Decanalization and the origin of complex disease

Human genomic disease variants: a neutral evolutionary explanation

Many perspectives on the role of evolution in human health include nonempirical assumptions concerning the adaptive evolutionary origins of human diseases. Evolutionary analyses of the increasing wealth of clinical and population genomic data have begun to challenge these presumptions. In order to systematically evaluate such claims, the time has come to build a common framework for an empirical and intellectual unification of evolution and modern medicine. We review the emerging evidence and provide a supporting conceptual framework that establishes the classical neutral theory of molecular evolution (NTME) as the basis for evaluating disease- associated genomic variations in health and medicine. For over a decade, the NTME has already explained the origins and distribution of variants implicated in diseases and has illuminated the power of evolutionary thinking in genomic medicine. We suggest that a majority of disease variants in modern populations will have neutral evolutionary origins (previously neutral), with a relatively smaller fraction exhibiting adaptive evolutionary origins (previously adaptive). This pattern is expected to hold true for common as well as rare disease variants. Ultimately, a neutral evolutionary perspective will provide medicine with an informative and actionable framework that enables objective clinical assessment beyond convenient tendencies to invoke past adaptive events in human history as a root cause of human disease.

Artificial neural networks modeling gene-environment interaction

Background

Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only.

Results

In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor.

Conclusion

Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

Phenotypic plasticity of the Drosophila transcriptome

Phenotypic plasticity is the ability of a single genotype to produce different phenotypes in response to changing environments. We assessed variation in genome-wide gene expression and four fitness-related phenotypes of an outbred Drosophila melanogaster population under 20 different physiological, social, nutritional, chemical, and physical environments; and we compared the phenotypically plastic transcripts to genetically variable transcripts in a single environment. The environmentally sensitive transcriptome consists of two transcript categories, which comprise ∼15% of expressed transcripts. Class I transcripts are genetically variable and associated with detoxification, metabolism, proteolysis, heat shock proteins, and transcriptional regulation. Class II transcripts have low genetic variance and show sexually dimorphic expression enriched for reproductive functions. Clustering analysis of Class I transcripts reveals a fragmented modular organization and distinct environmentally responsive transcriptional signatures for the four fitness-related traits. Our analysis suggests that a restricted environmentally responsive segment of the transcriptome preserves the balance between phenotypic plasticity and environmental canalization.

Unlike Mendelian traits, where the genotype allows a direct prediction of the phenotype, predicting phenotypic values is not straightforward for complex traits, which arise from multiple segregating genes and their interactions with the environment. Here, a single genotype can often express different phenotypes in different environments. Such phenotypic plasticity is the counterpoint to “environmental canalization,” whereby genotypes produce the same phenotype in different environments. Whereas phenotypic plasticity allows organisms to respond rapidly to changing environments, environmental canalization buffers phenotypes against environmental perturbations. The balance between plasticity and robustness is crucial for optimal fitness, but the genetic basis for phenotypic plasticity is poorly defined. Here, we present the most comprehensive analysis to date of variation in genome-wide gene expression of an outbred Drosophila melanogaster population under 20 different environments. We find that a restricted environmentally responsive segment of the transcriptome (∼15%) preserves the balance between phenotypic plasticity and environmental canalization. Environmentally plastic transcripts can be grouped into two categories. Class I transcripts are genetically variable and associated with detoxification, metabolism, proteolysis, heat shock proteins, and transcriptional regulation. Class II transcripts have low genetic variance and show sexually dimorphic expression enriched for reproductive functions. Despite low genetic variance these transcripts evolve rapidly.

Rare and common variants: twenty arguments

Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.

Role of pleiotropy in the evolution of a cryptic developmental variation in Caenorhabditis elegans

Using vulval phenotypes in Caenorhabditis elegans, the authors show that cryptic genetic variation can evolve through selection for pleiotropic effects that alter fitness, and identify a cryptic variant that has conferred enhanced fitness on domesticated worms under laboratory conditions.

