Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction

BACKGROUND

Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare organizations. This research aims to identify the interaction between Ultrasound scanners (US) maintenance variables as a function of maintenance indicators: US in service or decommissioned, excessive number of failures, and failure rate. Knowing those interactions, specific maintenance measures will be developed to improve the reliability of the US.

RESEARCH DESIGN AND METHODS

Multifactor Dimensionality Reduction (MDR) method was eployed to analyze data from 222 US and their four-year maintenance history. Models were developed based on the variables with the greatest influence on maintenance indicators, where US were classified according to the associated risk.

RESULTS

US with more than one major failure or at least one major component replacement had up to 496.4% more failures than the average. Failure rate increased by up to 188.7% over the average for those US with more than three moderate failures, three replacements, or both.

CONCLUSIONS

This study identifies and quantifies the causes of risk to establish a specific maintenance plan for US. It helps to better understand the degradation of US to optimize their operation and maintenance.

Multifactor dimensionality reduction reveals the effect of interaction between ERAP1 and IFIH1 polymorphisms in psoriasis susceptibility genes

Background: Psoriasis is a common immune-mediated hyperproliferative skin dysfunction with known genetic predisposition. Gene-gene interaction (e.g., between HLA-C and ERAP1) in the psoriasis context has been reported in various populations. As ERAP1 has been recognized as a psoriasis susceptibility gene and plays a critical role in antigen presentation, we performed this study to identify interactions between ERAP1 and other psoriasis susceptibility gene variants. Methods: We validated psoriasis susceptibility gene variants in an independent cohort of 5,414 patients with psoriasis and 5,556 controls. Multifactor dimensionality reduction (MDR) analysis was performed to identify the interaction between variants significantly associated with psoriasis in the validation cohort and ERAP1 variants. We then conducted a meta-analysis of those variants with datasets from exome sequencing, target sequencing, and validation analyses and used MDR to identify the best gene-gene interaction model, including variants that were significant in the meta-analysis and ERAP1 variants. Results: We found that 19 of the replicated variants were identified with p < 0.05 and detected six single-nucleotide polymorphisms of psoriasis susceptibility genes in the meta-analysis. MDR analysis revealed that the best predictive model was that between the rs27044 polymorphism of ERAP1 and the rs7590692 polymorphism of IFIH1 (cross-validation consistency = 9/10, test accuracy = 0.53, odds ratio = 1.32 (95% CI, 1.09-1.59), p < 0.01). Conclusion: Our findings suggest that the interaction between ERAP1 and IFIH1 affects the development of psoriasis. This hypothesis needs to be tested in basic biological studies.

Possible Association Between Polymorphisms in ESR1, COL1A2, BGLAP, SPARC, VDR, and MMP2 Genes and Dental Fluorosis in a Population from an Endemic Region of West Bengal

Dental fluorosis (DF) is the most prevalent form of fluorosis in India affecting millions of people all over the country. As estrogen receptor 1 (ESR1), collagen type 1 alpha 2 (COL1A2), bone γ-carboxyglutamic acid protein (BGLAP), secreted protein acidic and cysteine-rich (SPARC), vitamin D receptor (VDR), and matrix metallopeptidase 2 (MMP2) genes play critical roles in bone metabolism, bone formation, mineral metabolism, and mineralization, variants in these genes could influence susceptibility to DF. The present study was aimed at evaluating the association between 15 single-nucleotide polymorphisms (SNPs) in the six candidate genes (namely, ESR1, COL1A2, BGLAP, SPARC, VDR, and MMP2) and DF among 132 individuals (case = 71 and control = 61) living in a fluoride endemic region of West Bengal, India. No statistically significant association with disease risk was found when the genotypes and allele frequencies of each of the 15 SNPs was analyzed individually using odd's ratio with 95% confidence interval. "CC" and "AG" haplotypes of the COL1A2 gene showed a borderline association with DF. The present study is the first in India to use multifactor dimensionality reduction (MDR) analysis for identifying gene-gene and gene-environment interactions in fluorosis. The biomarker of serum fluoride showed a significant association with the disease state among the 17 attributes (15 SNPs and 2 biomarkers of urine fluoride and serum fluoride) (P value = 0.011). The best model of MDR analysis with maximized testing accuracy involved two SNPs from the ESR1 gene (rs9340799 and rs2077647) and one SNP from BGLAP gene (rs1543294) (P value < 0.0001).

Synergistic influence of cytokine gene polymorphisms over the risk of dementia: A multifactor dimensionality reduction analysis

OBJECTIVE

Evidence supports the important role of neuroinflammation in some types of dementia. This study aimed to evaluate the effect of epistasis of gene cytokines such as interleukin (IL)-α, IL-6, tumor necrosis factor (TNFα), and interferon-gamma (IFN-γ) on the susceptibility to the development of dementia.

MATERIALS AND METHODS

In the study, 221 patients diagnosed with dementia and 710 controls were included. The multifactor-dimensionality reduction (MDR) analysis was performed to identify the epistasis between SNP located in genes of IL-α (rs1800587), IL-6 (rs1800796), TNFα (rs361525 and rs1800629), and IFNγ (rs2069705). The best risk prediction model was identified based on precision and cross-validation consistency.

RESULTS

Multifactor-dimensionality reduction analysis detected a significant model with the genes TNFα, IFNγ, IL1α, and IL6 (prediction success: 72%, p < 0.0001). When risk factors were analyzed with these polymorphisms, the model achieved a similar prediction for dementia as the genes-only model.

CONCLUSION

These data indicate that gene-gene interactions form significant models to identify populations susceptible to dementia.

Detecting genetic epistasis by differential departure from independence

Countering prior beliefs that epistasis is rare, genomics advancements suggest the other way. Current practice often filters out genomic loci with low variant counts before detecting epistasis. We argue that this practice is far from optimal because it can throw away strong epistatic patterns. Instead, we present the compensated Sharma-Song test to infer genetic epistasis in genome-wide association studies by differential departure from independence. The test does not require a minimum number of replicates for each variant. We also introduce algorithms to simulate epistatic patterns that differentially depart from independence. Using two simulators, the test performed comparably to the original Sharma-Song test when variant frequencies at a locus are marginally uniform; encouragingly, it has a marked advantage over alternatives when variant frequencies are marginally nonuniform. The test further revealed uniquely clean epistatic variants associated with chicken abdominal fat content that are not prioritized by other methods. Genes involved in most numbers of inferred epistasis between single nucleotide polymorphisms (SNPs) belong to pathways known for obesity regulation; many top SNPs are located on chromosome 20 and in intergenic regions. Measuring differential departure from independence, the compensated Sharma-Song test offers a practical choice for studying epistasis robust to nonuniform genetic variant frequencies.

