Multifactor Dimensionality Reduction Analysis Identifies Specific Nucleotide Patterns Promoting Genetic Polymorphisms

The fidelity of DNA replication serves as the nidus for both genetic evolution and genomic instability fostering disease. Single nucleotide polymorphisms (SNPs) constitute greater than 80% of the genetic variation between individuals. A new theory regarding DNA replication fidelity has emerged in which selectivity is governed by base-pair geometry through interactions between the selected nucleotide, the complementary strand, and the polymerase active site. We hypothesize that specific nucleotide combinations in the flanking regions of SNP fragments are associated with mutation

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

<p>Abstract</p> <p>Background</p> <p>Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.</p> <p>Methods</p> <p>To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.</p> <p>Results</p> <p>Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between <it>AKT3 rs2125230-PRKCQ rs571715 </it>and disease aggressiveness using SEN-guided MDR (p = 0.011).</p> <p>Conclusions</p> <p>In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between <it>AKT3-PRKCQ </it>and aggressive PCA requires further validation using independent observational studies.</p

Pharmacogenetics of advanced colorectal cancer treatment

Even though treatment of several types of solid tumors has improved in the past few years with the introduction of the monoclonal antibodies against the epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF), the clinical benefit of these targeted therapies is modest. Pharmacogenetics has the potential to select patients with higher chance of response to agents that target these pathways. In the thesis, we describe the association of the FCGR3A Phe158Val polymorphism with progression-free survival in advanced colorectal cancer patients treated with cetuximab added to chemotherapy and bevacizumab. Following this finding, we found that cetuximab activates type 2 macrophages, which could have a negative effect on the clinical efficacy of cetuximab. Furthermore, we detected a genetic interaction profile consisting of the VEGF +405G>C and TYMS TSER polymorphisms, that was associated with the efficacy of capecitabine, oxaliplatin and bevacizumab in advanced colorectal cancer patients. Finally, we performed a genome wide association study with the same treatment, in which polymorphisms in the proximity of the AGPAT5 gene were associated with progression-free survivalUBL - phd migration 201

Prediction Accuracy of SNP Epistasis Models Generated by Multifactor Dimensionality Reduction and Stepwise Penalized Logistic Regression

Conventional statistical modeling techniques, used to detect high-order interactions between SNPs, lead to issues with high-dimensionality due to the number of interactions which need to be evaluated using sparse data. Statisticians have developed novel methods Multifactor Dimensionality Reduction (MDR), Generalized Multifactor Dimensionality Reduction (GMDR), and stepwise Penalized Logistic Regression (stepPLR) to analyze SNP epistasis associated with the development of or outcomes for genetic disease. Due to inconsistencies in published results regarding the performance of these three methods, this thesis used data from the very large GenIMS study to compare the prediction accuracies of 90-day mortality in SNP epistasis models. Comparisons were made using prediction accuracy, sensitivity, specificity, model consistency, chi-square tests, sign tests, and biological plausibility. Testing accuracies were generally higher for GMDR compared to MDR, and stepPLR yielded substandard performance since the models predicted that all subjects were alive at ninety days. Stepwise PLR, however, determined that IL-1A SNPs IL1A_M889, rs1894399, rs1878319, and rs2856837 were each significant predictors of 90-day mortality when adjusting for the other SNPs in the model. In addition, the model included a borderline significant, second-order interaction between rs28556838 and rs3783520 associated with 90-day mortality in a cohort of patients hospitalized with community-acquired pneumonia (CAP). The public health importance of this thesis is that the relative risk for CAP may be higher for a set of SNPs across different genes. The ability to predict which patients will experience a poor outcome may lead to more effective prevention strategies or treatments at earlier stages. Furthermore, identification of significant SNP interactions can also expand the scientific knowledge about biological mechanisms affecting disease outcomes. Altogether, the GMDR method yielded higher prediction accuracies than MDR, and MDR performed better than stepPLR when establishing SNP epistasis models associated with 90-day mortality in the GenIMS cohort

Cross-sectional study of the association of 5 single nucleotide polymorphisms with enalapril treatment response among South African adults with hypertension

This study investigates the association of 5 single nucleotide polymorphisms (SNPs) in selected genes (ABO, VEGFA, BDKRB2, NOS3, and ADRB2) with blood pressure (BP) response to enalapril. The study further assessed genetic interactions that exist within these genes and their implications in enalapril treatment response among South African adults with hypertension.A total of 284 participants belonging to the Nguni tribe of South Africa on continuous treatment for hypertension were recruited. Five SNPs in enalapril pharmacogenes were selected and genotyped using MassArray. Uncontrolled hypertension was defined as BP ≥140/90 mm Hg. The association between genotypes, alleles, and BP response to treatment was determined by fitting multivariate logistic regression model analysis, and genetic interactions between SNPs were assessed by multifactor dimensionality reduction.Majority of the study participants were female (75.00%), Xhosa (78.87%), and had uncontrolled hypertension (69.37%). All 5 SNPs were exclusively detected among Swati and Zulu participants. In the multivariate (adjusted) logistic model analysis, ADRB2 rs1042714 GC (adjusted odds ratio [AOR] = 2.31; 95% confidence interval [CI] 1.02-5.23; P = .044) and BDKRB2 rs1799722 CT (AOR = 2.74; 95% CI 1.19-6.28; P = .017) were independently associated with controlled hypertension in response to enalapril. While the C allele of VEGFA rs699947 (AOR = 0.37; 95% CI 0.15-0.94; P = .037) was significantly associated with uncontrolled hypertension. A significant interaction between rs699947, rs495828, and rs2070744 (cross-validation consistency = 10/10; P = .0005) in response to enalapril was observed.We confirmed the association of rs1042714 (ADRB2) and rs1799722 (BDKRB2) with controlled hypertension and established an interaction between rs699947 (VEGFA), rs495828 (ABO), and rs2070744 (NOS3) with BP response to enalapril. Our findings have provided substantial evidence for the use of SNPs as predictors for enalapril response among South Africans adults with hypertension. Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc

DETECTING CANCER-RELATED GENES AND GENE-GENE INTERACTIONS BY MACHINE LEARNING METHODS

To understand the underlying molecular mechanisms of cancer and therefore to improve pathogenesis, prevention, diagnosis and treatment of cancer, it is necessary to explore the activities of cancer-related genes and the interactions among these genes. In this dissertation, I use machine learning and computational methods to identify differential gene relations and detect gene-gene interactions. To identify gene pairs that have different relationships in normal versus cancer tissues, I develop an integrative method based on the bootstrapping K-S test to evaluate a large number of microarray datasets. The experimental results demonstrate that my method can find meaningful alterations in gene relations. For gene-gene interaction detection, I propose to use two Bayesian Network based methods: DASSO-MB (Detection of ASSOciations using Markov Blanket) and EpiBN (Epistatic interaction detection using Bayesian Network model) to address the two critical challenges: searching and scoring. DASSO-MB is based on the concept of Markov Blanket in Bayesian Networks. In EpiBN, I develop a new scoring function, which can reflect higher-order gene-gene interactions and detect the true number of disease markers, and apply a fast Branch-and-Bound (B&B) algorithm to learn the structure of Bayesian Network. Both DASSO-MB and EpiBN outperform some other commonly-used methods and are scalable to genome-wide data