Genetical genomics: combining genetics with gene expression analysis

Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato

The genetic analysis of potato is hampered by the complexity of tetrasomic inheritance. An ongoing effort aims to transform the clonally propagated tetraploid potato into a seed-propagated diploid crop, which would make genetic analyses much easier owing to disomic inheritance. Here, we construct and report the large-scale genetic and heterotic characteristics of a diploid F2 potato population derived from the cross of two highly homozygous inbred lines. We investigate 20,382 traits generated from multi-omics dataset and identify 25,770 quantitative trait loci (QTLs). Coupled with gene expression data, we construct a systems-genetics network for gene discovery in potatoes. Importantly, we explore the genetic basis of heterosis in this population, especially for yield and male fertility heterosis. We find that positive heterotic effects of yield-related QTLs and negative heterotic effects of metabolite QTLs (mQTLs) contribute to yield heterosis. Additionally, we identify a PME gene with a dominance heterotic effect that plays an important role in male fertility heterosis. This study provides genetic resources for the potato community and will facilitate the application of heterosis in diploid potato breeding.

Evolutionary Analyses of Gene Expression Divergence in Panicum hallii: Exploring Constitutive and Plastic Responses Using Reciprocal Transplants

The evolution of gene expression is thought to be an important mechanism of local adaptation and ecological speciation. Gene expression divergence occurs through the evolution of cis- polymorphisms and through more widespread effects driven by trans-regulatory factors. Here, we explore expression and sequence divergence in a large sample of Panicum hallii accessions encompassing the species range using a reciprocal transplantation experiment. We observed widespread genotype and transplant site drivers of expression divergence, with a limited number of genes exhibiting genotype-by-site interactions. We used a modified FST-QST outlier approach (QPC analysis) to detect local adaptation. We identified 514 genes with constitutive expression divergence above and beyond the levels expected under neutral processes. However, no plastic expression responses met our multiple testing correction as QPC outliers. Constitutive QPC outlier genes were involved in a number of developmental processes and responses to abiotic environments. Leveraging earlier expression quantitative trait loci results, we found a strong enrichment of expression divergence, including for QPC outliers, in genes previously identified with cis and cis-environment interactions but found no patterns related to trans-factors. Population genetic analyses detected elevated sequence divergence of promoters and coding sequence of constitutive expression outliers but little evidence for positive selection on these proteins. Our results are consistent with a hypothesis of cis-regulatory divergence as a primary driver of expression divergence in P. hallii.

Integrating Phenotypic and Gene Expression Linkage Mapping to Dissect Rust Resistance in Chickling Pea

Rusts are among the most important foliar biotrophic fungal diseases in legumes. Lathyrus cicera crop can be severely damaged by Uromyces pisi, to which partial resistance has been identified. Nevertheless, the underlying genetic basis and molecular mechanisms of this resistance are poorly understood in L. cicera. To prioritise the causative variants controlling partial resistance to rust in L. cicera, a recombinant inbred line (RIL) population, segregating for response to this pathogen, was used to combine the detection of related phenotypic- and expression-quantitative trait loci (pQTLs and eQTLs, respectively). RILs' U. pisi disease severity (DS) was recorded in three independent screenings at seedling (growth chamber) and in one season of exploratory screening at adult plant stage (semi-controlled field conditions). A continuous DS range was observed in both conditions and used for pQTL mapping. Different pQTLs were identified under the growth chamber and semi-controlled field conditions, indicating a distinct genetic basis depending on the plant developmental stage and/or the environment. Additionally, the expression of nine genes related to U. pisi resistance in L. cicera was quantified for each RIL individual and used for eQTL mapping. One cis-eQTL and one trans-eQTL were identified controlling the expression variation of one gene related to rust resistance - a member of glycosyl hydrolase family 17. Integrating phenotyping, gene expression and linkage mapping allowed prioritising four candidate genes relevant for disease-resistance precision breeding involved in adaptation to biotic stress, cellular, and organelle homeostasis, and proteins directly involved in plant defence.

Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Background

Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows.

RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results

WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = − 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion

The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2553-z) contains supplementary material, which is available to authorized users.

Quantitative trait locus mapping of Populus bark features and stem diameter

BACKGROUND

Bark plays important roles in photosynthate transport and storage, along with physical and chemical protection. Bark texture varies extensively among species, from smooth to fissured to deeply furrowed, but its genetic control is unknown. This study sought to determine the main genomic regions associated with natural variation in bark features and stem diameter. Quantitative trait loci (QTL) were mapped using an interspecific pseudo-backcross pedigree (Populus trichocarpa x P. deltoides and P. deltoides) for bark texture, bark thickness and diameter collected across three years, two sites and three biological replicates per site.

RESULTS

QTL specific to bark texture were highly reproducible in shared intervals across sites, years and replicates. Significant positive correlations and co-localization between trait QTL suggest pleiotropic regulators or closely linked genes. A list of candidate genes with related putative function, location close to QTL maxima and with the highest expression level in the phloem, xylem and cambium was identified.

CONCLUSION

Candidate genes for bark texture included an ortholog of Arabidopsis ANAC104 (PopNAC128), which plays a role in lignified fiber cell and ray development, as well as Pinin and Fasciclin (PopFLA) genes with a role in cell adhesion, cell shape and migration. The results presented in this study provide a basis for future genomic characterization of genes found within the QTL for bark texture, bark thickness and diameter in order to better understand stem and bark development in Populus and other woody perennial plants. The QTL mapping approach identified a list of prime candidate genes for further validation using functional genomics or forward genetics approaches.

Interference with ethylene perception at receptor level sheds light on auxin and transcriptional circuits associated with the climacteric ripening of apple fruit (Malus x domestica Borkh.)

