Tests d'association génétique pour des durées de vie en grappes

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2015-2016Les outils statistiques développés dans cette thèse par articles visent à détecter de nouvelles associations entre des variants génétiques et des données de survie en grappes. Le développement méthodologique en analyse des durées de vie est aujourd'hui ininterrompu avec la prolifération des tests d'association génétique et, de façon ultime, de la médecine personnalisée qui est centrée sur la prévention de la maladie et la prolongation de la vie. Dans le premier article, le problème suivant est traité : tester l'égalité de fonctions de survie en présence d'un biais de sélection et de corrélation intra-grappe lorsque l'hypothèse des risques proportionnels n'est pas valide. Le nouveau test est basé sur une statistique de type Cramérvon Mises. La valeur de p est estimée en utilisant une procédure novatrice de bootstrap semiparamétrique qui implique de générer des observations corrélées selon un devis non-aléatoire. Pour des scénarios de simulations présentant un écart vis-à-vis l'hypothèse nulle avec courbes de survie qui se croisent, la statistique de Cramer-von Mises offre de meilleurs résultats que la statistique de Wald du modèle de Cox à risques proportionnels pondéré. Le nouveau test a été utilisé pour analyser l'association entre un polymorphisme nucléotidique (SNP) candidat et le risque de cancer du sein chez des femmes porteuses d'une mutation sur le gène suppresseur de tumeur BRCA2. Un test d'association sequence kernel (SKAT) pour détecter l'association entre un ensemble de SNPs et des durées de vie en grappes provenant d'études familiales a été développé dans le deuxième article. La statistique de test proposée utilise la matrice de parenté de l'échantillon pour modéliser la corrélation intra-famille résiduelle entre les durées de vie via une copule gaussienne. La procédure de test fait appel à l'imputation multiple pour estimer la contribution des variables réponses de survie censurées à la statistique du score, laquelle est un mélange de distributions du khi-carré. Les résultats de simulations indiquent que le nouveau test du score de type noyau ajusté pour la parenté contrôle de façon adéquate le risque d'erreur de type I. Le nouveau test a été appliqué à un ensemble de SNPs du locus TERT. Le troisième article vise à présenter le progiciel R gyriq, lequel implante une version bonifiée du test d'association génétique développé dans le deuxième article. La matrice noyau identical-by-state (IBS) pondérée a été ajoutée, les tests d'association génétique actuellement disponibles pour des variables réponses d'âge d'apparition ont été brièvement revus de pair avec les logiciels les accompagnant, l'implantation du progiciel a été décrite et illustrée par des exemples.The statistical tools developed in this manuscript-based thesis aim at detecting new associations between genetic variants and clustered survival data. Methodological development in lifetime data analysis is today ongoing with the proliferation of genetic association testing and, ultimately, personalized medicine which focuses on preventing disease and prolonging life. In the first paper, the following problem is considered: testing the equality of survival functions in the presence of selection bias and intracluster correlation when the assumption of proportional hazards does not hold. The new proposed test is based on a Cramér-von Mises type statistic. The p-value is approximated using an innovative semiparametric bootstrap procedure which implies generating correlated observations according to a non-random design. For simulation scenarios of departures from the null hypothesis with crossing survival curves, the Cramer-von Mises statistic clearly outperformed the Wald statistic from the weighted Cox proportional hazards model. The new test was used to analyse the association between a candidate single nucleotide polymorphism (SNP) and breast cancer risk in women carrying a mutation in the BRCA2 tumor suppressor gene. A sequence kernel association test (SKAT) to detect the association between a set of genetic variants and clustered survival outcomes from family studies is developed in the second manuscript. The proposed statistic uses the kinship matrix of the sample to model the residual intra-family correlation between survival outcomes via a Gaussian copula. The test procedure relies on multiple imputation to estimate the contribution of the censored survival outcomes to the score statistic which is a mixture of chi-square distributions. Simulation results show that the new kinship-adjusted kernel score test controls adequately for the type I error rate. The new test was applied to a set of SNPs from the TERT locus. The third manuscript aims at presenting the R package gyriq which implements an enhanced version of the genetic association test developed in the second manuscript. The weighted identical-by-state (IBS) kernel matrix is added, genetic association tests and accompanying software currently available for age-at-onset outcomes are briefly reviewed, the implementation of the package is described, and illustrated through examples

The transcription factor Rreb1 regulates epithelial architecture, invasiveness, and vasculogenesis in early mouse embryos.

Ras-responsive element-binding protein 1 (Rreb1) is a zinc-finger transcription factor acting downstream of RAS signaling. Rreb1 has been implicated in cancer and Noonan-like RASopathies. However, little is known about its role in mammalian non-disease states. Here, we show that Rreb1 is essential for mouse embryonic development. Loss of Rreb1 led to a reduction in the expression of vasculogenic factors, cardiovascular defects, and embryonic lethality. During gastrulation, the absence of Rreb1 also resulted in the upregulation of cytoskeleton-associated genes, a change in the organization of F-ACTIN and adherens junctions within the pluripotent epiblast, and perturbed epithelial architecture. Moreover, Rreb1 mutant cells ectopically exited the epiblast epithelium through the underlying basement membrane, paralleling cell behaviors observed during metastasis. Thus, disentangling the function of Rreb1 in development should shed light on its role in cancer and other diseases involving loss of epithelial integrity

