Functional Characterization of the Frost Gene in Drosophila melanogaster: Importance for Recovery from Chill Coma

Background

Almost all animals, including insects, need to adapt to temperature fluctuations. The molecular basis of thermal adaptation is not well understood, although a number of candidate genes have been proposed. However, a functional link between candidate genes and thermal tolerance has rarely been established. The gene Frost (Fst) was first discovered when Drosophila flies were exposed to cold stress, but the biological function(s) of Fst has so far not been characterized. Because Fst is up-regulated after a cold stress, we tested whether it was essential for chill-coma recovery.

Methodology/Principal Findings

A marked increase in Fst expression was detected (by RT-PCR) during recovery from cold stress, peaking at 42-fold after 2 h. The GAL4/UAS system was used to knock down expression of Fst and recovery ability was assessed in transgenic adults following 12 h of chill coma at 0°C. The ability to recover from cold stress (short-, medium- and long-term) was significantly altered in the transgenic adults that had Fst silenced. These findings show that Fst plays an essential role in the recovery from chill coma in both males and females.

Conclusions/Significance

The Frost gene is essential for cold tolerance in Drosophila melanogaster and may play an important role in thermal adaptation.     

Bayesian neural networks for detecting epistasis in genetic association studies

Background

Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.

Results

A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.

Conclusions

The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users.     

Multiple pathway-based genetic variations associated with tobacco related multiple primary neoplasms

Background

In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN).

Methods

Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit.

Results

Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg²¹³His, mEH Tyr¹¹³His, hOGG1 Ser³²⁶Cys, XRCC1 Arg²⁸⁰His and BRCA2 Asn³⁷²His. Gene - environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg³⁹⁹His)/mEH(Tyr¹¹³His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis.

Conclusion

This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis.     

Contributions of Renin-Angiotensin system-related gene interactions to obesity in a chinese population

Background

Gene-gene interactions may be partly responsible for complex traits such as obesity. Increasing evidence suggests that the renin-angiotensin system (RAS) contributes to the etiology of obesity. How the epistasis of genes in the RAS contributes to obesity is still under research. We aim to evaluate the contribution of RAS-related gene interactions to a predisposition of obesity in a Chinese population.

Methodology and Principal Findings

We selected six single nucleotide polymorphisms (SNPs) located in angiotensin (AGT), angiotensin converting enzyme (ACE), angiotensin type 1 receptor (AGTR1), MAS1, nitric oxide synthase 3 (NOS3) and the bradykinin B2 receptor gene (BDKRB2), and genotyped them in 324 unrelated individuals with obesity (BMI ≥28 kg/m²) and 373 non-obese controls (BMI 18.5 to <24 kg/m²) from a large scale population-based cohort. We analyzed gene-gene interactions among 6 polymorphic loci using the Generalized Multifactor Dimensionality Reduction (GMDR) method, which has been shown to be effective for detecting gene-gene interactions in case-control studies with relatively small samples. Then we used logistic regression models to confirm the best combination of loci identified in the GMDR. It showed a significant gene-gene interaction between the rs220721 polymorphism in the MAS1 gene and the rs1799722 polymorphism in the gene BDKB2R. The best two-locus combination scored 9 for cross-validation consistency and 9 for sign test (p = 0.0107). This interaction showed the maximum consistency and minimum prediction error among all gene-gene interaction models evaluated. Moreover, the combination of the MAS1 rs220721 and the BDKRB2 rs1799722 was associated with a significantly increased risk of obesity (OR 1.82, CI 95%: 1.15–2.88, p = 0.0103).

Conclusions and Significance

These results suggest that the SNPs from the RAS-related genes may contribute to the risk of obesity in an interactive manner in a Chinese population. The gene-gene interaction may serve as a novel area for obesity research.     

Gene-Gene Combination Effect and Interactions among ABCA1, APOA1, SR-B1, and CETP Polymorphisms for Serum High-Density Lipoprotein-Cholesterol in the Japanese Population

Background/Objective

Gene-gene interactions in the reverse cholesterol transport system for high-density lipoprotein-cholesterol (HDL-C) are poorly understood. The present study observed gene-gene combination effect and interactions between single nucleotide polymorphisms (SNPs) in ABCA1, APOA1, SR-B1, and CETP in serum HDL-C from a cross-sectional study in the Japanese population.

Methods

The study population comprised 1,535 men and 1,515 women aged 35–69 years who were enrolled in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study. We selected 13 SNPs in the ABCA1, APOA1, CETP, and SR-B1 genes in the reverse cholesterol transport system. The effects of genetic and environmental factors were assessed using general linear and logistic regression models after adjusting for age, sex, and region.