Apple (Malus x domestica Borkh.) is a model species for studying the metabolic changes that occur at the onset of ripening in fruit crops, and the physiological mechanisms that are governed by the hormone ethylene. In this study, to dissect the climacteric interplay in apple, a multidisciplinary approach was employed. To this end, a comprehensive analysis of gene expression together with the investigation of several physiological entities (texture, volatilome and content of polyphenolic compounds) was performed throughout fruit development and ripening. The transcriptomic profiling was conducted with two microarray platforms: a dedicated custom array (iRIPE) and a whole genome array specifically enriched with ripening-related genes for apple (WGAA). The transcriptomic and phenotypic changes following the application of 1-methylcyclopropene (1-MCP), an ethylene inhibitor leading to important modifications in overall fruit physiology, were also highlighted. The integrative comparative network analysis showed both negative and positive correlations between ripening-related transcripts and the accumulation of specific metabolites or texture components. The ripening distortion caused by the inhibition of ethylene perception, in addition to affecting the ethylene pathway, stimulated the de-repression of auxin-related genes, transcription factors and photosynthetic genes. Overall, the comprehensive repertoire of results obtained here advances the elucidation of the multi-layered climacteric mechanism of fruit ripening, thus suggesting a possible transcriptional circuit governed by hormones and transcription factors.

Genetics of gene expression characterizes response to selective breeding for alcohol preference

Numerous selective breeding experiments have been performed with rodents, in an attempt to understand the genetic basis for innate differences in preference for alcohol consumption. Quantitative trait locus (QTL) analysis has been used to determine regions of the genome that are associated with the behavioral difference in alcohol preference/consumption. Recent work suggests that differences in gene expression represent a major genetic basis for complex traits. Therefore, the QTLs are likely to harbor regulatory regions (eQTLs) for the differentially expressed genes that are associated with the trait. In this study, we examined brain gene expression differences over generations of selection of the third replicate lines of high and low alcohol-preferring (HAP3 and LAP3) mice, and determined regions of the genome that control the expression of these differentially expressed genes (de eQTLs). We also determined eQTL regions (rv eQTLs) for genes that showed a decrease in variance of expression levels over the course of selection. We postulated that de eQTLs that overlap with rv eQTLs, and also with phenotypic QTLs, represent genomic regions that are affected by the process of selection. These overlapping regions controlled the expression of candidate genes (that displayed differential expression and reduced variance of expression) for the predisposition to differences in alcohol consumption by the HAP3/LAP3 mice.

RNA-Seq using two populations reveals genes and alleles controlling wood traits and growth in Eucalyptus nitens

Eucalyptus nitens is a perennial forest tree species grown mainly for kraft pulp production in many parts of the world. Kraft pulp yield (KPY) is a key determinant of plantation profitability and increasing the KPY of trees grown in plantations is a major breeding objective. To speed up the breeding process, molecular markers that can predict KPY are desirable. To achieve this goal, we carried out RNA-Seq studies on trees at extremes of KPY in two different trials to identify genes and alleles whose expression correlated with KPY. KPY is positively correlated with growth measured as diameter at breast height (DBH) in both trials. In total, six RNA bulks from two treatments were sequenced on an Illumina HiSeq platform. At 5% false discovery rate level, 3953 transcripts showed differential expression in the same direction in both trials; 2551 (65%) were down-regulated and 1402 (35%) were up-regulated in low KPY samples. The genes up-regulated in low KPY trees were largely involved in biotic and abiotic stress response reflecting the low growth among low KPY trees. Genes down-regulated in low KPY trees mainly belonged to gene categories involved in wood formation and growth. Differential allelic expression was observed in 2103 SNPs (in 1068 genes) and of these 640 SNPs (30%) occurred in 313 unique genes that were also differentially expressed. These SNPs may represent the cis-acting regulatory variants that influence total gene expression. In addition we also identified 196 genes which had Ka/Ks ratios greater than 1.5, suggesting that these genes are under positive selection. Candidate genes and alleles identified in this study will provide a valuable resource for future association studies aimed at identifying molecular markers for KPY and growth.

Is the alcohol deprivation effect genetically mediated? Studies with HXB/BXH recombinant inbred rat strains

BACKGROUND

Two features of alcohol addiction that have been widely studied in animal models are relapse drinking following periods of alcohol abstinence and the escalation of alcohol consumption after chronic continuous or intermittent alcohol exposure. The genetic contribution to these phenotypes has not been systematically investigated.

METHODS

HXB/BXH recombinant inbred (RI) rat strains were given access to alcohol sequentially as follows: alcohol (10%) as the only fluid for 1 week; alcohol (10%) and water in a 2-bottle choice paradigm for 7 weeks ("pre-alcohol deprivation effect [ADE] alcohol consumption"); 2 weeks of access to water only (alcohol deprivation); and 2 weeks of reaccess to 10% alcohol and water ("post-ADE alcohol consumption"). The periods of deprivation and reaccess to alcohol were repeated 3 times. The ADE was defined as the amount of alcohol consumed in the first 24 hours after deprivation minus the average daily amount of alcohol consumed in the week prior to deprivation. Heritability of the phenotypes was determined by analysis of variance, and quantitative trait loci (QTLs) were identified.

RESULTS

All strains showed increased alcohol consumption, compared to the predeprivation period, in the first 24 hours after each deprivation (ADE). Broad-sense heritability of the ADEs was low (ADE1, 9.1%; ADE2, 26.2%; ADE3, 16.3%). Alcohol consumption levels were relatively stable over weeks 2 to 7. Post-ADE alcohol consumption levels consistently increased in some strains and were decreased or unchanged in others. Heritability of pre- and post-ADE alcohol consumption was high and increased over time (week 2, 38.5%; week 7, 51.1%; week 11, 56.8%; week 15, 63.3%). QTLs for pre- and post-ADE alcohol consumption were similar, but the strength of the QTL association with the phenotype decreased over time.

CONCLUSIONS

In the HXB/BXH RI rat strains, genotypic variance does not account for a large proportion of phenotypic variance in the ADE phenotype (low heritability), suggesting a role of environmental factors. In contrast, a large proportion of the variance across the RI strains in pre- and post-ADE alcohol consumption is due to genetically determined variance (high heritability).

Genetics of gene expression in CNS

Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.

