On the use of multifactor dimensionality reduction (MDR) and classification and regression tree (CART) to identify haplotype–haplotype interactions in genetic studies

The Enigmatic Etiology of Oculo-Auriculo-Vertebral Spectrum (OAVS): An Exploratory Gene Variant Interaction Approach in Candidate Genes

The clinical diagnosis of oculo-auriculo-vertebral spectrum (OAVS) is established when microtia is present in association with hemifacial hypoplasia (HH) and/or ocular, vertebral, and/or renal malformations. Genetic and non-genetic factors have been associated with microtia/OAVS. Although the etiology remains unknown in most patients, some cases may have an autosomal dominant, autosomal recessive, or multifactorial inheritance. Among the possible genetic factors, gene−gene interactions may play important roles in the etiology of complex diseases, but the literature lacks related reports in OAVS patients. Therefore, we performed a gene−variant interaction analysis within five microtia/OAVS candidate genes (HOXA2, TCOF1, SALL1, EYA1 and TBX1) in 49 unrelated OAVS Mexican patients (25 familial and 24 sporadic cases). A statistically significant intergenic interaction (p-value < 0.001) was identified between variants p.(Pro1099Arg) TCOF1 (rs1136103) and p.(Leu858=) SALL1 (rs1965024). This intergenic interaction may suggest that the products of these genes could participate in pathways related to craniofacial alterations, such as the retinoic acid (RA) pathway. The absence of clearly pathogenic variants in any of the analyzed genes does not support a monogenic etiology for microtia/OAVS involving these genes in our patients. Our findings could suggest that in addition to high-throughput genomic approaches, future gene−gene interaction analyses could contribute to improving our understanding of the etiology of microtia/OAVS.

Association analysis in a Latin American population revealed ethnic differences in rheumatoid arthritis-associated SNPs in Caucasian and Asian populations

Large genome-wide association studies (GWAS) have increased our knowledge of the genetic risk factors of rheumatoid arthritis (RA). However, little is known about genetic susceptibility in populations with a large admixture of Amerindian ancestry. The aim of the present study was to test the generalizability of previously reported RA loci in a Latin American (LA) population with admixed ancestry. We selected 128 single nucleotide polymorphisms (SNPs) in linkage equilibrium, with high association to RA in multiple populations of non-Amerindian origin. Genotyping of 118 SNPs was performed in 313 RA patients/487 healthy control subjects by mid-density arrays of polymerase chain reaction (PCR). Some of the identified associations were validated in an additional cohort (250 cases/290 controls). One marker, the SNP rs2451258, located upstream of T Cell Activation RhoGTPase Activating Protein (TAGAP) gene, showed significant association with RA (p = 5 × 10-3), whereas 18 markers exhibited suggestive associations (p < 0.05). Haplotype testing showed association of some groups of adjacent SNPs around the signal transducer and activator of transcription 4 (STAT4) gene (p = 9.82 × 10-3 to 2.04 × 10-3) with RA. Our major finding was little replication of previously reported genetic associations with RA. These results suggest that performing GWAS and admixture mapping in LA populations has the potential to reveal novel loci associated with RA. This in turn might help to gain insight into the 'pathogenomics' of this disease and to explore trans-population differences for RA in general.

Association of HLA-G 3'UTR 14-bp Ins/Del polymorphism with breast cancer among South Indian women

AIM

Human leucocyte antigen-G (HLA-G) and tumour necrosis factor-alpha (TNF-α) are potent immune mediators implicated in the pathogenesis of breast cancer. The polymorphisms in the 3' untranslated region (3'UTR) of HLA-G and promoter region of TNF-α are well known to influence their expression levels and may consequently contribute to varied disease predisposition. Therefore, in the present study, we explored the effect of HLA-G 3'UTR (14-bp Ins/Del and +3142 C/G) and TNF-α promoter (-238 G/A and -308 G/A) polymorphisms on breast cancer risk among South Indian women.

METHODS

A total of 342 women (100 patients with breast cancer, 142 patients with benign breast disorder and 100 healthy women volunteers) were enrolled for this study. Genotyping of HLA-G and TNF-α polymorphisms were performed by direct PCR DNA amplification and amplification refractory mutation system PCR methods, respectively.

RESULTS

Significantly higher frequencies of HLA-G 14-bp Ins allele and Ins/+3142G haplotype were observed in patients with breast cancer than healthy controls (OR=1.56, P_c=0.036) and patients with benign breast disorder (OR=1.47, P_c=0.046). Similarly, subgroup analysis based on age at diagnosis (age≤50 years and >50 years) of breast cancer revealed higher frequencies of 14-bp Ins allele and Ins/+3142G haplotype in the patients of age >50 years than healthy controls (OR=1.77, P_c=0.03). Additionally, the extended haplotypes and multifactor dimensionality reduction analysis of the studied polymorphisms revealed significant contribution of HLA-G 14-bp Ins/Del polymorphism towards breast cancer risk.

