An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.     

Effects of methylation of deiodinase 3 gene on gene expression and severity of Kashin–Beck disease

Kashin–Beck disease (KBD) is a complex endemic osteoarthropathy, which mainly occurs in the northeast to southwest China. Iodothyronine deiodinases 3 (DIO3) is one of the selenoproteins, which is closely related to bone metabolism and unclear to KBD. This study aims to investigate the role and associated mechanisms of methylation and expression of DIO3 with disease severity in patients with KBD. We performed a bioinformatics analysis first to identify the biological mechanisms involved in selenoproteins. The methylation status of the DIO3 gene and DIO3 gene expression, as well as DIO3-related regulatory genes in patients with KBD, were analyzed. We found that 15 CpG sites of six selenoproteins were hypomethylated with 5-azacytidine treatment. DIO3 hypermethylation was associated with an increased risk of KBD and may lead to downregulation of DIO3 gene expression as well as be an indicator of the severity of KBD, which may provide a new insight for gene–environment correlations and interactions in etiology and pathogenesis of KBD.     

Contribution of gene conversion in the evolution of the human β-like globin gene family

Gene conversion is referred to as one of two types of mechanisms known to act on gene families, mainly to maintain their sequence homogeneity or, in certain cases, to produce sequence diversity. The concept of gene conversion was established 20 years ago by researchers working with fungi. A few years later, gene conversion was also observed in the human genome, i.e. the γ-globin locus. The aim of this article is to emphasize the role of genetic recombination, particularly of gene conversion, in the evolution of the human β-like globin genes and further to summarize its contribution to the convergent evolution of the fetal globin genes. Finally, this article attempts to re-examine the origin and spread of specific mutations of the β-globin cluster, such as the sickle cell or β-thalassemia mutations, on the basis of repeated gene conversion events. Received: 13 February 1997 / Accepted: 15 May 1998     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Characterisation of the beta-tubulin gene of Cylicocyclus nassatus?

mRNA and genomic DNA were isolated from adult Cylicocyclus nassatus, and the mRNA was reverse transcribed. The cDNA was PCR amplified using degenerate primers designed according to the alignment of the β-tubulin amino acid sequences of other species. To complete the coding sequence, the 3′ end was amplified with the 3′-RACE, and for amplification of the 5′ end the SL1-primer was used. The cDNA of the β-tubulin gene of C. nassatus spans 1429 bp and encodes a protein of 448 amino acids. Specific primers were developed from the cDNA sequence to amplify the genomic DNA sequence and to analyse the genomic organisation of the β-tubulin gene. The complete sequence of the genomic DNA of the β-tubulin gene of C. nassatus has a size of 2652 bp and is organised into nine exons and eight introns. The identities with the exons of the gru-1 β-tubulin gene of Haemonchus contortus range between 79% and 97%.     

The α-glucuronidase-encoding gene of Trichoderma reesei

The Trichoderma reesei cDNA coding for α-glucuronidase (GLRI), which releases glucuronic acid attached to xylose units of xylan, was cloned and sequenced. The deduced N-terminal amino acid (aa) sequence of the protein was verified by sequencing of the purified GLRI. The aa sequence of the GLRI displayed no similarity with any aa sequence available in the data bases.     

Evolution of a Vκ gene family

To examine the evolution of multigene families we have selected as an example an immunoglobulin light chain variable region subgroup (V24) which has been extensively characterized in inbred mice (Mus musculus domesticus). Homologous genes have been isolated and sequenced from Mus pahari, a genetically and geographically isolated species believed to be the oldest living representative of the genus. Southern blot analysis using probes corresponding to individual genes in this subgroup reveals changes in the overall size of the family occurring at the level of individual genes but not at the level of the entire family. Nucleotide sequence analysis indicates an absence of regulatory sequences such as the CAT and TATA boxes 5 to the coding region, but a decanucleotide sequence involved in light chain expression is highly conserved. Within coding regions highly complex patterns of variation are seen which appear to reflect quite different selective pressures on various subregions of the coding sequence. Complementarity determining regions (CDR) are conserved to different extents, with the first CDR region in all family members being among the most conserved segments of the molecule. Conservation is similarly variable among framework segments, indicating complex and variable evolutionary pressures not only at the level of individual genes or their products but also at subregions within homologous molecules.     

Culture–gene coevolution of individualism–collectivism and the serotonin transporter gene

Culture–gene coevolutionary theory posits that cultural values have evolved, are adaptive and influence the social and physical environments under which genetic selection operates. Here, we examined the association between cultural values of individualism–collectivism and allelic frequency of the serotonin transporter functional polymorphism (5-HTTLPR) as well as the role this culture–gene association may play in explaining global variability in prevalence of pathogens and affective disorders. We found evidence that collectivistic cultures were significantly more likely to comprise individuals carrying the short (S) allele of the 5-HTTLPR across 29 nations. Results further show that historical pathogen prevalence predicts cultural variability in individualism–collectivism owing to genetic selection of the S allele. Additionally, cultural values and frequency of S allele carriers negatively predict global prevalence of anxiety and mood disorder. Finally, mediation analyses further indicate that increased frequency of S allele carriers predicted decreased anxiety and mood disorder prevalence owing to increased collectivistic cultural values. Taken together, our findings suggest culture–gene coevolution between allelic frequency of 5-HTTLPR and cultural values of individualism–collectivism and support the notion that cultural values buffer genetically susceptible populations from increased prevalence of affective disorders. Implications of the current findings for understanding culture–gene coevolution of human brain and behaviour as well as how this coevolutionary process may contribute to global variation in pathogen prevalence and epidemiology of affective disorders, such as anxiety and depression, are discussed.     

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.