SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Single Nucleotide Polymorphisms (SNPs). Genome-wide association studies (GWAS) use single-locus analysis where each SNP is independently tested for association with phenotypes. The limitation with this approach, however, is its inability to explain genetic variation in complex diseases. Alternative approaches are required to model the intricate relationships between SNPs. Our proposed approach extends GWAS by combining deep learning stacked autoencoders (SAEs) and association rule mining (ARM) to identify epistatic interactions between SNPs. Following traditional GWAS quality control and association analysis, the most significant SNPs are selected and used in the subsequent analysis to investigate epistasis. SAERMA controls the classification results produced in the final fully connected multi-layer feedforward artificial neural network (MLP) by manipulating the interestingness measures, support and confidence, in the rule generation process. The best classification results were achieved with 204 SNPs compressed to 100 units (77% AUC, 77% SE, 68% SP, 53% Gini, logloss=0.58, and MSE=0.20), although it was possible to achieve 73% AUC (77% SE, 63% SP, 45% Gini, logloss=0.62, and MSE=0.21) with 50 hidden units - both supported by close model interpretation

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

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Gene-environment and gene-gene interactions in myopia

Motivated by the release of the UK Biobank data and the lack of documented gene-environment (GxE) and gene-gene (GxG) interactions in myopia, I sought to apply various statistical tools to provide a quantitative assessment of the interplay between environmental and genetic risk factors shaping refractive error. The comparison between the two different risk measurement scales with which GxE interactions can be identified suggested that the additive risk scale can lead to a more informative perspective about refractive error aetiology. The evaluation of two indirect methods for detecting genetic variants affecting refractive error via interaction effects suggested the enrichment of GxG and GxE among the variants that display marginal SNP effects. For genetic variants already known from prior GWAS studies to influence refractive error, genetic effect sizes were highly non-uniform; individuals from the tails of the refractive error distribution (i.e. high myopes and hyperopes) displayed much larger effects compared to individuals in the middle of the distribution (i.e. emmetropes). Prediction of refractive error using GxE interactions indicated that although some of the variance of refractive error could be explained by a risk score constructed using interaction effects, the contribution of GxE was already accounted for by a risk score constructed using marginal SNP effects only. Although a handful of candidate genes were identified using multifactor dimensionality reduction technique, none displayed compelling evidence of involvement in a GxG interaction. There was, however, suggestive evidence that the candidate genes constitute a genetic interaction network which is regulated by hub gene ZMAT4. In summary, the analyses reported in this thesis provide further support for the challenging nature of definitively identifying loci involved in GxE and GxG interactions. The thesis provides several guidelines that future studies could take into account to obtain more insightful results regarding the extent of interactions in refractive error

RaSaR: A Novel Methodology for the Detection of Epistasis

Complex diseases which affect a large proportion of our population today demand more strategic methods to produce significant association results. As it currently stands there are numerous disorders and diseases which are yet to be identified with a genetic causal variant despite evidence produced by research efforts which indicate the existence of high genetic concordance. Breast Cancer is one of the most prominent cancers in the female population with approximately 55K new cases each year in the UK and approximately 11K deaths. The genetic component of Breast Cancer is a popular research area and has uncovered many genetic associations from high to low penetrance. The dataset used within this research is obtained from the DRIVE project, one of five introduced under the GAME-ON initiative. The general research use DRIVE dataset contains approximately 533K single-nucleotide polymorphisms (SNPs), with more than 280K sequenced with reference to the 5 most prominent cancers; colon, breast, ovarian, prostate and lung. SNP’s are sequenced for approximately 28K subjects, of which approximately 14K were diagnosed with one of three stages of Breast Cancer; unknown, in-situ and invasive. Epistasis is a progressive approach that complements the ‘common disease, common variant’ hypothesis that highlights the potential for connected networks of genetic variants collaborating to produce a phenotypic expression. Epistasis is commonly performed as a pairwise or limitless-arity capacity that considers variant networks as either variant vs variant or as high order interactions. This type of analysis extends the number of tests that were previously performed in a standard approach such as GWAS, in which FDR was already an issue, therefore by multiplying the number of tests up to a factorial rate also increases the issue of FDR. Further to this, epistasis introduces its own limitations of computational complexity that are generated based on the analysis performed; to consider the most intense approach, a multivariate analysis introduces a time complexity of ( !) On . Throughout this thesis, approaches, methods and techniques for epistasis analysis and GWAS are discussed, as well as the limitations that exist and how to address these issues. Proposed in this thesis is a novel methodology, methodology and methods for the detection of epistasis using interpretable methods and best practice to outline interactions through filtering processes. RaSaR refers to process of Random Sampling Regularisation which randomly splits and produces sample sets to conduct a voting system to regularise the significance and reliability of biological markers, SNPs. Parallel to this, the proposed methodology takes into consideration and adjusts for the common limitations of computational complexity and false discovery using filter selection and a novel method to association analysis. Preliminary results are promising, outlining a concise detection of interactions using benchmarking standard approaches that consider the common approaches to multiple testing. Results for the detection of epistasis, in the classification of breast cancer patients, indicated nine outlined risk candidate interactions from five variants and a singular candidate variant with high protective association

AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects

Identifying gene-gene interaction is a hot topic in genome wide association studies. Two fundamental challenges are: (1) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and (2) how to test epistatic interaction when all potential combinations are available. We developed AprioriGWAS, which brings two innovations. (1) Based on Apriori, a successful method in field of Frequent Itemset Mining (FIM) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space, AprioriGWAS can efficiently identify genetically associated genotype patterns. (2) To test the hypotheses of epistasis, we adopt a new conditional permutation procedure to obtain reliable statistical inference of Pearson's chi-square test for the 2 x f contingency table generated by associated variants. By applying AprioriGWAS to age-related macular degeneration (AMD) data, we found that: (1) angiopoietin 1 (ANGPT1) and four retinal genes interact with Complement Factor H (CFH). (2) GO term "glycosaminoglycan biosynthetic process" was enriched in AMD interacting genes. The epistatic interactions newly found by AprioriGWAS on AMD data are likely true interactions, since genes interacting with CFH are retinal genes, and GO term enrichment also verified that interaction between glycosaminoglycans (GAGs) and CFH plays an important role in disease pathology of AMD. By applying AprioriGWAS on Bipolar disorder in WTCCC data, we found variants without marginal effect show significant interactions. For example, multiple-SNP genotype patterns inside gene GABRB2 and GRIA1 (AMPA subunit 1 receptor gene). AMPARs are found in many parts of the brain and are the most commonly found receptor in the nervous system. The GABRB2 mediates the fastest inhibitory synaptic transmission in the central nervous system. GRIA1 and GABRB2 are relevant to mental disorders supported by multiple evidences