Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

Clustering of Cases from Di erent Subtypes of Breast Cancer Using a Hop eld Network Built from Multi-omic Data

Tesis de Graduación (Maestría en Computación) Instituto Tecnológico de Costa Rica, Escuela de Computación, 2018Despite scienti c advances, breast cancer still constitutes a worldwide major cause of death among women. Given the great heterogeneity between cases, distinct classi cation schemes have emerged. The intrinsic molecular subtype classi cation (luminal A, luminal B, HER2- enriched and basal-like) accounts for the molecular characteristics and prognosis of tumors, which provides valuable input for taking optimal treatment actions. Also, recent advancements in molecular biology have provided scientists with high quality and diversity of omiclike data, opening up the possibility of creating computational models for improving and validating current subtyping systems. On this study, a Hop eld Network model for breast cancer subtyping and characterization was created using data from The Cancer Genome Atlas repository. Novel aspects include the usage of the network as a clustering mechanism and the integrated use of several molecular types of data (gene mRNA expression, miRNA expression and copy number variation). The results showed clustering capabilities for the network, but even so, trying to derive a biological model from a Hop eld Network might be di cult given the mirror attractor phenomena (every cluster might end up with an opposite). As a methodological aspect, Hop eld was compared with kmeans and OPTICS clustering algorithms. The last one, surprisingly, hints at the possibility of creating a high precision model that di erentiates between luminal, HER2-enriched and basal samples using only 10 genes. The normalization procedure of dividing gene expression values by their corresponding gene copy number appears to have contributed to the results. This opens up the possibility of exploring these kind of prediction models for implementing diagnostic tests at a lower cost

Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

Investigating the relationship between religious coping, appraisals, social support, and symptoms of Posttraumatic Stress Disorder (PTSD): A correlational study using an Islamic community sample.

Background Contemporary models of PTSD view posttraumatic appraisals and social support as important factors in the onset and maintenance of this condition (e.g., Ehlers & Clark, 2000). Islam is central to the lives of its adherents (e.g., Hamdan, 2007) and religion influences its followers’ beliefs and coping with adversity (Pargament, 1997). The impact of religious beliefs on coping with psychological distress has received increasing attention in the last two decades (Braam et al., 2010). However, like the literature on PTSD (e.g., Foa et al., 2009), this research has almost exclusively focused on Christian, Western populations (e.g., Abu-Raiya & Pargament, 2014). Therefore, this study aimed to better understand how religious coping, appraisals (religious and non-religious), and perceived social support influence the posttraumatic adjustment of Muslim trauma survivors. Method A cross-sectional, correlational design was conducted to study the relationships between PTSD symptoms and posttraumatic appraisals, negative religious coping, negative Islamic appraisals, and perceived social support. Eighty-eight Arabic-speaking Muslim trauma survivors, recruited from the community, completed a questionnaire booklet measuring the study variables. Results Contrary to expectations, symptoms of PTSD were not significantly associated with negative religious coping, negative Islamic appraisals, and perceived social support. However, posttraumatic appraisals were associated with, and predictive of, PTSD Doctoral Thesis: Investigating the relationship between Azi Berzengi religious coping, appraisals, social support, and symptoms of Posttraumatic Stress Disorder (PTSD): A correlational study using an Islamic community sample iii symptoms. Exploratory mediation analyses revealed that posttraumatic appraisals also mediated the relationships between negative religious coping and PTSD symptoms, and between negative Islamic appraisals and PTSD symptoms. Discussion The current theoretical and clinical emphasis on posttraumatic cognitive appraisals in PTSD may also be applicable to Muslim trauma survivors. Contrary to previous research, however, negative religious coping and negative Islamic appraisals appear to have an indirect effect on PTSD symptoms. Several methodological limitations, including the heterogeneous sample composition, could account for some of the findings. These limitations, alongside the theoretical and clinical implications of the results, are discussed

