Reconstructing regulatory networks from high-throughput post-genomic data using MCMC methods

Modern biological research aims to understand when genes are expressed and how certain genes in uence the expression of other genes. For organizing and visualizing gene expression activity gene regulatory networks are used. The architecture of these networks holds great importance, as they enable us to identify inconsistencies between hypotheses and observations, and to predict the behavior of biological processes in yet untested conditions. Data from gene expression measurements are used to construct gene regulatory networks. Along with the advance of high-throughput technologies for measuring gene expression statistical methods to predict regulatory networks have also been evolving. This thesis presents a computational framework based on a Bayesian modeling technique using state space models (SSM) for the inference of gene regulatory networks from time-series measurements. A linear SSM consists of observation and hidden state equations. The hidden variables can unfold effects that cannot be directly measured in an experiment, such as missing gene expression. We have used a Bayesian MCMC approach based on Gibbs sampling for the inference of parameters. However the task of determining the dimension of the hidden state space variables remains crucial for the accuracy of network inference. For this we have used the Bayesian evidence (or marginal likelihood) as a yardstick. In addition, the Bayesian approach also provides the possibility of incorporating prior information, based on literature knowledge. We compare marginal likelihoods calculated from the Gibbs sampler output to the lower bound calculated by a variational approximation. Before using the algorithm for the analysis of real biological experimental datasets we perform validation tests using numerical experiments based on simulated time series datasets generated by in-silico networks. The robustness of our algorithm can be measured by its ability to recapture the input data and generating networks using the inferred parameters. Our developed algorithm, GBSSM, was used to infer a gene network using E. coli data sets from the different stress conditions of temperature shift and acid stress. The resulting model for the gene expression response under temperature shift captures the effects of global transcription factors, such as fnr that control the regulation of hundreds of other genes. Interestingly, we also observe the stress-inducible membrane protein OsmC regulating transcriptional activity involved in the adaptation mechanism under both temperature shift and acid stress conditions. In the case of acid stress, integration of metabolomic and transcriptome data suggests that the observed rapid decrease in the concentration of glycine betaine is the result of the activation of osmoregulators which may play a key role in acid stress adaptation

Single-Cell Gene Expression Variation as A Cell-Type Specific Trait: A Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing

In this dissertation, we used single-cell RNA sequencing data from five mammalian tissues to characterize patterns of gene expression across single cells, transcriptome-wide and in a cell-type-specific manner (Part 1). Additionally, we characterized single-cell RNA sequencing methods as a resource for experimental design and data analysis (Part 2). Part 1: Differentiation of metazoan cells requires execution of different gene expression programs but recent single cell transcriptome profiling has revealed considerable variation within cells of seemingly identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. We used high quality single cell RNA sequencing for 107 single cells from five mammalian tissues, along with 30 control samples, to characterize transcriptome heterogeneity across single cells. We developed methods to filter genes for reliable quantification and to calibrate biological variation. We found evidence that ubiquitous expression across cells may be indicative of critical gene function and that, for a subset of genes, biological variability within each cell type may be regulated in order to perform dynamic functions. We also found evidence that single-cell variability of mouse pyramidal neurons was correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Part 2: Many researchers are interested in single-cell RNA sequencing for use in identification and classification of cell types, finding rare cells, and studying single-cell expression variation; however, experimental and analytic methods for single-cell RNA sequencing are young and there is little guidance available for planning experiments and interpreting results. We characterized single-cell RNA sequencing measurements in terms of sensitivity, precision and accuracy through analysis of data generated in a collaborative control project, where known reference RNA was diluted to single-cell levels and amplified using one of three single-cell RNA sequencing protocols. All methods perform comparably overall, but individual methods demonstrate unique strengths and biases. Measurement reliability increased with expression level for all methods and we conservatively estimated measurements to be quantitative at an expression level of ~5-10 molecules

Applications and extensions of Random Forests in genetic and environmental studies