Natural variation in the histone demethylase, KDM4C, influences expression levels of specific genes including those that affect cell growth

DNA sequence variants influence gene expression and cellular phenotypes. In this study, we focused on natural variation in the gene encoding the histone demethylase, KDM4C, which promotes transcriptional activation by removing the repressive histone mark, H3K9me3, from its target genes. We uncovered cis-acting variants that contribute to extensive individual differences in KDM4C expression. We also identified the target genes of KDM4C and demonstrated that variation in KDM4C expression leads to differences in the growth of normal and some cancer cells. Together, our results from genetic mapping and molecular analysis provide an example of how genetic variation affects epigenetic regulation of gene expression and cellular phenotype.

The roles of cis- and trans-regulation in the evolution of regulatory incompatibilities and sexually dimorphic gene expression

Evolutionary changes in gene expression underlie many aspects of phenotypic diversity within and among species. Understanding the genetic basis for evolved changes in gene expression is therefore an important component of a comprehensive understanding of the genetic basis of phenotypic evolution. Using interspecific introgression hybrids, we examined the genetic basis for divergence in genome-wide patterns of gene expression between Drosophila simulans and Drosophila mauritiana. We find that cis-regulatory and trans-regulatory divergences differ significantly in patterns of genetic architecture and evolution. The effects of cis-regulatory divergence are approximately additive in heterozygotes, quantitatively different between males and females, and well predicted by expression differences between the two parental species. In contrast, the effects of trans-regulatory divergence are associated with largely dominant introgressed alleles, have similar effects in the two sexes, and generate expression levels in hybrids outside the range of expression in both parental species. Although the effects of introgressed trans-regulatory alleles are similar in males and females, expression levels of the genes they regulate are sexually dimorphic between the parental D. simulans and D. mauritiana strains, suggesting that pure-species genotypes carry unlinked modifier alleles that increase sexual dimorphism in expression. Our results suggest that independent effects of cis-regulatory substitutions in males and females may favor their role in the evolution of sexually dimorphic phenotypes, and that trans-regulatory divergence is an important source of regulatory incompatibilities.

What is next after the genes for autoimmunity?

Clinical pathologies draw us to envisage disease as either an independent entity or a diverse set of traits governed by common physiopathological mechanisms, prompted by environmental assaults throughout life. Autoimmune diseases are not an exception, given they represent a diverse collection of diseases in terms of their demographic profile and primary clinical manifestations. Although they are pleiotropic outcomes of non-specific disease genes underlying similar immunogenetic mechanisms, research generally focuses on a single disease. Drastic technologic advances are leading research to organize clinical genomic multidisciplinary approaches to decipher the nature of human biological systems. Once the currently costly omic-based technologies become universally accessible, the way will be paved for a cleaner picture to risk quantification, prevention, prognosis and diagnosis, allowing us to clearly define better phenotypes always ensuring the integrity of the individuals studied. However, making accurate predictions for most autoimmune diseases is an ambitious challenge, since the understanding of these pathologies is far from complete. Herein, some pitfalls and challenges of the genetics of autoimmune diseases are reviewed, and an approximation to the future of research in this field is presented.

Genome-wide haplotype analysis of cis expression quantitative trait loci in monocytes

In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ~2,1 × 10(9) haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >10(4)-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2 × 10(-4) (~0.05/412), 193 haplotypic signals replicated. 1000 G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000 G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.

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.

Identifying the genetic variation of gene expression using gene sets: application of novel gene Set eQTL approach to PharmGKB and KEGG

Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set - expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.

Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

The burgeoning interest in the field of epigenetics has precipitated the need to develop approaches to strengthen causal inference when considering the role of epigenetic mediators of environmental exposures on disease risk. Epigenetic markers, like any other molecular biomarker, are vulnerable to confounding and reverse causation. Here, we present a strategy, based on the well-established framework of Mendelian randomization, to interrogate the causal relationships between exposure, DNA methylation and outcome. The two-step approach first uses a genetic proxy for the exposure of interest to assess the causal relationship between exposure and methylation. A second step then utilizes a genetic proxy for DNA methylation to interrogate the causal relationship between DNA methylation and outcome. The rationale, origins, methodology, advantages and limitations of this novel strategy are presented.

Drosophila melanogaster as a model for lead neurotoxicology and toxicogenomics research

Drosophila melanogaster is an excellent model animal for studying the neurotoxicology of lead. It has been known since ancient Roman times that long-term exposure to low levels of lead results in behavioral abnormalities, such as what is now known as attention deficit hyperactivity disorder (ADHD). Because lead alters mechanisms that underlie developmental neuronal plasticity, chronic exposure of children, even at blood lead levels below the current CDC community action level (10 μg/dl), can result in reduced cognitive ability, increased likelihood of delinquency, behaviors associated with ADHD, changes in activity level, altered sensory function, delayed onset of sexual maturity in girls, and changes in immune function. In order to better understand how lead affects neuronal plasticity, we will describe recent findings from a Drosophila behavioral genetics laboratory, a Drosophila neurophysiology laboratory, and a Drosophila quantitative genetics laboratory who have joined forces to study the effects of lead on the Drosophila nervous system. Studying the effects of lead on Drosophila nervous system development will give us a better understanding of the mechanisms of Pb neurotoxicity in the developing human nervous system.

When one and one gives more than two: challenges and opportunities of integrative omics

Since the dawn of the post-genomic era a myriad of novel high-throughput technologies have been developed that are capable of measuring thousands of biological molecules at once, giving rise to various “omics” platforms. These advances offer the unique opportunity to study how individual parts of a biological system work together to produce emerging phenotypes. Today, many research laboratories are moving toward applying multiple omics platforms to analyze the same biological samples. In addition, network information of interacting molecules is being incorporated more and more into the analysis and interpretation of these multiple omics datasets, which provides novel ways to integrate multiple layers of heterogeneous biological information into a single coherent picture. Here, we provide a perspective on how such recent “integrative omics” efforts are likely going to shift biological paradigms once again, and what challenges lie ahead.

