An examination of single nucleotide polymorphism selection prioritization strategies for tests of gene-gene interaction

Association between BHMT gene rs3733890 polymorphism and cancer risk: evidence from a meta-analysis

BACKGROUND AND OBJECTIVE

The gene betaine-homocysteine methyltransferase (BHMT) has drawn much attention during the past decades. An increasing number of clinical and genetic investigations have supposed that BHMT rs3733890 polymorphism might be associated with risk of breast cancer and ovarian cancer. As no consistent conclusion has been achieved, we conducted an up-to-date summary of BHMT rs3733890 polymorphism and cancer risk through a meta-analysis.

MATERIALS AND METHODS

The articles were collected from PubMed, Google Scholar, and CNKI (Chinese) databases up to December 2015. Then, the correlations were determined by reading the titles and abstracts and by further reading the full text to filter the unqualified articles. Odds ratio (OR) and the corresponding 95% confidence intervals (CI) were used to assess the results.

RESULTS

Among 187 articles collected in the analysis, seven studies with a total of 2,832 cases and 3,958 controls were included for evaluation of the association between BHMT rs3733890 polymorphism and susceptibility of cancer risk. The heterogeneity test showed no significant differences. Furthermore, we found that BHMT -742G>A polymorphism in case and control groups showed no statistically significant association with susceptibility in various cancer types except for uterine cervical cancer (A vs G: OR =0.641, 95% CI =0.445-0.923, P=0.017; AA+AG vs GG: OR =0.579, 95% CI =0.362-0.924, P=0.022). In addition, no statistically significant association was uncovered when stratification analyses were conducted by ethnicity and genotyping methods.

CONCLUSION

Our results have shown no obvious evidence that rs3733890 polymorphism in BHMT gene affected the susceptibility of head and neck squamous cell carcinoma, breast cancer, ovarian cancer, colorectal adenoma, and liver cancer. In contrast, we found the protective role of BHMT -742G>A polymorphism in uterine cervical cancer incidence. Future well-designed studies comprising larger sample size are warranted to verify our findings.

Schizophrenia

Schizophrenia is a chronic psychiatric disorder with a heterogeneous genetic and neurobiological background that influences early brain development, and is expressed as a combination of psychotic symptoms - such as hallucinations, delusions and disorganization - and motivational and cognitive dysfunctions. The mean lifetime prevalence of the disorder is just below 1%, but large regional differences in prevalence rates are evident owing to disparities in urbanicity and patterns of immigration. Although gross brain pathology is not a characteristic of schizophrenia, the disorder involves subtle pathological changes in specific neural cell populations and in cell-cell communication. Schizophrenia, as a cognitive and behavioural disorder, is ultimately about how the brain processes information. Indeed, neuroimaging studies have shown that information processing is functionally abnormal in patients with first-episode and chronic schizophrenia. Although pharmacological treatments for schizophrenia can relieve psychotic symptoms, such drugs generally do not lead to substantial improvements in social, cognitive and occupational functioning. Psychosocial interventions such as cognitive-behavioural therapy, cognitive remediation and supported education and employment have added treatment value, but are inconsistently applied. Given that schizophrenia starts many years before a diagnosis is typically made, the identification of individuals at risk and those in the early phases of the disorder, and the exploration of preventive approaches are crucial.

How genome-wide SNP-SNP interactions relate to nasopharyngeal carcinoma susceptibility

This study is the first to use genome-wide association study (GWAS) data to evaluate the multidimensional genetic architecture underlying nasopharyngeal cancer. Since analysis of data from GWAS confirms a close and consistent association between elevated risk for nasopharyngeal carcinoma (NPC) and major histocompatibility complex class 1 genes, our goal here was to explore lesser effects of gene-gene interactions. We conducted an exhaustive genome-wide analysis of GWAS data of NPC, revealing two-locus interactions occurring between single nucleotide polymorphisms (SNPs), and identified a number of suggestive interaction loci which were missed by traditional GWAS analyses. Although none of the interaction pairs we identified passed the genome-wide Bonferroni-adjusted threshold for significance, using independent GWAS data from the same population (Stage 2), we selected 66 SNP pairs in 39 clusters with P<0.01. We identified that in several chromosome regions, multiple suggestive interactions group to form a block-like signal, effectively reducing the rate of false discovery. The strongest cluster of interactions involved the CREB5 gene and a SNP rs1607979 on chromosome 17q22 (P = 9.86×10(-11)) which also show trans-expression quantitative loci (eQTL) association in Chinese population. We then detected a complicated cis-interaction pattern around the NPC-associated HLA-B locus, which is immediately adjacent to copy-number variations implicated in male susceptibility for NPC. While it remains to be seen exactly how and to what degree SNP-SNP interactions such as these affect susceptibility for nasopharyngeal cancer, future research on these questions holds great promise for increasing our understanding of this disease's genetic etiology, and possibly also that of other gene-related cancers.