Transcriptional regulation refers to the molecular systems that control the concentration of mRNA species within the cell. Variation in these controlling systems is not only responsible for many diseases, but also contributes to the vast phenotypic diversity in the biological world. There are powerful experimental approaches to probe these regulatory systems, and the focus of my doctoral research has been to develop and apply effective computational methods that exploit these rich data sets more completely. First, I present a method for mapping genetic regulators of gene expression (expression quantitative trait loci, or eQTL) using Random Forests. This approach allows for flexible modeling and feature selection, and results in eQTL that are more biologically supportable than those mapped with competing methods. Next, I present a method that finds interactions between genes that in turn regulate the expression of other genes. This is accomplished by finding recurring decision motifs in the forest structure that represent dependencies between genetic loci. Third, I present a method to use distributional differences in eQTL data to establish the regulatory roles of genes relative to other disease-associated genes. Using this method, we found that genes that are master regulators of other disease genes are more likely to be consistently associated with the disease in genetic association studies. Finally, I present a novel application of Random Forests to determine the mode of regulation of toxin-perturbed genes, using time-resolved gene expression. The results demonstrate a novel approach to supervised weighted clustering of gene expression data

Expression data dnalysis and regulatory network inference by means of correlation patterns

With the advance of high-throughput techniques, the amount of available data in the bio-molecular field is rapidly growing. It is now possible to measure genome-wide aspects of an entire biological system as a whole. Correlations that emerge due to internal dependency structures of these systems entail the formation of characteristic patterns in the corresponding data. The extraction of these patterns has become an integral part of computational biology. By triggering perturbations and interventions it is possible to induce an alteration of patterns, which may help to derive the dependency structures present in the system. In particular, differential expression experiments may yield alternate patterns that we can use to approximate the actual interplay of regulatory proteins and genetic elements, namely, the regulatory network of a cell. In this work, we examine the detection of correlation patterns from bio-molecular data and we evaluate their applicability in terms of protein contact prediction, experimental artifact removal, the discovery of unexpected expression patterns and genome-scale inference of regulatory networks. Correlation patterns are not limited to expression data. Their analysis in the context of conserved interfaces among proteins is useful to estimate whether these may have co-evolved. Patterns that hint on correlated mutations would then occur in the associated protein sequences as well. We employ a conceptually simple sampling strategy to decide whether or not two pathway elements share a conserved interface and are thus likely to be in physical contact. We successfully apply our method to a system of ABC-transporters and two-component systems from the phylum of Firmicute bacteria. For spatially resolved gene expression data like microarrays, the detection of artifacts, as opposed to noise, corresponds to the extraction of localized patterns that resemble outliers in a given region. We develop a method to detect and remove such artifacts using a sliding-window approach. Our method is very accurate and it is shown to adapt to other platforms like custom arrays as well. Further, we developed Padesco as a way to reveal unexpected expression patterns. We extract frequent and recurring patterns that are conserved across many experiments. For a specific experiment, we predict whether a gene deviates from its expected behaviour. We show that Padesco is an effective approach for selecting promising candidates from differential expression experiments. In Chapter 5, we then focus on the inference of genome-scale regulatory networks from expression data. Here, correlation patterns have proven useful for the data-driven estimation of regulatory interactions. We show that, for reliable eukaryotic network inference, the integration of prior networks is essential. We reveal that this integration leads to an over-estimate of network-wide quality estimates and suggest a corrective procedure, CoRe, to counterbalance this