Mosquito genomics: progress and challenges

The whole-genome sequencing of mosquitoes has facilitated our understanding of fundamental biological processes at their basic molecular levels and holds potential for application to mosquito control and prevention of mosquito-borne disease transmission. Draft genome sequences are available for Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus. Collectively, these represent the major vectors of African malaria, dengue fever and yellow fever viruses, and lymphatic filariasis, respectively. Rapid advances in genome technologies have revealed detailed information on genome architecture as well as phenotype-specific transcriptomics and proteomics. These resources allow for detailed comparative analyses within and across populations as well as species. Next-generation sequencing technologies will likely promote a proliferation of genome sequences for additional mosquito species as well as for individual insects. Here we review the current status of genome research in mosquitoes and identify potential areas for further investigations.

Gene clusters, molecular evolution and disease: a speculation

Traditionally eukaryotic genes are considered independently expressed under the control of their promoters and cis-regulatory domains. However, recent studies in worms, flies, mice and humans have shown that genes co-habiting a chromatin domain or “genomic neighborhood” are frequently co-expressed. Often these co-expressed genes neither constitute part of an operon nor function within the same biological pathway. The mechanisms underlying the partitioning of the genome into transcriptional genomic neighborhoods are poorly defined. However, cross-species analyses find that the linkage among the co-expressed genes of these clusters is significantly conserved and that the expression patterns of genes within clusters have coevolved with the clusters. Such selection could be mediated by chromatin interactions with the nuclear matrix and long-range remodeling of chromatin structure. In the context of human disease, we propose that dysregulation of gene expression across genomic neighborhoods will cause highly pleiotropic diseases. Candidate genomic neighborhood diseases include the nuclear laminopathies, chromosomal translocations and genomic instability disorders, imprinting disorders of errant insulator function, syndromes from impaired cohesin complex assembly, as well as diseases of global covalent histone modifications and DNA methylation. The alteration of transcriptional genomic neighborhoods provides an exciting and novel model for studying epigenetic alterations as quantitative traits in complex common human diseases.

Genetic dissection of behavioral flexibility: reversal learning in mice

BACKGROUND

Behavioral inflexibility is a feature of schizophrenia, attention-deficit/hyperactivity disorder, and behavior addictions that likely results from heritable deficits in the inhibitory control over behavior. Here, we investigate the genetic basis of individual differences in flexibility, measured using an operant reversal learning task.

METHODS

We quantified discrimination acquisition and subsequent reversal learning in a cohort of 51 BXD strains of mice (2-5 mice/strain, n = 176) for which we have matched data on sequence, gene expression in key central nervous system regions, and neuroreceptor levels.

RESULTS

Strain variation in trials to criterion on acquisition and reversal was high, with moderate heritability (∼.3). Acquisition and reversal learning phenotypes did not covary at the strain level, suggesting that these traits are effectively under independent genetic control. Reversal performance did covary with dopamine D2 receptor levels in the ventral midbrain, consistent with a similar observed relationship between impulsivity and D2 receptors in humans. Reversal, but not acquisition, is linked to a locus on mouse chromosome 10 with a peak likelihood ratio statistic at 86.2 megabase (p < .05 genome-wide). Variance in messenger RNA levels of select transcripts expressed in neocortex, hippocampus, and striatum correlated with the reversal learning phenotype, including Syn3, Nt5dc3, and Hcfc2.

CONCLUSIONS

This work demonstrates the clear trait independence between, and genetic control of, discrimination acquisition and reversal and illustrates how globally coherent data sets for a single panel of highly related strains can be interrogated and integrated to uncover genetic sources and molecular and neuropharmacological candidates of complex behavioral traits relevant to human psychopathology.

Social immunity in honeybees (Apis mellifera): transcriptome analysis of varroa-hygienic behaviour

Honeybees have evolved a social immunity consisting of the cooperation of individuals to decrease disease in the hive. We identified a set of genes involved in this social immunity by analysing the brain transcriptome of highly varroa-hygienic bees, who efficiently detect and remove brood infected with the Varroa destructor mite. The function of these candidate genes does not seem to support a higher olfactory sensitivity in hygienic bees, as previously hypothesized. However, comparing their genomic profile with those from other behaviours suggests a link with brood care and the highly varroa-hygienic Africanized honeybees. These results represent a first step toward the identification of genes involved in social immunity and thus provide first insights into the evolution of social immunity.

Relationships between genetic polymorphisms and transcriptional profiles for outcome prediction in anticancer agent treatment

In the era of personal genomics, predicting the individual response to drug-treatment is a challenge of biomedical research. The aim of this study was to validate whether interaction information between genetic and transcriptional signatures are promising features to predict a drug response. Because drug resistance/susceptibilities result from the complex associations of genetic and transcriptional activities, we predicted the inter-relationships between genetic and transcriptional signatures. With this concept, captured genetic polymorphisms and transcriptional profiles were prepared in cancer samples. By splitting ninety-nine samples into a trial set (n = 30) and a test set (n = 69), the outperformance of relationship-focused model (0.84 of area under the curve in trial set, P = 2.90 x 10⁻⁴) was presented in the trial set and validated in the test set, respectively. The prediction results of modeling show that considering the relationships between genetic and transcriptional features is an effective approach to determine outcome predictions of drug-treatment.

Variable set enrichment analysis in genome-wide association studies

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.

Common genetic variation in the 3'-untranslated region of gonadotropin-releasing hormone receptor regulates gene expression in cella and is associated with thyroid function, insulin secretion as well as insulin sensitivity in polycystic ovary syndrome patients

Gonadotropin-releasing hormone receptor (GNRHR) is a member of the G protein-coupled Ca(2+)-dependent family of receptors. It interacts with GnRH, whose signaling plays an important role in thyroid-stimulating hormone (TSH) secretion and insulin activity. There has been no study on the genetic effect of GNRHR on TSH secretion and insulin action in polycystic ovary syndrome (PCOS). We decided to investigate whether naturally occurring genetic variation at the human GNRHR locus is associated with thyroid function, insulin secretion and insulin sensitivity in PCOS. We undertook a systematic search for polymorphisms in GNRHR by resequencing the gene and then genotyped common single-nucleotide polymorphisms across the locus in 261 PCOS patients well-phenotyped for several metabolic traits to determine associations. A test for association of common genetic variants with susceptibility to PCOS was carried out in a large cohort of 948 subjects. Finally, we experimentally validated the marker-on-trait associations using GNRHR 3'-UTR region/reporter analysis in 293T cells. The 3'-UTR variant rs1038426 was associated with serum thyroid concentration (P = 0.007), change of insulin levels during oral glucose tolerance test (P = 0.004) and insulin sensitivity index (P = 0.014). In a functional study, 3'-UTR variant T allele increased reporter expression by a transfected luciferase reporter/GNRHR 3'-UTR expression plasmid. In conclusion, our results strongly suggest that common genetic variant in GNRHR contributes to the phenotypic expression of PCOS. The findings suggest novel pathophysiological links between the GNRHR locus and thyroid function and insulin secretion in PCOS.

XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments

XGAP, a software platform for the integration and analysis of genotype and phenotype data.

We present an extensible software model for the genotype and phenotype community, XGAP. Readers can download a standard XGAP (http://www.xgap.org) or auto-generate a custom version using MOLGENIS with programming interfaces to R-software and web-services or user interfaces for biologists. XGAP has simple load formats for any type of genotype, epigenotype, transcript, protein, metabolite or other phenotype data. Current functionality includes tools ranging from eQTL analysis in mouse to genome-wide association studies in humans.

Modeling expression quantitative trait loci in data combining ethnic populations

Background

Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to analyzing each population independently. In such studies, however, the genetic diversity of minor allele frequencies among populations has rarely been taken into account. Due to the fact that allele frequency diversity and population-level expression differences are present in populations, a consensus regarding the optimal statistical approach for analysis of eQTL in data combining different populations remains inconclusive.

Results

In this report, we explored the applicability of a constrained two-way model to identify eQTL for combined ethnic data that might contain genetic diversity among ethnic populations. In addition, gene expression differences resulted from ethnic allele frequency diversity between populations were directly estimated and analyzed by the constrained two-way model. Through simulation, we investigated effects of genetic diversity on eQTL identification by examining gene expression data pooled from normal quantile transformation of each population. Using the constrained two-way model to reanalyze data from Caucasians and Asian individuals available from HapMap, a large number of eQTL were identified with similar genetic effects on the gene expression levels in these two populations. Furthermore, 19 single nucleotide polymorphisms with inter-population differences with respect to both genotype frequency and gene expression levels directed by genotypes were identified and reflected a clear distinction between Caucasians and Asian individuals.

Conclusions

This study illustrates the influence of minor allele frequencies on common eQTL identification using either separate or combined population data. Our findings are important for future eQTL studies in which different datasets are combined to increase the power of eQTL identification.

Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.

Search for genetic markers and functional variants involved in the development of opiate and cocaine addiction and treatment

Addiction to opiates and illicit use of psychostimulants is a chronic, relapsing brain disease that, if left untreated, can cause major medical, social, and economic problems. This article reviews recent progress in studies of association of gene variants with vulnerability to develop opiate and cocaine addictions, focusing primarily on genes of the opioid and monoaminergic systems. In addition, we provide the first evidence of a cis-acting polymorphism and a functional haplotype in the PDYN gene, of significantly higher DNA methylation rate of the OPRM1 gene in the lymphocytes of heroin addicts, and significant differences in genotype frequencies of three single-nucleotide polymorphisms of the P-glycoprotein gene (ABCB1) between "higher" and "lower" methadone doses in methadone-maintained patients. In genomewide and multigene association studies, we found association of several new genes and new variants of known genes with heroin addiction. Finally, we describe the development and application of a novel technique: molecular haplotyping for studies in genetics of drug addiction.

Genetic mapping and positional cloning

Genetic mapping and positional cloning of genetically complex traits in the laboratory rat (Rattus norvegicus) has recently led to the identification of various susceptibility genes in different rat models. Rat genetics has benefited from revolutionary advances in molecular biology, genetics, genomics and informatics and provide an unparalleled resource for molecular genetic investigation of mammalian physiopathology and its underlying complex genetic architecture. In this review, we will consider different strategies that are being used in the successful positional cloning of rat complex trait genes in the context of recent progress in rodent and human genetics.

Genetic polymorphisms associated with acute lung injury

Acute lung injury and acute respiratory distress syndrome are the result of intense inflammation in the lungs leading to respiratory failure. The causes of acute lung injury/acute respiratory distress syndrome are numerous (e.g., pneumonia, sepsis and trauma) but the reasons why certain individuals develop lung injury in response to these stimuli and others do not are not well understood. There is ample evidence in the literature that gene-host and gene-environment interactions may play a large role in the morbidity and mortality associated with this syndrome. In this review, we initially discuss methods for identification of candidate acute lung injury/acute respiratory distress syndrome susceptibility genes using a number of model systems including in vitro cell systems and inbred mice. We then describe examples of polymorphisms in genes that have been associated with the pathogenesis of acute lung injury/acute respiratory distress syndrome in human case-control studies. Systematic bench to bedside approaches to understand the genetic contribution to acute lung injury/acute respiratory distress syndrome have provided important insight to this complex disease and continuation of these investigations could lead to the development of novel prevention or intervention strategies.

Gemcitabine and arabinosylcytosin pharmacogenomics: genome-wide association and drug response biomarkers

Cancer patients show large individual variation in their response to chemotherapeutic agents. Gemcitabine (dFdC) and AraC, two cytidine analogues, have shown significant activity against a variety of tumors. We previously used expression data from a lymphoblastoid cell line-based model system to identify genes that might be important for the two drug cytotoxicity. In the present study, we used that same model system to perform a genome-wide association (GWA) study to test the hypothesis that common genetic variation might influence both gene expression and response to the two drugs. Specifically, genome-wide single nucleotide polymorphisms (SNPs) and mRNA expression data were obtained using the Illumina 550K(R) HumanHap550 SNP Chip and Affymetrix U133 Plus 2.0 GeneChip, respectively, for 174 ethnically-defined "Human Variation Panel" lymphoblastoid cell lines. Gemcitabine and AraC cytotoxicity assays were performed to obtain IC(50) values for the cell lines. We then performed GWA studies with SNPs, gene expression and IC(50) of these two drugs. This approach identified SNPs that were associated with gemcitabine or AraC IC(50) values and with the expression regulation for 29 genes or 30 genes, respectively. One SNP in IQGAP2 (rs3797418) was significantly associated with variation in both the expression of multiple genes and gemcitabine and AraC IC(50). A second SNP in TGM3 (rs6082527) was also significantly associated with multiple gene expression and gemcitabine IC50. To confirm the association results, we performed siRNA knock down of selected genes with expression that was associated with rs3797418 and rs6082527 in tumor cell and the knock down altered gemcitabine or AraC sensitivity, confirming our association study results. These results suggest that the application of GWA approaches using cell-based model systems, when combined with complementary functional validation, can provide insights into mechanisms responsible for variation in cytidine analogue response.

