An Exploration of Gene-Gene Interactions and Their Effects on Hypertension

Omnibus testing approach for gene-based gene-gene interaction

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

Hypertension genetics past, present and future applications

Essential hypertension is a complex trait where the underlying aetiology is not completely understood. Left untreated it increases the risk of severe health complications including cardiovascular and renal disease. It is almost 15 years since the first genome-wide association study for hypertension, and after a slow start there are now over 1000 blood pressure (BP) loci explaining ∼6% of the single nucleotide polymorphism-based heritability. Success in discovery of hypertension genes has provided new pathological insights and drug discovery opportunities and translated to the development of BP genetic risk scores (GRSs), facilitating population disease risk stratification. Comparing highest and lowest risk groups shows differences of 12.9 mm Hg in systolic-BP with significant differences in risk of hypertension, stroke, cardiovascular disease and myocardial infarction. GRSs are also being trialled in antihypertensive drug responses. Drug targets identified include NPR1, for which an agonist drug is currently in clinical trials. Identification of variants at the PHACTR1 locus provided insights into regulation of EDN1 in the endothelin pathway, which is aiding the development of endothelin receptor EDNRA antagonists. Drug re-purposing opportunities, including SLC5A1 and canagliflozin (a type-2 diabetes drug), are also being identified. In this review, we present key studies from the past, highlight current avenues of research and look to the future focusing on gene discovery, epigenetics, gene-environment interactions, GRSs and drug discovery. We evaluate limitations affecting BP genetics, including ancestry bias and discuss streamlining of drug target discovery and applications for treating and preventing hypertension, which will contribute to tailored precision medicine for patients.

Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes

Genotype-phenotype causal modeling has evolved significantly since Johannsen's and Wright's original designs were published. The development of genomewide assays to interrogate and detect possible causal variants associated with complex traits has expanded the scope of genotype-phenotype research considerably. Clusters of causal variants discovered by genomewide assays and associated with complex traits have been used to develop polygenic risk scores to predict clinical diagnoses of multidimensional human disorders. However, genomewide investigations have met with many challenges to their research designs and statistical complexities which have hindered the reliability and validity of their predictions. Findings linked to differences in heritability estimates between causal clusters and complex traits among unrelated individuals remain a research area of some controversy. Causal models developed from case-control studies as opposed to experiments, as well as other issues concerning the genotype-phenotype causal model and the extent to which various forms of pleiotropy and the concept of the endophenotype add to its complexity, will be reviewed.

A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions

BACKGROUND

Non-additive interactions among genes are frequently associated with a number of phenotypes, including known complex diseases such as Alzheimer's, diabetes, and cardiovascular disease. Detecting interactions requires careful selection of analytical methods, and some machine learning algorithms are unable or underpowered to detect or model feature interactions that exhibit non-additivity. The Random Forest method is often employed in these efforts due to its ability to detect and model non-additive interactions. In addition, Random Forest has the built-in ability to estimate feature importance scores, a characteristic that allows the model to be interpreted with the order and effect size of the feature association with the outcome. This characteristic is very important for epidemiological and clinical studies where results of predictive modeling could be used to define the future direction of the research efforts. An alternative way to interpret the model is with a permutation feature importance metric which employs a permutation approach to calculate a feature contribution coefficient in units of the decrease in the model's performance and with the Shapely additive explanations which employ cooperative game theory approach. Currently, it is unclear which Random Forest feature importance metric provides a superior estimation of the true informative contribution of features in genetic association analysis.

RESULTS

To address this issue, and to improve interpretability of Random Forest predictions, we compared different methods for feature importance estimation in real and simulated datasets with non-additive interactions. As a result, we detected a discrepancy between the metrics for the real-world datasets and further established that the permutation feature importance metric provides more precise feature importance rank estimation for the simulated datasets with non-additive interactions.

CONCLUSIONS

By analyzing both real and simulated data, we established that the permutation feature importance metric provides more precise feature importance rank estimation in the presence of non-additive interactions.

Identifying the degree of genetic interactions using Restricted Boltzmann Machine-A study on colorectal cancer

The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples. At the first stage of this work we have used restricted Boltzmann machine model to find such significant interactions present in normal and/or cancer samples of every gene pairs of 20 genes of colorectal cancer data set (GDS4382) along with the weight/degree of those interactions. Later on, we are looking for those interactions present in adenoma and/or carcinoma samples of the same 20 genes of colorectal cancer data set (GDS1777). The weight/degree of those interactions represents how strong/weak an interaction is. At the end we will create a gene regulatory network with the help of those interactions, where the regulatory genes are identified by using Naïve Bayes Classifier. Experimental results are validated biologically by comparing the interactions with NCBI databases.

G2G: A web-server for the prediction of human synthetic lethal interactions

Genetic interactions (GIs) are fundamental to our understanding of biological processes in the cell. While GIs have been systematically mapped in yeast, there is scarce information about them in humans. Recently, we have suggested a state-of-the-art hierarchical method that leverages gene ontology information for predicting GIs in yeast. Here, we adapt this method and apply it for the first time to predict GIs in human. We introduce a web service called G2G for this task that is available at http://bnet.cs.tau.ac.il/g2g/.

Familial aggregation and heritability of hypertension in Han population in Shanghai China: a case-control study

Background

To explore the familial aggregation and heritability of hypertension in Han in Shanghai China.