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Learning by Fusing Heterogeneous Data

It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. This often gives rise to data that are represented in totally different input spaces. A basic premise behind the study of learning from heterogeneous data is that in many such cases, there exists some correspondence among certain input dimensions of different input spaces. In our work we found that a key bottleneck that prevents us from better understanding and truly fusing heterogeneous data at large scales is identifying the kind of knowledge that can be transferred between related data views, entities and tasks. We develop interesting and accurate data fusion methods for predictive modeling, which reduce or entirely eliminate some of the basic feature engineering steps that were needed in the past when inferring prediction models from disparate data. In addition, our work has a wide range of applications of which we focus on those from molecular and systems biology: it can help us predict gene functions, forecast pharmacological actions of small chemicals, prioritize genes for further studies, mine disease associations, detect drug toxicity and regress cancer patient survival data. Another important aspect of our research is the study of latent factor models. We aim to design latent models with factorized parameters that simultaneously tackle multiple types of data heterogeneity, where data diversity spans across heterogeneous input spaces, multiple types of features, and a variety of related prediction tasks. Our algorithms are capable of retaining the relational structure of a data system during model inference, which turns out to be vital for good performance of data fusion in certain applications. Our recent work included the study of network inference from many potentially nonidentical data distributions and its application to cancer genomic data. We also model the epistasis, an important concept from genetics, and propose algorithms to efficiently find the ordering of genes in cellular pathways. A central topic of our Thesis is also the analysis of large data compendia as predictions about certain phenomena, such as associations between diseases and involvement of genes in a certain phenotype, are only possible when dealing with lots of data. Among others, we analyze 30 heterogeneous data sets to assess drug toxicity and over 40 human gene association data collections, the largest number of data sets considered by a collective latent factor model up to date. We also make interesting observations about deciding which data should be considered for fusion and develop a generic approach that can estimate the sensitivities between different data sets

A Copula-based approach to differential gene expression analysis

Thesis submitted in total fulfillment of the requirements for the degree of Doctor of Philosophy in Biostatistics at Strathmore UniversityMicroarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. The main objective in microarray experiments is to identify a panel of genes that are associated with a disease outcome or trait. In this thesis, we develop and evaluate a semi-parametric copula-based algorithm for gene selection that does not depend on the distributions of the covariates, except that their marginal distributions are continuous. A comparison of the developed method with the existing methods is done based on power to identify differentially expressed genes (DEGs) and control of Type I error rate via a simulation study. Simulations indicate that the copula-based model has a reasonable power in selecting differentially expressed gene and has a good control of Type I error rate. These results are validated in a publicly available melanoma dataset. The copula-based approach turns out to be useful in finding genes that are clinically important. Relaxing parametric assumptions on microarray data may yield procedures that have good power for differential gene expression analysis

Statistical learning methods for multi-omics data integration in dimension reduction, supervised and unsupervised machine learning

Over the decades, many statistical learning techniques such as supervised learning, unsupervised learning, dimension reduction technique have played ground breaking roles for important tasks in biomedical research. More recently, multi-omics data integration analysis has become increasingly popular to answer to many intractable biomedical questions, to improve statistical power by exploiting large size samples and different types omics data, and to replicate individual experiments for validation. This dissertation covers the several analytic methods and frameworks to tackle with practical problems in multi-omics data integration analysis. Supervised prediction rules have been widely applied to high-throughput omics data to predict disease diagnosis, prognosis or survival risk. The top scoring pair (TSP) algorithm is a supervised discriminant rule that applies a robust simple rank-based algorithm to identify rank-altered gene pairs in case/control classes. TSP usually generates greatly reduced accuracy in inter-study prediction (i.e., the prediction model is established in the training study and applied to an independent test study). In the first part, we introduce a MetaTSP algorithm that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. One important objective of omics data analysis is clustering unlabeled patients in order to identify meaningful disease subtypes. In the second part, we propose a group structured integrative clustering method to incorporate a sparse overlapping group lasso technique and a tight clustering via regularization to integrate inter-omics regulation flow, and to encourage outlier samples scattering away from tight clusters. We show by two real examples and simulated data that our proposed methods improve the existing integrative clustering in clustering accuracy, biological interpretation, and are able to generate coherent tight clusters. Principal component analysis (PCA) is commonly used for projection to low-dimensional space for visualization. In the third part, we introduce two meta-analysis frameworks of PCA (Meta-PCA) for analyzing multiple high-dimensional studies in common principal component space. Theoretically, Meta-PCA specializes to identify meta principal component (Meta-PC) space; (1) by decomposing the sum of variances and (2) by minimizing the sum of squared cosines. Applications to various simulated data shows that Meta-PCAs outstandingly identify true principal component space, and retain robustness to noise features and outlier samples. We also propose sparse Meta-PCAs that penalize principal components in order to selectively accommodate significant principal component projections. With several simulated and real data applications, we found Meta-PCA efficient to detect significant transcriptomic features, and to recognize visual patterns for multi-omics data sets. In the future, the success of data integration analysis will play an important role in revealing the molecular and cellular process inside multiple data, and will facilitate disease subtype discovery and characterization that improve hypothesis generation towards precision medicine, and potentially advance public health research

Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide