Browsing by study line "Bioinformatics and Systems Medicine"

Polygenic risk scores (PRSs) estimate the genetic risk of an individual for a certain polygenic disease trait by summing up the effects of multiple variants across the genome affecting the disease risk. Currently, polygenic risk scores (PRSs) are calculated from imputed array genotyping data which is inexpensive to produce use and has standard procedures and pipelines available. However, genotyping arrays are prone to ascertainment bias, which can also lead to biased PRS results in some populations. If PRSs are utilized in healthcare for screening rare diseases, usage of whole-genome sequencing (WGS) instead of array genotyping is desirable, because also individual samples can be analyzed easily. While high-coverage WGS is still significantly more expensive than array genotyping, low-coverage whole genome sequencing (lcWGS) with imputation has been proposed as an alternative for genotyping arrays. In this project, the utility of imputed low-coverage whole-genome sequencing (lcWGS) data in PRS estimation compared to genotyping array data and the impact of the choice of imputation tool for lcWGS data was studied. Down-sampled WGS data with six different low coverages (0.1x-2x) was used to represent lcWGS data. Two different pipelines were used in genotype imputation and haplotype phasing: in the first one, pre-phasing and imputation were performed directly for the genotype likelihoods (GLs) calculated from the down-sampled data, whereas in the second one, the GLs were converted to genotype calls before imputation and phasing. In both pipelines, PRS for 27 disease phenotypes were calculated from the imputed and phased lcWGS data. Imputation and PRS calculation accuracy of the two pipelines were calculated in relation to both genotyping array and high-coverage whole-genome sequencing (hcWGS) data. In both pipelines, imputation and PRS calculation accuracy increased when the down-sampled coverage increased. The second imputation and phasing pipeline lead to better results in both imputation and PRS calculation accuracy. Some differences in PRS accuracy between different phenotypes were also detected. The results show similar patterns to what is seen in other similar publications. However, not quite as high imputation and PRS accuracy as seen in earlier studies could be attained, but possible limitations leading to lower accuracy could be identified. The results also emphasize the importance of choosing suitable imputation and phasing methods for lcWGS data and suggest that methods and pipelines designed particularly for lcWGS should be developed and published.

Single-cell RNA sequencing (scRNA-seq) allows the analysis of differences in the RNA expression between individual cells. While this is usually performed by short read sequencing, long read sequencing like Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) is also applied by researchers. As long read technologies allow to capture entire RNA molecules, combined with single-cell sequencing, this enables the exploration of cell-specific isoform expression patterns. In single-cell sequencing each cell is tagged by a different oligonucleotide, called barcode, during sequencing, to enable the identification of the origin of each read. With short reads, these are straighforward to identify and correct. However, with the higher error rate of long reads, the identification of the barcodes becomes more challenging. Tools exist for the identification and correction of barcodes in short reads and for combinations of long and short reads, but only few tools work with long reads exclusively. Additionally, most tools are focused on one specific scRNA-seq protocol. While most protocols work in a similar way, the location, length or other characteristics of the barcodes might differ, meaning not all tools work for all protocols. This thesis introduces a novel barcode calling algorithm for long reads called BArcoDe callinG via Edit distance gRaph, or Badger, which can accomodate for different scRNAseq protocols. The algorithm uses a novel data structure called edit distance graph, which is based on the Hamming distance graph. Within the graph, every distinct barcode is represented by a node. Edges are added between nodes where the represented barcodes have an edit distance below a certain threshold between them. As calculating the edit distance is computationally expensive, a filter is used to find similar barcodes, and only between those the edit distance is calculated. Additionally, the algorithm is implemented and its performance evaluated, both on its own and in comparison to the existing method scTagger, where Badger outperforms scTagger in both precision and recall.

Cancer cells accumulate somatic mutations in their DNA throughout their lifetime. The advances in cancer prevention and treatment methods call for a deeper understanding of carcinogenesis on the genetic sequence level. Mutational signatures present a novel and promising way to capture somatic mutation patterns and define their causes, allowing to summarize the mutational landscape of cancer as a combination of distinct mutagenic processes acting with different levels of strength. While the majority of previous studies assume an additive relationship between the mutational processes, this Master’s thesis provides tentative evidence that contemporary methods with additivity constraints, e.g. non-negative matrix factorization (NMF), are not sufficient to comprehensively explain the observed mutations in cancer genomes and the observed deviations are not random. To quantify these residues, two metrics are defined – additive and multiplicative residues – and hierarchical clustering algorithms are used to identify cancer subsets with similar residual profiles. It is shown that in certain cancer sample subsets there is a systematic mutational burden overestimation that can only be solved by a multiplicatively acting process, as well as non-random underestimation, requiring additional mutational signatures. Here an extension to the additive mutational signature model is proposed – a probabilistic model that incorporates a selectively active modulatory mutational process that is able to act in a multiplicative manner together with the known mutational signatures, reducing systematic variability.