Principal Findings

Alcohol consumption and daily activity were positively associated with HDL-C levels, whereas smoking had a negative relationship. The T allele of CETP, rs3764261, was correlated with higher HDL-C levels and had the highest coefficient (2.93 mg/dL/allele) among the 13 SNPs, which was statistically significant after applying the Bonferroni correction (p<0.001). Gene-gene combination analysis revealed that CETP rs3764261 was associated with high HDL-C levels with any combination of SNPs from ABCA1, APOA1, and SR-B1, although no gene-gene interaction was apparent. An increasing trend for serum HDL-C was also observed with an increasing number of alleles (p<0.001).

Conclusions

The present study identified a multiplier effect from a polymorphism in CETP with ABCA1, APOA1, and SR-B1, as well as a dose-dependence according to the number of alleles present.     

The CXCL12 G801A Polymorphism Is Associated with Cancer Risk: A Meta-Analysis

Background

CXCL12 is a small chemotactic cytokine belonging to the CXC chemokine family expressed in various organs. It contributes to the migration, invasion and angiogenesis of cancer cells. Recently, the CXCL12 G801A polymorphism was shown to be associated with an increased risk of various kinds of cancers, but the results were too inconsistent to be conclusive.

Methods

To solve the problem of inadequate statistical power and conflicting results, a meta-analysis of published case-control studies was performed, including 4,435 cancer cases and 6,898 controls. Odds ratios (ORs) and their 95% confidence intervals (CIs) were used to determine the strength of association between CXCL12 G801A polymorphism and cancer risk.

Results

A significant association between CXCL12 G801A polymorphism and cancer risk was found under all genetic models. Further, subgroup analysis stratified by ethnicity suggested a significant association between CXCL12 G801A polymorphism and cancer risk in the Asian subgroup under all genetic models. However, in the Caucasian subgroup, a significant association was only found under an additive genetic model and a dominant genetic model. The analysis stratified by cancer type found that CXCL12 G801A polymorphism may increase the risk of breast cancer, lung cancer, and “other” cancers. Based on subgroup stratified by source of controls, a significant association was observed in hospital-based studies under all genetic models.

Conclusions

The CXCL12 G801A polymorphism is associated with an increased risk of cancer based on current published data. In the future, large-scale well-designed studies with more information are needed to better estimate possible gene-gene or gene-environment interactions.     

Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals

Background

While the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits.

Results

Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed.

Conclusions

We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users.     

The strength of genetic interactions scales weakly with mutational effects

Background

Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study, we analyze a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae.

Results

We discuss a geometric definition of epistasis that reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilized this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher''s geometric model of epistasis, and discuss the consequences for evolutionary dynamics.

Conclusions

Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques, it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.     

Prioritizing causal disease genes using unbiased genomic features

Background

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits.

Results

To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM.

Conclusion

Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0534-8) contains supplementary material, which is available to authorized users.     

Evaluation of Five Candidate Genes from GWAS for Association with Oligozoospermia in a Han Chinese Population

Background

Oligozoospermia is one of the severe forms of idiopathic male infertility. However, its pathology is largely unknown, and few genetic factors have been defined. Our previous genome-wide association study (GWAS) has identified four risk loci for non-obstructive azoospermia (NOA).

Objective

To investigate the potentially functional genetic variants (including not only common variants, but also less-common and rare variants) of these loci on spermatogenic impairment, especially oligozoospermia.

Design, Setting, and Participants

A total of 784 individuals with oligozoospermia and 592 healthy controls were recruited to this study from March 2004 and January 2011.

Measurements

We conducted a two-stage study to explore the association between oligozoospermia and new makers near NOA risk loci. In the first stage, we used next generation sequencing (NGS) in 96 oligozoospermia cases and 96 healthy controls to screen oligozoospermia-susceptible genetic variants. Next, we validated these variants in a large cohort containing 688 cases and 496 controls by SNPscan for high-throughput Single Nucleotide Polymorphism (SNP) genotyping.

Results and Limitations

Totally, we observed seven oligozoospermia associated variants (rs3791185 and rs2232015 in PRMT6, rs146039840 and rs11046992 in Sox5, rs1129332 in PEX10, rs3197744 in SIRPA, rs1048055 in SIRPG) in the first stage. In the validation stage, rs3197744 in SIRPA and rs11046992 in Sox5 were associated with increased risk of oligozoospermia with an odds ratio (OR) of 4.62 (P  =  0.005, 95%CI 1.58-13.4) and 1.82 (P  =  0.005, 95%CI 1.01-1.64), respectively. Further investigation in larger populations and functional characterizations are needed to validate our findings.