Robust biological systems are expected to accumulate cryptic genetic variation that does not affect the system output in standard conditions yet may play an evolutionary role once phenotypically expressed under a strong perturbation. Genetic variation that is cryptic relative to a robust trait may accumulate neutrally as it does not change the phenotype, yet it could also evolve under selection if it affects traits related to fitness in addition to its cryptic effect. Cryptic variation affecting the vulval intercellular signaling network was previously uncovered among wild isolates of Caenorhabditis elegans. Using a quantitative genetic approach, we identify a non-synonymous polymorphism of the previously uncharacterized nath-10 gene that affects the vulval phenotype when the system is sensitized with different mutations, but not in wild-type strains. nath-10 is an essential protein acetyltransferase gene and the homolog of human NAT10. The nath-10 polymorphism also presents non-cryptic effects on life history traits. The nath-10 allele carried by the N2 reference strain leads to a subtle increase in the egg laying rate and in the total number of sperm, a trait affecting the trade-off between fertility and minimal generation time in hermaphrodite individuals. We show that this allele appeared during early laboratory culture of N2, which allowed us to test whether it may have evolved under selection in this novel environment. The derived allele indeed strongly outcompetes the ancestral allele in laboratory conditions. In conclusion, we identified the molecular nature of a cryptic genetic variation and characterized its evolutionary history. These results show that cryptic genetic variation does not necessarily accumulate neutrally at the whole-organism level, but may evolve through selection for pleiotropic effects that alter fitness. In addition, cultivation in the laboratory has led to adaptive evolution of the reference strain N2 to the laboratory environment, which may modify other phenotypes of interest.

Robustness is a property of biological systems that ensures the production of reproducible phenotypes in spite of underlying environmental, stochastic, and genetic variability. A consequence of robustness is that potentially functional genetic variation is free to accumulate in natural populations because it is buffered at the phenotypic level. Even if this so-called “cryptic” genetic variation has no obvious effects under standard conditions, it may become phenotypically expressed upon major genetic or environmental perturbations. Here we used the model organism Caenorhabditis elegans to identify genetic variations involved in the cryptic evolution of vulval cell fate induction between wild strains. We found that a mutation in the essential nath-10 gene not only contributes to cryptic genetic variation in the vulval system, but also affects key life history traits that are expected to be under a strong selective pressure (brood size, age at sexual maturity, sperm number and rate of progeny production). Indeed, an allele of nath-10 that emerged during the laboratory domestication of C. elegans about 50 years ago confers a strong competitive advantage over the ancestral allele under laboratory conditions. A genetic variation that is cryptic for a robust trait can therefore affect more sensitive phenotypes and thus evolve under selection.

Natural variation in biogenesis efficiency of individual Arabidopsis thaliana microRNAs

Like protein-coding genes, loci that produce microRNAs (miRNAs) are generally considered to be under purifying selection, consistent with miRNA polymorphisms being able to cause disease. Nevertheless, it has been hypothesized that variation in miRNA genes may contribute to phenotypic diversity. Here we demonstrate that a naturally occurring polymorphism in the MIR164A gene affects leaf shape and shoot architecture in Arabidopsis thaliana, with the effects being modified by additional loci in the genome. A single base pair substitution in the miRNA complementary sequence alters the predicted stability of the miRNA:miRNA(∗) duplex. It thereby greatly reduces miRNA accumulation, probably because it interferes with precursor processing. We demonstrate that this is not a rare exception and that natural strains of Arabidopsis thaliana harbor dozens of similar polymorphisms that affect processing of a wide range of miRNA precursors. Our results suggest that natural variation in miRNA biogenesis resulting from cis mutations is a common contributor to phenotypic variation in plants.

Narrow-sense heritability of body size and its response to different developmental temperatures in Culex quinquefasciatus (Say 1923)

Body size is an important trait involved in overall fitness through its effects on mating success, fecundity, resource acquisition and mortality, and desiccation resistance. In this study, we raised inbred Culex quinquefasciatus mosquito cohorts at different developmental temperatures of 20°, 23°, and 27° C. As an indicator of the amount of genetic variation in body size, we estimated the narrow-sense heritability of body sizes defined as wing aspect ratios. Our results show that narrow-sense heritability of the body size increased as the developmental temperature increased. We also detected the presence of strong genotype-by-environment (G × E) interaction from low cross-environmental correlations. The body size of each temperature regime followed the general rule that higher temperatures produce smaller individuals. We suggest that the increase in genetic variation with increasing temperature might be due to an unleashing of the cryptic genetic variation of the putative genes affecting body size. We conclude that this increase in genetic variation tracking the environmental (developmental temperature) change could have considerable implications for the distribution and range expansion of Cx. quinquefasciatus, especially in warmer environments.

Distinct genetic architectures for male and female inflorescence traits of maize

We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines. Four traits from the male inflorescence (tassel) and three traits from the female inflorescence (ear) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population. Inflorescence loci have larger effects than flowering and leaf loci, and ear effects are larger than tassel effects. Ear trait models also have lower predictive ability than tassel, flowering, or leaf trait models. Pleiotropic loci were identified that control elongation of ear and tassel, consistent with their common developmental origin. For these pleiotropic loci, the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved. This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms. Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time, since the traits that changed most during the relatively recent domestication of maize have the largest effects.