Imagery and emotion in chronic pain

Psychological factors have important implications for adjustment to chronic pain, which itself has a variety of emotional consequences. Mental imagery has historically been assumed to be closely connected to emotional responses, and some experimental and clinical evidence has supported this claim. Around two in five people with chronic pain spontaneously report having mind‟s-eye mental images of their pain, although this phenomenon has received only limited research attention. This study aimed to see whether, for people with chronic pain who report these images, evoking their pain images is different from describing their pain using only single descriptive words. It was hypothesised that evoking the images would result in a stronger negative emotional response, weaker positive emotional response and an increase in the perceived pain intensity. It was also hypothesised that, compared to baseline scores, emotional and pain intensity ratings would be higher under both experimental conditions. Thirty-six participants completed an experiment interview, which employed a repeated measures design. The dependent variables were visual analogue scale ratings of pain intensity and strength of emotional experience (fear, sadness, anger, disgust and happiness). Other measures completed assessed the nature of the imagery and level of overall psychological distress. The study found that evoking pain-related mental images resulted in a temporary increase in pain intensity, sadness, anger and disgust and a decrease in happiness. However, these emotional responses were no different from those experienced when participants described their pain in single words, although this verbal task did not result in the increase in pain intensity seen when images were evoked. These results suggest that for this group of people, pain imagery is no more closely connected to emotional responses than equivalent verbal representations. However, the fact that imagery evocation resulted in a temporary increase in pain intensity where the verbal condition did not perhaps suggests that this represents a qualitatively different kind of paying attention to pain. The next steps for this small but growing field of research are considered

Riskiskoorid ja nende prognoosivõime komplekshaiguste jaoks

Väitekirja elektrooniline versioon ei sisalda publikatsiooneGenotüpiseerimise ja sekveneerimisega seotud tehnoloogiate odavnemine on plahvatuslikult kasvatanud geneetiliste andmete hulka, võimaldades nende ja olemasolevate fenotüübiliste andmete kombineerimisel paljude tunnuste ning haiguste geneetilist tausta uurida. Kõige uuritumad geneetilise varieeruvuse allikad on ühenukleotiidilised polümorfismid (SNPd). Enamasti on sagedaste SNPide mõjud tunnustele üsna väikesed ning seetõttu on nad eraldiseisvana väikese prognoosivõimega. Seevastu paljude SNPide efektide kokku kombineerimisel saadav tunnus, mida nimetatakse geneetiliseks riskiskooriks, on mitmete komplekshaiguste prognoosimisel osutunud väga väärtuslikuks. Töö raames tutvustatakse topeltkaalumise meetodit, mis kaasab geneetilisse riskiskoori korraga paljusid vähekorreleeritud SNPe olemasolevast ülegenoomsest uuringust (GWAS). Antud metoodikat rakendatakse nii simulatsioonides kui ka Eesti Geenivaramu (EGV) andmetel, et iseloomustada selle töötavust erinevate haiguste korral ning võrrelda seda eelnevalt kasutatud lihtsamate meetoditega. Samuti uuriti, kas ja kuidas on geneetiliste riskiskooride prognoosimisvõime seotud sellega, millisest GWASist SNPide kaalud võetakse. Ilmnes, et erinevate GWASide põhjal tehtud geneetilised riskiskoorid samale haigusele ei pruugi olla üksteisega eriti korreleeritud ning seetõttu sõltub konkreetse isiku jaoks geneetilise eelsoodumuse hindamine vaadeldavast geneetilisest riskiskoorist ega ole seega üheselt määratud. Veel uuriti, kuidas geneetiliste riskiskooride jaotus käitub erinevates etnilistes populatsioonides. Leiti, et geneetiliste riskiskooride jaotus sõltub uuritavate populatsioonide geneetilisest struktuurist ning seetõttu ei saa geneetilise riskiskoori abil geneetilist eelsoodumust määrata populatsioonistruktuuri arvesse võtmata. Viimaks uuriti kolme tuntud mittegeneetilist riskiskoori ja nende prognoosivõimet kardiovaskulaarhaiguste jaoks EGV andmetes. Kaks riskiskoori olid Eesti andmetes hästi kalibreeritud, kuid kõige uuem ja keerulisema algoritmiga neist (QRISK2) alahindas tekkivate juhtude arvu. Samuti selgus, et antud mittegeneetiliste skooridega kaasas käivate ravijuhiste järgi tuleks pea pooltele keskealistele meestele ning veerandile keskealistele naistele, kes uuringus osalesid, soovitada kolesterooli alandavate ravimite manustamist südameveresoonkonna haiguste riski vähendamiseks.The prices of genotyping and whole genome sequencing have been decreasing rapidly over the past few years. Due to that, genotypic data has become available in large quantities, allowing for extensive investigation of the genetic background of many common complex diseases. The most studied genetic variants are single nucleotide polymorphisms (SNPs). Each SNP separately tends to have a small effect on common complex diseases. However, by combining the effects of many SNPs together into one variable – called genetic risk score (GRS) – one can compose a useful predictor for determining the genetic predisposition for a disease. In this thesis, a new method called doubly-weighting will be introduced, which allows for inclusion of many uncorrelated markers instead of including only few genome-wide significant ones from genome-wide association study(GWAS) and at the same time, intends to correct for winner’s curse bias problem. We illustrate its predictive ability under several scenarios with both simulations and Estonian Biobank data to show that it systematically performs better than more simple methods. In the second article, it was investigated how the selection of GWAS study affects the predictive ability of GRSs for breast cancer. We also tried combining several GRS together into one metaGRS to achieve the best predictive genetic score. We also addressed the problem that different genetic risk scores with similar predictive ability are not necessarily highly correlated for the same disease. Another important aspect influencing the predictive ability of GRSs is the similarity between discovery and target dataset of which the GRS is intended for. This is investigated in the third article, where it is showed that the distributions of GRSs heavily depend on ancestral background of the population. In the fourth article, three known non-genetic risk scores for ASCVD are validated in the Estonian Biobank data. Two of them were well calibrated, but the newest and most complicated algorithm developed in the UK estimated almost twice as less cases than observed. We also compared the statin treatment recommendations based on guideline specific criteria and found that statins for primary prevention were recommended for almost half of the men and quarter of women under investigation, illustrating high risk levels of ASCVD in Estonia.https://www.ester.ee/record=b522905