Melatonin Receptor 1B Genetic Variants on Susceptibility to Gestational Diabetes Mellitus: A Hospital-Based Case-Control Study in Wuhan, Central China

PURPOSE

The aim of the study was to find out the associations of Melatonin receptor 1B (MTNR1B) genetic variants with gestational diabetes mellitus (GDM) in Wuhan of central China.

PATIENTS AND METHODS

A hospital-based case-control study that included 1679 women was carried out to explore the associations of MTNR1B single nucleotide polymorphisms (SNPs) with GDM risk, which were analyzed through logistic regression analysis by adjusting age, pre-pregnancy BMI and family history of diabetes. Multifactor dimensionality reduction was applied to determine gene-gene interactions between SNPs.

RESULTS

MTNR1B SNPs rs10830962, rs10830963, rs1387153, rs7936247 and rs4753426 were significantly associated with GDM risk (P<0.05). The rs10830962/G, rs10830963/G, rs1387153/T, and rs7936247/T were risk variants, whereas rs4753426/T was protective variant for GDM development. Fasting plasma glucose (FPG) and 1h-plasma glucose (PG) were significantly different among genotypes at rs10830962 and rs10830963, whereas 2h-PG levels were not. Gene-gene interactions were not found among the five SNPs on GDM risk.

CONCLUSION

MTNR1B genetic variants have significant associations but no gene-gene interactions with GDM risk in central Chinese population. Furthermore, MTNR1B SNPs have significant relationships with glycemic traits.

Ethnicity Differences in the Association of UCP1-3826A/G, UCP2-866G/A and Ala55Val, and UCP3-55C/T Polymorphisms with Type 2 Diabetes Mellitus Susceptibility: An Updated Meta-Analysis

BACKGROUND

The relationship between uncoupling protein (UCP) 1-3 polymorphisms and susceptibility to type 2 diabetes mellitus (T2DM) has been extensively studied, while conclusions remain contradictory. Thus, we performed this meta-analysis to elucidate whether the UCP1-3826A/G, UCP2-866G/A, Ala55Val, and UCP3-55C/T polymorphisms are associated with T2DM.

METHODS

Eligible studies were searched from PubMed, Cochrane Library, and Web of Science database before 12 July 2020. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the strength of the association. Heterogeneity analysis, subgroup analysis, sensitivity analysis, and publication bias were also performed.

RESULTS

A total of 38 case-control studies were included in this meta-analysis. The overall results revealed significant association between T2DM and the UCP2 Ala55Val polymorphism (recessive model: OR = 1.25, 95% CI 1.12-1.40, P < 0.01; homozygous model: OR = 1.33, 95% CI 1.03-1.72, P = 0.029, respectively). In subgroup analysis stratified by ethnicity, T2DM risk was increased with the UCP2 Ala55Val polymorphism (allele model: OR = 1.17, 95% CI 1.02-1.34, P = 0.023; recessive model: OR = 1.28, 95% CI 1.13-1.45, P < 0.01; homozygous model: OR = 1.39, 95% CI 1.05-1.86, P = 0.023, respectively), while decreased with the UCP2-866G/A polymorphism in Asians (dominant model: OR = 0.86, 95% CI 0.74-1.00, P = 0.045).

CONCLUSIONS

Our results demonstrate that the UCP2-866G/A polymorphism is protective against T2DM, while the UCP2 Ala55Val polymorphism is susceptible to T2DM in Asians.

Machine Learning to Identify Interaction of Single-Nucleotide Polymorphisms as a Risk Factor for Chronic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene-gene and/or gene-environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP-SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene-gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Simple Summary

The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset.

Abstract

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

Association of CASR, CALCR, and ORAI1 Genes Polymorphisms With the Calcium Urolithiasis Development in Russian Population

Kidney stone disease is an urgent medical and social problem. Genetic factors play an important role in the disease development. This study aims to establish an association between polymorphisms in genes coding for proteins involved in calcium metabolism and the development of calcium urolithiasis in Russian population. In this case-control study, we investigated 50 patients with calcium urolithiasis (experimental group) and 50 persons lacking signs of kidney stone disease (control group). For molecular genetic analysis we used a previously developed gene panel consisting of 33 polymorphisms in 15 genes involved in calcium metabolism: VDR, CASR, CALCR, OPN, MGP, PLAU, AQP1, DGKH, SLC34A1, CLDN14, TRPV6, KLOTHO, ORAI1, ALPL, and RGS14. High-throughput target sequencing was utilized to study the loci of interest. Odds ratios and 95% confidence intervals were used to estimate the association between each SNP and risk of urolithiasis development. Multifactor dimensionality reduction analysis was also carried out to analyze the gene-gene interaction. We found statistically significant (unadjusted p-value < 0.05) associations between calcium urolithiasis and the polymorphisms in the following genes: CASR rs1042636 (OR = 3.18 for allele A), CALCR rs1801197 (OR = 6.84 for allele A), and ORAI1 rs6486795 (OR = 2.25 for allele C). The maximum OR was shown for AA genotypes in loci rs1042636 (CASR) and rs1801197 (CALCR) (OR = 4.71, OR = 11.8, respectively). After adjustment by Benjamini-Hochberg FDR we found only CALCR (rs1801197) was significantly associated with the risk of calcium urolithiasis development. There was no relationship between recurrent course of the disease and family history of urolithiasis in investigated patients. Thus we found a statistically significant association of polymorphism rs1801197 (gene CALCR) with calcium urolithiasis in Russian population.

Association of vitamin D receptor gene polymorphisms with gestational diabetes mellitus-a case control study in Wuhan, China

BACKGROUND

Gestational diabetes mellitus (GDM) increased risk of perinatal complications for both the women and the fetuses. The association between the vitamin D receptor (VDR) gene polymorphism and GDM has not been thoroughly investigated in Chinese pregnant women. Therefore, we aimed to determine whether VDR gene single nucleotide polymorphisms (SNPs) rs154410, rs7975232, rs731236, rs2228570 and rs739837 contribute to GDM risk in Wuhan, China. Moreover, we aimed to explore their combined effects on the risk of GDM.

METHODS

Pregnant women who had prenatal examinations at 24 to 28 weeks' gestation in our hospital from January 15, 2018 to March 31, 2019 were included in this case-control study. After exclusion, a total of 1684 pregnant women (826 GDM patients and 858 non-diabetic controls) were recruited. The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of candidate SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test and logistic regression were performed to the data with SPSS Software to evaluate differences in genotype distribution and associations with GDM risk. Multifactor dimensionality reduction method was used to explore the gene-gene interactions on the risk of GDM.

RESULTS

Differences in age, pre-pregnancy BMI, family history of diabetes and previous history of GDM between the case and control groups were statistically significant (P < 0.05), whereas no significant differences were found in height, gravidity, parity, and age of menarche (P > 0.05). There were no significant differences at genotype distributions of the examined VDR gene SNPs (P > 0.05). After adjusting by age, pre-pregnancy BMI, family history of diabetes, the results of logistic regression analysis showed no associations of the five SNPs with GDM in all the four genotype models(P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the five examined VDR gene SNPs.

CONCLUSIONS

The VDR gene SNPs rs154410, rs7975232, rs731236, rs2228570 and rs739837 showed neither significant associations nor gene-gene interactions with GDM in Wuhan, China.

Association of uncoupling protein-2 -866G/A and Ala55Val polymorphisms with susceptibility to type 2 diabetes mellitus: A meta-analysis of case-control studies

BACKGROUND

Recently, the relationships between uncoupling protein-2 (UCP2) -866G/A (rs659366) and Ala55Val (rs660339) polymorphisms and the risk of type 2 diabetes mellitus (T2DM) have been explored considerably, but the results are greatly inconsistent. This meta-analysis was performed to further identify the association of UCP2 rs659366 and rs660339 with the risk of T2DM.

METHODS

Eligible studies were searched from PubMed, Embase, Cochrane Library, VIP database, Chinese National Knowledge Infrastructure, and Chinese WanFang database until March 8, 2020. The odds ratios with corresponding 95% confidence intervals (CIs), and P-values were used to assess the strength of the association.

RESULTS

A total of 26 studies were included in this study. UCP2 rs659366 was associated with the risk of T2DM in allele model (OR: 1.112, 95%CI: 1.009-1.224, P = 0.032), dominant model (OR: 1.189, 95%CI: 1.035-1.366, P = 0.014), and heterozygous model (OR: 1.177, 95%CI: 1.032-1.342, P = .015). A significantly increased risk of T2DM was detected in Asians by UCP2 rs659366 allele (OR: 1.132, 95%CI: 1.016-1.262, P = .025), dominant (OR: 1.218, 95%CI: 1.046-1.418, P = .011), homozygous (OR: 1.254, 95%CI: 1.022-1.540, P = .031) or heterozygous (OR: 1.198, 95%CI: 1.047-1.371, P = .009) models. There was no significant correlation between UCP2 rs660339 and the risk of T2DM (P>.05).

CONCLUSIONS

The UCP2 rs65366 is significantly associated with the risk of T2DM, especially in Asian population, while no evidence is found between the UCP2 rs660339 and the susceptibility to T2DM.

A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data

Goal: Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framework is proposed for the efficient identification of n-Way interactions. Methods: The framework was applied on a Multiple Sclerosis dataset with 725 subjects and 147 tagging SNPs. The first two steps of the framework are quality control and feature selection. The next step uses clustering and binary encodes the features. The final step performs the n-Way interaction testing. Results: The feature space was reduced to 7 SNPs and using the proposed binary encoding, more 2-SNP and 3-SNP interactions were identified compared to using the initial encoding. Conclusions: The framework selects informative features and with the proposed binary encoding it is able to identify more n-way interactions by increasing the power of the statistical analysis.

Epistasis of polymorphisms related to the articular cartilage extracellular matrix in knee osteoarthritis: Analysis-based multifactor dimensionality reduction

Osteoarthritis (OA) is a complex disease with a multifactorial etiology. The genetic component is one of the main associated factors, resulting from interactions between genes and environmental factors. The aim of this study was to identify gene-gene interactions (epistasis) of the articular cartilage extracellular matrix (ECM) in knee OA. Ninety-two knee OA patients and 147 healthy individuals were included. Participants were genotyped in order to evaluate nine variants of eight genes associated with ECM metabolism using the OpenArray technology. Epistasis was analyzed using the multifactor dimensionality reduction (MDR) method. The MDR analysis showed significant gene-gene interactions between MMP3 (rs679620) and COL3A1 (rs1800255), and between COL3A1 (rs1800255) and VEGFA (rs699947) polymorphisms, with information gain values of 3.21% and 2.34%, respectively. Furthermore, in our study we found interactions in high-risk genotypes of the HIF1AN, MMP3 and COL3A1 genes; the most representative were [AA+CC+GA], [AA+CT+GA] and [AA+CT+GG], respectively; and low-risk genotypes [AA+CC+GG], [GG+TT+GA] and [AA+TT+GA], respectively. Knowing the interactions of these polymorphisms involved in articular cartilage ECM metabolism could provide a new tool to identify individuals at high risk of developing knee OA.

Non-additive effects of ACVR2A in preeclampsia in a Philippine population

BACKGROUND

Multiple interrelated pathways contribute to the pathogenesis of preeclampsia, and variants in susceptibility genes may play a role among Filipinos, an ethnically distinct group with high prevalence of the disease. The objective of this study was to examine the association between variants in maternal candidate genes and the development of preeclampsia in a Philippine population.

METHODS

A case-control study involving 29 single nucleotide polymorphisms (SNPs) in 21 candidate genes was conducted in 150 patients with preeclampsia (cases) and 175 women with uncomplicated normal pregnancies (controls). Genotyping for the GRK4 and DRD1 gene variants was carried out using the TaqMan Assay, and all other variants were assayed using the Sequenom MassARRAY Iplex Platform. PLINK was used for SNP association testing. Multilocus association analysis was performed using multifactor dimensionality reduction (MDR) analysis.

RESULTS

Among the clinical factors, older age (P <  1 × 10-4), higher BMI (P <  1 × 10-4), having a new partner (P = 0.006), and increased time interval from previous pregnancy (P = 0.018) associated with preeclampsia. The MDR algorithm identified the genetic variant ACVR2A rs1014064 as interacting with age and BMI in association with preeclampsia among Filipino women.

CONCLUSIONS

The MDR algorithm identified an interaction between age, BMI and ACVR2A rs1014064, indicating that context among genetic variants and demographic/clinical factors may be crucial to understanding the pathogenesis of preeclampsia among Filipino women.

HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> SPOCK1) and (LINGO2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).