effect. CoRe drastically improves the false discovery rate of the originally predicted networks. We further suggest a consensus approach in combination with an extended set of topological features to obtain a more accurate estimate of the eukaryotic regulatory network for yeast. In the course of this work we show how correlation patterns can be detected and how they can be applied for various problem settings in computational molecular biology. We develop and discuss competitive approaches for the prediction of protein contacts, artifact repair, differential expression analysis, and network inference and show their applicability in practical setups.Mit der Weiterentwicklung von Hochdurchsatztechniken steigt die Anzahl verfügbarer Daten im Bereich der Molekularbiologie rapide an. Es ist heute möglich, genomweite Aspekte eines ganzen biologischen Systems komplett zu erfassen. Korrelationen, die aufgrund der internen Abhängigkeits-Strukturen dieser Systeme enstehen, führen zu charakteristischen Mustern in gemessenen Daten. Die Extraktion dieser Muster ist zum integralen Bestandteil der Bioinformatik geworden. Durch geplante Eingriffe in das System ist es möglich Muster-Änderungen auszulösen, die helfen, die Abhängigkeits-Strukturen des Systems abzuleiten. Speziell differentielle Expressions-Experimente können Muster-Wechsel bedingen, die wir verwenden können, um uns dem tatsächlichen Wechselspiel von regulatorischen Proteinen und genetischen Elementen anzunähern, also dem regulatorischen Netzwerk einer Zelle. In der vorliegenden Arbeit beschäftigen wir uns mit der Erkennung von Korrelations-Mustern in molekularbiologischen Daten und schätzen ihre praktische Nutzbarkeit ab, speziell im Kontext der Kontakt-Vorhersage von Proteinen, der Entfernung von experimentellen Artefakten, der Aufdeckung unerwarteter Expressions-Muster und der genomweiten Vorhersage regulatorischer Netzwerke. Korrelations-Muster sind nicht auf Expressions-Daten beschränkt. Ihre Analyse im Kontext konservierter Schnittstellen zwischen Proteinen liefert nützliche Hinweise auf deren Ko-Evolution. Muster die auf korrelierte Mutationen hinweisen, würden in diesem Fall auch in den entsprechenden Proteinsequenzen auftauchen. Wir nutzen eine einfache Sampling-Strategie, um zu entscheiden, ob zwei Elemente eines Pathways eine gemeinsame Schnittstelle teilen, berechnen also die Wahrscheinlichkeit für deren physikalischen Kontakt. Wir wenden unsere Methode mit Erfolg auf ein System von ABC-Transportern und Zwei-Komponenten-Systemen aus dem Firmicutes Bakterien-Stamm an. Für räumlich aufgelöste Expressions-Daten wie Microarrays enspricht die Detektion von Artefakten der Extraktion lokal begrenzter Muster. Im Gegensatz zur Erkennung von Rauschen stellen diese innerhalb einer definierten Region Ausreißer dar. Wir entwickeln eine Methodik, um mit Hilfe eines Sliding-Window-Verfahrens, solche Artefakte zu erkennen und zu entfernen. Das Verfahren erkennt diese sehr zuverlässig. Zudem kann es auf Daten diverser Plattformen, wie Custom-Arrays, eingesetzt werden. Als weitere Möglichkeit unerwartete Korrelations-Muster aufzudecken, entwickeln wir Padesco. Wir extrahieren häufige und wiederkehrende Muster, die über Experimente hinweg konserviert sind. Für ein bestimmtes Experiment sagen wir vorher, ob ein Gen von seinem erwarteten Verhalten abweicht. Wir zeigen, dass Padesco ein effektives Vorgehen ist, um vielversprechende Kandidaten eines differentiellen Expressions-Experiments auszuwählen. Wir konzentrieren uns in Kapitel 5 auf die Vorhersage genomweiter regulatorischer Netzwerke aus Expressions-Daten. Hierbei haben sich Korrelations-Muster als nützlich für die datenbasierte Abschätzung regulatorischer Interaktionen erwiesen. Wir zeigen, dass für die Inferenz eukaryotischer Systeme eine Integration zuvor bekannter Regulationen essentiell ist. Unsere Ergebnisse ergeben, dass diese Integration zur Überschätzung netzwerkübergreifender Qualitätsmaße führt und wir schlagen eine Prozedur - CoRe - zur Verbesserung vor, um diesen Effekt auszugleichen. CoRe verbessert die False Discovery Rate der ursprünglich vorhergesagten Netzwerke drastisch. Weiterhin schlagen wir einen Konsensus-Ansatz in Kombination mit einem erweiterten Satz topologischer Features vor, um eine präzisere Vorhersage für das eukaryotische Hefe-Netzwerk zu erhalten. Im Rahmen dieser Arbeit zeigen wir, wie Korrelations-Muster erkannt und wie sie auf verschiedene Problemstellungen der Bioinformatik angewandt werden können. Wir entwickeln und diskutieren Ansätze zur Vorhersage von Proteinkontakten, Behebung von Artefakten, differentiellen Analyse von Expressionsdaten und zur Vorhersage von Netzwerken und zeigen ihre Eignung im praktischen Einsatz