Gene editing holds tremendous potential for treating a variety of diseases, but concerns about safety, particularly the risk of edited cells becoming cancerous, must be addressed. This thesis explores a safety mechanism to prevent unwanted cell proliferation and tumor formation in induced pluripotent stem cells that have been edited for use in gene therapy. The mechanism bases on the genetic disruption (knockout) of the thymidylate synthase gene (TYMS), the only enzyme in charge of synthesizing deoxythymidine monophosphate (dTMP), an essential building block of DNA. Without dTMP, cells cannot successfully proliferate, while RNA synthesis remains unaffected. Through RNA sequencing analysis, we investigate the early response of TYMS knockout cells to dTMP withdrawal and find evidence of the activation of apoptosis and stress pathways, as well as differentiation and changes in the cell cycle. In addition, we demonstrate the effectiveness of the TYMS knockout mechanism in preventing proliferation of cancerous cells in a laboratory setting.

The increasing demand for comprehensive datasets to address complex diseases has resulted in a widespread popularity of biobank-based research. However, the collection of biobank-level data may be susceptible to biases when fundamental aspects of study design, such as sampling approach, are overlooked. FinnGen is a large-scale cohort study aiming to improve diagnoses and prevent diseases through genetic research by combining biobank data with registry data.However, FinnGen’s hospital-based recruitment strategy makes FinnGen suffer from selection bias and thus epidemiologically less representative of its sampling population. In this study, we examine the profound impact of selection bias in FinnGen. We use well-established epidemiological methods and leverage representative data on the Finnish population to try and correct for the bias. By comparing key demographic characteristics and association statistics of interest between FinnGen and a comprehensive registry-based study, FinRegistry, we highlight the extent to which selection bias within FinnGen results in distorted association estimates and a dataset that is highly non - representative of its underlying population. In response to these findings, we estimate Iterative Proportional Fitting (IPF) weights to estimate association statistics that are representative of the true sampling population of FinnGen and unaffected by selection bias. By comparing weighted associations estimated in the FinnGen with associations estimated using FinRegistry data, we infer that the use of our IPF weights mitigates volunteer bias in FinnGen.

Modern developments in biology and bioinformatics has enabled unprecedented computational capabilities in developmental biology. Novel biological single cell-resolution assays, as well as advanced data processing algorithms and workflows, define directions in which genomics research transitions in the 21st century. This thesis uses Single Cell Assay for Transposase Accessible Chromatin using Sequencing (scATAC-seq) high-throughput data to study gene regulatory principles responsible for the development of a distinguished cellular lineage in the embryonic brainstem in mouse. In particular, a ventral area of the rhombomere 1 region (rV2) in the embryonic brainstem generates two, antagonistic cellular lineages. These lineages–an inhibitory GABAergic lineage and an exhibitory glutamatergic lineage–have been well characterized in recent literature. Functionally, the cells originating from these lineages have been shown to regulate fundamental, evolutionary behavioral traits, present in cognitively more complex organisms such as humans, as well. Studying the cellular development of the two lineages is thus crucial for understanding the development of congenital disorders related to the higher-level behavioral traits. Coupled with auxiliary single cell mRNA sequencing (scRNA-seq) data analysis, the scATAC-seq data analysis conducted in this thesis first successfully rebuilds the lineage structure of the rV2 area in both, scRNA-seq data and scATAC-seq data. Moreover, by the use of open-source data analysis libraries, the work presented in this thesis integrates the structure of the rV2 area across the two modalities, creating a multiomics data set in which both, the transcriptomics and the chromatin accessibility landscapes of the rV2 lineages can be studied jointly. Previous research has described the molecular and genetic signature of the rV2 area. In particular, the induction of the GABAergic rV2 lineage is known to depend on presence of a combination of a few key transcription factors. The analysis of chromatin accessibility data obtained through the scATAC-seq assay, enables the determination of regulatory elements, putatively responsible for the activation the Tal1 gene – a crucial fate-determining gene coding one of the key transcription factors. Indeed, the work presented in this thesis shows that at least three proximal regulatory elements exhibit potential accessibility trends associated with elevated Tal1 gene expression. Through transcription factor footprinting analysis algorithms, this thesis finally predicts how a limited number of known transcription factors binds to the proximal Tal1 regulatory elements and orchestrates lineage defining Tal1 activation in the rV2 area of mouse brainstem. The thesis ends with a critical assessment of the analysis pipelines and computational tools used in the thesis, and suggests directions for research efforts, which can computationally and biologically validate the observations and results of the thesis work.