Here, there and everywhere: Resistin-like molecules in infection, inflammation, and metabolic disorders

The Resistin-Like Molecules (RELM) α, β, and γ and their namesake, resistin, share structural and sequence homology but exhibit significant diversity in expression and function within their mammalian host. RELM proteins are expressed in a wide range of diseases, such as: microbial infections (eg. bacterial and helminth), inflammatory diseases (eg. asthma, fibrosis) and metabolic disorders (eg. diabetes). While the expression pattern and molecular regulation of RELM proteins are well characterized, much controversy remains over their proposed functions, with evidence of host-protective and pathogenic roles. Moreover, the receptors for RELM proteins are unclear, although three receptors for resistin, decorin, adenylyl cyclase-associated protein 1 (CAP1), and Toll-like Receptor 4 (TLR4) have recently been proposed. In this review, we will first summarize the molecular regulation of the RELM gene family, including transcription regulation and tissue expression in humans and mouse disease models. Second, we will outline the function and receptor-mediated signaling associated with RELM proteins. Finally, we will discuss recent studies suggesting that, despite early misconceptions that these proteins are pathogenic, RELM proteins have a more nuanced and potentially beneficial role for the host in certain disease settings.

Epistasis analysis of metabolic genes polymorphisms associated with ischemic heart disease in Yucatan

OBJECTIVE

Epistasis is a type of genetic interaction that could explain much of the phenotypic variability of complex diseases. In this work, the effect of epistasis of metabolic genes and cardiovascular risk on the susceptibility to the development of ischemic heart disease in Yucatan was determined.

METHODS

Case-control study in 79 Yucatecan patients with ischemic heart disease and 101 healthy controls matched by age and origin with cases. The polymorphisms -108CT, Q192R, L55M (paraoxonase 1; PON1), C677T, A1298C (methylenetetrahydrofolate reductase; MTHFR), and the presence/absence of the glutathione S-transferase T1 (GSTT1) gene were genotyped. Epistasis analysis was performed using the multifactorial dimensional reduction method. The best risk prediction model was selected based on precision (%), statistical significance (P<0.05), and cross-validation consistency.

RESULTS

We found an independent association of the null genotype GSTT1*0/0 (OR=3.39, CI: 1.29-8.87, P=0.017) and the null allele (OR=1.86, CI: 1.19-2.91, P=0.007) with ischemic heart disease. The GSTT1*0 deletion and the 677TT genotype (MTHFR) were identified as being at a high cardiovascular risk, whereas the GSTT1*1 wild type genotype and the CC677 variant were at low risk. The gene-environment interaction identified the GSTT1 gene, C677T polymorphism (MTHFR), and hypertension as the factors that best explain ischemic heart disease in the study population.

CONCLUSIONS

The interaction of the MTHFR, GSTT1 and hypertension may constitute a predictive model of risk for early onset ischemic heart disease in the population of Yucatan.

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population

Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait.

Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases

BACKGROUND

With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities. Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular problem. As a comprehensive review of all possible data fusion methods was outside the scope of this paper, we have focused on fusion approaches that employ dimensionality reduction (DR).

METHODS

In this work, we quantitatively evaluate 4 non-overlapping existing instantiations of DR-based data fusion, within 3 different biomedical applications comprising over 100 studies. These instantiations utilized different knowledge representation and knowledge fusion methods, allowing us to examine the interplay of these modules in the context of data fusion. The use cases considered in this work involve the integration of (a) radiomics features from T2w MRI with peak area features from MR spectroscopy for identification of prostate cancer in vivo, (b) histomorphometric features (quantitative features extracted from histopathology) with protein mass spectrometry features for predicting 5 year biochemical recurrence in prostate cancer patients, and (c) volumetric measurements on T1w MRI with protein expression features to discriminate between patients with and without Alzheimers' Disease.

RESULTS AND CONCLUSIONS

Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based DR approach resulted in the highest area under the ROC curve of over 0.8. By contrast non-optimized DR-based representation and fusion methods yielded the worst predictive performance across all 3 applications. Our results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either. In summary, to outperform the predictive ability of individual modalities, methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.

SNP-SNP Interaction Analysis on Soybean Oil Content under Multi-Environments

Soybean oil content is one of main quality traits. In this study, we used the multifactor dimensionality reduction (MDR) method and a soybean high-density genetic map including 5,308 markers to identify stable single nucleotide polymorphism (SNP)—SNP interactions controlling oil content in soybean across 23 environments. In total, 36,442,756 SNP-SNP interaction pairs were detected, 1865 of all interaction pairs associated with soybean oil content were identified under multiple environments by the Bonferroni correction with p <3.55×10⁻¹¹. Two and 1863 SNP-SNP interaction pairs detected stable across 12 and 11 environments, respectively, which account around 50% of total environments. Epistasis values and contribution rates of stable interaction (the SNP interaction pairs were detected in more than 2 environments) pairs were detected by the two way ANOVA test, the available interaction pairs were ranged 0.01 to 0.89 and from 0.01 to 0.85, respectively. Some of one side of the interaction pairs were identified with previously research as a major QTL without epistasis effects. The results of this study provide insights into the genetic architecture of soybean oil content and can serve as a basis for marker-assisted selection breeding.

Discovering causal interactions using Bayesian network scoring and information gain

BACKGROUND

The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.

RESULTS

We develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist.

CONCLUSIONS

We conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data.

Genome-wide search followed by replication reveals genetic interaction of CD80 and ALOX5AP associated with systemic lupus erythematosus in Asian populations

OBJECTIVES

Genetic interaction has been considered as a hallmark of the genetic architecture of systemic lupus erythematosus (SLE). Based on two independent genome-wide association studies (GWAS) on Chinese populations, we performed a genome-wide search for genetic interactions contributing to SLE susceptibility.

METHODS

The study involved a total of 1 659 cases and 3 398 controls in the discovery stage and 2 612 cases and 3 441 controls in three cohorts for replication. Logistic regression and multifactor dimensionality reduction were used to search for genetic interaction.

RESULTS

Interaction of CD80 (rs2222631) and ALOX5AP (rs12876893) was found to be significantly associated with SLE (OR_int=1.16, P_int_all=7.7E-04 at false discovery rate<0.05). Single nuclear polymorphism rs2222631 was found associated with SLE with genome-wide significance (P_all=4.5E-08, OR=0.86) and is independent of rs6804441 in CD80, whose association was reported previously. Significant correlation was observed between expression of these two genes in healthy controls and SLE cases, together with differential expression of these genes between cases and controls, observed from individuals from the Hong Kong cohort. Genetic interactions between BLK (rs13277113) and DDX6 (rs4639966), and between TNFSF4 (rs844648) and PXK (rs6445975) were also observed in both GWAS data sets.