Genetic architecture is of broad interest in evolutionary biology, plant and animal breeding, and medicine, because it influences both the response to selection and the success of trait mapping. Results from the most rigorously studied genetic systems suggest a similar genetic architecture across all species and traits studied, with many loci of small effect. A few strongly selected traits in domesticated organisms show unusual genetic architecture, for reasons that are unclear. We compare maize inflorescence, flowering, and leaf traits and show that inflorescence traits have distinct genetic architectures characterized by larger effects. Female inflorescences (ears) have larger effects than male inflorescences (tassels) even though the two structures have similar developmental origins. Analysis of pleiotropic loci shows that these larger effects are not inherent features of the underlying polymorphisms. Rather, maize inflorescences appear to be exceptionally labile, with female inflorescences more labile than male inflorescences. These results support the canalization hypothesis, which predicts that rapidly changing traits will have larger effects. We suggest that maize inflorescence traits, and ear traits in particular, have larger effects than flowering or leaf traits as a result of strong directional selection during maize domestication.

The QTN program and the alleles that matter for evolution: all that's gold does not glitter

The search for the alleles that matter, the quantitative trait nucleotides (QTNs) that underlie heritable variation within populations and divergence among them, is a popular pursuit. But what is the question to which QTNs are the answer? Although their pursuit is often invoked as a means of addressing the molecular basis of phenotypic evolution or of estimating the roles of evolutionary forces, the QTNs that are accessible to experimentalists, QTNs of relatively large effect, may be uninformative about these issues if large-effect variants are unrepresentative of the alleles that matter. Although 20th century evolutionary biology generally viewed large-effect variants as atypical, the field has recently undergone a quiet realignment toward a view of readily discoverable large-effect alleles as the primary molecular substrates for evolution. I argue that neither theory nor data justify this realignment. Models and experimental findings covering broad swaths of evolutionary phenomena suggest that evolution often acts via large numbers of small-effect polygenes, individually undetectable. Moreover, these small-effect variants are different in kind, at the molecular level, from the large-effect alleles accessible to experimentalists. Although discoverable QTNs address some fundamental evolutionary questions, they are essentially misleading about many others.

Rare copy number deletions predict individual variation in human brain metabolite concentrations in individuals with alcohol use disorders

BACKGROUND

Although variations in neurometabolite concentrations occur in diverse neuropsychiatric and neurodegenerative disorders, little is known about the nature of underlying genetic influences. The current study investigated the importance of a specific type of genetic mutation, copy number variation (CNV), for neurometabolite concentrations in a bilateral anterior cingulate voxel.

METHODS

These neurometabolic signals were quantified using proton magnetic resonance spectroscopy ((1)H-MRS): N-acetylaspartate (NAA), creatine-phosphocreatine (Cre), glutamate/glutamine (Glx), myoinositol (mI), and phosphorylcholine-glycerol phosphorylcholine (Cho). Genetic data were collected using the Illumina 1MDuoBeadChip Array from a sample adults with alcohol use disorders (n = 146).

RESULTS

The number of base pairs lost through rare copy number deletions (occurring in less than 5% of our sample) predicted lower NAA, Cre, mI, and Glx. More total rare deletions also predicted lower NAA, Cre, and Glx. Principal components analyses of the five neurometabolites identified two correlated components, the first comprised of NAA, Glx, and Cre, and the second comprised of Cho, mI, and to a lesser extent, Cre. The number and length of rare deletions were correlated with the first component, capturing approximately 10% of phenotypic variance, but not the second component.

CONCLUSIONS

These results suggest that mutation load affects neurometabolite concentrations, potentially increasing risk for neuropsychiatric disorders. The greater effect of CVNs on NAA, Glx, and Cre may reflect a greater sensitivity to the effects of mutations (i.e., reduced canalization) for neurometabolites related to metabolic activity and cellular energetics, due to extensive recent selection pressure on these phenotypes in the human lineage.

Gene set analysis and network analysis for genome-wide association studies

The application of high-throughput genotyping in humans has yielded numerous insights into the genetic basis of human phenotypes and an unprecedented amount of genetic data. Genome-wide association studies (GWAS) have increased in number in recent years, but the variants that have been found have generally explained only a tiny proportion of the estimated genetic contribution to phenotypic variation. This article summarizes the progress made in the development of gene set analysis (GSA) and network analysis for GWAS was a way to identify the underlying molecular processes of human phenotypes. It also highlights some promising findings and indicates future directions that may greatly enhance the analysis and interpretation of GWAS.