Conclusions

Our study provides evidence of independent oligozoospermia risk alleles driven by variants in the potentially functional regions of genes discovered by GWAS. Our findings suggest that integrating sequence data with large-scale genotyping will serve as an effective strategy for discovering risk alleles in the future.     

Statistic Complexity: Combining Kolmogorov Complexity with an Ensemble Approach

Background

The evaluation of the complexity of an observed object is an old but outstanding problem. In this paper we are tying on this problem introducing a measure called statistic complexity.

Methodology/Principal Findings

This complexity measure is different to all other measures in the following senses. First, it is a bivariate measure that compares two objects, corresponding to pattern generating processes, on the basis of the normalized compression distance with each other. Second, it provides the quantification of an error that could have been encountered by comparing samples of finite size from the underlying processes. Hence, the statistic complexity provides a statistical quantification of the statement ‘ is similarly complex as ’.

Conclusions

The presented approach, ultimately, transforms the classic problem of assessing the complexity of an object into the realm of statistics. This may open a wider applicability of this complexity measure to diverse application areas.     

Dopaminergic Neuronal Imaging in Genetic Parkinson's Disease: Insights into Pathogenesis

Objectives

To compare the dopaminergic neuronal imaging features of different subtypes of genetic Parkinson''s Disease.

Methods

A retrospective study of genetic Parkinson''s diseases cases in which DaTSCAN (123I-FP-CIT) had been performed. Specific non-displaceable binding was calculated for bilateral caudate and putamen for each case. The right:left asymmetry index and striatal asymmetry index was calculated.

Results

Scans were available from 37 cases of monogenetic Parkinson''s disease (7 glucocerebrosidase (GBA) mutations, 8 alpha-synuclein, 3 LRRK2, 7 PINK1, 12 Parkin). The asymmetry of radioligand uptake for Parkinson''s disease with GBA or LRRK2 mutations was greater than that for Parkinson''s disease with alpha synuclein, PINK1 or Parkin mutations.

Conclusions

The asymmetry of radioligand uptake in Parkinsons disease associated with GBA or LRRK2 mutations suggests that interactions with additional genetic or environmental factors may be associated with dopaminergic neuronal loss.     

Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

Background

Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases.

Results

Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present.

Conclusion

This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.

     

Genetic diversity and population structure of the secondary symbiont of tsetse flies, Sodalis glossinidius, in sleeping sickness foci in Cameroon

Background

Previous studies have shown substantial differences in Sodalis glossinidius and trypanosome infection rates between Glossina palpalis palpalis populations from two Cameroonian foci of human African trypanosomiasis (HAT), Bipindi and Campo. We hypothesized that the geographical isolation of the two foci may have induced independent evolution in the two areas, resulting in the diversification of symbiont genotypes.

Methodology/Principal Findings

To test this hypothesis, we investigated the symbiont genetic structure using the allelic size variation at four specific microsatellite loci. Classical analysis of molecular variance (AMOVA) and differentiation statistics revealed that most of the genetic diversity was observed among individuals within populations and frequent haplotypes were shared between populations. The structure of genetic diversity varied at different geographical scales, with almost no differentiation within the Campo HAT focus and a low but significant differentiation between the Campo and Bipindi HAT foci.

Conclusions/Significance

The data provided new information on the genetic diversity of the secondary symbiont population revealing mild structuring. Possible interactions between S. glossinidius subpopulations and Glossina species that could favor tsetse fly infections by a given trypanosome species should be further investigated.     

Plant resource-use strategies: the importance of phenotypic plasticity in response to a productivity gradient for two subalpine species

Background and Aims

Functional traits are indicators of plant interactions with their environment and the resource-use strategies of species can be defined through some key functional traits. The importance of genetic variability and phenotypic plasticity in trait variations in response to a common environmental change was investigated in two subalpine species.

Methods

Two species with contrasted resource-use strategies, Dactylis glomerata and Festuca paniculata, were grown along a productivity gradient in a greenhouse experiment. Functional traits of different genotypes were measured to estimate the relative roles of phenotypic plasticity and genetic variability, and to compare their levels of phenotypic plasticity.

Key Results

Trait variability in the field for the two species is more likely to be the result of phenotypic plasticity rather than of genetic differentiation between populations. The exploitative species D. glomerata expressed an overall higher level of phenotypic plasticity compared with the conservative species F. paniculata. In addition to different amplitudes of phenotypic plasticity, the two species differed in their pattern of response for three functional traits relevant to resource use (specific leaf area, leaf dry matter content and leaf nitrogen content).