CONCLUSIONS

Our study represents the first genome-wide evaluation of epistasis interactions on SLE and the findings suggest interactions and independent variants may help partially explain missing heritability for complex diseases.

Evaluation of a two-stage framework for prediction using big genomic data

We are in the era of abundant 'big' or 'high-dimensional' data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, 'genome-wide association studies' examine millions of single-nucleotide polymorphisms (SNPs), along with disease status. We can learn SNPs that affect disease status from these data sets, and use the knowledge learned to predict disease likelihood. Owing to the large number of features, it is difficult for many prediction methods to use all the features directly. The ReliefF algorithm ranks a set of features in terms of how well they predict a target. It can be used to identify good predictors, which can then be provided to a prediction method. We compared the performance of eight prediction methods when predicting binary outcomes using high-dimensional discrete data sets. We performed two-stage prediction, where ReliefF is used in the first stage to identify good predictors. Bayesian network (BN)-based methods performed best overall. Furthermore, ReliefF did not improve their performance. The BN-based methods use the Bayesian Dirichlet Equivalent Uniform score to evaluate candidate models, and use BN inference algorithms to perform prediction. This score and these algorithms were developed for discrete variables. This perhaps explains why they perform better in this domain. Many prediction methods are available, and researchers have little reason for choosing one over the other in the domain of binary prediction using high-dimensional data sets. Our results indicate that the best choices overall are BN-based methods.

Genetic variations in the homologous recombination repair pathway genes modify risk of glioma

Accumulative epidemiological evidence suggests that single nucleotide polymorphisms (SNPs) in genes involved in homologous recombination (HR) DNA repair pathway play an important role in glioma susceptibility. However, the effects of such SNPs on glioma risk remain unclear. We used a used a candidate pathway-based approach to elucidate the relationship between glioma risk and 12 putative functional SNPs in genes involved in the HR pathway. Genotyping was conducted on 771 histologically-confirmed glioma patients and 752 cancer-free controls from the Chinese Han population. Odds ratios (OR) were calculated both for each SNP individually and for grouped analyses, examining the effects of the numbers of adverse alleles on glioma risk, and evaluated their potential gene-gene interactions using the multifactor dimensionality reduction (MDR). In the single-locus analysis, two variants, the NBS1 rs1805794 (OR 1.42, 95% CI 1.15-1.76, P = 0.001), and RAD54L rs1048771 (OR 1.61, 95% CI 1.17-2.22, P = 0.002) were significantly associated with glioma risk. When we examined the joint effects of the risk-conferring alleles of these three SNPs, we found a significant trend indicating that the risk increases as the number of adverse alleles increase (P = 0.005). Moreover, the MDR analysis suggested a significant three-locus interaction model involving NBS1 rs1805794, MRE11 rs10831234, and ATM rs227062. These results suggested that these variants of the genes involved in the HR pathway may contribute to glioma susceptibility.

A survey about methods dedicated to epistasis detection

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

Gene-gene and gene-environment interactions in the etiology of type 2 diabetes mellitus in the population of Hyderabad, India

Fifteen SNPs from nine different genes were genotyped on 1379 individuals, 758 T2DM patients and 621 controls, from the city of Hyderabad, India, using Sequenom Massarray platform. These data were analyzed to examine the role of gene-gene and gene-environment interactions in the manifestation of T2DM. The multivariate analysis suggests that TCF7L2, CDKAL1, IGF2BP2, HHEX and PPARG genes are significantly associated with T2DM, albeit only the first two of the above 5 were associated in the univariate analysis. Significant gene-gene and gene-environment interactions were also observed with reference to TCF7L2, CAPN10 and CDKAL1 genes, highlighting their importance in the pathophysiology of T2DM. In the analysis for cumulative effect of risk alleles, SLC30A8 steps in as significant contributor to the disease by its presence in all combinations of risk alleles. A striking difference between risk allele categories, 1-4 and 5-6, was evident in showing protective and susceptible roles, respectively, while the latter was characterized by the presence of TCF7L2 and CDKAL1 variants. Overall, these two genes TCF7L2 and CDKAL1 showed strong association with T2DM, either individually or in interaction with the other genes. However, we need further studies on gene-gene and gene-environment interactions among heterogeneous Indian populations to obtain unequivocal conclusions that are applicable for the Indian population as a whole.

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.

Bridging the gap between statistical and biological epistasis in Alzheimer's disease

Alzheimer's disease affects millions of people worldwide and incidence is expected to rise as the population ages, but no effective therapies exist despite decades of research and more than 20 known disease markers. Research has shown that Alzheimer's disease's missing heritability remains extensive with an estimated 25% of phenotypic variance unexplained by known variants. The missing heritability may be explained by missing variants or by epistasis. Researchers often focus on individual loci rather than epistatic interactions, which is likely an oversimplification of the underlying biology since most phenotypes are affected by multiple genes. Focusing research efforts on epistasis will be critical to resolving Alzheimer's disease etiology, and a major key to identifying and properly interpreting key epistatic interactions will be bridging the gap between statistical and biological epistasis. This review covers the current state of epistasis research in Alzheimer's disease and how researchers can bridge the gap between statistical and biological epistasis to help resolve Alzheimer's disease etiology.

Forward LASSO analysis for high-order interactions in genome-wide association study

Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits. Within the frameworks of linear model and generalized linear model, the LASSO with coordinate descent step can be used to simultaneously analyze thousands and thousands of SNPs for normal and discrete traits. With consideration of high-order interactions among SNPs, a huge number of genetic effects make the LASSO failing to work under the presented condition of computation. Forward LASSO analysis is, therefore, proposed to shrink most of genetic effects to be zeros stage by stage. Simulation demonstrates that our proposed method could be used instead of the LASSO method for full model in mapping high-order interactions. Application of forward LASSO method is provided to GWAS for carcass traits and meat quality traits in beef cattle.

LEAP: biomarker inference through learning and evaluating association patterns

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability.

A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets

OBJECTIVE

The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.

METHOD

We evaluate how well eight methods perform binary prediction using high-dimensional discrete genomic datasets containing epistatic interactions. The methods are as follows: naive Bayes (NB), model averaging NB (MANB), feature selection NB (FSNB), EBMC, logistic regression (LR), support vector machines (SVM), Lasso, and extreme learning machines (ELM). We use a hundred 1000-single nucleotide polymorphism (SNP) simulated datasets, ten 10,000-SNP datasets, six semi-synthetic sets, and two real genome-wide association studies (GWAS) datasets in our evaluation.