Methods

According to l:l matched pairs design, 342 patients of hypertension and 342 controls were selected and investigate their nuclear family members in the case-control study. The method of genetic epidemiology research was used to explore the familial aggregation and heritability of hypertension.

Results

The prevalence rate of hypertension of first-degree relatives was significantly higher (34.44%) than that of second- degree relatives (17.60%) and third-degree relatives (13.51%) in Han Population in Shanghai China. Separation ratio p was 0.217, and prevalence rate of case group relatives was higher than that of control group relatives. The results showed a phenomenon of familial aggregation in the distribution of hypertension. The heritability of first- degree relatives was 49.51%; that of second-degree relatives and third-degree relatives were respectively 23.42 and 21.41%.

Conclusion

The distribution of essential hypertension has phenomenon of familial aggregation in Han Population in Shanghai China. The separation ratio of essential hypertension in this study shows that essential hypertension conform to the characteristics of multigene genetic disease. The heritability of first-degree relatives is bigger than that of second-degree relatives and third-degree relatives.

Economic evaluation of a pharmacogenomic multi-gene panel test to optimize anti-hypertension therapy: simulation study

AIMS

Hypertension is the strongest modifiable risk factor for cardiovascular disease, affecting 80 million individuals in the US and responsible for ∼360,000 deaths, at total annual costs of $93.5 billion. Antihypertension therapies guided by single genotypes are clinically more effective and may avert more adverse events than the standard of care of layering anti-hypertensive drug therapies, thus potentially decreasing costs. This study aimed to determine the economic benefits of the implementation of multi-gene panel guided therapies for hypertension from the payer perspective within a 3-year time horizon.

MATERIALS AND METHODS

A simulation analysis was conducted for a panel of 10 million insured patients categorized clinically as untreated, treated but uncontrolled, and treated and controlled over a 3-year treatment period. Inputs included research data; empirical data from a 11-gene panel with known functional, heart, blood vessel, and kidney genotypes; and therapy efficacy and safety estimates from literature. Cost estimates were categorized as related to genetic testing, evaluation and management, medication, or adverse events.

RESULTS

Multi-gene panel guided therapy yielding savings of $6,256,607,500 for evaluation and management, $908,160,000 for medications, and $37,467,508,716 for adverse events, after accounting for incremental genetic testing costs of $2,355,540,000. This represents total 3-year savings of $42,276,736,216, or a 47% reduction, and 3-year savings of $4,228 and annual savings of $1,409 per covered patient.

CONCLUSIONS

A precision medicine approach to genetically guided therapy for hypertension patients using a multi-gene panel reduced total 3-year costs by 47%, yielding savings exceeding $42.3 billion in an insured panel of 10 million patients. Importantly, 89% of these savings are generated by averting specific adverse events and, thus, optimizing choice of therapy in function of both safety and efficacy.

Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine

Elucidation of the systems biology foundation underlying the effect of Fangji, which are multi-herbal traditional Chinese medicine (TCM) formulas, is one of the major aims in the field. The numerous bioactive ingredients of a Fangji deal with the multiple targets of a complex disease, which is influenced by a number of genes and their interactions with the environment. Genome-wide association study (GWAS) is an unbiased approach for dissecting the genetic variants underlying complex diseases and individual response to a given treatment. GWAS has great potential for the study of systems biology from the point of view of genomics, but the capacity using current analysis models is largely handicapped, as evidenced by missing heritability. Recent development of a full genetic model, in which gene-gene interactions (dominance and epistasis) and gene-environment interactions are all considered, has addressed these problems. This approach has been demonstrated to substantially increase model power, remarkably improving the detection of association of GWAS and the construction of the molecular architecture. This analysis does not require a very large sample size, which is often difficult to meet for a GWAS of treatment response. Furthermore, this analysis can integrate other omic information and allow for variations of Fangji, which is very promising for Fangjiomic study and detection of the sophisticated molecular architecture of the function of Fangji, as well as for the delineation of the systems biology of personalized medicine in TCM in an unbiased and comprehensive manner.

Interaction between polymorphisms in cell-cycle genes and environmental factors in regulating cholinesterase activity in people with exposure to omethoate

Cholinesterase activity (ChA), the effective biomarker for organophosphate pesticide exposure, is possibly affected by single nucleotide polymorphisms (SNPs) in cell-cycle-related genes. One hundred and eighty workers with long-term exposure to omethoate and 115 healthy controls were recruited to explore the gene-gene and gene-environment interactions. The acetylthiocholine and dithio-bis-(nitrobenzoic acid) method was used to detect the cholinesterase activities in whole blood, erythrocytes and plasma. Genetic polymorphisms were determined by the PCR-RFLP and direct PCR electrophoresis methods. Statistical results showed that the cholinesterase activities of whole blood, erythrocytes and plasma in the exposure group were significantly lower than those in the control group (p < 0.001), and erythrocyte cholinesterase activities were associated with gender, smoking and drinking in the exposure group (p < 0.05). Single-locus analyses showed that there is a statistically significant difference in the ChA among the genotypes CC, CA and AA of the p21 rs1801270 locus in the control group (p = 0.033), but not in the exposure group. A significant interaction between genes and environmental factors (i.e. p53, p21, mdm2, gender, smoking and drinking) affecting ChA was found through a generalized multifactor dimensionality reduction analysis. These obtained markers will be useful in further marker-assisted selection in workers with exposure to omethoate.