Conclusions

Functional trait variability was mainly the result of phenotypic plasticity, with the exploitative species showing greater variability. In addition to average trait values, two species with different resource-use strategies differed in their plastic responses to productivity.     

The DAO Gene Is Associated with Schizophrenia and Interacts with Other Genes in the Taiwan Han Chinese Population

Background

Schizophrenia is a highly heritable disease with a polygenic mode of inheritance. Many studies have contributed to our understanding of the genetic underpinnings of schizophrenia, but little is known about how interactions among genes affect the risk of schizophrenia. This study aimed to assess the associations and interactions among genes that confer vulnerability to schizophrenia and to examine the moderating effect of neuropsychological impairment.

Methods

We analyzed 99 SNPs from 10 candidate genes in 1,512 subject samples. The permutation-based single-locus, multi-locus association tests, and a gene-based multifactorial dimension reduction procedure were used to examine genetic associations and interactions to schizophrenia.

Results

We found that no single SNP was significantly associated with schizophrenia. However, a risk haplotype, namely A-T-C of the SNP triplet rsDAO7-rsDAO8-rsDAO13 of the DAO gene, was strongly associated with schizophrenia. Interaction analyses identified multiple between-gene and within-gene interactions. Between-gene interactions including DAO*DISC1 , DAO*NRG1 and DAO*RASD2 and a within-gene interaction for CACNG2 were found among schizophrenia subjects with severe sustained attention deficits, suggesting a modifying effect of impaired neuropsychological functioning. Other interactions such as the within-gene interaction of DAO and the between-gene interaction of DAO and PTK2B were consistently identified regardless of stratification by neuropsychological dysfunction. Importantly, except for the within-gene interaction of CACNG2, all of the identified risk haplotypes and interactions involved SNPs from DAO.

Conclusions

These results suggest that DAO, which is involved in the N-methyl-d-aspartate receptor regulation, signaling and glutamate metabolism, is the master gene of the genetic associations and interactions underlying schizophrenia. Besides, the interaction between DAO and RASD2 has provided an insight in integrating the glutamate and dopamine hypotheses of schizophrenia.     

Complete genome sequence of the Clostridium difficile laboratory strain 630Δerm reveals differences from strain 630, including translocation of the mobile element CTn5

Background

Clostridium difficile strain 630Δerm is a spontaneous erythromycin sensitive derivative of the reference strain 630 obtained by serial passaging in antibiotic-free media. It is widely used as a defined and tractable C. difficile strain. Though largely similar to the ancestral strain, it demonstrates phenotypic differences that might be the result of underlying genetic changes. Here, we performed a de novo assembly based on single-molecule real-time sequencing and an analysis of major methylation patterns.

Results

In addition to single nucleotide polymorphisms and various indels, we found that the mobile element CTn5 is present in the gene encoding the methyltransferase rumA rather than adhesin CD1844 where it is located in the reference strain.

Conclusions

Together, the genetic features identified in this study may help to explain at least part of the phenotypic differences. The annotated genome sequence of this lab strain, including the first analysis of major methylation patterns, will be a valuable resource for genetic research on C. difficile.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1252-7) contains supplementary material, which is available to authorized users.     

Next-generation sequencing of flow-sorted wheat chromosome 5D reveals lineage-specific translocations and widespread gene duplications

Background

The ~17 Gb hexaploid bread wheat genome is a high priority and a major technical challenge for genomic studies. In particular, the D sub-genome is relatively lacking in genetic diversity, making it both difficult to map genetically, and a target for introgression of agriculturally useful traits. Elucidating its sequence and structure will therefore facilitate wheat breeding and crop improvement.

Results

We generated shotgun sequences from each arm of flow-sorted Triticum aestivum chromosome 5D using 454 FLX Titanium technology, giving 1.34× and 1.61× coverage of the short (5DS) and long (5DL) arms of the chromosome respectively. By a combination of sequence similarity and assembly-based methods, ~74% of the sequence reads were classified as repetitive elements, and coding sequence models of 1314 (5DS) and 2975 (5DL) genes were generated. The order of conserved genes in syntenic regions of previously sequenced grass genomes were integrated with physical and genetic map positions of 518 wheat markers to establish a virtual gene order for chromosome 5D.

Conclusions

The virtual gene order revealed a large-scale chromosomal rearrangement in the peri-centromeric region of 5DL, and a concentration of non-syntenic genes in the telomeric region of 5DS. Although our data support the large-scale conservation of Triticeae chromosome structure, they also suggest that some regions are evolving rapidly through frequent gene duplications and translocations.

Sequence accessions

EBI European Nucleotide Archive, Study no. ERP002330

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1080) contains supplementary material, which is available to authorized users.