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- Smolinska Garbulowska, Karolina
(författare)
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Elucidation of complex diseases by machine learning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Uncovering the interpretability of models for complex health-related problems is a crucial task that is often neglected in machine learning (ML). The amount of available data makes the problem even more complicated. The focal point of my research was building and applying specialized tools that identify relevant descriptors (features and their values). These tools cover a spectrum of methods that originate in ML, statistics and network visualization.In the first part of the thesis, we predicted regulatory elements with potential regulatory impact on gene expression by incorporating several annotations tracks. Then, we created the funMotifs framework that enables the identification and analysis of functional transcription factor (TF) motifs in a tissue-specific manner (Paper I). The TF motifs were described by different chromatin signals from various genomics platforms. Afterwards, the data were merged into a functional score of the motif using logistic regression.Subsequently, funMotifs was used to characterize a map of regulatory mutations and regulatory elements in 37 cancer types from 2,515 samples (Paper II). We were able to identify 5,749 mutated regulatory elements containing 11,962 regulatory mutations. Additionally, we identified several dysregulated cancer-associated genes nearby the mutated elements. Finally, enrichment of cancer-related pathways was observed for the genes linked with the mutated elements.In the second part, we focused on interpretable ML modeling with rule-based classifiers. A rule-based model (RBM) consists of a set of IF-THEN rules, which are legible and allow to determine combinations of descriptors. To analyze RBMs, we created the R.ROSETTA R package that is a wrapper of ROSETTA (Paper III). As a result R.ROSETTA gained several additional functionalities that simplify validation and interpretation of RBMs.Visual inspection of RBMs is an essential step towards the identification of interesting descriptors of a classifier. In order to support the analysis of complex RBMs, we created the VisuNet R tool for rule network (RN) visualization (Paper IV). These networks are constructed from IF-THEN rules that constitute RBM; nodes are descriptors in rules, and an edge connects two nodes if the corresponding descriptors occur in the same rule. By creating RN for RBM, we are able to use network concepts to analyze complex health-related processes. We applied VisuNet on various datasets to illustrate the properties of the tool.In our studies, we showed the importance of identification of relevant descriptors for biological problems. Moreover, our methods may contribute to a better understanding of complex diseases.
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| 2. |
- Schmitz, Daniel, 1995-
(författare)
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Beyond GWAS : Novel Methods and Resources for Genetic Epidemiology
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the first human genome assembly’s release, our knowledge of the genetic architecture of complex traits and diseases has grown steadily. Genome-wide association studies (GWAS) played a major role but are limited to common traits and single-nucleotide polymorphisms (SNPs). Technologies and resources like next-generation sequencing, Mendelian Randomization (MR), long-read sequencing and improved reference genomes enable the investigation of variants inaccessible to GWAS, such as copy number variations (CNVs), rare variants and variants in previously unresolved regions.In project I, we performed a GWAS of estradiol measurements using data from UK Biobank and quantified estradiol’s effect on bone mineral density (BMD) using MR. 14 loci were associated with estradiol levels in males, of which one was also significant in females and an additional female-specific locus. We found a significant effect of estradiol on BMD, confirming previous research of estrogen’s importance for skeletal health.In project II, we used the GWAS results from project I to investigate the effect of endogenous estradiol on breast, endometrial and ovarian cancer using MR. Estradiol was associated with ovarian cancer and nominally associated with estrogen receptor-positive breast cancer, demonstrating the effect of endogenous estrogen on cancer risk. In project III, we quantified the effect of 184,182 CNVs on 438 blood plasma proteins using whole-genome sequencing (WGS) data from a Northern Swedish cohort and validated our findings using long-read sequencing in a subcohort. 15 CNVs were associated with 16 proteins of which four could be validated using long reads and three more were more complex variation. Our findings show the effects of CNVs on the plasma proteome and highlight the application different sequencing technologies for CNV detection.In project IV, we evaluated the use of T2T-CHM13 as reference for the SweGen cohort. Compared to GRCh38, mapping quality improved and we identified 9.8 million more variants. Sensitivity for rare, singleton and functionally relevant variants was higher. These findings show how research and clinical applications benefit from T2T-CHM13 by improving detection of previously unknown functionally relevant variation.This thesis demonstrates the application of novel technologies and resources in genomics to detect variation and study its impact on quantitative traits. By using genotyping and WGS variants from short and long reads, I showed how we can leverage these technologies for research beyond GWAS.
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