A framework for trend mining with application to medical data

This thesis presents research work conducted in the field of knowledge discovery. It presents an integrated trend-mining framework and SOMA, which is the application of the trend-mining framework in diabetic retinopathy data. Trend mining is the process of identifying and analysing trends in the context of the variation of support of the association/classification rules that have been extracted from longitudinal datasets. The integrated framework concerns all major processes from data preparation to the extraction of knowledge. At the pre-process stage, data are cleaned, transformed if necessary, and sorted into time-stamped datasets using logic rules. At the next stage, time-stamp datasets are passed through the main processing, in which the ARM technique of matrix algorithm is applied to identify frequent rules with acceptable confidence. Mathematical conditions are applied to classify the sequences of support values into trends. Afterwards, interestingness criteria are applied to obtain interesting knowledge, and a visualization technique is proposed that maps how objects are moving from the previous to the next time stamp. A validation and verification (external and internal validation) framework is described that aims to ensure that the results at the intermediate stages of the framework are correct and that the framework as a whole can yield results that demonstrate causality. To evaluate the thesis, SOMA was developed. The dataset is, in itself, also of interest, as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The Royal Liverpool University Hospital has been a major centre for retinopathy research since 1991. Retinopathy is a generic term used to describe damage to the retina of the eye, which can, in the long term, lead to visual loss. Diabetic retinopathy is used to evaluate the framework, to determine whether SOMA can extract knowledge that is already known to the medics. The results show that those datasets can be used to extract knowledge that can show causality between patients’ characteristics such as the age of patient at diagnosis, type of diabetes, duration of diabetes, and diabetic retinopathy

Metabolic profiling on 2D NMR TOCSY spectra using machine learning

Due to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach