Transcriptome Prediction Performance Across Machine Learning Models and Diverse Ancestries

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits

Generative Models of Biological Variations in Bulk and Single-cell RNA-seq

The explosive growth of next-generation sequencing data enhances our ability to understand biological process at an unprecedented resolution. Meanwhile organizing and utilizing this tremendous amount of data becomes a big challenge. High-throughput technology provides us a snapshot of all underlying biological activities, but this kind of extremely high-dimensional data is hard to interpret. Due to the curse of dimensionality, the measurement is sparse and far from enough to shape the actual manifold in the high-dimensional space. On the other hand, the measurements may contain structured noise such as technical or nuisance biological variation which can interfere downstream interpretation. Generative modeling is a powerful tool to make sense of the data and generate compact representations summarizing the embedded biological information. This thesis introduces three generative models that help amplifying biological signals buried in the noisy bulk and single-cell RNA-seq data. In Chapter 2, we propose a semi-supervised deconvolution framework called PLIER which can identify regulations in cell-type proportions and specific pathways that control gene expression. PLIER has inspired the development of MultiPLIER and has been used to infer context-specific genotype effects in the brain. In Chapter 3, we construct a supervised transformation named DataRemix to normalize bulk gene expression profiles in order to maximize the biological findings with respect to a variety of downstream tasks. By reweighing the contribution of hidden factors, we are able to reveal the hidden biological signals without any external dataset-specific knowledge. We apply DataRemix to the ROSMAP dataset and report the first replicable trans-eQTL effect in human brain. In Chapter 4, we focus on scRNA-seq and introduce NIFA which is an unsupervised decomposition framework that combines the desired properties of PCA, ICA and NMF. It simultaneously models uni- and multi-modal factors isolating discrete cell-type identity and continuous pathway-level variations into separate components. The work presented in Chapter 2 has been published as a journal article. The work in Chapter 3 and Chapter 4 are under submission and they are available as preprints on bioRxiv

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much attention, prompted by the advance of high-throughput technologies for quantifying gene, protein and metabolite levels. Of great interest is the detection of weak trans-regulatory effects between a genetic variant and a distal gene product. In particular, hotspot genetic variants, which remotely control the levels of many molecular outcomes, may initiate decisive functional mechanisms underlying disease endpoints. This thesis proposes a Bayesian hierarchical approach for joint analysis of QTL data on a genome-wide scale. We consider a series of parallel sparse regressions combined in a hierarchical manner to flexibly accommodate high-dimensional responses (molecular levels) and predictors (genetic variants), and we present new methods for large-scale inference. Existing approaches have limitations. Conventional marginal screening does not account for local dependencies and association patterns common to multiple outcomes and genetic variants, whereas joint modelling approaches are restricted to relatively small datasets by computational constraints. Our novel framework allows information-sharing across outcomes and variants, thereby enhancing the detection of weak trans and hotspot effects, and implements tailored variational inference procedures that allow simultaneous analysis of data for an entire QTL study, comprising hundreds of thousands of predictors, and thousands of responses and samples. The present work also describes extensions to leverage spatial and functional information on the genetic variants, for example, using predictor-level covariates such as epigenomic marks. Moreover, we augment variational inference with simulated annealing and parallel expectation-maximisation schemes in order to enhance exploration of highly multimodal spaces and allow efficient empirical Bayes estimation. Our methods, publicly available as packages implemented in R and C++, are extensively assessed in realistic simulations. Their advantages are illustrated in several QTL applications, including a large-scale proteomic QTL study on two clinical cohorts that highlights novel candidate biomarkers for metabolic disorders

Explainable deep learning models for biological sequence classification

Biological sequences - DNA, RNA and proteins - orchestrate the behavior of all living cells and trying to understand the mechanisms that govern and regulate the interactions among these molecules has motivated biological research for many years. The introduction of experimental protocols that analyze such interactions on a genome- or transcriptome-wide scale has also established the usage of machine learning in our field to make sense of the vast amounts of generated data. Recently, deep learning, a branch of machine learning based on artificial neural networks, and especially convolutional neural networks (CNNs) were shown to deliver promising results for predictive tasks and automated feature extraction. However, the resulting models are often very complex and thus make model application and interpretation hard, but the possibility to interpret which features a model has learned from the data is crucial to understand and to explain new biological mechanisms. This work therefore presents pysster, our open source software library that enables researchers to more easily train, apply and interpret CNNs on biological sequence data. We evaluate and implement different feature interpretation and visualization strategies and show that the flexibility of CNNs allows for the integration of additional data beyond pure sequences to improve the biological feature interpretability. We demonstrate this by building, among others, predictive models for transcription factor and RNA-binding protein binding sites and by supplementing these models with structural information in the form of DNA shape and RNA secondary structure. Features learned by models are then visualized as sequence and structure motifs together with information about motif locations and motif co-occurrence. By further analyzing an artificial data set containing implanted motifs we also illustrate how the hierarchical feature extraction process in a multi-layer deep neural network operates. Finally, we present a larger biological application by predicting RNA-binding of proteins for transcripts for which experimental protein-RNA interaction data is not yet available. Here, the comprehensive interpretation options of CNNs made us aware of potential technical bias in the experimental eCLIP data (enhanced crosslinking and immunoprecipitation) that were used as a basis for the models. This allowed for subsequent tuning of the models and data to get more meaningful predictions in practice

