Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits

Laser-Induced Breakdown Spectroscopy Combined with Nonlinear Manifold Learning for Improvement Aluminum Alloy Classification Accuracy

Laser-induced breakdown spectroscopy (LIBS) spectra often include many intensity lines, and obtaining meaningful information from the input dataset and condensing the dimensions of the original data has become a significant challenge in LIBS applications. This study was conducted to classify five different types of aluminum alloys rapidly and noninvasively, utilizing the manifold dimensionality reduction technique and a support vector machine (SVM) classifier model integrated with LIBS technology. The augmented partial residual plot was used to determine the nonlinearity of the LIBS spectra dataset. To circumvent the curse of dimensionality, nonlinear manifold learning techniques, such as local tangent space alignment (LTSA), local linear embedding (LLE), isometric mapping (Isomap), and Laplacian eigenmaps (LE) were used. The performance of linear techniques, such as principal component analysis (PCA) and multidimensional scaling (MDS), was also investigated compared to nonlinear techniques. The reduced dimensions of the dataset were assigned as input datasets in the SVM classifier. The prediction labels indicated that the Isomap-SVM model had the best classification performance with the classification accuracy, the number of dimensions and the number of nearest neighbors being 96.67%, 11, and 18, respectively. These findings demonstrate that the combination of nonlinear manifold learning and multivariate analysis has the potential to classify the samples based on LIBS with reasonable accuracy.

Polygenetic-Risk Scores Related to Crystallin Metabolism Are Associated with Age-Related Cataract Formation and Interact with Hyperglycemia, Hypertension, Western-Style Diet, and Na Intake

Age-related cataract (ARC) development is associated with loss of crystalline lens transparency related to interactions between genetic and environmental factors. We hypothesized that polygenetic risk scores (PRS) of the selected genetic variants among the ARC-related genes might reveal significant genetic impacts on ARC risk, and the PRS might have gene-gene and gene-lifestyle interactions. We examined the hypothesis in 1972 and 39,095 subjects aged ≥50 years with and without ARC, respectively, in a large-scale hospital-based cohort study conducted from 2004 to 2013. Single nucleotide polymorphisms (SNPs) of the genes related to ARC risk were identified, and polygenetic risk scores (PRS) were generated based on the results of a generalized multifactor dimensionality reduction analysis. Lifestyle interactions with PRS were evaluated. The PRS derived from the best model included the following six SNPs related to crystallin metabolism: ULK4_rs1417380362, CRYAB_rs2070894, ACCN1_rs55785344, SSTR2_rs879419608, PTN_rs322348, and ICA1_rs200053781. The risk of ARC in the high-PRS group was 2.47-fold higher than in the low-PRS group after adjusting for confounders. Age, blood pressure, and glycemia interacted with PRS to influence the risk of ARC: the incidence of ARC was much higher in the elderly (≥65 years) and individuals with hypertension or hyperglycemia. The impact of PRS on ARC risk was greatest in middle-aged individuals with hypertension or hyperglycemia. Na, coffee, and a Western-style diet intake also interacted with PRS to influence ARC risk. ARC risk was higher in the high-PRS group than in the low-PRS group, and high Na intake, Western-style diet, and low coffee intake elevated its risk. In conclusion, ARC risk had a positive association with PRS related to crystallin metabolism. The genetic impact was greatest among those with high Na intake or hypertension. These results can be applied to precision nutrition interventions to prevent ARC.

Endothelin-1 rs9296344 associates with the susceptibility of childhood primary nephrotic syndrome

BACKGROUND

Recently, the rs5370 single nucleotide polymorphisms (SNPs) of Endothelin-1 (EDN1) showed association with the susceptibility of childhood primary nephrotic syndrome (CPNS). This study aims to investigate potential relationships between other EDN1 SNPs and CPNS.

METHODS

Seven SNPs (rs5370, rs10478723, rs1476046, rs1800541, rs2070698, rs2071942, and rs9296344) of the EDN1 gene were genotyped in 579 CPNS patients and 586 age-matched healthy children. Then, we analyzed potential associations of the six SNPs with susceptibility of CPNS by using rs5370 as a conditional variant in a logistic regression model. SNP-SNP interaction analysis was performed to investigate the joint effects of the seven SNPs in the pathogenesis of CPNS.

RESULTS

Independent with rs5370, only rs9296344 significantly associated (T vs C, odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.57-0.88, P = .001) with the susceptibility of CPNS. Meanwhile, no joint effect among the analyzed seven SNPs was discovered in this study.

CONCLUSIONS

This study discovered that C allele of rs9296344 on EDN1 is a novel independent risk factor for CPNS.

