GEIRA: gene-environment and gene–gene interaction research application

The GEIRA (Gene-Environment and Gene–Gene Interaction Research Application) algorithm and subsequent program is dedicated to genome-wide gene-environment and gene–gene interaction analysis. It implements concepts of both additive and multiplicative interaction as well as calculations based on dominant, recessive and co-dominant genetic models, respectively. Estimates of interactions are incorporated in a single table to make the output easily read. The algorithm is coded in both SAS and R. GEIRA is freely available to non-commercial users at . Additional information, including user’s manual and example datasets is available online at .     

Exploring gene–gene interaction in family-based data with an unsupervised machine learning method: EPISFA

Gene–gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa .     

Relationship among metabolizing genes,smoking and alcohol used as modifier factors on prostate cancer risk: Exploring some gene–gene and gene–environment interactions

Background: Prostate cancer (PCa) is one of the most common male cancers, but the burden of this disease shows remarkable worldwide variation. The role of susceptibility low penetrance genes and environmental factors in the etiology of (PCa) is unclear, but may involve, in some cases, multiple alleles at multiple loci and environmental factors. Study Objectives: To assess whether CYP1A1, GSTM1, GSTT1 susceptibility genotypes, smoking status and alcohol consumption factors contribute to PCa risk, gene–gene and gene–environment interactions were analyzed. Design and Participants: We explored interactions on a multiplicative scale conducting a population-based case–control and a case–only study on 103 incident PCa patients and 132 unrelated controls. Main Results: The interaction odds ratios (IOR) for PCa risk were increased in men who had both susceptibility genotypes GST (M1; T1) null and CYP1A1-M1* in a case–control and case-only design (IOR_cc: 1.11; 95% CI: 0.12–10.02; IOR_cc: 6.23; 95%, CI: 0.51–75.89; IOR_co: 2.80; 95% CI: 0.44–17.45 and IOR_co: 2.65; 95%, CI: 0.30–25.40). No clear evidence for interaction on a multiplicative scale between smoking status, alcohol consumption and genetic polymorphisms in PCa risk was observed. Conclusions: Our findings suggest that the interaction between genetic polymorphisms in GST (T1; M1) and CYP1A1-M1* would play a significant role as a modifying factor on PCa risk in Chilean people. However, these preliminary exploratory results should be confirmed in a larger study.     

Identification of gene–environment interactions with marginal penalization

Gene–environment (G–E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other “standard”) estimation with each marginal model, and then select significant G–E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the “main effects, interactions” hierarchy, which has been stressed in recent G–E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of “non-normal” distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.     

Exploring data from genetic association studies using bayesian variable selection and the dirichlet process: application to searching for gene × gene patterns

We construct data exploration tools for recognizing important covariate patterns associated with a phenotype, with particular focus on searching for association with gene-gene patterns. To this end, we propose a new variable selection procedure that employs latent selection weights and compare it to an alternative formulation. The selection procedures are implemented in tandem with a Dirichlet process mixture model for the flexible clustering of genetic and epidemiological profiles. We illustrate our approach with the aid of simulated data and the analysis of a real data set from a genome-wide association study.     

SiRNA technology, the gene therapy of the future?

A new era in genetics started 17 years ago, when co-suppression in petunia was discovered. Later, co-suppression was identified as RNA interference (RNAi) in many plant and lower eukaryote animals. Although an ancient antiviral host defense mechanism in plants, the physiologic role of RNAi in mammals is still not completely understood. RNAi is directed by short interfering RNAs (siRNAs), one subtype of short double stranded RNAs. In this review we summarize the history and mechanisms of RNAi. We also aim to highlight the correlation between structure and efficacy of siRNAs. Delivery is the most important obstacle for siRNA based gene therapy. Viral and nonviral deliveries are discussed. In vivo delivery is the next obstacle to clinical trials with siRNAs. Although hydrodynamic treatment is effective in animals, it cannot be used in human therapy. One possibility is organ selective catheterization. The known side effects of synthesized siRNAs are also discussed. Although there are many problems to face in this new field of gene therapy, successful in vitro and in vivo experiments raise hope for treating human disease with siRNA.     