CONSERVED NONCODING SEQUENCES REGULATE STEADY-STATE mRNA LEVELS IN Arabidopsis thaliana

Arabidopsis thaliana has undergone three whole genome duplications within its ancestry, and these events have dramatically affected its gene complement. Of the most recent whole genome duplication events (&alpha event), there remain 11,452 conserved noncoding sequences (CNSs) that have been retained proximal to &alpha duplicate gene pairs. As functional DNA elements are expected to diverge in sequence at a slower rate than nonfunctional DNA elements, the retained CNSs likely encode gene regulatory function. Within this dissertation I provide evidence for the regulatory role of CNSs within Arabidopsis thaliana. Using a collection of over 5,000 microarray RNA expression profiling datasets, I demonstrate that the presence of CNSs near &alpha duplicate pairs is correlated with changes in average expression intensity (AEI), &alpha duplicate pair co-expression, mRNA stability, and breadth of gene expression. The effects of CNSs on AEI, co-expression, and mRNA stability vary relative to their subgene position, because they are located in nontranscribed (5\u27-upstream and 3\u27-downstream) and transcribed (5\u27-UTR, intronic and 3\u27-UTR) regions. Modeling gene interactions through the generation of co-expression networks, I also demonstrate that a portion of CNSs participate in known gene regulatory networks. Collectively, this body of work demonstrates that CNSs regulate steady-state mRNA levels within Arabidopsis thailiana through both transcriptional and post-transcriptional mechanisms

Biomarker lists stability in genomic studies: analysis and improvement by prior biological knowledge integration into the learning process