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multifactor dimensionality reduction (MDR). Early MDR methods mainly focused on binary traits. More recently, several extensions of MDR have been developed for analyzing various traits such as quantitative traits and survival times. Newer technologies, such as genome-wide association studies (GWAS), have now been developed for assessing multiple traits, to simultaneously identify genetic variants associated with various pathological phenotypes. It has also been well demonstrated that analyzing multiple traits has several advantages over single trait analysis. While there remains a need to find GGIs for multiple traits, such studies have become more difficult, due to a lack of novel methods and software. Herein, we propose a novel multi-CMDR method, by combining fuzzy clustering and MDR, to find GGIs for multiple traits. Multi-CMDR showed similar power to existing methods, when phenotypes followed bivariate normal distributions, and showed better power than others for skewed distributions. The validity of multi-CMDR was confirmed by analyzing real-life Korean GWAS data.

High genetic risk scores of SLIT3, PLEKHA5 and PPP2R2C variants increased insulin resistance and interacted with coffee and caffeine consumption in middle-aged adults

BACKGROUNDS AND AIMS

Insulin resistance is a common feature of metabolic syndrome that may be influenced by genetic risk factors. We hypothesized that genetic risk scores (GRS) of SNPs that influence insulin resistance and signaling interact with lifestyles to modulate insulin resistance in Korean adults.

METHODS AND RESULTS

Genome-wide association studies (GWAS) of subjects aged 40-65 years who participated in the Ansung/Ansan cohorts (8842 adults) in Korea revealed 52 genetic variants that influence insulin resistance. The best gene-gene interaction model was explored using the generalized multifactor dimensionality reduction (GMDR) method. GRS from the best model were calculated and the GRS were divided into low, medium and high groups. The best model for representing insulin resistance included SLIT3_rs2974430, PLEKHA5_rs1077044, and PPP2R2C_rs16838853. The odds ratios for insulin resistance were increased by 150% in the High-GRS group compared to the Low-GRS group. However, ORs for insulin secretion capacity, measured by HOMA-B, were not associated with GRS. Coffee and caffeine intake and GRS had an interaction with insulin resistance: In subjects with high coffee (≥10 cups/week) or caffeine intake (≥220 mg caffeine/day), insulin resistance was significantly elevated in the High-GRS group, but not in the Low-GRS. However, alcohol intake, smoking and physical activity did not have an interaction with GRS. Insulin secretion capacity was not significantly influenced by GRS when evaluating the adjusted odds ratios.

CONCLUSIONS

Subjects with High-GRS may be susceptible to increased insulin resistance by 50% and its risk may be exacerbated by consuming more than 10 cups coffee/week or 220 mg caffeine/day.

Childhood trauma interacted with BDNF Val66Met influence schizophrenic symptoms

The gene - environment (G × E) interaction effect is involved in severe mental disorders. However, whether the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism participates in the childhood-abuse influenced schizophrenic symptoms remains unclear. We examined the interaction between BDNF Val66Met, and childhood trauma (ChT) on psychotic symptoms in a Chinese Han population.To estimate the G × E interaction, psychiatric interviews, self-report questionnaires for ChT, and genotyping for BDNF Val66Met were carried out on 201 schizophrenic patients. G × E interactions were analyzed by generalized multifactor dimensionality reduction (GMDR).Among all patients, 11.9%, 19.4%, 23.4%, 26.4%, and 73.6% reported emotional abuses, physical abuses (PA), sexual abuses (SA), emotional neglects (EN), and physical neglects (PN), respectively. Significant negative correlations were observed between anxiety/depression factors, and ChT total scores. Patients with 3 different BDNF genotypes showed significant differences in anxiety/depression scores. Significant 2-way interactions were found for Val66Met × PN, 3-way interactions were found for Val66Met × PN × PA, and four-way interactions were found for Val66Met × PN × PA × EN with regard to the excitement scores.Our findings suggested an involvement of BDNF Val66Met polymorphism after ChT in terms of risk for schizophrenia symptoms.

Genetically-Driven Enhancement of Dopaminergic Transmission Affects Moral Acceptability in Females but Not in Males: A Pilot Study

Moral behavior has been a key topic of debate for philosophy and psychology for a long time. In recent years, thanks to the development of novel methodologies in cognitive sciences, the question of how we make moral choices has expanded to the study of neurobiological correlates that subtend the mental processes involved in moral behavior. For instance, in vivo brain imaging studies have shown that distinct patterns of brain neural activity, associated with emotional response and cognitive processes, are involved in moral judgment. Moreover, while it is well-known that responses to the same moral dilemmas differ across individuals, to what extent this variability may be rooted in genetics still remains to be understood. As dopamine is a key modulator of neural processes underlying executive functions, we questioned whether genetic polymorphisms associated with decision-making and dopaminergic neurotransmission modulation would contribute to the observed variability in moral judgment. To this aim, we genotyped five genetic variants of the dopaminergic pathway [rs1800955 in the dopamine receptor D4 (DRD4) gene, DRD4 48 bp variable number of tandem repeat (VNTR), solute carrier family 6 member 3 (SLC6A3) 40 bp VNTR, rs4680 in the catechol-O-methyl transferase (COMT) gene, and rs1800497 in the ankyrin repeat and kinase domain containing 1 (ANKK1) gene] in 200 subjects, who were requested to answer 56 moral dilemmas. As these variants are all located in genes belonging to the dopaminergic pathway, they were combined in multilocus genetic profiles for the association analysis. While no individual variant showed any significant effects on moral dilemma responses, the multilocus genetic profile analysis revealed a significant gender-specific influence on human moral acceptability. Specifically, those genotype combinations that improve dopaminergic signaling selectively increased moral acceptability in females, by making their responses to moral dilemmas more similar to those provided by males. As females usually give more emotionally-based answers and engage the "emotional brain" more than males, our results, though preliminary and therefore in need of replication in independent samples, suggest that this increase in dopamine availability enhances the cognitive and reduces the emotional components of moral decision-making in females, thus favoring a more rationally-driven decision process.