Evaluation of recombinant fowlpox virus expressing infectious bronchitis virus S1 gene and chicken interferon-γ gene for immune protection against heterologous strains

A recombinant fowlpox virus (rFPV-IFNγS1) that co-expressed the infectious bronchitis virus (IBV) S1 gene and the chicken interferon-γ gene has been constructed. To evaluate the efficacy of the recombinant fowlpox virus vaccine against heterotypic IBV strains, 60 4-week-old Specific-Pathogen-Free (SPF) chickens were inoculated with this vaccine and 3 weeks post inoculation challenged with the homotypic IBV strain LX4 and the heterotypic IBV strains LHB, LHLJ04XI, LTJ95I and LSC99I. Antibodies against IBV were detected in vaccinated chickens 1-week post inoculation. The number of CD4⁺ and CD8⁺ T-lymphocytes in the peripheral blood increased rapidly in the vaccinated groups challenged with strains LX4, LHB and LHLJ04XI. There were significant differences in the number of CD4⁺ and CD8⁺ T-lymphocytes between the vaccinated groups challenged with strains LTJ95I and LSC99I and all the control groups. The morbidity was below 30% in vaccinated groups challenge with strains LX4, LHB and LHLJ04XI, but was 40% greater than that in the other groups. In addition, the lesions and the amount of virus shedding were less severe in the vaccinated groups challenged by strains LX4, LHB and LHLJ04XI when compared with the other groups, but there was no significant difference in the average body weight of the chickens in all groups (all p > 0.05). These results indicate that the rFPV-IFNγS1 protected chickens against challenge with homotypic IBV strain LX4 and heterotypic strains LHLJ04XI and LHB.     

VitA对小鼠胚胎组织Hox gene蛋白表达的影响

目的：从发育基因角度探讨维生素A（VA）缺乏导致先天性畸形的机理,方法。通过建立小鼠VA缺乏模型,用免疫组织化学等方法研究VA缺乏对小鼠胚胎组织Hox gene蛋白表达水平的影响。结果：维生素A缺乏使胎鼠的Hox gene蛋白表达明显低于正常对照组（P＜0．05）。VA补充组明显高于VA缺乏组,但仍比正常对照组低,结论：VA缺乏可能在翻译水平影响小鼠胚胎组织Hox gene蛋白表达。     

Comparisons of power of statistical methods for gene–environment interaction analyses

Any genome-wide analysis is hampered by reduced statistical power due to multiple comparisons. This is particularly true for interaction analyses, which have lower statistical power than analyses of associations. To assess gene–environment interactions in population settings we have recently proposed a statistical method based on a modified two-step approach, where first genetic loci are selected by their associations with disease and environment, respectively, and subsequently tested for interactions. We have simulated various data sets resembling real world scenarios and compared single-step and two-step approaches with respect to true positive rate (TPR) in 486 scenarios and (study-wide) false positive rate (FPR) in 252 scenarios. Our simulations confirmed that in all two-step methods the two steps are not correlated. In terms of TPR, two-step approaches combining information on gene-disease association and gene–environment association in the first step were superior to all other methods, while preserving a low FPR in over 250 million simulations under the null hypothesis. Our weighted modification yielded the highest power across various degrees of gene–environment association in the controls. An optimal threshold for step 1 depended on the interacting allele frequency and the disease prevalence. In all scenarios, the least powerful method was to proceed directly to an unbiased full interaction model, applying conventional genome-wide significance thresholds. This simulation study confirms the practical advantage of two-step approaches to interaction testing over more conventional one-step designs, at least in the context of dichotomous disease outcomes and other parameters that might apply in real-world settings.     

Mixture toxicity and gene inductions: can we predict the outcome?

As a consequence of the nature of most real-life exposure scenarios, the last decade of ecotoxicological research has seen increasing interest in the assessment of mixture ecotoxicology. Often, mixtures are considered to follow one of two models, concentration addition (CA) or response addition (RA), both of which have been described in the literature. Nevertheless, mixtures that deviate from either or both models exist; they typically exhibit phenomena like synergism, ratio or concentration dependency, or inhibition. Moreover, both CA and RA have been challenged and evaluated mainly for acute responses at relatively high levels of biological organization (e.g., whole-organism mortality), and applicability to genetic responses has not received much attention. Genetic responses are considered to be the primary reaction in case of toxicant exposure and carry valuable mechanistic information. Effects at the gene-expression level are at the heart of the mode of action by toxicants and mixtures. The ability to predict mixture responses at this primary response level is an important asset in predicting and understanding mixture effects at different levels of biological organization. The present study evaluated the applicability of mixture models to stress gene inductions in Escherichia coli employing model toxicants with known modes of action in binary combinations. The results showed that even if the maximum of the dose-response curve is not known, making a classical ECx (concentration causing x% effect) approach impossible, mixture models can predict responses to the binary mixtures based on the single-toxicant response curves. In most cases, the mode of action of the toxicants does not determine the optimal choice of model (i.e., CA, RA, or a deviation thereof).     

AMH gene mutations in two Egyptian families with persistent müllerian duct syndrome

The anti-müllerian hormone (AMH) is responsible for regression of müllerian ducts during male sexual differentiation. Mutations in the AMH gene or its type II receptor gene AMHR2 lead to persistence of the uterus and fallopian tubes in male children, i.e. persistent müllerian duct syndrome (PMDS). Both conditions are transmitted according to an autosomal recessive pattern and are symptomatic only in males. We report on 2 unrelated Egyptian consanguineous families with PMDS. The first family comprised 3 affected prepubertal sibs complaining of undescended testes. Pelvic exploration and laparotomy revealed müllerian duct derivatives. The other family was presenting with an adolescent male with impalpable left testis, and pelvic exploration showed remnants of fallopian tubes and rudimentary uterus. AMH levels were very low and almost undetectable in all affected patients in both families. Direct sequencing of the coding region of the AMH gene identified 2 homozygous mutations in exon 1, R95X in the first family and V12G in the second family. These data confirmed the autosomal recessive type of PMDS. Molecular investigation of this rare disorder in a larger number of cases with undescended testes in Egypt is warranted for proper diagnosis and genetic counseling.     

Vitamin C-related nutrient–nutrient and nutrient–gene interactions that modify folate status

Purpose

Folate-related nutrient–nutrient and nutrient–gene interactions modify disease risk; we therefore examined synergistic relationships between dietary folic acid, vitamin C and variant folate genes with respect to red cell folate status.

Methods

Two hundred and twelve subjects were examined using chemiluminescent immunoassay, PCR and food frequency questionnaire to determine red cell and serum folate, 14 folate gene polymorphisms, dietary folate (natural and synthetic) and vitamin C.

Results

When examined independently, synthetic PteGlu correlates best with red cell folate at higher levels of intake (p = 0.0102), while natural 5CH₃-H₄-PteGlu_n correlates best with red cell folate at lower levels of intake (p = 0.0035). However, dietary vitamin C and 5CH₃-H₄-PteGlu_n interact synergistically to correlate with red cell folate at higher levels of intake (p = 0.0005). No interaction between dietary vitamin C and PteGlu was observed. This ‘natural’ nutrient–nutrient interaction may provide an alternative to synthetic PteGlu supplementation that is now linked to adverse phenomena/health outcomes. On its own, vitamin C also correlates with red cell folate (p = 0.0150) and is strongly influenced by genetic variation in TS, MTHFR and MSR, genes critical for DNA and methionine biosynthesis that underpin erythropoiesis. Similarly, dietary vitamin C and 5CH₃-H₄-PteGlu_n act synergistically to modify red cell folate status according to variation in folate genes: of note, heterozygosity for 2R3R-TS (p = 0.0181), SHMT (p = 0.0046) and all three MTHFR SNPs (p = 0.0023, 0.0015 and 0.0239 for G1793A, C677T and A1298C variants, respectively) promote a significant association with red cell folate. Again, all these genes are critical for nucleic acid biosynthesis. Folate variants with the strongest independent effect on folate status were C677T-MTHFR (p = 0.0004) and G1793A-MTHFR (p = 0.0173).

Conclusions

5CH₃-H₄-PteGlu_n assimilation and variant folate gene expression products may be critically dependent on dietary vitamin C.