The analysis of high-throughput sequencing, microarray and mass spectrometry data has been demonstrated extremely helpful for the identification of those genes and proteins, called biomarkers, helpful for answering to both diagnostic/prognostic and functional questions. In this context, robustness of the results is critical both to understand the biological mechanisms underlying diseases and to gain sufficient reliability for clinical/pharmaceutical applications. Recently, different studies have proved that the lists of identified biomarkers are poorly reproducible, making the validation of biomarkers as robust predictors of a disease a still open issue. The reasons of these differences are referable to both data dimensions (few subjects with respect to the number of features) and heterogeneity of complex diseases, characterized by alterations of multiple regulatory pathways and of the interplay between different genes and the environment. Typically in an experimental design, data to analyze come from different subjects and different phenotypes (e.g. normal and pathological). The most widely used methodologies for the identification of significant genes related to a disease from microarray data are based on computing differential gene expression between different phenotypes by univariate statistical tests. Such approach provides information on the effect of specific genes as independent features, whereas it is now recognized that the interplay among weakly up/down regulated genes, although not significantly differentially expressed, might be extremely important to characterize a disease status. Machine learning algorithms are, in principle, able to identify multivariate nonlinear combinations of features and have thus the possibility to select a more complete set of experimentally relevant features. In this context, supervised classification methods are often used to select biomarkers, and different methods, like discriminant analysis, random forests and support vector machines among others, have been used, especially in cancer studies. Although high accuracy is often achieved in classification approaches, the reproducibility of biomarker lists still remains an open issue, since many possible sets of biological features (i.e. genes or proteins) can be considered equally relevant in terms of prediction, thus it is in principle possible to have a lack of stability even by achieving the best accuracy. This thesis represents a study of several computational aspects related to biomarker discovery in genomic studies: from the classification and feature selection strategies to the type and the reliability of the biological information used, proposing new approaches able to cope with the problem of the reproducibility of biomarker lists. The study has highlighted that, although reasonable and comparable classification accuracy can be achieved by different methods, further developments are necessary to achieve robust biomarker lists stability, because of the high number of features and the high correlation among them. In particular, this thesis proposes two different approaches to improve biomarker lists stability by using prior information related to biological interplay and functional correlation among the analyzed features. Both approaches were able to improve biomarker selection. The first approach, using prior information to divide the application of the method into different subproblems, improves results interpretability and offers an alternative way to assess lists reproducibility. The second, integrating prior information in the kernel function of the learning algorithm, improves lists stability. Finally, the interpretability of results is strongly affected by the quality of the biological information available and the analysis of the heterogeneities performed in the Gene Ontology database has revealed the importance of providing new methods able to verify the reliability of the biological properties which are assigned to a specific feature, discriminating missing or less specific information from possible inconsistencies among the annotations. These aspects will be more and more deepened in the future, as the new sequencing technologies will monitor an increasing number of features and the number of functional annotations from genomic databases will considerably grow in the next years.L’analisi di dati high-throughput basata sull’utilizzo di tecnologie di sequencing, microarray e spettrometria di massa si è dimostrata estremamente utile per l’identificazione di quei geni e proteine, chiamati biomarcatori, utili per rispondere a quesiti sia di tipo diagnostico/prognostico che funzionale. In tale contesto, la stabilità dei risultati è cruciale sia per capire i meccanismi biologici che caratterizzano le malattie sia per ottenere una sufficiente affidabilità per applicazioni in campo clinico/farmaceutico. Recentemente, diversi studi hanno dimostrato che le liste di biomarcatori identificati sono scarsamente riproducibili, rendendo la validazione di tali biomarcatori come indicatori stabili di una malattia un problema ancora aperto. Le ragioni di queste differenze sono imputabili sia alla dimensione dei dataset (pochi soggetti rispetto al numero di variabili) sia all’eterogeneità di malattie complesse, caratterizzate da alterazioni di più pathway di regolazione e delle interazioni tra diversi geni e l’ambiente. Tipicamente in un disegno sperimentale, i dati da analizzare provengono da diversi soggetti e diversi fenotipi (e.g. normali e patologici). Le metodologie maggiormente utilizzate per l’identificazione di geni legati ad una malattia si basano sull’analisi differenziale dell’espressione genica tra i diversi fenotipi usando test statistici univariati. Tale approccio fornisce le informazioni sull’effetto di specifici geni considerati come variabili indipendenti tra loro, mentre è ormai noto che l’interazione tra geni debolmente up/down regolati, sebbene non differenzialmente espressi, potrebbe rivelarsi estremamente importante per caratterizzare lo stato di una malattia. Gli algoritmi di machine learning sono, in linea di principio, capaci di identificare combinazioni non lineari delle variabili e hanno quindi la possibilità di selezionare un insieme più dettagliato di geni che sono sperimentalmente rilevanti. In tale contesto, i metodi di classificazione supervisionata vengono spesso utilizzati per selezionare i biomarcatori, e diversi approcci, quali discriminant analysis, random forests e support vector machines tra altri, sono stati utilizzati, soprattutto in studi oncologici. Sebbene con tali approcci di classificazione si ottenga un alto livello di accuratezza di predizione, la riproducibilità delle liste di biomarcatori rimane ancora una questione aperta, dato che esistono molteplici set di variabili biologiche (i.e. geni o proteine) che possono essere considerati ugualmente rilevanti in termini di predizione. Quindi in teoria è possibile avere un’insufficiente stabilità anche raggiungendo il massimo livello di accuratezza. Questa tesi rappresenta uno studio su diversi aspetti computazionali legati all’identificazione di biomarcatori in genomica: dalle strategie di classificazione e di feature selection adottate alla tipologia e affidabilità dell’informazione biologica utilizzata, proponendo nuovi approcci in grado di affrontare il problema della riproducibilità delle liste di biomarcatori. Tale studio ha evidenziato che sebbene un’accettabile e comparabile accuratezza nella predizione può essere ottenuta attraverso diversi metodi, ulteriori sviluppi sono necessari per raggiungere una robusta stabilità nelle liste di biomarcatori, a causa dell’alto numero di variabili e dell’alto livello di correlazione tra loro. In particolare, questa tesi propone due diversi approcci per migliorare la stabilità delle liste di biomarcatori usando l’informazione a priori legata alle interazioni biologiche e alla correlazione funzionale tra le features analizzate. Entrambi gli approcci sono stati in grado di migliorare la selezione di biomarcatori. Il primo approccio, usando l’informazione a priori per dividere l’applicazione del metodo in diversi sottoproblemi, migliora l’interpretabilità dei risultati e offre un modo alternativo per verificare la riproducibilità delle liste. Il secondo, integrando l’informazione a priori in una funzione kernel dell’algoritmo di learning, migliora la stabilità delle liste. Infine, l’interpretabilità dei risultati è fortemente influenzata dalla qualità dell’informazione biologica disponibile e l’analisi delle eterogeneità delle annotazioni effettuata sul database Gene Ontology rivela l’importanza di fornire nuovi metodi in grado di verificare l’attendibilità delle proprietà biologiche che vengono assegnate ad una specifica variabile, distinguendo la mancanza o la minore specificità di informazione da possibili inconsistenze tra le annotazioni. Questi aspetti verranno sempre più approfonditi in futuro, dato che le nuove tecnologie di sequencing monitoreranno un maggior numero di variabili e il numero di annotazioni funzionali derivanti dai database genomici crescer`a considerevolmente nei prossimi anni