Hypothesis-based analysis of gene-gene interactions and risk of myocardial infarction

The genetic loci that have been found by genome-wide association studies to modulate risk of coronary heart disease explain only a fraction of its total variance, and gene-gene interactions have been proposed as a potential source of the remaining heritability. Given the potentially large testing burden, we sought to enrich our search space with real interactions by analyzing variants that may be more likely to interact on the basis of two distinct hypotheses: a biological hypothesis, under which MI risk is modulated by interactions between variants that are known to be relevant for its risk factors; and a statistical hypothesis, under which interacting variants individually show weak marginal association with MI. In a discovery sample of 2,967 cases of early-onset myocardial infarction (MI) and 3,075 controls from the MIGen study, we performed pair-wise SNP interaction testing using a logistic regression framework. Despite having reasonable power to detect interaction effects of plausible magnitudes, we observed no statistically significant evidence of interaction under these hypotheses, and no clear consistency between the top results in our discovery sample and those in a large validation sample of 1,766 cases of coronary heart disease and 2,938 controls from the Wellcome Trust Case-Control Consortium. Our results do not support the existence of strong interaction effects as a common risk factor for MI. Within the scope of the hypotheses we have explored, this study places a modest upper limit on the magnitude that epistatic risk effects are likely to have at the population level (odds ratio for MI risk 1.3-2.0, depending on allele frequency and interaction model).

Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder

Most pathway and gene-set enrichment methods prioritize genes by their main effect and do not account for variation due to interactions in the pathway. A portion of the presumed missing heritability in genome-wide association studies (GWAS) may be accounted for through gene-gene interactions and additive genetic variability. In this study, we prioritize genes for pathway enrichment in GWAS of bipolar disorder (BD) by aggregating gene-gene interaction information with main effect associations through a machine learning (evaporative cooling) feature selection and epistasis network centrality analysis. We validate this approach in a two-stage (discovery/replication) pathway analysis of GWAS of BD. The discovery cohort comes from the Wellcome Trust Case Control Consortium (WTCCC) GWAS of BD, and the replication cohort comes from the National Institute of Mental Health (NIMH) GWAS of BD in European Ancestry individuals. Epistasis network centrality yields replicated enrichment of Cadherin signaling pathway, whose genes have been hypothesized to have an important role in BD pathophysiology but have not demonstrated enrichment in previous analysis. Other enriched pathways include Wnt signaling, circadian rhythm pathway, axon guidance and neuroactive ligand-receptor interaction. In addition to pathway enrichment, the collective network approach elevates the importance of ANK3, DGKH and ODZ4 for BD susceptibility in the WTCCC GWAS, despite their weak single-locus effect in the data. These results provide evidence that numerous small interactions among common alleles may contribute to the diathesis for BD and demonstrate the importance of including information from the network of gene-gene interactions as well as main effects when prioritizing genes for pathway analysis.

Six Degrees of Epistasis: Statistical Network Models for GWAS

There is growing evidence that much more of the genome than previously thought is required to explain the heritability of complex phenotypes. Recent studies have demonstrated that numerous common variants from across the genome explain portions of genetic variability, spawning various avenues of research directed at explaining the remaining heritability. This polygenic structure is also the motivation for the growing application of pathway and gene set enrichment techniques, which have yielded promising results. These findings suggest that the coordination of genes in pathways that are known to occur at the gene regulatory level also can be detected at the population level. Although genes in these networks interact in complex ways, most population studies have focused on the additive contribution of common variants and the potential of rare variants to explain additional variation. In this brief review, we discuss the potential to explain additional genetic variation through the agglomeration of multiple gene-gene interactions as well as main effects of common variants in terms of a network paradigm. Just as is the case for single-locus contributions, we expect each gene-gene interaction edge in the network to have a small effect, but these effects may be reinforced through hubs and other connectivity structures in the network. We discuss some of the opportunities and challenges of network methods for analyzing genome-wide association studies (GWAS) such as the study of hubs and motifs, and integrating other types of variation and environmental interactions. Such network approaches may unveil hidden variation in GWAS, improve understanding of mechanisms of disease, and possibly fit into a network paradigm of evolutionary genetics.

Multifactor dimensionality reduction as a filter-based approach for genome wide association studies

Advances in genotyping technology and the multitude of genetic data available now provide a vast amount of data that is proving to be useful in the quest for a better understanding of human genetic diseases through the study of genetic variation. This has led to the development of approaches such as genome wide association studies (GWAS) designed specifically for interrogating variants across the genome for association with disease, typically by testing single locus, univariate associations. More recently it has been accepted that epistatic (interaction) effects may also be great contributors to these genetic effects, and GWAS methods are now being applied to find epistatic effects. The challenge for these methods still remain in prioritization and interpretation of results, as it has also become standard for initial findings to be independently investigated in replication cohorts or functional studies. This is motivating the development and implementation of filter-based approaches to prioritize variants found to be significant in a discovery stage for follow-up for replication. Such filters must be able to detect both univariate and interactive effects. In the current study we present and evaluate the use of multifactor dimensionality reduction (MDR) as such a filter, with simulated data and a wide range of effect sizes. Additionally, we compare the performance of the MDR filter to a similar filter approach using logistic regression (LR), the more traditional approach used in GWAS analysis, as well as evaporative cooling (EC)-another prominent machine learning filtering method. The results of our simulation study show that MDR is an effective method for such prioritization, and that it can detect main effects, and interactions with or without marginal effects. Importantly, it performed as well as EC and LR for main effect models. It also significantly outperforms LR for various two-locus epistatic models, while it has equivalent results as EC for the epistatic models. The results of this study demonstrate the potential of MDR as a filter to detect gene-gene interactions in GWAS studies.