Genetical toxicogenomics in Drosophila identifies master-modulatory loci that are regulated by developmental exposure to lead

The genetics of gene expression in recombinant inbred lines (RILs) can be mapped as expression quantitative trait loci (eQTLs). So-called "genetical genomics" studies have identified locally acting eQTLs (cis-eQTLs) for genes that show differences in steady-state RNA levels. These studies have also identified distantly acting master-modulatory trans-eQTLs that regulate tens or hundreds of transcripts (hotspots or transbands). We expand on these studies by performing genetical genomics experiments in two environments in order to identify trans-eQTL that might be regulated by developmental exposure to the neurotoxin lead. Flies from each of 75 RIL were raised from eggs to adults on either control food (made with 250 microM sodium acetate), or lead-treated food (made with 250 microM lead acetate, PbAc). RNA expression analyses of whole adult male flies (5-10 days old) were performed with Affymetrix DrosII whole genome arrays (18,952 probesets). Among the 1389 genes with cis-eQTL, there were 405 genes unique to control flies and 544 genes unique to lead-treated ones (440 genes had the same cis-eQTLs in both samples). There are 2396 genes with trans-eQTL which mapped to 12 major transbands with greater than 95 genes. Permutation analyses of the strain labels but not the expression data suggests that the total number of eQTL and the number of transbands are more important criteria for validation than the size of the transband. Two transbands, one located on the 2nd chromosome and one on the 3rd chromosome, co-regulate 33 lead-induced genes, many of which are involved in neurodevelopmental processes. For these 33 genes, rather than allelic variation at one locus exerting differential effects in two environments, we found that variation at two different loci are required for optimal effects on lead-induced expression.

Regulatory divergence in Drosophila melanogaster and D. simulans, a genomewide analysis of allele-specific expression

Species-specific regulation of gene expression contributes to the development and maintenance of reproductive isolation and to species differences in ecologically important traits. A better understanding of the evolutionary forces that shape regulatory variation and divergence can be developed by comparing expression differences among species and interspecific hybrids. Once expression differences are identified, the underlying genetics of regulatory variation or divergence can be explored. With the goal of associating cis and/or trans components of regulatory divergence with differences in gene expression, overall and allele-specific expression levels were assayed genomewide in female adult heads of Drosophila melanogaster, D. simulans, and their F1 hybrids. A greater proportion of cis differences than trans differences were identified for genes expressed in heads and, in accordance with previous studies, cis differences also explained a larger number of species differences in overall expression level. Regulatory divergence was found to be prevalent among genes associated with defense, olfaction, and among genes downstream of the Drosophila sex determination hierarchy. In addition, two genes, with critical roles in sex determination and micro RNA processing, Sxl and loqs, were identified as misexpressed in hybrid female heads, potentially contributing to hybrid incompatibility.

Genetics of human gene expression: mapping DNA variants that influence gene expression

There is extensive natural variation in human gene expression. As quantitative phenotypes, expression levels of genes are heritable. Genetic linkage and association mapping have identified cis- and trans-acting DNA variants that influence expression levels of human genes. New insights into human gene regulation are emerging from genetic analyses of gene expression in cells at rest and following exposure to stimuli. The integration of these genetic mapping results with data from co-expression networks is leading to a better understanding of how expression levels of individual genes are regulated and how genes interact with each other. These findings are important for basic understanding of gene regulation and of diseases that result from disruption of normal gene regulation.

A common intronic variant of CXCR3 is functionally associated with gene expression levels and the polymorphic immune cell responses to stimuli

BACKGROUND

CXCR3 is a chemokine receptor that plays important roles in mediating chemotactic signals and modulating the activation of lymphocytes. We have previously conducted a case-control study by using a candidate gene approach to investigate the association of CXCR3 polymorphisms with the risk of asthma. Results from the epidemiologic study showed that a common nucleotide variant in the CXCR3 intron (rs2280964G>A) was associated with disease susceptibility (1006 cases and 384 control subjects; odds ratio, 0.81; 95% CI, 0.69-0.94; P = .007).

OBJECTIVE

The aim of our study was to evaluate the epidemiologic study and provide functional evidence for the association of rs2280964G>A with asthma by investigating the effects of intronic variant on chemokine-mediated phenotypes of human-derived T cells.

METHODS

We used cell line-based in vitro and human primary T cell-based ex vivo studies to examine the functional consequences of the intronic polymorphism, focusing on the regulation of gene expression, splicing, and immune responsiveness toward activating signals.

RESULTS

We present functional evidence indicating that the rs2280964A allele significantly correlates with decreased CXCR3 gene expression, which would lead to variation in immune cell responses to chemokine-cytokine signals in vitro and ex vivo that includes a decrease in chemotactic activity.

CONCLUSION

These findings, in conjunction with those of our previous epidemiologic studies, might implicate a functional link between a common nucleotide variant of a chemokine receptor gene, CXCR3, and a cause for a complex-trait disease, asthma.

Estimating heritability of gene expression using parent-offspring regression with 2-channel microarrays

With the advent of microarrays, it is possible to look at the entire transcriptome of an organism as a suite of quantitative traits. An obvious question to now ask is: To what extent is gene expression heritable? In quantitative genetics, single parent-offspring regression is the most straightforward method in situations where the progenies are produced by cross-pollination to many male parents of unknown location. However, estimation of the heritability of gene expression with single parent-offspring regression has not yet been examined with 2-channel microarrays. Here we introduce 3 experimental designs: chain design, independent quartets design, and completely independent design. We then compare them with common reference design in respect to statistical power and bias of the estimates. In our simulations, we also incorporated a model of simple inheritance with one gene. The results of our simulations indicate the efficiency of the chain design over the alternative design considered.