Feature selection and modelling methods for microarray data from acute coronary syndrome

Acute coronary syndrome (ACS) represents a leading cause of mortality and morbidity worldwide. Providing better diagnostic solutions and developing therapeutic strategies customized to the individual patient represent societal and economical urgencies. Progressive improvement in diagnosis and treatment procedures require a thorough understanding of the underlying genetic mechanisms of the disease. Recent advances in microarray technologies together with the decreasing costs of the specialized equipment enabled affordable harvesting of time-course gene expression data. The high-dimensional data generated demands for computational tools able to extract the underlying biological knowledge. This thesis is concerned with developing new methods for analysing time-course gene expression data, focused on identifying differentially expressed genes, deconvolving heterogeneous gene expression measurements and inferring dynamic gene regulatory interactions. The main contributions include: a novel multi-stage feature selection method, a new deconvolution approach for estimating cell-type specific signatures and quantifying the contribution of each cell type to the variance of the gene expression patters, a novel approach to identify the cellular sources of differential gene expression, a new approach to model gene expression dynamics using sums of exponentials and a novel method to estimate stable linear dynamical systems from noisy and unequally spaced time series data. The performance of the proposed methods was demonstrated on a time-course dataset consisting of microarray gene expression levels collected from the blood samples of patients with ACS and associated blood count measurements. The results of the feature selection study are of significant biological relevance. For the first time is was reported high diagnostic performance of the ACS subtypes up to three months after hospital admission. The deconvolution study exposed features of within and between groups variation in expression measurements and identified potential cell type markers and cellular sources of differential gene expression. It was shown that the dynamics of post-admission gene expression data can be accurately modelled using sums of exponentials, suggesting that gene expression levels undergo a transient response to the ACS events before returning to equilibrium. The linear dynamical models capturing the gene regulatory interactions exhibit high predictive performance and can serve as platforms for system-level analysis, numerical simulations and intervention studies

Statistical inference from large-scale genomic data

This thesis explores the potential of statistical inference methodologies in their applications in functional genomics. In essence, it summarises algorithmic findings in this field, providing step-by-step analytical methodologies for deciphering biological knowledge from large-scale genomic data, mainly microarray gene expression time series. This thesis covers a range of topics in the investigation of complex multivariate genomic data. One focus involves using clustering as a method of inference and another is cluster validation to extract meaningful biological information from the data. Information gained from the application of these various techniques can then be used conjointly in the elucidation of gene regulatory networks, the ultimate goal of this type of analysis. First, a new tight clustering method for gene expression data is proposed to obtain tighter and potentially more informative gene clusters. Next, to fully utilise biological knowledge in clustering validation, a validity index is defined based on one of the most important ontologies within the Bioinformatics community, Gene Ontology. The method bridges a gap in current literature, in the sense that it takes into account not only the variations of Gene Ontology categories in biological specificities and their significance to the gene clusters, but also the complex structure of the Gene Ontology. Finally, Bayesian probability is applied to making inference from heterogeneous genomic data, integrated with previous efforts in this thesis, for the aim of large-scale gene network inference. The proposed system comes with a stochastic process to achieve robustness to noise, yet remains efficient enough for large-scale analysis. Ultimately, the solutions presented in this thesis serve as building blocks of an intelligent system for interpreting large-scale genomic data and understanding the functional organisation of the genome

