biological system, and genetic crosses, through the processes of recombination and segregation, randomize the distribution of these alleles among the progeny of a cross. The randomized genetic perturbations affect traits directly and indirectly, and the similarities and differences between traits in their responses to common perturbations allow inferences about whether variation in a trait is a cause of a phenotype (such as disease) or whether the trait variation is, instead, an effect of that phenotype. It is then possible to use this information about causes and effects to build models of probabilistic 'causal networks'. These networks are beginning to define the outlines of the 'genotype-phenotype map'.

Mapping of quantitative trait loci for cholesterol, LDL, HDL, and triglyceride serum concentrations in pigs

The fine mapping of polymorphisms influencing cholesterol (CT), triglyceride (TG), and lipoprotein serum levels in human and mouse has provided a wealth of knowledge about the complex genetic architecture of these traits. The extension of these genetic analyses to pigs would be of utmost importance since they constitute a valuable biological and clinical model for the study of coronary artery disease and myocardial infarction. In the present work, we performed a whole genome scan for serum lipid traits in a half-sib Duroc pig population of 350 individuals. Phenotypic registers included total CT, TG, and low (LDL)- and high (HDL)-density lipoprotein serum concentrations at 45 and 190 days of age. This approach allowed us to identify two genomewide significant quantitative trait loci (QTL) for HDL-to-LDL ratio at 45 days (SSC6, 84 cM) and for TG at 190 days (SSC4, 23 cM) as well as a number of chromosomewide significant QTL. The comparison of QTL locations at 45 and 190 days revealed a notable lack of concordance at these two time points, suggesting that the effects of these QTL are age specific. Moreover, we have observed a considerable level of correspondence among the locations of the most significant porcine lipid QTL and those identified in humans. This finding might suggest that, in mammals, diverse polymorphisms located in a common set of genes are involved in the genetic variation of serum lipid levels.