RESULTS

In fivefold cross-validation studies, the SVM performed best on the 1000-SNP dataset, while the BN-based methods performed best on the other datasets, with EBMC exhibiting the best overall performance. In-sample testing indicates that LR, SVM, Lasso, ELM, and NB tend to overfit the data.

DISCUSSION

EBMC performed better than NB when there are several strong predictors, whereas NB performed better when there are many weak predictors. Furthermore, for all BN-based methods, prediction capability did not degrade as the dimension increased.

CONCLUSIONS

Our results support the hypothesis that EBMC performs well at binary outcome prediction using high-dimensional discrete datasets containing epistatic-like interactions. Future research using more GWAS datasets is needed to further investigate the potential of EBMC.

Detecting epistatic interactions in metagenome-wide association studies by metaBOOST

Material and Methods. We recall the definition of epistasis and extend it for metagenomic biomarkers and then we describe the overview of our method metaBOOST and provide detailed information about each step of metaBOOST. Results. We describe the data sources for both simulation studies and real metagenomic datasets. Then, we describe the procedure of simulation studies and provide results for it. After that, we conduct real datasets studies and report the results. Conclusions and Discussion. Finally, we conclude our method and discuss some possible improvements for the future.

A SNP Harvester Analysis to Better Detect SNPs of CCDC158 Gene That Are Associated with Carcass Quality Traits in Hanwoo

The purpose of this study was to investigate interaction effects of genes using a Harvester method. A sample of Korean cattle, Hanwoo (n = 476) was chosen from the National Livestock Research Institute of Korea that were sired by 50 Korean proven bulls. The steers were born between the spring of 1998 and the autumn of 2002 and reared under a progeny-testing program at the Daekwanryeong and Namwon branches of NLRI. The steers were slaughtered at approximately 24 months of age and carcass quality traits were measured. A SNP Harvester method was applied with a support vector machine (SVM) to detect significant SNPs in the CCDC158 gene and interaction effects between the SNPs that were associated with average daily gains, cold carcass weight, longissimus dorsi muscle area, and marbling scores. The statistical significance of the major SNP combinations was evaluated with x (2)-statistics. The genotype combinations of three SNPs, g.34425+102 A>T(AA), g.4102636T>G(GT), and g.11614+19G>T(GG) had a greater effect than the rest of SNP combinations, e.g. 0.82 vs. 0.75 kg, 343 vs. 314 kg, 80.4 vs 74.7 cm(2), and 7.35 vs. 5.01, for the four respective traits (p<0.001). Also, the estimates were greater compared with single SNPs analyzed (the greatest estimates were 0.76 kg, 320 kg, 75.5 cm(2), and 5.31, respectively). This result suggests that the SNP Harvester method is a good option when multiple SNPs and interaction effects are tested. The significant SNPs could be applied to improve meat quality of Hanwoo via marker-assisted selection.

Genetic interactions within inositol-related pathways are associated with longitudinal changes in ventricle size

The genetic etiology of late-onset Alzheimer's disease (LOAD) has proven complex, involving clinical and genetic heterogeneity and gene-gene interactions. Recent genome wide association studies in LOAD have led to the discovery of novel genetic risk factors; however, the investigation of gene-gene interactions has been limited. Conventional genetic studies often use binary disease status as the primary phenotype, but for complex brain-based diseases, neuroimaging data can serve as quantitative endophenotypes that correlate with disease status and closely reflect pathological changes. In the Alzheimer's Disease Neuroimaging Initiative cohort, we tested for association of genetic interactions with longitudinal MRI measurements of the inferior lateral ventricles (ILVs), which have repeatedly shown a relationship to LOAD status and progression. We performed linear regression to evaluate the ability of pathway-derived SNP-SNP pairs to predict the slope of change in volume of the ILVs. After Bonferroni correction, we identified four significant interactions in the right ILV (RILV) corresponding to gene-gene pairs SYNJ2-PI4KA, PARD3-MYH2, PDE3A-ABHD12B, and OR2L13-PRKG1 and one significant interaction in the left ILV (LILV) corresponding to SYNJ2-PI4KA. The SNP-SNP interaction corresponding to SYNJ2-PI4KA was identical in the RILV and LILV and was the most significant interaction in each (RILV: p = 9.13 × 10(-12); LILV: p = 8.17 × 10(-13)). Both genes belong to the inositol phosphate signaling pathway which has been previously associated with neurodegeneration in AD and we discuss the possibility that perturbation of this pathway results in a down-regulation of the Akt cell survival pathway and, thereby, decreased neuronal survival, as reflected by increased volume of the ventricles.

Genetics of Alzheimer's disease

Alzheimer's disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer's disease, early onset and the more common late onset. The genetics of early-onset Alzheimer's disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer's disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer's disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer's disease.

An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies

This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study

OBJECTIVE

We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment.

DESIGN

Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors.

SETTING

Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait.

PARTICIPANTS

270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates.

OUTCOME MEASURES

Incident type 2 diabetes, hypertension and comorbidity.

RESULTS

Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign 'high' risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned 'low' risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models.

CONCLUSIONS

Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case-control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant to the predictive models. Risk assessments need to be developed using regional data as we demonstrate the applicability of the American Diabetes Association online calculator on data from Kuwait.

Association between resistin gene -420 C/G polymorphism and the risk of type 2 diabetes mellitus: a meta-analysis

Epidemiological studies on the association between the single nucleotide polymorphism (SNP) at -420 C/G (rs1862513) in the human resistin gene (RETN) and the risk of type 2 diabetes mellitus (T2DM) are conflicting. In order to derive a more precise estimation of the association, a meta-analysis was conducted. Twelve studies with 5,935 cases and 5,959 controls were enrolled by searching the databases of Medline, EMBASE, and Cochrane. Summary odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. The heterogeneity and publication bias were investigated. The main analysis indicated no significant association [for allelic model: OR = 0.97 (0.92-1.03); for additive model: OR = 0.95 (0.83-1.09); for recessive model: OR = 0.98 (0.86-1.12); for dominant model: OR = 0.95 (0.88-1.04)]. Overall, no significant heterogeneity was found. Subgroup analysis by race and source of controls indicated no significant association. In conclusion, the current meta-analysis did not observe any association between the polymorphism of RETN -420 C/G and the risk of T2DM. The study may help us further understand the genetics of T2DM. However, larger and prospective studies are warranted to confirm this finding.

