Risk score modeling of multiple gene to gene interactions using aggregated-multifactor dimensionality reduction

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

BACKGROUND

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

METHOD

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.

RESULTS

We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R^2-scores > 0.01 for 71.55% of metabolites.

CONCLUSION

The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction

BACKGROUND

Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD.

METHODS

We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction.

RESULTS

We identified 2 candidate genetic interactions with PFDR <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD.

CONCLUSIONS

In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

Impact of the gene-gene interactions related to the HIF-1α signaling pathway with the knee osteoarthritis development

INTRODUCTION/OBJECTIVES

Articular cartilage is the target tissue of osteoarthritis (OA), and because it lacks capillary networks, the microenvironment is hypoxic. Hypoxia inducible factor-1 alpha (HIF-1α) regulates the homeostasis of this tissue. The aim of this study was to investigate whether genetic polymorphisms of the HIF-1α signaling pathway are involved in the development of knee OA.

METHOD

We performed a case-control association study and genotyped 134 knee OA patients and 267 healthy controls. All participants were genotyped in order to evaluate 42 SNPs from 22 genes involved in the HIF-1α signaling pathway using the OpenArray technology. Gene-gene interactions (epistasis) were analyzed using the multifactor dimensionality reduction (MDR) method.

RESULTS

The MDR analysis showed epistasis between AKT2 (rs8100018) and IGF1 (rs2288377), AKT2 (rs8100018) and IGF1 (rs35767), IGF1 (rs35767) and COL2A1 (rs1793953), and between GSK3B (rs6438552) and IGF1 (rs35767) polymorphisms, with information gain values of 21.24%, 8.37%, 9.93%, and 5.73%, respectively. Additionally, our model allowed us to identify high- and low-risk genotypes among COL2A1 rs1793953, GSK3B rs6438552, AKT2 rs8100018, and IGF1 rs35767 polymorphisms.

CONCLUSIONS

Knowing the interactions of these polymorphisms involved in HIF-1α signaling pathway could provide a new diagnostic support tool to identify individuals at high risk of developing knee OA.

Discovering feature relevancy and dependency by kernel-guided probabilistic model-building evolution

Background

Discovering relevant features (biomarkers) that discriminate etiologies of a disease is useful to provide biomedical researchers with candidate targets for further laboratory experimentation while saving costs; dependencies among biomarkers may suggest additional valuable information, for example, to characterize complex epistatic relationships from genetic data. The use of classifiers to guide the search for biomarkers (the so–called wrapper approach) has been widely studied. However, simultaneously searching for relevancy and dependencies among markers is a less explored ground.

Results

We propose a new wrapper method that builds upon the discrimination power of a weighted kernel classifier to guide the search for a probabilistic model of simultaneous marginal and interacting effects. The feasibility of the method was evaluated in three empirical studies. The first one assessed its ability to discover complex epistatic effects on a large–scale testbed of generated human genetic problems; the method succeeded in 4 out of 5 of these problems while providing more accurate and expressive results than a baseline technique that also considers dependencies. The second study evaluated the performance of the method in benchmark classification tasks; in average the prediction accuracy was comparable to two other baseline techniques whilst finding smaller subsets of relevant features. The last study was aimed at discovering relevancy/dependency in a hepatitis dataset; in this regard, evidence recently reported in medical literature corroborated our findings. As a byproduct, the method was implemented and made freely available as a toolbox of software components deployed within an existing visual data–mining workbench.

Conclusions

The mining advantages exhibited by the method come at the expense of a higher computational complexity, posing interesting algorithmic challenges regarding its applicability to large–scale datasets. Extending the probabilistic assumptions of the method to continuous distributions and higher–degree interactions is also appealing. As a final remark, we advocate broadening the use of visual graphical software tools as they enable biodata researchers to focus on experiment design, visualisation and data analysis rather than on refining their scripting programming skills.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-017-0131-y) contains supplementary material, which is available to authorized users.

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Therapeutic advancements in juvenile idiopathic arthritis

The treatment of juvenile idiopathic arthritis (JIA) has substantially evolved over the past two decades. Research has been conducted and is ongoing on how therapies can best be utilized either as monotherapy or in combination for enhanced efficacy. The introduction of biologic therapies that selectively target specific cytokines has changed the acceptable clinical course of childhood arthritis. In addition to the development and utilization of new therapeutic agents, the pediatric rheumatology community has made vital progress toward defining disease activity, developing validated outcome measures, and establishing collaborative networks to assess both clinical outcomes and the long-term side effects related to therapeutics for juvenile arthritis. In this chapter, we will discuss the therapeutic evolution in JIA over the past two decades. Although the largest strides have been made with biologic agents, and these newer drugs have more rigorous data to support their use, select commonly used non-biologic therapies are included, with the discussion focused on more recent updated literature.

Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma

Determining the complex relationships between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has been proven to be capable of effectively detecting the statistical patterns of epistasis, although classification accuracy is required for this approach. The imbalanced dataset can cause seriously negative effects on classification accuracy. Moreover, MDR methods cannot quantitatively assess the disease risk of genotype combinations. Hence, we introduce a novel weighted risk score-based multifactor dimensionality reduction (WRSMDR) method that uses the Bayesian posterior probability of polymorphism combinations as a new quantitative measure of disease risk. First, we compared the WRSMDR to the MDR method in simulated datasets. Our results showed that the WRSMDR method had reasonable power to identify high-order gene-gene interactions, and it was more effective than MDR at detecting four-locus models. Moreover, WRSMDR reveals more information regarding the effect of genotype combination on the disease risk, and the result was easier to determine and apply than with MDR. Finally, we applied WRSMDR to a nasopharyngeal carcinoma (NPC) case-control study and identified a statistically significant high-order interaction among three polymorphisms: rs2860580, rs11865086 and rs2305806.