[Integrating genetic and gene expression data: methods and applications of eQTL mapping]

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to investigate genetic variations that influence levels of gene expression. Expression Quantitative Trait Locus (eQTL) mapping methods have been used to identify the genetic basis of gene expression. Similar to traditional QTL studies, the main goal of eQTL is to identify the genomic locations to which the expression traits are linked. Although microarrays provide the expression data of thousands of transcripts, standard QTL mapping methods, which are able to handle at most tens of traits, cannot be applied directly. As a result, it is necessary to consider the statistical principles involved in the design and analysis of these experiments. In this paper, we reviewed individual selection, experimental design of microarray, normalization of gene expression data, mapping methods, and explaining of results and proposed potential methodological problems for such analyses. Finally, we discussed the applications of this integrative genomic approach to estimate heritability of transcripts, identify candidate genes, construct gene networks, and understand interactions between genes, genes and environments.

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases

An integrative analysis combining genetic interactions and protein interactions can be used to identify candidate genes/proteins for type 1 diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

Innate immune modulation by RNA viruses: emerging insights from functional genomics

By providing a global and integrated view of the host response to infection, functional genomic and systems-biology approaches are contributing to our understanding of RNA virus–host interactions. One area in which these approaches are being put to particularly good use is in shedding new light on the components of innate antiviral defence mechanisms and the viral strategies used to regulate or overcome them.

Genomic analyses have helped to reveal virus-specific differences in the way that viral recognition through pathogen-recognition receptors (PRRs) initiates intracellular signalling cascades. Whereas influenza virus appears to signal primarily through retinoic-acid-inducible gene I (RIG-I), West Nile virus signals through both RIG-I and melanoma differentiation-associated gene 5 (MDA5). Both viruses induce the expression of interferon (IFN)-regulatory factor 3 (IRF3) target genes and IFN-stimulated genes (ISGs).

Genomic analyses have provided a comprehensive view of the transcriptional programmes that are induced by Toll-like receptor (TLR) activation. One transcriptional profile is universally activated by all TLRs and a second profile is specific to TLR3 and TLR4. Nuclear factor-κB (NF-κB) is the key regulator of the universal response, which occurs early after TLR stimulation, and the IFN-stimulated response element (ISRE) is the key component of the TLR3/TLR4 response, which is induced after the NF-κB response.

Some highly virulent viruses, such as Ebola virus and rabies virus, are successful at inhibiting ISG expression, resulting in the marked suppression of genes in key innate antiviral pathways, including those mediated by IRF3. There seems to be a correlation between the antagonism of the IFN response and virulence.

Genomic analyses of the host response to the reconstructed 1918 pandemic influenza virus have revealed similarities and differences to contemporary influenza virus infection. Contemporary and 1918 influenza viruses each trigger an innate immune response that includes the expression of NF-κB and IRF3 target genes, and both viruses trigger a robust cytokine response that attracts immune-cell infiltration to infected tissues. Unlike contemporary virus strains, in which the early response to infection is resolved, the innate immune response triggered by the 1918 influenza virus is characterized by a strong and sustained induction that is associated with massive tissue damage and death.

Global gene-expression profiling has revealed that many effective, attenuated live-virus vaccines transiently induce a stronger type I IFN response than the cognate pathogen, and therefore implicates modulation of this response as an important strategy in rational vaccine design.

By providing a global view of the host response to infection, functional genomic approaches are proving useful in deciphering complex virus–host interactions. Here, the authors reveal how such approaches are being used to better understand viral triggering and regulation of host innate immune responses.

Although often encoding fewer than a dozen genes, RNA viruses can overcome host antiviral responses and wreak havoc on the cells they infect. Some manage to evade host antiviral defences, whereas others elicit an aberrant or disproportional immune response. Both scenarios can result in the disruption of intracellular signalling pathways and significant pathology in the host. Systems-biology approaches are increasingly being used to study the processes of viral triggering and regulation of host immune responses. By providing a global and integrated view of cellular events, these approaches are beginning to unravel some of the complexities of virus–host interactions and provide new insights into how RNA viruses cause disease.

Psychiatric genetics: progress amid controversy

Several psychiatric disorders--such as bipolar disorder, schizophrenia and autism--are highly heritable, yet identifying their genetic basis has been challenging, with most discoveries failing to be replicated. However, inroads have been made by the incorporation of intermediate traits (endophenotypes) and of environmental factors into genetic analyses, and through the identification of rare inherited variants and novel structural mutations. Current efforts aim to increase sample sizes by gathering larger samples for case-control studies or through meta-analyses of such studies. More attention on unique families, rare variants, and on incorporating environment and the emerging knowledge of biological function and pathways into genetic analysis is warranted.

Genetic variation and gene expression in antioxidant related enzymes and risk of COPD: a systematic review

BACKGROUND

Observational epidemiological studies of dietary antioxidant intake, serum antioxidant concentration and lung outcomes suggest that lower levels of antioxidant defences are associated with decreased lung function. Another approach to understanding the role of oxidant/antioxidant imbalance in the risk of chronic obstructive pulmonary disease (COPD) is to investigate the role of genetic variation in antioxidant enzymes, and indeed family based studies suggest a heritable component to lung disease. Many studies of the genes encoding antioxidant enzymes have considered COPD or COPD related outcomes, and a systematic review is needed to summarise the evidence to date, and to provide insights for further research.

METHODS

Genetic association studies of antioxidant enzymes and COPD/COPD related traits, and comparative gene expression studies with disease or smoking as the exposure were systematically identified and reviewed. Antioxidant enzymes considered included enzymes involved in glutathione metabolism, in the thioredoxin system, superoxide dismutases (SOD) and catalase.