Associations between UCP1 -3826A/G, UCP2 -866G/A, Ala55Val and Ins/Del, and UCP3 -55C/T polymorphisms and susceptibility to type 2 diabetes mellitus: case-control study and meta-analysis

Background

Some studies have reported associations between five uncoupling protein (UCP) 1–3 polymorphisms and type 2 diabetes mellitus (T2DM). However, other studies have failed to confirm the associations. This paper describes a case-control study and a meta-analysis conducted to attempt to determine whether the following polymorphisms are associated with T2DM: -3826A/G (UCP1); -866G/A, Ala55Val and Ins/Del (UCP2) and -55C/T (UCP3).

Methods

The case-control study enrolled 981 T2DM patients and 534 nondiabetic subjects, all of European ancestry. A literature search was run to identify all studies that investigated associations between UCP1–3 polymorphisms and T2DM. Pooled odds ratios (OR) were calculated for allele contrast, additive, recessive, dominant and co-dominant inheritance models. Sensitivity analyses were performed after stratification by ethnicity.

Results

In the case-control study the frequencies of the UCP polymorphisms did not differ significantly between T2DM and nondiabetic groups (P>0.05). Twenty-three studies were eligible for the meta-analysis. Meta-analysis results showed that the Ala55Val polymorphism was associated with T2DM under a dominant model (OR = 1.27, 95% CI 1.03–1.57); while the -55C/T polymorphism was associated with this disease in almost all genetic models: allele contrast (OR = 1.17, 95% CI 1.02–1.34), additive (OR = 1.32, 95% CI 1.01–1.72) and dominant (OR = 1.18, 95% CI 1.02–1.37). However, after stratification by ethnicity, the UCP2 55Val and UCP3 -55C/T alleles remained associated with T2DM only in Asians (OR = 1.25, 95% CI 1.02–1.51 and OR = 1.22, 95% CI 1.04–1.44, respectively; allele contrast model). No significant association of the -3826A/G, -866G/A and Ins/Del polymorphisms with T2DM was observed.

Conclusions

In our case-control study of people with European ancestry we were not able to demonstrate any association between the UCP polymorphisms and T2DM; however, our meta-analysis detected a significant association between the UCP2 Ala55Val and UCP3 -55C/T polymorphisms and increased susceptibility for T2DM in Asians.

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

BACKGROUND

Multifactor Dimensionality Reduction (MDR) has been widely applied to detect gene-gene (GxG) interactions associated with complex diseases. Existing MDR methods summarize disease risk by a dichotomous predisposing model (high-risk/low-risk) from one optimal GxG interaction, which does not take the accumulated effects from multiple GxG interactions into account.

RESULTS

We propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for and detects significant GxG interactions to generate an epistasis enriched gene network. An aggregated epistasis enriched risk score, which takes into account multiple GxG interactions simultaneously, replaces the dichotomous predisposing risk variable and provides higher resolution in the quantification of disease susceptibility. We evaluate this new A-MDR approach in a broad range of simulations. Also, we present the results of an application of the A-MDR method to a data set derived from Juvenile Idiopathic Arthritis patients treated with methotrexate (MTX) that revealed several GxG interactions in the folate pathway that were associated with treatment response. The epistasis enriched risk score that pooled information from 82 significant GxG interactions distinguished MTX responders from non-responders with 82% accuracy.

CONCLUSIONS

The proposed A-MDR is innovative in the MDR framework to investigate aggregated effects among GxG interactions. New measures (pOR, pRR and pChi) are proposed to detect multiple GxG interactions.

Significant interaction between RETN -420 G/G genotype and lower BMI on decreased risk of type 2 diabetes mellitus (T2DM) in Japanese--the J-MICC Study

We examined the association of the RETN (resistin) -420 C>G polymorphism (rs1862513) with risk of diabetes mellitus (DM), considering lifestyle factors, in Japanese. Subjects were participants of J-MICC Study, where 2,651 participants aged 35-69 years provided their blood for genotyping and lifestyle data after informed consent. Odds ratio (OR) of DM for RETN-420 G/G genotype was estimated using unconditional logistic regression model. Statistically significant interaction on risk of DM was observed between RETN-420 G/G genotype and BMI<25 (OR for interaction = 0.12; P = 0.046), and when subjects with RETN-420 C/C+C/G and BMI ≥ 25 (n = 69 for DM and 544 for non-DM) were defined as the reference, the adjusted ORs for subjects with RETN-420 G/G genotype and BMI>25 (n = 10 for DM and 111 for non-DM), RETN-420 C/C+C/G and BMI<25 (n = 81 for DM and 1,605 for non-DM), and RETN-420 G/G and BMI<25 (n = 1 for DM and 230 for non-DM) were demonstrated to be 0.72 (95% confidence interval: 0.36-1.46), 0.40 (0.28-0.56) and 0.03 (0.005-0.25), respectively. The present study revealed the significant interaction of RETN-420 G/G genotype with lower BMI on the decreased risk of DM, but the direction was opposite to the reported ones in Japanese. We should be careful in interpretation of the present study results because of the limited sample sizes. Further investigation of this association as well as of the actual biological roles of RETN in the genesis of human metabolic disorders including DM will be required.

Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality

BACKGROUND

The interaction between loci to affect phenotype is called epistasis. It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility. A difficulty when mining epistatic interactions from high-dimensional datasets concerns the curse of dimensionality. There are too many combinations of SNPs to perform an exhaustive search. A method that could locate strict epistasis without an exhaustive search can be considered the brass ring of methods for analyzing high-dimensional datasets.

METHODOLOGY/FINDINGS

A SNP pattern is a Bayesian network representing SNP-disease relationships. The Bayesian score for a SNP pattern is the probability of the data given the pattern, and has been used to learn SNP patterns. We identified a bound for the score of a SNP pattern. The bound provides an upper limit on the Bayesian score of any pattern that could be obtained by expanding a given pattern. We felt that the bound might enable the data to say something about the promise of expanding a 1-SNP pattern even when there are no marginal effects. We tested the bound using simulated datasets and semi-synthetic high-dimensional datasets obtained from GWAS datasets. We found that the bound was able to dramatically reduce the search time for strict epistasis. Using an Alzheimer's dataset, we showed that it is possible to discover an interaction involving the APOE gene based on its score because of its large marginal effect, but that the bound is most effective at discovering interactions without marginal effects.

CONCLUSIONS/SIGNIFICANCE

We conclude that the bound appears to ameliorate the curse of dimensionality in high-dimensional datasets. This is a very consequential result and could be pivotal in our efforts to reveal the dark matter of genetic disease risk from high-dimensional datasets.