A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis

<p>Abstract</p> <p>Background</p> <p>The success achieved by genome-wide association (GWA) studies in the identification of candidate loci for complex diseases has been accompanied by an inability to explain the bulk of heritability. Here, we describe the algorithm V-Bay, a variational Bayes algorithm for multiple locus GWA analysis, which is designed to identify weaker associations that may contribute to this missing heritability.</p> <p>Results</p> <p>V-Bay provides a novel solution to the computational scaling constraints of most multiple locus methods and can complete a simultaneous analysis of a million genetic markers in a few hours, when using a desktop. Using a range of simulated genetic and GWA experimental scenarios, we demonstrate that V-Bay is highly accurate, and reliably identifies associations that are too weak to be discovered by single-marker testing approaches. V-Bay can also outperform a multiple locus analysis method based on the lasso, which has similar scaling properties for large numbers of genetic markers. For demonstration purposes, we also use V-Bay to confirm associations with gene expression in cell lines derived from the Phase II individuals of HapMap.</p> <p>Conclusions</p> <p>V-Bay is a versatile, fast, and accurate multiple locus GWA analysis tool for the practitioner interested in identifying weaker associations without high false positive rates.</p

Modeling the polygenic architecture of complex traits

Die Genomforschung ist innerhalb der letzten Jahre stark gewachsen. Fortschritte in der Sequenzierungstechnologie haben zu einer wahren Flut von genomweiten Daten geführt, die es uns ermöglichen, die genetische Architektur von komplexen Phänotypen detaillierter als jemals zuvor zu untersuchen. Selbst die modernsten Analysemethoden stoßen jedoch an ihre Grenzen, wenn die Effektgrößen zwischen den Markern zu stark schwanken, Störfaktoren die Analyse erschweren, oder die Abhängigkeiten zwischen verwandten Phänotypen ignoriert werden. Das Ziel dieser Arbeit ist es, mehrere Methoden zu entwickeln, die diese Herausforderungen effizient bewältigen können. Unser erster Beitrag ist der LMM-Lasso, ein Hybrid-Modell, das die Vorteile von Variablenselektion mit linearen gemischten Modellen verbindet. Dafür zerlegt er die phänotypische Varianz in zwei Komponenten: die erste besteht aus individuellen genetischen Effekten. Die zweite aus Effekten, die entweder durch Störfaktoren hervorgerufen werden oder zwar genetischer Natur sind, sich aber nicht auf individuelle Marker zurückführen lassen. Der Vorteil unseres Modells ist zum einen, dass die selektierten Koeffizienten leichter zu interpretieren sind als bei etablierte Standardverfahren und zum anderem diese auch an Vorhersagegenauigkeit übertroffen werden. Der zweite Beitrag beschreibt eine kritische Evaluierung verschiedener Lasso- Methoden, die a-priori bekannte strukturelle Informationen über die genetische Marker und den untersuchten Phänotypen benutzen. Wir bewerten die verschiedenen Ansätze auf Grund ihrer Vorhersagegenauigkeit auf simulierten Daten und auf Genexpressionsdaten in Hefe. Beide Experimente zeigen, dass Strukturinformationen nur dann helfen, wenn ihre Annahmen gerechtfertigt sind – sobald die Annahmen verletzt sind, hat die Zuhilfenahme der Strukturinformation den gegenteiligen Effekt. Um dem vorzubeugen, schlagen wir in unserem nächstem Beitrag vor, die Struktur zwischen den Phänotypen aus den Daten zu lernen. Im dritten Beitrag stellen wir ein effizientes Rechenverfahren für Multi-Task Gauss-Prozesse auf, das sowohl die genetische Verwandtschaft zwischen den Phänotypen als auch die Verwandtschaft der Residuen lernt. Unser Inferenzverfahren zeichnet sich durch einen verminderten Laufzeit- und Speicherbedarf aus und ermöglicht uns damit, die gemeinsame Heritabilität von Phänotypen auf großen Datensätzen zu untersuchen. Das Kapitel wird durch zwei Versuchsstudien vervollständigt; einer genomweiten Assoziationsstudie von Arabidopsis thaliana und einer Genexpressionsanalyse in Hefe, die bestätigen dass die neue Methode bessere Vorhersagen liefert. Die Vorteile der gemeinsamen Modellierung von Variablenselektion und Störfaktoren, sowie von Multi-Task Learning, werden in all unseren Versuchsreihen deutlich. Während sich unsere Experimente vor allem auf Anwendungen aus dem Bereich der Genomik konzentrieren, sind die von uns entwickelten Methoden jedoch allgemeingültig und können auch in anderen Feldern Anwendung finden