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Elucidating the mechanisms of complex diseases such as cardiovascular disease (CVD) remains a significant challenge due to multidimensional alterations at molecular, cellular, tissue, and organ levels. To better understand CVD and offer insights into the underlying mechanisms and potential therapeutic strategies, data from multiple omics types (genomics, epigenomics, transcriptomics, metabolomics, proteomics, microbiomics) from both humans and model organisms have become available. However, individual omics data types capture only a fraction of the molecular mechanisms. To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular insights. In this review, we summarize recent methodological advances in multidimensional omics integration, exemplify their applications in cardiovascular research, and pinpoint challenges and future directions in this incipient field.

Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes

To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.

GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits

Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today’s genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http://www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.

Effect of High-Density Lipoprotein Metabolic Pathway Gene Variations and Risk Factors on Neovascular Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy in China

Purpose

To investigate the effect of genetic variants in the high-density lipoprotein (HDL) metabolic pathway and risk factors on neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) in China.

Methods

A total of 742 Chinese subjects, including 221 controls, 230 cases with nAMD, and 291 cases with PCV, were included in the present study. Five single nucleotide polymorphisms (SNPs) from three genes in the HDL metabolic pathway (HDLMP) including cholesteryl ester transfer protein (CETP), hepatic lipase (LIPC) and lipoprotein lipase (LPL) were genotyped in all study subjects with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Risk factors including gender, hypertension, hyperlipidemia, diabetes mellitus, and coronary artery disease were identified. Chi-square tests or Fisher’s exact tests were applied to discover associations between SNPs and risk factors for PCV and nAMD. Gene-gene interactions and gene-environment interactions were evaluated by the multifactor-dimensionality reduction (MDR) method.

Results

CETP rs3764261 were significantly associated with an increased risk for PCV (odds ratio (OR) = 1.444, P = 0.0247). LIPC rs1532085 conferred an increased risk for PCV (OR = 1.393, P = 0.0094). We found no association between PCV and LPL rs12678919, LIPC rs10468017 or CETP rs173539. No association was found between five SNPs with nAMD. Regarding risk factors, females were found to have significantly decreased risks for both PCV and nAMD (P = 0.006 and 0.001, respectively). Coronary artery disease (CAD) was a risk factor in PCV patients but played a protective role in nAMD patients. Hyperlipidemia was associated with PCV but not with nAMD. Neither hypertension nor diabetes mellitus was associated with PCV or nAMD. The MDR analysis revealed that a three-locus model with rs12678919, rs1532085, and gender was the best model for nAMD, while a five-locus model consisting of rs10468017, rs3764261, rs1532085, gender, and hyperlipidemia was best for PCV.

Conclusion

Our large-sample study suggested that CETP rs3764261 conferred an increased risk for PCV. We also first found the association between rs1532085 and PCV. The result of present study also showed that gender and CAD are associated with PCV and nAMD. Significant association was found between hyperlipidemia and PCV but not nAMD.

Pharmacogenomics of long-acting β2-agonists

INTRODUCTION

Long-acting β2-agonists are an effective class of drugs, when combined with inhaled corticosteroids, for reducing symptoms and exacerbations in patients with asthma that is not adequately controlled by inhaled corticosteroids alone. However, because this class of drugs has been associated with severe adverse events, including hospitalization and death in small numbers of patients, efforts to identify a pharmacogenetic profile for patients at risk has been diligently investigated.

AREAS COVERED

The PubMed search engine of the National Library of Medicine was used to identify English-language and non-English language articles published from 1947 to March 2015 pertinent to asthma, pharmacogenomics, and long-acting β2-agonists. Keywords and topics included: asthma, asthma control, long-acting β2-agonists, salmeterol, formoterol, pharmacogenetics, and pharmacogenomics. This strategy was also used for the Cochrane Library Database and CINAHL. Reference types were randomized controlled trials, reviews, and editorials. Additional publications were culled from reference lists. The publications were reviewed by the authors and those most relevant were used to support the topics covered in this review.

EXPERT OPINION

Children, who carry the ADRB2 Arg16Arg genotype, may be at greater risk than adults for severe adverse events. Rare ADRB2 variants appear to provide better clues for identifying the at-risk population of asthmatics.

Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks

This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.