Multilocus analysis of candidate genes involved in neurogenic inflammation in pediatric asthma and related phenotypes: a case-control study

OBJECTIVES

Asthma is a heterogenous complex disorder caused by chronic inflammation of the airways. The key issue in genetic association studies of complex disorders is the identification of multiple low-risk genes that individually have little impact on the phenotype, but in combination account for the clinical manifestation of asthma. Since neurogenic inflammation is emerging as a candidate factor in the pathogenesis of asthma, the aim of the study was to investigate whether genetic variants of neurotrophin genes are associated with asthma disease severity or asthma-related phenotypes in a pediatric population.

METHODS

We genotyped 27 polymorphisms located in neurotrophin genes, using TaqMan SNP genotyping assays or Polymerase Chain Reaction - Restriction Fragments Lengths Polymorphism (PCR-RFLP) in 200 children diagnosed with asthma and 226 controls. Interactions between 27 polymorphic loci and asthma-related phenotypes were determined using the Multifactor Dimensionality Reduction (MDR) method.

RESULTS

In single marker analysis, we observed an association of MAP3K1 gene polymorphisms (rs702689 and rs889312) with asthma. We also observed that four Single Nucleotide Polymorphisms (SNPs) were associated with severe asthma. Analysis stratified by asthma-related phenotype revealed an association between atopy and NGFR (rs3785931), while BDNF (rs7124442), NTRK2 (rs1212171), NGFR (rs2072446), and FYN (rs3730353) variants were associated with increased exhaled nitric oxide (exNO). In addition, gene-gene interaction analysis revealed a significant epistatic interaction between MAPK (rs889312) and NGF (rs11102930) variants in asthma susceptibility.

CONCLUSIONS

Our results suggest that genetic variants of MAP3K1 and NGF genes involved in the regulation of neurogenic inflammation may contribute to asthma, possibly via enhanced NGF expression and MAPK signaling pathway activation.

Gene-gene and gene-environmental interactions of childhood asthma: a multifactor dimension reduction approach

Background

The importance of gene-gene and gene-environment interactions on asthma is well documented in literature, but a systematic analysis on the interaction between various genetic and environmental factors is still lacking.

Methodology/Principal Findings

We conducted a population-based, case-control study comprised of seventh-grade children from 14 Taiwanese communities. A total of 235 asthmatic cases and 1,310 non-asthmatic controls were selected for DNA collection and genotyping. We examined the gene-gene and gene-environment interactions between 17 single-nucleotide polymorphisms in antioxidative, inflammatory and obesity-related genes, and childhood asthma. Environmental exposures and disease status were obtained from parental questionnaires. The model-free and non-parametrical multifactor dimensionality reduction (MDR) method was used for the analysis. A three-way gene-gene interaction was elucidated between the gene coding glutathione S-transferase P (GSTP1), the gene coding interleukin-4 receptor alpha chain (IL4Ra) and the gene coding insulin induced gene 2 (INSIG2) on the risk of lifetime asthma. The testing-balanced accuracy on asthma was 57.83% with a cross-validation consistency of 10 out of 10. The interaction of preterm birth and indoor dampness had the highest training-balanced accuracy at 59.09%. Indoor dampness also interacted with many genes, including IL13, beta-2 adrenergic receptor (ADRB2), signal transducer and activator of transcription 6 (STAT6). We also used likelihood ratio tests for interaction and chi-square tests to validate our results and all tests showed statistical significance.

Conclusions/Significance

The results of this study suggest that GSTP1, INSIG2 and IL4Ra may influence the lifetime asthma susceptibility through gene-gene interactions in schoolchildren. Home dampness combined with each one of the genes STAT6, IL13 and ADRB2 could raise the asthma risk.

A novel method to detect gene-gene interactions in structured populations: MDR-SP

Complex diseases are presumed to be the result of multiple genes and environmental factors, which emphasize the importance of gene - gene and gene - environment interactions. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions with undetectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method was developed. MDR is well suited for examining high-order interactions and detecting interactions without main effects. Like most statistical methods in genetic association studies, MDR may also lead to a false positive in the presence of population stratification. Although many statistical methods have been proposed to detect main effects and control for population stratification using genomic markers, not many methods are available to detect interactions and control for population stratification at the same time. In this article, we developed a novel test, MDR in structured populations (MDR-SP), to detect the interactions and control for population stratification. MDR-SP is applicable to both quantitative and qualitative traits and can incorporate covariates. We present simulation studies to demonstrate the validity of the test and to evaluate its power.

UCP2 -866G/A and Ala55Val, and UCP3 -55C/T polymorphisms in association with type 2 diabetes susceptibility: a meta-analysis study

AIMS/HYPOTHESIS

A meta-analysis was performed to assess the association between the UCP2 -866G/A, UCP2 Ala55Val and UCP3 -55C/T polymorphisms and type 2 diabetes susceptibility.

METHODS

A literature-based search was conducted to identify all relevant studies. The fixed or random effect pooled measure was calculated mainly at the allele level to determine heterogeneity bias among studies. Further analyses were performed that stratified for ethnicity.

RESULTS

We examined 17 publications. Stratified analysis for ethnicity and sensitivity analysis revealed that there was no heterogeneity between studies for these variants. Using an additive model, no significant association of the UCP2 -866G/A polymorphism with type 2 diabetes risk was observed, either in participants of Asian (OR 1.05, 95% CI 0.96, 1.16) or of European (OR 1.03, 95% CI 0.99, 1.07) descent. Neither the UCP2 Ala55Val nor the UCP3 -55C/T polymorphism showed any significant association with type 2 diabetes risk in Europeans (OR 1.04, 95% CI 0.98, 1.09 for Ala55Val; OR 1.04, 95% CI 1.00, 1.09 for -55C/T). In contrast, a statistically significant association was observed for both polymorphisms in participants of Asian descent (OR 1.23, 95% CI 1.12, 1.36 for Ala55Val; OR 1.15, 95% CI 1.03, 1.28 for -55C/T).

CONCLUSIONS/INTERPRETATION

Our meta-analysis suggests that the UCP2 -866G/A polymorphism is unlikely to be associated with increased type 2 diabetes risk in the populations investigated. In contrast, our results indicate that the UCP2 Ala55Val and UCP3 -55C/T polymorphisms may indeed be risk factors for susceptibility to type 2 diabetes in individuals of Asian descent, but not in individuals of European descent. This conclusion warrants confirmation by further studies.