RESULTS

A total of 29 genetic association and 15 comparative gene expression studies met the inclusion criteria. The strongest and most consistent effects were in the genes GCL, GSTM1, GSTP1 and SOD3. This review also highlights the lack of studies for genes of interest, particularly GSR, GGT and those related to TXN. There were limited opportunities to evaluate the contribution of a gene to disease risk through synthesis of results from different study designs, as the majority of studies considered either association of sequence variants with disease or effect of disease on gene expression.

CONCLUSION

Network driven approaches that consider potential interaction between and among genes, smoke exposure and antioxidant intake are needed to fully characterise the role of oxidant/antioxidant balance in pathogenesis.

Identifying genetic variants that contribute to chemotherapy-induced cytotoxicity

Patients treated with anticancer chemotherapy exhibit variation, both in terms of tumor response and the incidence and severity of adverse effects. The etiology of this variation is multifactorial with genetic factors likely contributing to a significant extent. Pharmacogenetic and genomic studies can be used to identify the genetic variants that contribute to interindividual variation in susceptibility to chemotherapy-induced cytotoxicity. This review will describe candidate and whole-genome approaches, describe the advantages and disadvantages of each, and illustrate how they can be used to obtain clinically relevant information. Specific emphasis is given to recent advances emerging from the International HapMap Project and to the development of genetic signatures, as opposed to expression signatures, to explain drug sensitivity and resistance.

Cell-to-cell stochastic variation in gene expression is a complex genetic trait

The genetic control of common traits is rarely deterministic, with many genes contributing only to the chance of developing a given phenotype. This incomplete penetrance is poorly understood and is usually attributed to interactions between genes or interactions between genes and environmental conditions. Because many traits such as cancer can emerge from rare events happening in one or very few cells, we speculate an alternative and complementary possibility where some genotypes could facilitate these events by increasing stochastic cell-to-cell variations (or ‘noise’). As a very first step towards investigating this possibility, we studied how natural genetic variation influences the level of noise in the expression of a single gene using the yeast S. cerevisiae as a model system. Reproducible differences in noise were observed between divergent genetic backgrounds. We found that noise was highly heritable and placed under a complex genetic control. Scanning the genome, we mapped three Quantitative Trait Loci (QTL) of noise, one locus being explained by an increase in noise when transcriptional elongation was impaired. Our results suggest that the level of stochasticity in particular molecular regulations may differ between multicellular individuals depending on their genotypic background. The complex genetic architecture of noise buffering couples genetic to non-genetic robustness and provides a molecular basis to the probabilistic nature of complex traits.

Although most inter-individual phenotypic variabilities are largely attributable to DNA differences, a wealth of examples illustrate how a single biological system can vary stochastically over time and between individuals. Identical twins are not identical, and similarly, clonal microbial cells differ in many aspects even when grown simultaneously in a common environment. Using yeast as a model system, we show that a population of isogenic cells all carrying genotype A showed higher cell-to-cell heterogeneity in gene expression than a population of isogenic cells of genotype B. We considered this level of intra-clonal heterogeneity as a quantitative trait and performed genetic linkage (on AxB) to search for regulators of it. This led to the demonstration that transcriptional elongation impairment increases stochastic variation in gene expression in vivo. Our results show that the two levels of inter-individual diversity, genetic and stochastic, are connected by a complex control of the former on the latter. We invite the community to revisit the interpretation of incomplete penetrance, which defines cases where a mutation does not cause the associated phenotype in all its carriers. We propose that, in the case of cancer or other diseases triggered by single cells, such mutations might increase stochastic molecular fluctuations and thereby the fraction of deviant cellular phenotypes in a human body.

From classical genetics to quantitative genetics to systems biology: modeling epistasis

Gene expression data has been used in lieu of phenotype in both classical and quantitative genetic settings. These two disciplines have separate approaches to measuring and interpreting epistasis, which is the interaction between alleles at different loci. We propose a framework for estimating and interpreting epistasis from a classical experiment that combines the strengths of each approach. A regression analysis step accommodates the quantitative nature of expression measurements by estimating the effect of gene deletions plus any interaction. Effects are selected by significance such that a reduced model describes each expression trait. We show how the resulting models correspond to specific hierarchical relationships between two regulator genes and a target gene. These relationships are the basic units of genetic pathways and genomic system diagrams. Our approach can be extended to analyze data from a variety of experiments, multiple loci, and multiple environments.

Epistasis has long had two slightly different meanings depending on the context in which it is discussed. The classical definition describes an allele at one locus completely masking the effect of an allele at a second locus. Such relationships can be interpreted as hierarchical, and they can be combined to infer genetic pathways. In quantitative genetics, epistasis encompasses a wide range of interactions and can be extended to more than two loci. These two definitions coexist because they are typically applied to different types of study populations and different types of traits. The current trend is to treat gene expression as a trait in a variety of genetic backgrounds. This provides reason to revisit epistasis in this new context. We accommodate the continuous nature of gene expression using ideas from quantitative genetics, but retain the hierarchical interpretation of the classical experiment. These hierarchical relationships are the building blocks of systems diagrams and genetic pathways. This framework can serve as a foundation for future epistasis analyses based on genomic data.

Identifying trait clusters by linkage profiles: application in genetical genomics

MOTIVATION

Genes often regulate multiple traits. Identifying clusters of traits influenced by a common group of genes helps elucidate regulatory networks and can improve linkage mapping.

METHODS

We show that the Pearson correlation coefficient, rho L, between two LOD score profiles can, with high specificity and sensitivity, identify pairs of genes that have their transcription regulated by shared quantitative trait loci (QTL). Furthermore, using theoretical and/or empirical methods, we can approximate the distribution of rho L under the null hypothesis of no common QTL. Therefore, it is possible to calculate P-values and false discovery rates for testing whether two traits share common QTL. We then examine the properties of rho L through simulation and use rho L to cluster genes in a genetical genomics experiment examining Saccharomyces cerevisiae.

RESULTS

Simulations show that rho L can have more power than the clustering methods currently used in genetical genomics. Combining experimental results with Gene Ontology (GO) annotations show that genes within a purported cluster often share similar function.

SOFTWARE

R-code included in online Supplementary Material.