Browsing by Subject "whole-genome sequencing"
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(2021)Uterine leiomyomas are benign smooth muscle tumors arising in myometrium. They are very common, and the incidence in women is up to 70% by the age of 50. Usually, leiomyomas are asymptomatic, but some patients suffer from various symptoms, including abnormal uterine bleeding, pelvic pain, urinary frequency, and constipation. Uterine leiomyomas may also cause subfertility. Genetic alterations in the known driver genes MED12, HMGA2, FH, and COL4A5-6 account for about 90 % of all leiomyomas. These initiator mutations result in distinct molecular subtypes of leiomyomas. The majority of whole-genome sequencing (WGS) studies analyzing chromosomal rearrangements have been performed using fresh frozen tissues. One aim of this study was to examine the feasibility of detecting chromosomal rearrangements from WGS data of formalin-fixed paraffin embedded (FFPE) tissue samples. Previous results from 3’RNA-sequencing data revealed a subset of uterine leiomyoma samples that displayed similar gene expression patterns with HMGA2-positive leiomyomas but were previously classified as HMGA2-negative by immunohistochemistry. According to 3’RNA-sequencing, all these tumors overexpressed PLAG1, and some of them overexpressed HMGA2 or HMGA1. Thus, the second aim of this study was to identify driver mutations in these leiomyoma samples using WGS. In this study, WGS was performed for 16 leiomyoma and 4 normal myometrium FFPE samples. The following bioinformatic tools were used to detect somatic alterations at multiple levels: Delly for chromosomal rearrangements, CNVkit for copy-number alterations, and Mutect for point mutations and small insertions and deletions. Sanger sequencing was used to validate findings. The quality of WGS data obtained from FFPE samples was sufficient for detecting chromosomal rearrangements, although the number of calls were quite high. We identified recurrent chromosomal rearrangements affecting HMGA2, HMGA1, and PLAG1, mutually exclusively. One sample did not harbor any of these rearrangements, but a deletion in COL4A5-6 was found. Biallelic loss of DEPDC5 was seen in one sample with an HMGA2 rearrangement and in another sample with an HMGA1 rearrangement. HMGA2 and HMGA1 encode architectural chromatin proteins regulating several transcription factors. It is well-known that HMGA2 upregulates PLAG1 expression. The structure and functionality of HMGA2 and HMGA1 are very similar and conserved, so it might be that HMGA1 may also regulate PLAG1 expression. The results of this study suggest that HMGA2 and HMGA1 drive tumorigenesis by regulating PLAG1, and thus, PLAG1 rearrangements resulting in PLAG1 overexpression can also drive tumorigenesis. A few samples, previously classified as HMGA2-negative by immunohistochemistry, revealed to harbor HMGA2 rearrangements, suggesting that the proportion of HMGA2-positive leiomyomas might be underestimated in previous studies using immunohistochemistry. Only one study has previously reported biallelic inactivation of DEPDC5 in leiomyomas, and the results of this study support the idea that biallelic loss of DEPDC5 is a secondary driver event in uterine leiomyomas.
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(2024)Silver birch (Betula pendula) is both the third most commercially important, as well as abundant, tree in Finland. Faster growth is of interest from an industry, as well as climate change, perspective. In addition to higher biomass yield from a commercial point of view, faster growth in trees leads to absorption of more carbon dioxide, thus increasing their effect as a carbon sink to lessen the impact of climate change. By conducting a genome-wide association study, the aim of this thesis was to investigate the genetical background of B. pendula on three first-year growth traits: maximum height, maximum growth rate and time to cessation. Whole-genome sequencing data from 405 mother trees was used to provide a high-resolution genome-wide map for assessing genes associated with the three growth traits. A high-quality genotypic dataset of 5,727,473 single-nucleotide polymorphisms (SNP), which span across the whole genome, was established through rigorous filtering. Phenotypic data was estimated with a mixed linear model by best linear unbiased prediction from mother trees’ 7,266 seedlings, which were grown in a greenhouse under controlled conditions to monitor their height trajectories. Association testing was done as both a uni- and multivariate mixed linear model with the GEMMA algorithm. Overall, 16 unique suggestively significant SNPs were discovered among the three traits. Genes putatively associated with the SNPs were related to metabolic regulation and cell transportation processes, as well as biotic and abiotic stress. On each of the three traits, SNPs with the largest effects had around 5% deviation against the average phenotypic value, which could be considered a major effect for a polygenic trait. Only 4 out of the 16 suggestively associated SNPs were within gene regions, even though the putative genes themselves had also variants present abundantly in the dataset. This could suggest that the associated SNPs could be related to gene expression regulation, or they could be in linkage disequilibrium with multiple different genes, and thus be markers for combined effects of multiple genes on the focal trait. To validate the results, further studies such as gene editing on the associated genes, or a duplicated study on a different population, would be needed.
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