CONCLUSION

The findings of the present study suggest that the HLA-G 14-bp Ins/Del polymorphism could influence breast cancer pathogenesis among South Indian women.

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.

Interactive potential of genetic polymorphism in Xenobiotic metabolising and DNA repair genes for predicting lung cancer predisposition and overall survival in North Indians

INTRODUCTION

Cancer, a multi-step, multifactorial and multi-gene disease, not only damages the genomic integrity of the cell but also hinders the DNA repair mechanisms of the body. Gene-gene and gene environment interactions amongst the genetic polymorphisms together modulate the susceptibility towards a cancer. We have studied the high order gene interactions between the genetic polymorphism of detoxifying genes (CYP1A1, Ahr, XRCC and GST1) that play a key role in the metabolism of the xenobiotics and have been proved to be prognostic markers for lung cancer METHODS: 237 cases and 250 controls have been genotyped using PCR-RFLP technique. In order to find out the association, unconditional logistic regression approach was used and to analyse high order interactions MDR and CART was used.

RESULTS

In the MDR analysis, the best model was one factor model which included GSTM1 (CVC 10/10, Prediction error = 0.43, p < .001). The best three factor model comprised of XRCC1 632, XRCC1 206, GSTM1 (CVC 10/10, Prediction error = 0.45, p < .0001). The CART analysis exhibited that Node 1 carrying mutant type of GSTM1 imposed the highest risk towards lung cancer (OR = 11.0, 95%C.I. = 6.05-20.03, p = .000001). Wild type of GSTM1 when combined with mutant type of CYP1A1 M2 and XRCC1 632, an 8 fold risk towards lung cancer was observed (95%C.I. = 4.07-16.29, p = .00001). The high order interactions were used to predict the prognosis of lung cancer patients. Of all the genetic variants, XRCC1 632, GSTM1 and AhR rs2066853 was the most important determinant of overall survival of lung cancer patients CONCLUSION: Through the study we introduced the concept of polygenic approach to get an insight about the various polymorphic variants in determining cancer susceptibility. Lesser number of subjects were found in the high risk subgroups. Further studies with larger sample size are required to warranty the above findings.

Haplotype-based gene-gene interaction of bone morphogenetic protein 4 and interferon regulatory factor 6 in the etiology of non-syndromic cleft lip with or without cleft palate in a Chilean population

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, the etiology of which can be dependent on the interactions of multiple genes. We previously reported haplotype associations for polymorphic variants of interferon regulatory factor 6 (IRF6), msh homeobox 1 (MSX1), bone morphogenetic protein 4 (BMP4), and transforming growth factor beta 3 (TGFB3) in Chile. Here, we analyzed the haplotype-based gene-gene interaction for markers of these genes and NSCL/P risk in the Chilean population. We genotyped 15 single nucleoptide polymorphisms (SNPs) in 152 Chilean patients and 164 controls. Linkage disequilibrium (LD) blocks were determined using the Haploview software, and phase reconstruction was performed by the Phase program. Haplotype-based interactions were evaluated using the multifactor dimensionality reduction (MDR) method. We detected two LD blocks composed of two SNPs from BMP4 (Block 1) and three SNPs from IRF6 (Block 2). Although MDR showed no statistical significance for the global interaction model involving these blocks, we found four combinations conferring a statistically significantly increased NSCL/P risk (Block 1-Block 2): T-T/T-G C-G-T/G-A-T; T-T/T-G C-G-C/C-G-C; T-T/T-G G-A-T/G-A-T; and T-T/C-G G-A-T/G-A-T. These findings may reflect the presence of a genomic region containing potential causal variants interacting in the etiology of NSCL/P and may contribute to disentangling the complex etiology of this birth defect.

Confident gene activity prediction based on single histone modification H2BK5ac in human cell lines

BACKGROUND

The histones in the core of nucleosomes may be subject to covalent post-transcriptional modifications. These modifications are thought to correlate with and possibly affect various genomic functions, including transcription. Each modification may alone or in combination with other modifications influence or be influenced by transcription. We aimed to identify correlations between single modifications or combinations of modifications at specific nucleosome sized gene regions with transcription activity based on global histone modification and transcription data of human CD4+ T cells and three other human cell lines. Transcription activity was defined in a binary fashion as either on or off. The analysis was done using the Classification and Regression Tree (CART) data mining protocol, and the Multifactorial Dimensionality Reduction (MDR) method was performed to confirm the CART results. These powerful methods have not previously been used for analysis of histone modification data.

RESULTS

We showed that analysis of the single histone modification H2BK5ac at only four gene regions correctly predicted transcription activity status of over 75% of genes in CD4+ T-cells. The H2BK5ac modification status also had high power for prediction of gene transcription activity in the three other cell lines studied. The informative gene regions with the H2BK5ac modification were all positioned proximal to transcription initiation sites. The CART and MDR methods were appropriate tools for the analysis performed. In the study, we also developed a non-arbitrary protocol for binary classification of genes as transcriptionally active or inactive.