Complex phenotypes and phenomenon of genome-wide inter-chromosomal linkage disequilibrium in the human genome

Studies of non-human species show that loci on non-homologous chromosomes can be in linkage disequilibrium (LD). I focus on the Framingham Heart Study (FHS) participants to explore whether the phenomenon of inter-chromosomal LD can be caused by non-stochastic bio-genetic mechanisms in the human genome and be associated with complex, polygenic phenotypes. This paper documents remarkably strong and extensive LD among SNPs at loci on multiple non-homologous chromosomes genotyped using two independent (Affymetrix 50K and 500K) arrays. The analyses provided compelling evidences that the observed inter-chromosomal LD was unlikely generated by stochasticity, population or family structure, or mis-genotyping. The analyses show that this LD is associated with complex heritable phenotypes characterizing poor health. The inter-chromosomal LD was observed in parental and offspring generations of the FHS participants. These findings suggest that inter-chromosomal LD can be caused by bio-genetic mechanisms possibly associated with favorable or unfavorable epistatic evolution. This phenomenon can challenge our understanding of the role of genes and gene networks in regulating complex, polygenic phenotypes in humans.

Variance of gene expression identifies altered network constraints in neurological disease

Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance have fewer network connections and also tend to sit on the periphery of the cell. Using neural stem cells derived from patients suffering from Schizophrenia (SZ), Parkinson's disease (PD), and a healthy control group, we find marked differences in expression variance in cell signaling pathways that shed new light on potential mechanisms associated with these diverse neurological disorders. In particular, we find that expression variance of core networks in the SZ patient group was considerably constrained, while in contrast the PD patient group demonstrated much greater variance than expected. One hypothesis is that diminished variance in SZ patients corresponds to an increased degree of constraint in these pathways and a corresponding reduction in robustness of the stem cell networks. These results underscore the role that variation plays in biological systems and suggest that analysis of expression variance is far more important in disease than previously recognized. Furthermore, modeling patterns of variability in gene expression could fundamentally alter the way in which we think about how cellular networks are affected by disease processes.

Genes are a repository of information that provides the framework for cellular processes, with the flow of information from gene (DNA) to phenotype via an intermediate molecule—the messenger RNA. We understand that sequence variations in a gene may lead to phenotypic variations, but less well understood is how variation in the information flow itself might also impact on phenotype. In this study we demonstrated that disease phenotypes were correlated with expression variance. A change in expression variance might infer that the genetic networks representing information flow were less robust—surprisingly, we found that too little and too much variance were equally detrimental in the context of neurological disease.

Association of single nucleotide polymorphisms in the diamine oxidase gene with diamine oxidase serum activities

BACKGROUND

Histamine intolerance (HIT) is associated with an excess of histamine because of an impaired function of the histamine-degrading enzyme diamine oxidase (DAO). The genetic background of HIT is unknown yet.

METHODS

Case-control association study of all haplotype tagging and four previously reported DAO SNPs and one HNMT Single nucleotide polymorphism with symptoms of HIT and DAO serum activity in 484 German individuals including 285 patients with clinical symptoms of HIT and 199 controls.

RESULTS

Diamine oxidase serum activity was significantly associated with seven SNPs within the DAO gene. The minor allele at rs2052129, rs2268999, rs10156191 and rs1049742 increased the risk for a reduced DAO activity whereas showing a moderate protective effect at rs2071514, rs1049748 and rs2071517 in the genotypic (P = 2.1 × 10(-8) , 7.6 × 10(-10) , 8.3 × 10(-10) , 0.009, 0.005, 0.00001, 0.006, respectively) and allelic genetic model (P = 2.5 × 10(-11) , 5.4 × 10(-13) , 8.9 × 10(-13) , 0.00002, 0.006, 0.0003, 0.005, respectively). Reporter gene assays at rs2052129 revealed a lower promoter activity (P = 0.016) of the minor allele. DAO mRNA expression in peripheral blood mononuclear cells of homozygous carriers of the minor allele at rs2052129, rs2268999, rs10156191 was lower (P = 0.002) than homozygous carriers of the major allele. Diamine oxidase variants were not associated with the HIT phenotype per se, only with DAO activity alone and the subgroup of HIT patients displaying a reduced DAO activity.

CONCLUSIONS

DAO gene variants strongly influence DAO expression and activity but alone are not sufficient to fully effectuate the potentially associated disease state of HIT, suggesting an interplay of genetic and environmental factors.

Decanalization, brain development and risk of schizophrenia

Waddington's original description of canalization refers to the ability of an organism to maintain phenotypic fidelity in the face of environmental and/or genetic perturbation. Development of the human brain requires exposure to a 'wild-type' environment-one that supports the optimal set of instructions for development. Recently derived brain structures in our species, such as the expanded neocortex, may be more vulnerable to decanalization because there has been insufficient time to evolve buffering capacity. On the basis of modern notions of decanalization, we provide perspectives on selected environmental and genetic risk factors for schizophrenia, and we discuss strengths and weaknesses of this conceptual framework. We argue that if we are to build a solid foundation for translational psychiatry, we must explore models that attempt to capture the complexity of the interaction between genetic and non-genetic risk factors in mediating and modulating brain development.