Improving Risk Factor Identification of Human Complex Traits in Omics Data

With recent advances in various high throughput technologies, the rise of omics data offers a promise of personalized health care with its potential to expand both the depth and the width of the identification of risk factors that are associated with human complex traits. In genomics, the introduction of repeated measures and the increased sequencing depth provides an opportunity for deeper investigation of disease dynamics for patients. In transcriptomics, high throughput single-cell assays provide cellular level gene expression depicting cell-to-cell heterogeneity. The cell-level resolution of gene expression data brought the opportunities to promote our understanding of cell function, disease pathogenesis, and treatment response for more precise therapeutic development. Along with these advances are the challenges posed by the increasingly complicated data sets. In genomics, as repeated measures of phenotypes are crucial for understanding the onset of disease and its temporal pattern, longitudinal designs of omics data and phenotypes are being increasingly introduced. However, current statistical tests for longitudinal outcomes, especially for binary outcomes, depend heavily on the correct specification of the phenotype model. As many diseases are rare, efficient designs are commonly applied in epidemiological studies to recruit more cases. Despite the enhanced efficiency in the study sample, this non-random ascertainment sampling can be a major source of model misspecification that may lead to inflated type I error and/or power loss in the association analysis. In transcriptomics, the analysis of single-cell RNA-seq data is facing its particular challenges due to low library size, high noise level, and prevalent dropout events. The purpose of this dissertation is to provide the methodological foundation to tackle the aforementioned challenges. We first propose a set of retrospective association tests for the identification of genetic loci associated with longitudinal binary traits. These tests are robust to different types of phenotype model misspecification and ascertainment sampling design which is common in longitudinal cohorts. We then extend these retrospective tests to variant-set tests for genetic rare variants that have low detection power by incorporating the variance component test and burden test into the retrospective test framework. Finally, we present a novel gene-graph based imputation method to impute dropout events in single-cell transcriptomic data to recover true gene expression level by borrowing information from adjacent genes in the gene graph

Modeling the impact of genetic variation on gene expression

A complete, mechanistic understanding of the consequences of genetic variation could provide immense insights into disease development and, ultimately, human health. A powerful approach for filling in the missing links between genetic variation and higher order traits is to use molecular phenotypes, such as gene expression levels, as an intermediate phenotype. This dissertation advances our understanding of how the genetic regulation of gene expression changes as a function of cellular context or environment. Secondly, this dissertation provides a novel approach to identify functional rare variants via the incorporation of gene expression data. These advances have been achieved via the completion of four major projects. My first project quantified how genetic effects on gene expression, as measured by expression quantitative trait loci (eQTL), vary between tissues of the human body. For my second project, we identified changes in genetic regulation of gene expression along the continuous process of cellular differentiation by detecting dynamic eQTLs, or eQTLs whose effect size changed according to differentiation progress. Broadly, these first two projects identified instances of context-specific eQTLs, as well as describe their biological relevance. However, the relevant contexts, such as cell type or state, that actually modulate genetic effects may not be known a priori. Therefore, in the third project, we developed SURGE, a novel probabilistic model to learn a continuous representation of the cellular contexts that modulate genetic effects. In my fourth project we assessed how rare genetic variants contribute to extreme patterns of gene expression. We developed a probabilistic model, SPOT, to identify instances of abnormal splicing patterns. We also developed Watershed, a novel probabilistic model that identifies functional rare genetic variants by integrating patterns of gene expression data and genomic annotation data