CONCLUSIONS

The importance of H2BK5ac modification with regards to transcription control has not previously been emphasized. Analysis of this single modification at only four nucleosome sized gene regions, all of which are at or proximal to transcription initiation, has high power for prediction of gene transcription activity.

Genetic Polymorphisms of TGFB1, TGFBR1, SNAI1 and TWIST1 Are Associated with Endometrial Cancer Susceptibility in Chinese Han Women

Endometrial cancer (EC) is a complex disease involving multiple gene-gene and gene-environment interactions. TGF-β signaling plays pivotal roles in EC development. This study aimed to investigate whether the genetic polymorphisms of TGF-β signaling related genes TGFB1, TGFBR1, SNAI1 and TWIST1 contribute to EC susceptibility. Using the TaqMan Genotyping Assay, 19 tagging-SNPs of these four genes were genotyped in 516 EC cases and 707 controls among Chinese Han women. Logistic regression (LR) showed that the genetic variants of TGFB1 rs1800469, TGFBR1 rs6478974 and rs10733710, TWIST1 rs4721745 were associated with decreased EC risk, and these four loci showed a dose-dependent effect (Ptrend < 0.0001). Classification and regression tree (CART) demonstrated that women carrying both the genotypes of TGFBR1 rs6478974 TT and rs10512263 TC/CC had the highest risk of EC (aOR = 7.86, 95% CI = 3.42-18.07, P<0.0001). Multifactor dimensionality reduction (MDR) revealed that TGFB1 rs1800469 plus TGFBR1 rs6478974 was the best interactional model to detect EC risk. LR, CART and MDR all revealed that TGFBR1 rs6478974 was the most important protective locus for EC. In haplotype association study, TGFBR1 haplotype CACGA carrier showed the lowest EC risk among women with longer menarche-first full term pregnancy intervals (˃11 years) and BMI˂24 (aOR = 0.39, 95% CI = 0.17-0.90, P = 0.0275). These results suggest that polymorphisms in TGFB1, TGFBR1, SNAI1 and TWIST1 may modulate EC susceptibility, both separately and corporately.

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P=0.046), bladder cancer (entropy P=0.039), and rheumatoid arthritis (PheWAS case–control P<10⁻⁴). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

Using propensity score adjustment method in genetic association studies

BACKGROUND

The statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use propensity score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a propensity score adjustment method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1.

RESULTS

We assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access multifactor dimensionality reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 SNPs from the GAW16 NARAC dataset by reducing their original trend-test p-values from within 0.001 and 0.05 to p-values less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation.

CONCLUSIONS

PSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of rheumatoid arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

BACKGROUND

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making.

OBJECTIVE

The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.

METHODS

The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.

RESULTS

In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.

CONCLUSIONS

The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

TGFBR1 intralocus epistatic interaction as a risk factor for colorectal cancer

In colorectal cancer (CRC), an inherited susceptibility risk affects about 35% of patients, whereas high-penetrance germline mutations account for <6% of cases. A considerable proportion of sporadic tumors could be explained by the coinheritance of multiple low-penetrance variants, some of which are common. We assessed the susceptibility to CRC conferred by genetic variants at the TGFBR1 locus. We analyzed 14 polymorphisms and the allele-specific expression (ASE) of TGFBR1 in 1025 individuals from the Spanish population. A case-control study was undertaken with 504 controls and 521 patients with sporadic CRC. Fourteen polymorphisms located at the TGFBR1 locus were genotyped with the iPLEX Gold (MassARRAY-Sequenom) technology. Descriptive analyses of the polymorphisms and haplotypes and association studies were performed with the SNPator workpackage. No relevant associations were detected between individual polymorphisms or haplotypes and the risk of CRC. The TGFBR1*9A/6A polymorphism was used for the ASE analysis. Heterozygous individuals were analyzed for ASE by fragment analysis using cDNA from normal tissue. The relative level of allelic expression was extrapolated from a standard curve. The cutoff value was calculated with Youden's index. ASE was found in 25.4% of patients and 16.4% of controls. Considering both bimodal and continuous types of distribution, no significant differences between the ASE values of patients and controls were identified. Interestingly, a combined analysis of the polymorphisms and ASE for the association with CRC occurrence revealed that ASE-positive individuals carrying one of the most common haplotypes (H2: 20.7%) showed remarkable susceptibility to CRC (RR: 5.25; 95% CI: 2.547–5.250; p<0.001) with a synergy factor of 3.7. In our study, 54.1% of sporadic CRC cases were attributable to the coinheritance of the H2 haplotype and TGFBR1 ASE. These results support the hypothesis that the allelic architecture of cancer genes, rather than individual polymorphisms, more accurately defines the CRC risk.