Trade-off in the effects of the apolipoprotein E polymorphism on the ages at onset of CVD and cancer influences human lifespan

Progress in unraveling the genetic origins of healthy aging is tempered, in part, by a lack of replication of effects, which is often considered a signature of false-positive findings. We convincingly demonstrate that the lack of genetic effects on an aging-related trait can be because of trade-offs in the gene action. We focus on the well-studied apolipoprotein E (APOE) e2/3/4 polymorphism and on lifespan and ages at onset of cardiovascular diseases (CVD) and cancer, using data on 3924 participants of the Framingham Heart Study Offspring cohort. Kaplan-Meier estimates show that the e4 allele carriers live shorter lives than the non-e4 allele carriers (log rank = 0.016). The adverse effect was attributed to the poor survival of the e4 homozygotes, whereas the effect of the common e3/4 genotype was insignificant. The e3/4 genotype, however, was antagonistically associated with onsets of those diseases predisposing to an earlier onset of CVD and a later onset of cancer compared to the non-e4 allele genotypes. This trade-off explains the lack of a significant effect of the e3/4 genotype on survival; adjustment for it in the Cox regression model makes the detrimental effect of the e4 allele highly significant (P = 0.002). This trade-off is likely caused by the lipid-metabolism-related (for CVD) and nonrelated (for cancer) mechanisms. An evolutionary rationale suggests that genetic trade-offs should not be an exception in studies of aging-related traits. Deeper insights into biological mechanisms mediating gene action are critical for understanding the genetic regulation of a healthy lifespan and for personalizing medical care.

Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis

Although central to many studies of phenotypic variation and disease susceptibility, characterizing the genetic architecture of complex traits has been unexpectedly difficult. For example, most of the susceptibility genes that contribute to highly heritable conditions such as obesity and type 2 diabetes (T2D) remain to be identified despite intensive study. We took advantage of mouse models of diet-induced metabolic disease in chromosome substitution strains (CSSs) both to characterize the genetic architecture of diet-induced obesity and glucose homeostasis and to test the feasibility of gene discovery. Beginning with a survey of CSSs, followed with genetic and phenotypic analysis of congenic, subcongenic, and subsubcongenic strains, we identified a remarkable number of closely linked, phenotypically heterogeneous quantitative trait loci (QTLs) on mouse chromosome 6 that have unexpectedly large phenotypic effects. Although fine-mapping reduced the genomic intervals and gene content of these QTLs over 3000-fold, the average phenotypic effect on body weight was reduced less than threefold, highlighting the "fractal" nature of genetic architecture in mice. Despite this genetic complexity, we found evidence for 14 QTLs in only 32 recombination events in less than 3000 mice, and with an average of four genes located within the three body weight QTLs in the subsubcongenic strains. For Obrq2a1, genetic and functional studies collectively identified the solute receptor Slc35b4 as a regulator of obesity, insulin resistance, and gluconeogenesis. This work demonstrated the unique power of CSSs as a platform for studying complex genetic traits and identifying QTLs.

Progress and promise of genome-wide association studies for human complex trait genetics

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.

SNPs in the FCER1A gene region show no association with allergic rhinitis in a Han Chinese population

Background

Immunoglobulin E (IgE) is a central player in the allergic response, and raised total IgE levels are considered as an indicator of atopy or potential development of atopy. A recent genome-wide scan in a German population-based cohort of adults identified the gene encoding the alpha chain of the high affinity receptor for IgE (FCER1A) as a susceptibility locus influencing total serum IgE levels. The aim of this study was to investigate whether the polymorphisms in the FCER1A gene are associated with allergic rhinitis (AR) in a Han Chinese population.

Methodology/Principal Findings

A population of 378 patients with AR and 288 healthy controls was studied. Precise phenotyping of patients was accomplished by means of a questionnaire and clinical examination. Blood was drawn for DNA extraction and total serum immunoglobulin E (IgE) measurement. A total of 16 single nucleotide polymorphisms (SNPs) in FCER1A were selected and individually genotyped. None of the SNPs in the FCER1A showed an association with AR. Similarly, the lack of association was also evident in subgroup analysis for the presence of different allergen sensitivities. None of the selected SNPs in FCER1A was associated with total IgE level.

Conclusions

Although FCER1A presents itself as a good candidate for contributing to total serum IgE, this study failed to find an association between SNPs in the FCER1A gene region and IgE level or AR susceptibility.

Synthetic lethality: general principles, utility and detection using genetic screens in human cells

Synthetic lethality occurs when the simultaneous perturbation of two genes results in cellular or organismal death. Synthetic lethality also occurs between genes and small molecules, and can be used to elucidate the mechanism of action of drugs. This area has recently attracted attention because of the prospect of a new generation of anti-cancer drugs. Based on studies ranging from yeast to human cells, this review provides an overview of the general principles that underlie synthetic lethality and relates them to its utility for identifying gene function, drug action and cancer therapy. It also identifies the latest strategies for the large-scale mapping of synthetic lethalities in human cells which bring us closer to the generation of comprehensive human genetic interaction maps.

Improving disease gene prioritization using the semantic similarity of Gene Ontology terms

MOTIVATION

Many hereditary human diseases are polygenic, resulting from sequence alterations in multiple genes. Genomic linkage and association studies are commonly performed for identifying disease-related genes. Such studies often yield lists of up to several hundred candidate genes, which have to be prioritized and validated further. Recent studies discovered that genes involved in phenotypically similar diseases are often functionally related on the molecular level.

RESULTS

Here, we introduce MedSim, a novel approach for ranking candidate genes for a particular disease based on functional comparisons involving the Gene Ontology. MedSim uses functional annotations of known disease genes for assessing the similarity of diseases as well as the disease relevance of candidate genes. We benchmarked our approach with genes known to be involved in 99 diseases taken from the OMIM database. Using artificial quantitative trait loci, MedSim achieved excellent performance with an area under the ROC curve of up to 0.90 and a sensitivity of over 70% at 90% specificity when classifying gene products according to their disease relatedness. This performance is comparable or even superior to related methods in the field, albeit using less and thus more easily accessible information.

AVAILABILITY

MedSim is offered as part of our FunSimMat web service (http://www.funsimmat.de).

Mixture regression analysis on age at onset in bipolar disorder patients: investigation of the role of serotonergic genes

Bipolar Disorder (BPD) is a complex psychiatric disease with a relevant underlying genetic basis. HTR2A T102C, HTR2C Cys23Ser, SLC6A4 5-HTTLPR and rs25531 polymorphisms were genotyped in 230 BPD patients and inserted as covariates in a mixture regression model of age at onset (AAO). 5-HTTLPR polymorphism associated with early onset component under recessive and additive model. HTR2A T102C, HTR2C Cys23Ser and 5-HTTLPR interaction terms associated with early onset component under dominant, recessive and additive model. These findings suggest a role of genes codifying for elements of the serotonergic system in influencing the AAO in BPD.

Analysis of the high affinity IgE receptor genes reveals epistatic effects of FCER1A variants on eczema risk

BACKGROUND

High levels of total and allergen-specific IgE levels are a key feature in allergic diseases. The high-affinity receptor for IgE, which is composed of one alpha (FCER1A), one beta (FCER1B), and two gamma (FCER1G) subunits, represents the central receptor of IgE-induced reactions. In a genome-wide association scan, we recently identified associations between functional FCER1A variants and total serum IgE levels. Previous studies had reported linkage and association of FCER1B variants with IgE and atopic traits. The FCER1G gene has not yet been investigated with regard to atopy. Filaggrin (FLG) is the strongest known risk gene for eczema, in particular the allergic subtype of eczema.

METHODS

We investigated the association of FCER1A, FCER1B, and FCER1G variants with IgE in a large population-based cohort (n = 4261) and tested for epistatic effects using the model-based multifactor dimensionality reduction (MB-MDR) method. In addition, we investigated a potential interaction between FLG and FCER1A variants in a large collection of eczema cases (n = 1018) and population controls.

RESULTS

Three strongly correlated FCER1A polymorphisms were significantly associated with total and specific IgE levels as well as allergic sensitization. No associations were seen for FCER1B and FCER1G. After adjustment for FLG effects, a significant epistatic effect of the FCER1A variants rs10489854 and rs2511211 on eczema risk was detected.

CONCLUSIONS

These results suggest that FCER1A variants by themselves and in combination influence IgE levels and act synergistically to influence eczema risk.

Genotype-by-diet interactions drive metabolic phenotype variation in Drosophila melanogaster

The rising prevalence of complex disease suggests that alterations to the human environment are increasing the proportion of individuals who exceed a threshold of liability. This might be due either to a global shift in the population mean of underlying contributing traits, or to increased variance of such underlying endophenotypes (such as body weight). To contrast these quantitative genetic mechanisms with respect to weight gain, we have quantified the effect of dietary perturbation on metabolic traits in 146 inbred lines of Drosophila melanogaster and show that genotype-by-diet interactions are pervasive. For several metabolic traits, genotype-by-diet interactions account for far more variance (between 12 and 17%) than diet alone (1-2%), and in some cases have as large an effect as genetics alone (11-23%). Substantial dew point effects were also observed. Larval foraging behavior was found to be a quantitative trait exhibiting significant genetic variation for path length (P = 0.0004). Metabolic and fitness traits exhibited a complex correlation structure, and there was evidence of selection minimizing weight under laboratory conditions. In addition, a high fat diet significantly increases population variance in metabolic phenotypes, suggesting decreased robustness in the face of dietary perturbation. Changes in metabolic trait mean and variance in response to diet indicates that shifts in both population mean and variance in underlying traits could contribute to increases in complex disease.

Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression

Coordinated gene transcript levels across tissues (denoted “gene synchrony”) reflect converging influences of genetic, biochemical and environmental factors; hence they are informative of the biological state of an individual. So could brain gene synchrony also integrate the multiple factors engaged in neuropsychiatric disorders and reveal underlying pathologies? Using bootstrapped Pearson correlation for transcript levels for the same genes across distinct brain areas, we report robust gene transcript synchrony between the amygdala and cingulate cortex in the human postmortem brain of normal control subjects (n = 14; Control/Permutated data, p<0.000001). Coordinated expression was confirmed across distinct prefrontal cortex areas in a separate cohort (n = 19 subjects) and affected different gene sets, potentially reflecting regional network- and function-dependent transcriptional programs. Genewise regional transcript coordination was independent of age-related changes and array technical parameters. Robust shifts in amygdala-cingulate gene synchrony were observed in subjects with major depressive disorder (MDD, denoted here “depression”) (n = 14; MDD/Permutated data, p<0.000001), significantly affecting between 100 and 250 individual genes (10–30% false discovery rate). Biological networks and signal transduction pathways corresponding to the identified gene set suggested putative dysregulated functions for several hormone-type factors previously implicated in depression (insulin, interleukin-1, thyroid hormone, estradiol and glucocorticoids; p<0.01 for association with depression-related networks). In summary, we showed that coordinated gene expression across brain areas may represent a novel molecular probe for brain structure/function that is sensitive to disease condition, suggesting the presence of a distinct and integrated hormone-mediated corticolimbic homeostatic, although maladaptive and pathological, state in major depression.

Heterogeneity of genetic modifiers ensures normal cardiac development

BACKGROUND

Mutations of the transcription factor Nkx2-5 cause pleiotropic heart defects with incomplete penetrance. This variability suggests that additional factors can affect or prevent the mutant phenotype. We assess here the role of genetic modifiers and their interactions.

METHODS AND RESULTS

Heterozygous Nkx2-5 knockout mice in the inbred strain background C57Bl/6 frequently have atrial and ventricular septal defects. The incidences are substantially reduced in the Nkx2-5(+/-) progeny of first-generation (F1) outcrosses to the strains FVB/N or A/J. Defects recur in the second generation (F2) of the F1 X F1 intercross or backcrosses to the parental strains. Analysis of >3000 Nkx2-5(+/-) hearts from 5 F2 crosses demonstrates the profound influence of genetic modifiers on disease presentation. On the basis of their incidences and coincidences, anatomically distinct malformations have shared and unique modifiers. All 3 strains carry susceptibility alleles at different loci for atrial and ventricular septal defects. Relative to the other 2 strains, A/J carries polymorphisms that confer greater susceptibility to atrial septal defect and atrioventricular septal defects and C57Bl/6 to muscular ventricular septal defects. Segregation analyses reveal that > or = 2 loci influence membranous ventricular septal defect susceptibility, whereas > or = loci and at least 1 epistatic interaction affect muscular ventricular and atrial septal defects.

CONCLUSIONS

Alleles of modifier genes can either buffer perturbations on cardiac development or direct the manifestation of a defect. In a genetically heterogeneous population, the predominant effect of modifier genes is health. (Circulation. 2010;121:1313-1321.)

Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network

Systems biology is a general trend of contemporary scientific development. When coupling the classical traditional Chinese medicine (TCM) Cold Syndrome and methodology of systems biology, we conformed to the genome, transcriptome, proteome, and metabolome that are supposed to run through the overall macro behavior, and explored the macro and micro framework of systems biology of TCM Syndrome. We introduced a new way to probe into the implicit stratification of Cold Syndrome, after surveying 4575 cases of Cold Syndrome patients and examining gene expression information of a typical Cold Syndrome pedigree by microarray. We underlined the genetic background of the Cold Syndrome family based on the molecular foundation to understand Syndrome, one of our earlier discoveries in which genes and chemical compounds in neuro-endocrine-immune (NEI) system are scored as Cold or Hot (or both) property. Results indicate that Cold Syndrome related genes play an essential role in energy metabolism, which are tightly correlated with the genes of neurotransmitters, hormones and cytokines in the NEI interaction network. Therefore, NEI interaction not only opens out mechanism of classical TCM theory on Syndrome but also enriches current research on complex diseases as well as systems biology.

Gaining a pathway insight into genetic association data

The recent application of high throughput genotyping in humans has yielded numerous insights into the genetic basis of human phenotypes and unprecedented amount of genetic variation data. Each genome wide significant finding has explained only a tiny proportion of phenotypic variation, yet genome wide association studies (GWAS) in their entirety can provide unprecedented windows into the molecular genetics of these phenotypes. New methods are emerging to mine modest association signals from these data using information on biological pathways and networks underlying the phenotype variation. These methods promise to enhance the information extracted from GWAS providing grounds for follow up studies of both a genetic and molecular nature.

Geographical genomics of human leukocyte gene expression variation in southern Morocco

Studies of the genetics of gene expression can identify expression SNPs (eSNPs) that explain variation in transcript abundance. Here we address the robustness of eSNP associations to environmental geography and population structure in a comparison of 194 Arab and Amazigh individuals from a city and two villages in southern Morocco. Gene expression differed between pairs of locations for up to a third of all transcripts, with notable enrichment of transcripts involved in ribosomal biosynthesis and oxidative phosphorylation. Robust associations were observed in the leukocyte samples: cis eSNPs (P < 10(-08)) were identified for 346 genes, and trans eSNPs (P < 10(-11)) for 10 genes. All of these associations were consistent both across the three sample locations and after controlling for ancestry and relatedness. No evidence of large-effect trans-acting mediators of the pervasive environmental influence was found; instead, genetic and environmental factors acted in a largely additive manner.

Transient genotype-by-environment interactions following environmental shock provide a source of expression variation for essential genes

Understanding complex genotype-by-environment interactions (GEIs) is crucial for understanding phenotypic variation. An important factor often overlooked in GEI studies is time. We measured the contribution of GEIs to expression variation in four nonlaboratory Saccharomyces cerevisiae strains responding dynamically to a 25 degrees -37 degrees heat shock. GEI was a major force explaining expression variation, affecting 55% of the genes analyzed. Importantly, almost half of these expression patterns showed GEI influence only during the transition between environments, but not in acclimated cells. This class reveals a genotype-by-environment-by-time interaction that affected expression of a large fraction of yeast genes. Strikingly, although transcripts subject to persistent GEI effects were enriched for nonessential genes with upstream TATA elements, those displaying transient GEIs were enriched for essential genes regardless of TATA regulation. Genes subject to persistent GEI influences showed relaxed constraint on acclimated gene expression compared to the average yeast gene, whereas genes restricted to transient GEIs did not. We propose that transient GEI during the transition between environments provides a previously unappreciated source of expression variation, particularly for essential genes.

Replication in genome-wide association studies

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication; issues of heterogeneity; advantages and disadvantages of different methods of data synthesis across multiple studies; frequentist vs. Bayesian inferences for replication; and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.

Finding the missing heritability of complex diseases

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

[Genetic and environmental variations in an intercellular signaling network]

Interindividual variation, be it of environmental or genetic origin, is crucial for biological evolution as well as in the medical context. This variation is not always directly visible, yet may be revealed under some environmental or genetic condition. In this essay is presented the example of the developmental model system underlying vulva formation in the nematode Caenorhabditis elegans, where an intercellular signaling network (EGF-Ras-MAP kinase, Notch and Wnt pathways) is involved in spatial patterning of the fates of the vulva precursor cells. Variation may be studied at two levels: (1) rare deviations in the system's output, i.e. the spatial pattern of vulva precursor cell fates ; (2) so-called << cryptic >> variation in the underlying intercellular signaling network, without change in the system's output. Like every biological system, this network displays genetic and -environmental epistasis.

Epistasis and its implications for personal genetics

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

Robustness: mechanisms and consequences

Biological systems are robust to perturbation by mutations and environmental fluctuations. New data are shedding light on the biochemical and network-level mechanisms responsible for robustness. Robustness to mutation might have evolved as an adaptation to reduce the effect of mutations, as a congruent byproduct of adaptive robustness to environmental variation, or as an intrinsic property of biological systems selected for their primary functions. Whatever its mechanism or origin, robustness to mutation results in the accumulation of phenotypically cryptic genetic variation. Partial robustness can lead to pre-adaptation, and thereby might contribute to evolvability. The identification and characterization of phenotypic capacitors - which act as switches of the degree of robustness - are critical to understanding the mechanisms and consequences of robustness.